School of Computer Science

Carnegie Mellon University 1 School of Computer Science Andrew Moore, Dean Klaus Sutner, Associate Dean for Undergraduate Education Thomas Cortina, Assistant Dean for Undergraduate Education Undergraduate Office: GHC 4115 http://www.csd.cs.cmu.edu/education/bscs/ Carnegie Mellon founded one of the first Computer Science departments in the world in 165. Today, the Computer Science Department forms the centerpiece of the School of Computer Science, and is joined by the Human- Computer Interaction Institute, the Institute for Software Research, the Lane Center for Computational Biology, the Language Technologies Institute, the Machine Learning Department, and the Robotics Institute. Together, these units make the School of Computer Science a world leader in research and education. The B.S. program in Computer Science combines a solid core of Computer Science courses with the ability to gain substantial depth in another area through a required minor in a second subject. In addition, the curriculum provides numerous choices for science, engineering, humanities and fine arts courses. As computing is a discipline with strong links to many fields, this provides students with unparalleled flexibility to pursue allied (or non-allied) interests. The curriculum's mathematics and probability component ensures that students have the formal tools to remain current as technologies and systems change, rather than be limited by a narrow focus on programming alone. At the same time, students gain insight into the practical issues of building and maintaining systems by participating in intensive project-oriented courses. Due to the tremendous number of ongoing research projects within the School, many students obtain part-time or summer jobs, or receive independent study credit, working on research while pursuing their undergraduate degree. Students seeking a research/ graduate school career may pursue an intensive course of research, equivalent to four classroom courses, culminating in the preparation of a senior research thesis. Students apply to, and are directly admitted into, the undergraduate program in Computer Science and, upon successful completion, are awarded a Bachelor of Science in Computer Science. Suitably prepared students from other Carnegie Mellon colleges are eligible to apply for internal transfer to the School of Computer Science and will be considered for transfer if space is available. Computation-oriented programs are also available within the Departments of Biology, Chemistry, Physics, Electrical and Computer Engineering, Information Systems, Philosophy, Psychology, and Design. We also offer a B.S. degree in Computational Biology and joint degrees with the College of Fine Arts in Computer Science and Arts as well as Music and Technology. SCS offers double majors in Computer Science (for non-cs majors), Human-Computer Interaction, and Robotics, and minors in Computational Biology, Computer Science (for non-cs majors), Language Technologies, Machine Learning, Neural Computation, Robotics, and Software Engineering. Curriculum - B.S. in Computer Science Computer Science Computer Science Core: 15-8 Freshman Immigration Course 1 15-2 Principles of Imperative Computation 10 (students with no prior programming experience take 15-1 before 15-2) 15-150 Principles of Functional Programming 10 15-210 Parallel and Sequential Data Structures and Algorithms 15-213 Introduction to Computer Systems 15-251 Great Theoretical Ideas in Computer Science 15-451 Algorithm Design and Analysis One Communications course: 15-221 Technical Communication for Computer Scientists One Algorithms/Complexity elective (min. units): 15-354 Computational Discrete Mathematics 15-355 Modern Computer Algebra 15-453 Formal Languages, Automata, and Computability 15-455 Undergraduate Complexity Theory 15-456 Computational Geometry 21-301 Combinatorics 21-484 Graph Theory One Logics/Languages elective (min. units): 15-3 Foundations of Programming Languages 15-317 Constructive Logic 15-414 Bug Catching: Automated Program Verification and Testing 15-424 Foundations of Cyber-Physical Systems 21-300 Basic Logic 80-310 Formal Logic 80-311 Undecidability and Incompleteness One Software Systems elective (min. units): 15-410 Operating System Design and Implementation 15-411 Compiler Design 15-418 Parallel Computer Architecture and Programming 15-440 Distributed Systems 15-441 Computer Networks One Applications elective (min. units): 02-510 Computational Genomics 05-31 Designing Human Centered Software 10-601 Introduction to Machine Learning 11-411 Natural Language Processing 15-313 Foundations of Software Engineering 15-322 Introduction to Computer Music 15-323 Computer Music Systems and Information Processing 15-381 Artificial Intelligence: Representation and Problem Solving 15-415 Database Applications 15-462 Computer Graphics 16-384 Robot Kinematics and Dynamics 16-385 Computer Vision Two Computer Science electives: These electives can be from any SCS department; 200-level or above, at least units each: Computer Science [15-], Lane Center for Computational Biology [02-], Human Computer Interaction Institute [05-], Institute for Software Research [08-,17-], Machine Learning [10-], Language Technologies Institute [11-], and Robotics Institute [16-]. (NOTE: The following courses do NOT count as Computer Science electives: 02-223, 02-250, 02-261, 08-200, 15-351. Consult with a CS undergraduate advisor before registration to determine eligibility for this requirement.) Mathematics 18 21-0 Differential and Integral Calculus 10 21-2 Integration and Approximation 10 21-7 Concepts of Mathematics 10 one of the following Matrix Algebra courses: 21-241 Matrices and Linear Transformations 10 21-242 Matrix Theory 10 one of the following Probability courses: 15-35 Probability and Computing 21-325 Probability 36-217 Probability Theory and Random Processes 36-225 Introduction to Probability Theory

2 School of Computer Science Engineering and Natural Sciences Four engineering or science courses are required, of which at least one must have a laboratory component and at least two must be from the same department. At present, courses meeting the lab requirement are: 02-261 Quantitative Cell and Molecular Biology Laboratory 03-4 Modern Biology Laboratory 0-101 Introduction to Experimental Chemistry 3 (This 3 unit lab together with 0-105 satisfies the lab requirement.) 0-221 Laboratory I: Introduction to Chemical Analysis 15-321 Research Methods for Experimental Computer Science 27-100 Engineering the Materials of the Future 33-104 Experimental Physics 42-203 Biomedical Engineering Laboratory 85-310 Research Methods in Cognitive Psychology The following courses from the Lane Center for Computational Biology can be used to satisfy the Science and Engineering requirement and can be paired with a Biology [03-] course for two courses from one department: 02-223 Personalized Medicine: Understanding Your Own Genome 02-250 Introduction to Computational Biology (or 02-251 + 02-252) 02-261 Quantitative Cell and Molecular Biology Laboratory The following MCS and CIT courses cannot be used to satisfy the Engineering and Natural Sciences requirement: 06-262 Mathematical Methods of Chemical Engineering 0-103 Atoms, Molecules and Chemical Change 0-104 Fundamental Aspects of Organic Chemistry and Biochemistry 0-231 Mathematical Methods for Chemists 18-00 Introduction to Signal Processing for Creative 10 Practice 18-202 Mathematical Foundations of Electrical Engineering 18-345 Introduction to Telecommunication Networks 18-411 Computational Techniques in Engineering 18-487 Introduction to Computer & Network Security & Applied Cryptography 1-101 Introduction to Engineering and Public Policy 1-211 Ethics and Policy Issues in Computing 1-350 SP TP: Research Methods & Statistics for Engineering & Public Policy Analysis 1-402 Telecommunications Technology, Policy & Management 1-403 Policies of Wireless Systems and the Internet 1-411 Global Competitiveness: Firms, Nations and Technological Change 1-448 Science, Technology & Ethics 33-100 Basic Experimental Physics 6 33-115 Physics for Future Presidents 33-4 Introduction to Astronomy 33-232 Mathematical Methods of Physics 10 3-100 Special Topics: WHAT IS ENGINEERING? 3-200 Business for Engineers In addition, all Electrical and Computer Engineering graduate courses [18-6xx, 18-7xx, 18-8xx, 18-xx] cannot be used for this requirement. Consult with a CS undergraduate advisor about any course to be used for the Science and Engineering requirement before registration. Humanities and Arts All candidates for the bachelor's degree must complete a minimum of 63 units offered by the College of Humanities & Social Sciences and/or the College of Fine Arts as prescribed below: A. Writing Requirement ( units) Complete the following course: 76-101 Interpretation and Argument B. Breadth Requirement (27 units) Complete three courses, one each from Category 1, Category 2, and Category 3: Category 1: Cognition, Choice and Behavior - this requirement explores the process of thinking, decision making, and behavior in the context of the individual. 70-311 Organizational Behavior 80-130 Introduction to Ethics 80-150 Nature of Reason 80-180 Nature of Language 80-221 Philosophy of Social Science 80-230 Ethical Theory 80-241 Ethical Judgments in Professional Life 80-270 Philosophy of Mind 80-271 Philosophy and Psychology 80-275 Metaphysics 80-281 Language and Thought 85-102 Introduction to Psychology 85-211 Cognitive Psychology 85-221 Principles of Child Development 85-241 Social Psychology 85-251 Personality 85-261 Abnormal Psychology 88-0 Reason, Passion and Cognition 88-260 Organizations Category 2: Economic, Political and Social Institutions - this requirement explores the processes by which institutions organize individual preferences and actions into collective outcomes. 1-101 Introduction to Engineering and Public Policy 36-303 Sampling, Survey and Society 70-332 Business, Society and Ethics 73-100 Principles of Economics 73-230 Intermediate Microeconomics 73-240 Intermediate Macroeconomics 7-300 History of American Public Policy 7-331 Body Politics: Women and Health in America 7-335 Drug Use and Drug Policy 80-135 Introduction to Political Philosophy 80-136 Social Structure, Public Policy & Ethics 80-235 Political Philosophy 80-244 Environmental Ethics 80-245 Medical Ethics 80-341 Computers, Society and Ethics 88-104 Decision Processes in American Political Institutions 88-110 Experiments with Economic Principles 88-205 Comparative Politics 88-210 Comparative Political Systems 88-220 Policy Analysis I Category 3: Cultural Analysis - this requirement seeks to recognize cultures that have shaped and continue to shape the human experience; courses in this category are usually either broad in place, time, or cultural diversity. 57-173 Survey of Western Music History 60-205 Modern Visual Culture 178-160 70-342 Managing Across Cultures 76-232 African American Literature 76-23 Introduction to Film Studies 76-241 Introduction to Gender Studies 7-104 Global Histories 7-207 Development of European Culture 7-222 Between Revolutions: The Development of Modern Latin America 7-226 Introduction to African History: Earliest Times to 1780

Carnegie Mellon University 3 7-230 Arab-Israeli Conflict and Peace Process since 148 7-240 The Development of American Culture 7-241 African American History: Africa to the Civil War 7-242 African American History: Reconstruction to the Present 7-255 Irish History 7-281 Introduction to Religion 7-282 Europe and the World since 1800 6 7-311 Introduction to Anthropology 7-345 The Roots of Rock and Roll, 1870-170 7-350 Early Christianity 7-35 The Arts in Pittsburgh 7-36 Music and Society in 1th and 20th Century Europe and the U.S. 80-100 Introduction to Philosophy 80-250 Ancient Philosophy 80-251 Modern Philosophy 80-253 Continental Philosophy 80-254 Analytic Philosophy 80-255 Pragmatism 80-261 Empiricism and Rationalism 80-276 Philosophy of Religion 82-273 Introduction to Japanese Language and Culture 82-23 Introduction to Russian Culture 82-303 French Culture 82-304 The Francophone World 82-333 Introduction to Chinese Language and Culture Var. 82-342 Spain: Language and Culture 82-343 Latin America: Language and Culture 82-344 U.S. Latinos: Language and Culture 82-345 Introduction to Hispanic Literary and Cultural Studies C. Humanities and Arts Electives (27 units) Complete 3 non-technical courses of at least units each from any of the departments in the College of Humanities & Social Sciences or the College of Fine Arts. Some of the courses taught in these units are considered technical courses and may not be used to satisfy this requirement. Additionally, a select set of courses from Business Administration and from Environmental and Public Policy can also count for this requirement. The complete list of additions and deletions can be found at http:// www.csd.cs.cmu.edu/education/bscs/humanities-arts.html. Consult with a CS undergraduate advisor for additional information. Required Minor A sequence of courses proscribed by the requirements of the particular department. Completion of a second major (or double degree) also satisfies this requirement. If permitted by the minor or second major department, courses taken in satisfaction of the minor or second major may also count toward any category other than Computer Science and Mathematics. Consult with a CS undergraduate advisor and an advisor from the department of the minor (or double major) for specific restrictions on double counting. Computing @ Carnegie Mellon The following course is required of all students to familiarize them with the campus computing environment: -10x Computing @ Carnegie Mellon 3 Free Electives A free elective is any Carnegie Mellon course. However, a maximum of nine units of Physical Education and/or Military Science (ROTC) and/or Student- Led (StuCo) courses may be used toward fulfilling graduation requirements. Summary of Degree Requirements: Area Courses Computer Science 14 135 Mathematics 5 4 Science/Engineering 4 36 Humanities/Arts 7 63 Minor Requirement/Free electives Computing @ Carnegie Mellon Sample Course Sequence Freshman Year: 8 74 1 3 360 Fall 15-2 Principles of Imperative Computation 10 15-8 Freshman Immigration Course 1 15-131 Great Practical Ideas for Computer Scientists 2 (optional, not required for CS major) 21-0 Differential and Integral Calculus 10 21-7 Concepts of Mathematics 10 76-101 Interpretation and Argument -10x Computing Skills Workshop 3 Spring 15-150 Principles of Functional Programming 10 15-251 Great Theoretical Ideas in Computer Science 21-2 Integration and Approximation 10 xx-xxx Science/Engineering Course xx-xxx Humanities and Arts Elective Sophomore Year: Fall 15-213 Introduction to Computer Systems 15-221 Technical Communication for Computer Scientists 21-241 Matrices and Linear Transformations 10 xx-xxx Science/Engineering Course xx-xxx Minor Requirement / Free Elective Spring 15-210 Parallel and Sequential Data Structures and Algorithms xx-xxx Computer Science: Applications Elective xx-xxx Science/Engineering Course xx-xxx Humanities and Arts Electivs xx-xxx Minor Requirement / Free Elective Junior Year: Fall 15-451 Algorithm Design and Analysis xx-xxx Computer Science: Logic/Languages Elective xx-xxx Probability Course xx-xxx Humanities and Arts Elective xx-xxx Minor Requirement / Free Elective Spring 15-xxx Computer Science: Systems Elective xx-xxx Computer Science: Algorithms/Complexity Elective xx-xxx Humanities and Arts Elective xx-xxx Minor Requirement / Free Elective xx-xxx Science/Engineering Course Senior Year: Fall xx-xxx School of Computer Science Elective 45 50 4 48 48 48

4 School of Computer Science xx-xxx Humanities and Arts Elective xx-xxx Minor Requirement / Free Elective xx-xxx Minor Requirement / Free Elective Spring xx-xxx School of Computer Science Elective xx-xxx Humanities and Arts Elective xx-xxx Minor Requirement / Free Elective xx-xxx Minor Requirement / Free Elective 360Minimum number of units required for the degree: The flexibility in the curriculum allows many different schedules, of which the above is only one possibility. Some elective courses are offered only once per year (Fall or Spring). Constrained CS electives (algorithms/ complexity, logic/languages, systems and applications) may be taken in any order and in any semester if prerequisites are met and seats are available. Constrained electives are shown in the specific semesters in the schedule above as an example only. Students should consult with their academic advisor to determine the best elective options depending on course availability, their academic interests and their career goals. Additionally, the School of Computer Science offers a Double Major in Human-Computer Interaction and a Double Major in Robotics, as well as numerous computingoriented Minors available to majors and non-majors alike. Undergraduate Research Thesis Students considering going on to graduate school in Computer Science should take a wide variety of Computer Science and Mathematics courses, as well as consider getting involved in independent research as early as possible. This would be no later than the junior year and can begin even earlier. Students interested in graduate school are strongly encouraged to participate in the Undergraduate Research Thesis program. Additionally, graduate CS courses can be taken with permission of the instructor and in consultation with an academic advisor. The goal of the Undergraduate Research Thesis Program is to introduce students to the breadth of tasks involved in independent research, including library work, problem formulation, experimentation, analysis, writing and speaking. In particular, students write a survey paper summarizing prior results in their desired area of research, present a public poster session in December describing their current progress, present their final results in an oral summary in the year-end university-wide Undergraduate Research Symposium (Meeting of the Minds) and submit a written thesis at the end of their senior year. Students work closely with faculty advisors to plan and carry out their research. The Undergraduate Research Thesis (15-5) can start as early as the Spring semester of the junior year, and spans the entire senior year. Students receive a total of 36 units of academic credit for the thesis work. Up to 18 units can be counted toward CS elective requirements ( per semester). For most students, the thesis program requires at least one semester with 18 units of thesis work, so students in this program are advised to plan their schedules carefully to ensure there is ample time to perform the required research for the thesis. Students interested in research are urged to consult with their CS undergraduate advisor and Assistant Dean no later than the start of their junior year in order to plan their workload effectively. Computer Science Additional Majors and Minors The School of Computer Science offers an Additional Major in Computer Science, Human-Computer Interaction, and Robotics. It also offers Minors in Computer Science, Computational Biology, Human-Computer Interaction, Language Technologies, Neural Computation, Robotics, and Software Engineering. To see the additional majors and minors other than Computer Science, see Additional Majors and Minors in SCS (http:// coursecatalog.web.cmu.edu/schoolofcomputerscience/addlmajorsminors). Computer Science Additional Major Students interested in pursuing an additional major in Computer Science should first consult with an advisor in the CS Undergraduate Office after completion of prerequisites and at least two of the core courses for application requirements and availability of seats. 36 36 The following courses are required for the Additional Major in Computer Science: Prerequisites: 15-1 Fundamentals of Programming and Computer Science 15-2 Principles of Imperative Computation 10 (requires 21-7 as a co-requisite) 15-150 Principles of Functional Programming 10 21-0 Differential and Integral Calculus 10 21-2 Integration and Approximation 10 21-7 Concepts of Mathematics 10 Computer Science core: 15-210 Parallel and Sequential Data Structures and Algorithms 15-213 Introduction to Computer Systems 15-251 Great Theoretical Ideas in Computer Science 15-451 Algorithm Design and Analysis One of the following Matrix Algebra courses: 21-241 Matrices and Linear Transformations 10 21-242 Matrix Theory 10 One of the following Probability courses: 15-35 Probability and Computing 21-325 Probability 36-217 Probability Theory and Random Processes 36-225 Introduction to Probability Theory One Communications course: 15-221 Technical Communication for Computer Scientists One Algorithms & Complexity elective: 15-354 Computational Discrete Mathematics 15-355 Modern Computer Algebra 15-453 Formal Languages, Automata, and Computability 15-455 Undergraduate Complexity Theory 15-456 Computational Geometry 21-301 Combinatorics 21-484 Graph Theory One Logics & Languages elective: 15-3 Foundations of Programming Languages 15-317 Constructive Logic 15-414 Bug Catching: Automated Program Verification and Testing 15-424 Foundations of Cyber-Physical Systems 21-300 Basic Logic 80-310 Formal Logic 80-311 Undecidability and Incompleteness One Software Systems elective: 15-410 Operating System Design and Implementation 15-411 Compiler Design 15-418 Parallel Computer Architecture and Programming 15-440 Distributed Systems 15-441 Computer Networks One Applications elective: 02-510 Computational Genomics 05-31 Designing Human Centered Software 10-601 Introduction to Machine Learning 11-411 Natural Language Processing 15-313 Foundations of Software Engineering 15-322 Introduction to Computer Music 15-323 Computer Music Systems and Information Processing 15-381 Artificial Intelligence: Representation and Problem Solving

Carnegie Mellon University 5 15-415 Database Applications 15-462 Computer Graphics 16-384 Robot Kinematics and Dynamics 16-385 Computer Vision Two Computer Science electives: These electives can be from any SCS department; 200-level or above, at least units each: Computer Science [15-], Lane Center for Computational Biology [02-], Human Computer Interaction Institute [05-], Institute for Software Research [08-,17-], Machine Learning [10-], Language Technologies Institute [11-], and Robotics Institute [16-]. (NOTE: The following courses do NOT count as Computer Science electives: 02-223, 02-250, 02-261, 08-200, 15-351. Consult with the CS undergraduate office before registration to determine eligibility for this requirement.) Computer Science Minor 18 Students interested in pursuing a minor in Computer Science should first consult with an advisor in the CS Undergraduate Office after completion of the prerequisites and core courses for application requirements. The following courses are required for the Minor in Computer Science: Prerequisites: 15-1 Fundamentals of Programming and Computer Science 21-7 Concepts of Mathematics 10 Computer Science core courses: 15-2 Principles of Imperative Computation 10 15-150 Principles of Functional Programming 10 15-210 Parallel and Sequential Data Structures and Algorithms One of the following Computer Science core courses: 15-213 Introduction to Computer Systems 15-251 Great Theoretical Ideas in Computer Science Two Computer Science electives, of at least units each: CS elective courses must be 15-213 or higher, at least -units each. 15-221 and 15-351 cannot be used. One course can be from any SCS department, with prior approval. Note: Since ECE students must take 15-213/18-213, they are required to take three CS electives; two can be from any SCS department with prior approval. This three-elective stricture applies to any student minoring in CS who is required to take 15-213/18-213 or 15-251 for their home major requirements. Double-Counting Restriction 18 In order to avoid excessive double-counting, students pursuing a Double Major or Minor in Computer Science must complete at least 6 courses in their home department, of at least units each, none of which are required by (or are cognates for requirements in) the Computer Science major. Additional Majors and Minors in SCS Computational Biology Minor Director: Dr. Ziv Bar-Joseph Advisor: Dr. Karen Thickman Admin Coordinator: Thom Gulish Website: http://lane.compbio.cmu.edu/education/minor.html The computational biology minor is open to students in any major of any college at Carnegie Mellon. The curriculum and course requirements are designed to maximize the participation of students from diverse academic disciplines. The program seeks to produce students with both basic computational skills and knowledge in biological sciences that are central to computational biology. Why Minor in Computational Biology? Computational Biology is concerned with solving biological and biomedical problems using mathematical and computational methods. It is recognized as an essential element in modern biological and biomedical research. There have been fundamental changes in biology and medicine over the past two decades due to spectacular advances in high throughput data collection for genomics, proteomics and biomedical imaging. The resulting availability of unprecedented amounts of biological data demands the application of advanced computational tools to build integrated models of biological systems, and to use them to devise methods of prevent or treat disease. Computational Biologists inhabit and expand the interface of computation and biology, making them integral to the future of biology and medicine. A minor in Computational Biology will position students well for entering the job market and graduate school in this exciting and growing field. Admission Students must apply for admission no later than November 30 of their senior years; an admission decision will usually be made within one month. Students are encouraged to apply as early as possible in their undergraduate careers so that the advisor of the computational biology minor can provide advice on their curriculum. To apply, send email to Dr. Ziv Bar-Joseph <zivbj at andrew.cmu.edu> and Dr. Karen Thickman <krthickman at cmu.edu>. Include in your email: Full name Andrew ID Preferred email address (if different) Your class and College/School at Carnegie Mellon Semester you intend to graduate All (currently) declared majors and minors Statement of purpose (maximum 1 page) Describes why you want to take this minor and how it fits into your career goals Proposed schedule of courses for the minor (this is your plan, NOT a commitment) Curriculum The minor in computational biology requires a total of five courses: 3 core courses, 1 biology elective, and 1 computer science elective, for a total of at least 45 units. Prerequisites 03-1 Modern Biology 15-2 Principles of Imperative Computation 10 Core Classes 02-250 Introduction to Computational Biology 02-261 Quantitative Cell and Molecular Biology Laboratory (03-116 Phage Genomics Research or 03-343 Experimental Techniques in Molecular Biology may be substituted for 02-261 with permission of the minor advisor) plus one of the following courses: 02-510 Computational Genomics 02-5 Computational Methods for Biological Modeling and Simulation 02-530 Cell and Systems Modeling Biology Electives (one of the following): 03-231 Biochemistry I 03-240 Cell Biology 03-327 Phylogenetics 03-330 Genetics 03-362 Cellular Neuroscience 03-363 Systems Neuroscience 03-364 Developmental Neuroscience 03-43 Introduction to Biophysics 03-442 Molecular Biology 03-534 Biological Imaging and Fluorescence Spectroscopy 42-202 Physiology Computer Science Electives (one of the following): 02-422 Advanced Algorithms for Computational Structural Biology 02-450 Automation of Biological Research 02-500 Undergraduate Research in Computational Var. Biology 02-510 Computational Genomics

6 School of Computer Science 02-5 Computational Methods for Biological Modeling and Simulation 02-530 Cell and Systems Modeling 02-740 Bioimage Informatics 0-560 Computational Chemistry 10-601 Introduction to Machine Learning 15-381 Artificial Intelligence: Representation and Problem Solving 15-386 Neural Computation 15-415 Database Applications 16-721 Learning-based Methods in Vision A number of graduate courses in CS and Robotics may be taken in consultation with the minor advisor. Note: No more than two courses may be double counted with your major's core requirements. Courses in the minor may not be counted towards another SCS minor. Consult the advisor for the minor for more information. Human-Computer Interaction Additional Major The undergraduate major in HCI is available only as an additional major. If you have questions, please contact the Academic Program Coordinator at hciibachelors@cs.cmu.edu. Human-Computer Interaction (HCI) is devoted to the design, implementation, and evaluation of interactive computer-based technology. Examples of HCI products include intelligent computer tutors, wearable computers, and highly interactive web sites. Constructing an HCI product is a cyclic, iterative process that involves at least three stages. Human-Computer Interaction Minor The Minor in Human-Computer Interaction will give students core knowledge about techniques for building successful user interfaces, approaches for conceiving, refining, and evaluating interfaces that are useful and useable, and techniques for identifying opportunities for computational technology to improve the quality of people s lives. The students will be able to effectively collaborate in the design, implementation, and evaluation of easy-to-use, desirable, and thoughtful interactive systems. They will be prepared to contribute to multi-disciplinary teams that create new interactive products, services, environments, and systems. The key concepts, skills and methods that students will learn in the HCI Minor include: Fieldwork for understanding people s needs and the influence of context Generative approaches to imagining many possible solutions such as sketching and bodystorming Iterative refinement of designs Basic visual design including typography, grids, color, and the use of images Implementation of interactive prototypes Evaluation techniques including discount and empirical evaluation methods The HCI minor is targeted at undergraduates who expect to get jobs where they design and/or implement information technology-based systems for end users, and well as students with an interest in learning more about the design of socio-technical systems. It is appropriate for students with majors in Computer Science and Information Systems, as well as students in less software-focused majors, including Design, Architecture, Art, Business Administration, Psychology, Statistics, Decision Science, Mechanical Engineering, Electrical Engineering, English and many others in the university. Curriculum The only prerequisite for this Minor is an introductory-level college programming course (such as 15-110, 15-1, 15-1, or 51-257) and to be in good standing with the University. In addition to the programming prerequisite, the Minor has required two courses 05-31 Designing Human Centered Software (DHCS) and 05-4xx Interaction Design Overview (IDO) and four electives from an approved list. The student will be required to get a grade of C or better in each course in order for it to count as part of the Minor. There is no final project or research required for the Minor. Required Courses 05-31 Designing Human Centered Software (DHCS) 1 : This course provides an overview of the most important methods taught in the Additional Major in HCI, such as Contextual Inquiry, Prototyping and Iterative Design, Heuristic Evaluation, and Think Aloud User Studies. It covers in a more abbreviated form the content of 05-410 User-Centered Research and Evaluation, 05-430 Programming Usable Interfaces, and 05-433 User Interface Lab. 05-32 (IDO) 2 : This is a new design course that will combine material from 05-651 and 05-650 for students who do not have any previous experience with design, in a form that will fit appropriately in to a onesemester format. It will be first offered in Spring 2014. Electives The HCI minor requires four electives from the pre-approved list of electives (http://www.hcii.cmu.edu/undergraduate-electives) made available at the HCII website. Double Counting Students may double count up to two (2) of the required courses or electives with their primary major. relationship between the BHCI Major and Minor Admission BHCI Major: Application and admissions required, information on the HCII website (http://staging.cs.cmu.edu/academics/hci-undergraduate/ major/applying). BHCI Minor: Admissions form available at the HCII website (http:// staging.cs.cmu.edu/academics/hci-undergraduate/minor). Prerequisites BHCI Major: Freshman-level programming (51-257 or 15-110 or 15-1 or 15-1. Statistics (introductory) Cognitive Psychology Interaction Design Fundamentals or Communication Design Fundamentals BHCI Minor: Freshman-level programming (51-257 or 15-110 or 15-1 or 15-1. Core Courses BHCI Major: Interaction Design Studio (IDS) User Centered Research & Evaluation (UCRE) HCI Programming (PUI/SSUI) and Lab BHCI Project BHCI Minor: Interaction Design Overview (IDO) Designing Human Centered Systems (DHCS) Electives BHCI Major: Four (4) electives (2 from defined list and 2 free electives approved by the director of the BHCI major) BHCI Minor: Four (4) electives (from defined list) Double Counting BHCI Major: Two (2) courses with primary major. BHCI Minor: Two (2) courses with primary major. Footnotes 1 Alternatively, a student can take both the BS/MHCI empirical methods course (05-410) and the BS/MHCI core-programming course (either 05-430 Programming Usable Interfaces or05-431 Software Structures for User Interfaces, along with its associated 05-433 User Interface Lab). If students take this course sequence, they would get credit for fulfilling this requirement plus one elective.

Carnegie Mellon University 7 2 Alternatively, students can fulfill the design requirement by taking 05-650 and 05-651. If students take this course sequence, they would get credit for fulfilling this requirement plus one elective. These alternative ways of fulfilling the requirements for the HCI minor are designed for students who are in the HCI 2nd major who want to downgrade to the minor. These students can use some the courses completed for the HCI 2nd major as a way of fulfilling the requirements for the minor. Students who are in the HCI minor right from the start are strongly encouraged to follow the regular requirements outlined above and are strongly discouraged from trying these alternative ways of fulfilling the requirements. It can be extremely difficult to get into any of the alternative courses. This is true especially for 05-650, but for other courses as well. The fact that a student in the minor has already taken 05-651 will not give priority for getting into Studio. Language Technologies Minor Chair: Alan W. Black E-mail: awb@cs.cmu.edu Website: http://www.lti.cs.cmu.edu/lti_minor Human language technologies have become an increasingly central component of Computer Science in the last decade. Information retrieval, machine translation and speech technology are used daily by the general public, while text mining, natural language processing, and languagebased tutoring are used regularly within more specialized professional or educational environments. The Language Technologies Minor allows students to learn about language technologies and apply them through a directed project. Prerequisites Prerequisites 15-2 Principles of Imperative Computation 10 15-150 Principles of Functional Programming 10 Recommended 21-241 Matrices and Linear Transformations 10 or 21-341 Linear Algebra 36-217 Probability Theory and Random Processes Curriculum Core Course 11-721 Grammars and Lexicons Electives (choose 3) 11-411 Natural Language Processing 11-441 Search Engines and Web Mining 11-42 Speech Processing 11-711 Algorithms for NLP 11-731 Machine Translation 11-741 Information Retrieval 11-751 Speech Recognition and Understanding 11-752 Speech II: Phonetics, Prosody, Perception and Synthesis 11-761 Language and Statistics 80-180 Nature of Language or 80-280 Linguistic Analysis Project A semester-long directed research project OR paper to provide hands-on experience and an in-depth study of a topic (in same area as a chosen elective) Double Counting of Courses SCS undergraduates may use 11-721 Grammars and Lexicons as an elective for their CS degree and also as a required course for the LT minor. Courses in the minor may not be counted towards another SCS minor. Machine Learning Minor Chair: William W. Cohen E-mail: ml-minor@cs.cmu.edu Website: http://www.ml.cmu.edu/prospective-students/minor-in-machinelearning.html Machine learning and statistical methods are increasingly used in many application areas including natural language processing, speech, vision, robotics, and computational biology. The Minor in Machine Learning allows undergraduates to learn about the core principles of this field. Prerequisites 15-2 Principles of Imperative Computation 10 21-2 Integration and Approximation 10 36-217 Probability Theory and Random Processes or 36-225 Introduction to Probability Theory or 21-325 Probability 36-226 Introduction to Statistical Inference or 36-326 Mathematical Statistics (Honors) Core Courses 10-601 Introduction to Machine Learning or 10-701 Introduction to Machine Learning 36-401 Modern Regression Electives Total of 3 courses (36 units) from: 10-701 Introduction to Machine Learning 10-7xx certain ML grad courses as specified on the Minor web page 10-xxx year-long supervised research 36-315 Statistical Graphics and Visualization 36-402 Advanced Methods for Data Analysis 36-462 Special Topics: Data Mining 36-463 Special Topics: Multilevel and Hierarchical Models 36-464 Special Topics: Applied Multivariate Methods Additional electives can be found on the minor electives page (http:// www.ml.cmu.edu/prospective-students/minor-electives.html). Double Counting Any course in the Machine Learning minor, other than the prerequisites, may not be counted towards another SCS minor. The Minor in Neural Computation Director: Dr. Tai Sing Lee Administrative Coordinator: Melissa Stupka Website: http://www.cnbc.cmu.edu/upnc/nc_minor/ The minor in Neural Computation is an inter college minor jointly sponsored by the School of Computer Science, the Mellon College of Science, and the Dietrich College of Humanities and Social Sciences, and is coordinated by the Center for the Neural Basis of Cognition (CNBC) (http:// www.cnbc.cmu.edu). The Neural Computation minor is open to students in any major of any college at Carnegie Mellon. It seeks to attract undergraduate students from computer science, psychology, engineering, biology, statistics, physics, and mathematics from SCS, CIT, Dietrich College and MCS. The primary objective of the minor is to encourage students in biology and psychology to take computer science, engineering and mathematics courses, to encourage students in computer science, engineering, statistics and physics to take courses in neuroscience and psychology, and to bring students from different disciplines together to form a community. The curriculum and course requirements are designed to maximize the participation of students from diverse academic disciplines. The program seeks to produce students with both basic computational skills and knowledge in cognitive science and neuroscience that are central to computational neuroscience. Curriculum The minor in Neural Computation will require a total of five courses: four courses drawn from the four core areas (A: Neural Computation, B: Neuroscience, C: Cognitive Psychology, D: Intelligent System Analysis), one from each area, and one additional depth elective chosen from one of the core areas that is outside the student's major. The depth elective can be replaced by a one-year research project in computational neuroscience. No more than two courses can be double counted toward the student's major or other minors. However, courses taken for general education

8 School of Computer Science requirements of the student's degree are not considered to be double counted. A course taken to satisfy one core area cannot be used to satisfy the course requirement for another core area. The following listing presents a set of current possible courses in each area. Substitution is possible but requires approval by the director of the minor program. A. Neural Computation 15-386 Neural Computation 15-387 Computational Perception 15-883 Computational Models of Neural Systems 85-41 Introduction to Parallel Distributed Processing 86-375 Computational Perception Pitt-Mathematics-1800 Introduction to Mathematical Neuroscience B. Neuroscience 03-362 Cellular Neuroscience 03-363 Systems Neuroscience 03-761 Neural Plasticity 85-765 Cognitive Neuroscience Var. Pitt-Neuroscience 1000 Introduction to Neuroscience Pitt-Neuroscience 10 Neurophysiology C. Cognitive Psychology 85-211 Cognitive Psychology 85-213 Human Information Processing and Artifical Intelligence 85-4 Cognitive Modeling 85-41 Introduction to Parallel Distributed Processing 85-426 Learning in Humans and Machines 85-765 Cognitive Neuroscience Var. D. Intelligent System Analysis 10-601 Introduction to Machine Learning 15-381 Artificial Intelligence: Representation and Problem Solving 15-386 Neural Computation 15-387 Computational Perception 15-486 Artificial Neural Networks 15-44 Special Topic: Cognitive Robotics 16-2 Introduction to Feedback Control Systems 16-311 Introduction to Robotics 16-385 Computer Vision 18-20 Signals and Systems 24-352 Dynamic Systems and Controls 36-225 Introduction to Probability Theory 36-247 Statistics for Lab Sciences 36-401 Modern Regression 36-410 Introduction to Probability Modeling 42-631 Neural Data Analysis 42-632 Neural Signal Processing 86-375 Computational Perception 86-631 Neural Data Analysis Prerequisites The required courses in the above four core areas require a number of basic prerequisites: basic programming skills at the level of 15-110 Principles of Computing and basic mathematical skills at the level of 21-2 Integration and Approximation or their equivalents. Some courses in Area D require additional prerequisites. Area B Biology courses require, at minimum, 03-1 Modern Biology. Students might skip the prerequisites if they have the permission of the instructor to take the required courses. Prerequisite courses are typically taken to satisfy the students' major or other requirements. In the event that these basic skill courses are not part of the prerequisite or required courses of a student's major, one of them can potentially count toward the five required courses (e.g. the depth elective), conditional on approval by the director of the minor program. Research Requirements (Optional) The minor itself does not require a research project. The student however may replace the depth elective with a year-long research project. In special circumstances, a research project can also be used to replace one of the five courses, as long as (1) the project is not required by the student's major or other minor, (2) the student has taken a course in each of the four core areas (not necessarily for the purpose of satisfying this minor's requirements), and (3) has taken at least three courses in this curriculum not counted toward the student's major or other minors. Students interested in participating in the research project should contact any faculty engaged in computational neuroscience or neural computation research at Carnegie Mellon or in the University of Pittsburgh. A useful webpage that provides listing of faculty in neural computation is http://www.cnbc.cmu.edu/ computational-neuroscience. The director of the minor program will be happy to discuss with students about their research interest and direct them to the appropriate faculty. Fellowship Opportunities The Program in Neural Computation (PNC) administered by the Center for the Neural Basis of Cognition currently provides 3-4 competitive fullyear fellowships ($11,000) to Carnegie Mellon undergraduate students to carry out mentored research in neural computation. The fellowship has course requirements similar to the requirements of the minor. Students do not apply to the fellowship program directly. They have to be nominated by the faculty members who are willing to mentor them. Therefore, students interested in the full-year fellowship program should contact and discuss research opportunities with any CNBC faculty at Carnegie Mellon or University of Pittsburgh working in the area of neural computation or computational neuroscience and ask for their nomination by sending email to Dr. Tai Sing Lee, who also administers the undergraduate fellowship program at Carnegie Mellon. See http://www.cnbc.cmu.edu/ fellowcompneuro for details. The Program in Neural Computation also offers a summer training program for undergraduate students from any U.S. undergraduate college. The students will engage in a 10-week intense mentored research and attend a series of lectures in neural computation. See the http://www.cnbc.cmu.edu/ summercompneuro for application information. Robotics Additional Major Director: Dr. Howie Choset Administrative Coordinator: Julie Goldstein Website: http://addlmajor.ri.cmu.edu/#&panel1-1 The Additional Major in Robotics focuses on the theme that robotics is both multidisciplinary and interdisciplinary. This means that it draws from many fields, such as mechanical engineering, computer science and electrical engineering, and it also integrates these fields in a novel manner. The foundation of this program lies in motion and control. Upon this base, sensing, cognition, and action are layered. These foci are brought together by a unique systems perspective special to robotics. Since robotics involves building artifacts that embody these fundamentals, foci, and systems thinking, there is a "hands-on" course requirement. Lastly, students will complete a capstone course that will tie together previously learned skills and knowledge. Admission The Additional Major in Robotics is available to all Carnegie Mellon undergraduate students. Students should apply for the Robotics Additional Major in their Freshman year. Students in their Sophomore year may apply, provided they meet the requirements and their schedule would allow. The application is due early February and decisions on admittance to the Additional Major will be emailed to students in time for Fall registration. Application materials include: Full name and email address Home college, year you intend to graduate, and list of all declared Majors and Minors Statement of purpose (maximum 1 page, single spaced, to articulate why the student wants to pursue the Robotics Additional Major) Proposed schedule of required courses Unofficial Transcript (can be downloaded from SIO) Curriculum Prerequisites Calculus 21-25 Calculus in Three Dimensions Linear Algebra (choose one) 18-202 Mathematical Foundations of Electrical Engineering 21-240 Matrix Algebra with Applications 10 21-241 Matrices and Linear Transformations 10

Carnegie Mellon University 21-260 Differential Equations 24-311 Numerical Methods Programming in C 15-2 Principles of Imperative Computation 10 or knowledge and experience programming in C Required Courses Choose 10 courses total (one from each category plus two electives): Overview/Introductory 16-311 Introduction to Robotics Controls 16-2 Introduction to Feedback Control Systems 18-370 Fundamentals of Control 24-451 Feedback Control Systems 16-xxx Upper-level RI course with instructor and Program Director's permission Kinematics 16-384 Robot Kinematics and Dynamics 24-355 Kinematics and Dynamics of Mechanisms (not offered regularly) 16-xxx Upper-level RI course with instructor and Program Director's permission Machine Perception 15-463 Computational Photography 16-385 Computer Vision 16-421 Vision Sensors 85-370 Perception Upper-level RI course with Instructor and Program Director's permission Cognition and Reasoning 10-601 Introduction to Machine Learning 11-344 Machine Learning in Practice 15-381 Artificial Intelligence: Representation and Problem Solving 15-44 Special Topic: Cognitive Robotics 16-xxx Upper-level RI planning course with instructor and Program Director's permission "Hands-on Course" 15-41 Special Topic: CMRoboBits: Creating Intelligent Robots 16-362 Mobile Robot Programming Laboratory 18-578 Mechatronic Design 16-xxx Upper-level RI project course e.g., 16-861 or 16-865 or independent study with instructor and Program Director's permission Systems Engineering 16-450 Robotics Systems Engineering Capstone Course 16-474 Robotics Capstone Required Electives (choose two) 10-601 Introduction to Machine Learning 11-344 Machine Learning in Practice 15-381 Artificial Intelligence: Representation and Problem Solving 15-462 Computer Graphics 15-41 Special Topic: CMRoboBits: Creating Intelligent Robots 15-44 Special Topic: Cognitive Robotics 16-264 Humanoids 16-362 Mobile Robot Programming Laboratory 16-385 Computer Vision 16-421 Vision Sensors 18-342 Fundamentals of Embedded Systems 18-348 Embedded Systems Engineering 18-34 Embedded Real-Time Systems 18-54 Embedded Systems Design 18-578 Mechatronic Design 24-41 Department Research Honors Var. 24-675 Micro/Nano Robotics 3-500 Honors Research Project Var. 85-370 Perception 85-382 Consciousness and Cognition 85-35 Applications of Cognitive Science 85-4 Cognitive Modeling 85-41 Introduction to Parallel Distributed Processing Any of these can be independent study but only one independent study is allowed. A student can also take additional courses from the core; e.g., a student who takes 16-385 as a core can take 16-421 as an elective. Graduate level Robotics courses may be used to meet elective requirement with permission from the Program Director. Graduate level Mechanical Engineering and Electrical and Computer Engineering courses that are relevant to robotics may be used to meet the elective requirement with permission from the Program Director. A 3.0 QPA in the Additional Major curriculum is required for graduation. Robotics Minor Director: Dr. Howie Choset Administrative Coordinator: Julie Goldstein Website: http://www.ri.cmu.edu/education/ugrad_minor.html The Minor in Robotics provides an opportunity for undergraduate students at Carnegie Mellon to learn the principles and practices of robotics through theoretical studies and hands-on experience with robots. The Minor is open to students in any major of any college at Carnegie Mellon. Students initially learn the basics of robotics in an introductory robotics overview course. Additional required courses teach control systems and robotic manipulation. Students also choose from a wide selection of electives in robotics, perception, computer vision, cognition and cognitive science, or computer graphics. Students have a unique opportunity to undertake independent research projects, working under the guidance of Robotics Institute faculty members, this provides an excellent introduction to robotics research for those considering graduate studies. All Robotics Minors are required to take Introduction to Robotics (16-311). This course is designed to help students understand the big picture of what is going on in robotics through topics such as kinematics, mechanisms, motion planning, sensor based planning, mobile robotics, sensors, and vision. The minor also requires students to take a controls class and a manipulation, dynamics, or mechanism class. These courses provide students with the necessary intuition and technical background to move on to more advanced robotics courses. In addition to the required courses, students must take 2 electives. Students may satisfy the elective requirement by taking an approved course or upper-level Robotics course. The student must have course selection approved by the Director of the Minor during the application submission process. In order to be awarded the Minor in Robotics, a student must earn a cumulative QPA of 2.5 in these courses. Courses that are taken Pass/Fail or audited cannot be counted for the Minor. Admission Admission to the Undergraduate Minor in Robotics is limited to current Carnegie Mellon students. Students interested in signing up for the minor should fill out the application form (https://www-preview.ri.cmu.edu/ education/apply/ugrad_appform.html). Prerequisite Successful candidates for the Robotics Minor will have prerequisite knowledge of C language, basic programming skills, and familiarity with basic algorithms. Students can gain this knowledge by taking 15-2 Principles of Imperative Computation. Required Courses Overview: 16-311 Introduction to Robotics Controls (choose one of the following): 24-451 Feedback Control Systems 18-370 Fundamentals of Control 16-2 Introduction to Feedback Control Systems (Computer Science)

10 School of Computer Science Manipulation (choose one of the following): 16-384 Robot Kinematics and Dynamics 24-355 Kinematics and Dynamics of Mechanisms Electives Two Electives (chosen from the following): 10-601 Introduction to Machine Learning 11-344 Machine Learning in Practice 15-381 Artificial Intelligence: Representation and Problem Solving 15-424 Foundations of Cyber-Physical Systems 15-462 Computer Graphics 15-463 Computational Photography 15-41 Special Topic: CMRoboBits: Creating Intelligent Robots 15-44 Special Topic: Cognitive Robotics 16-264 Humanoids 16-362 Mobile Robot Programming Laboratory 16-385 Computer Vision 16-421 Vision Sensors 18-342 Fundamentals of Embedded Systems 18-348 Embedded Systems Engineering 18-34 Embedded Real-Time Systems 18-54 Embedded Systems Design 18-578 Mechatronic Design 24-41 Department Research Honors Var. 24-675 Micro/Nano Robotics 3-500 Honors Research Project Var. 85-370 Perception 85-382 Consciousness and Cognition 85-35 Applications of Cognitive Science 85-4 Cognitive Modeling 85-41 Introduction to Parallel Distributed Processing Graduate level Robotics courses may be used to meet elective requirement with permission from the Program Director. Graduate level Mechanical Engineering and Electrical and Computer Engineering courses that are relevant to robotics may be used to meet the elective requirement with permission from the Program Director. Double-Counting Restriction Courses in the Robotics Minor may not be counted towards another SCS minor. Computer Science (CS) Majors are permitted to double count a maximum of two courses (excluding General Education requirements) towards the Minor in Robotics. Software Engineering Minor Co-Director: Jonathan Aldrich Co-Director: Claire Le Goues Website: http://isri.cmu.edu/education/undergrad/ The Software Engineering minor is designed to teach the fundamental tools, techniques, and processes of software engineering.through internships and a mentored project experience, students gain an understanding of the issues of scale and complexity that motivate software engineering tools and techniques.the core curriculum includes material both on engineering the software product and on the process, teamwork, and management skills that are essential to successful engineering.graduates of the program should have the technical, process, and teamwork skills to be immediately productive in a mature engineering organization. Prerequisite 15-214 Principles of Software Construction: Objects, Design, and Concurrency Core Course Requirements 15-313 Foundations of Software Engineering 15-413 Software Engineering Practicum Electives The minor requires three elective courses, one selected from each of the following categories: 1. One domain-independent course focused on technical software engineering material: 15-414 Bug Catching: Automated Program Verification and Testing 17-651 Models of Software Systems 17-652 Methods: Deciding What to Design 17-653 Managing Software Development 17-654 Analysis of Software Artifacts 17-655 Architectures for Software Systems 17-60 Seminar in Software Process Var. 17-xxx Other Software Engineering graduate classes may be taken; get preapproval from the program director. 2. One engineering-focused course with a significant software component: 15-410 Operating System Design and Implementation 15-4 Operating System Practicum Var. 15-437 Web Application Development 15-440 Distributed Systems 15-441 Computer Networks 15-610 Engineering Distributed Systems 18-54 Embedded Systems Design 18-64 Distributed Embedded Systems Other courses may be acceptable, with prior approval from the director of the minor. 3. One course that explores computer science problems related to existing and emerging technologies and their associated social, political, legal, business, and organizational contexts: 08-200 Ethics and Policy Issues in Computing 08-300 Constructing Appropriate Technology 08-532 Law of Computer Technology 08-533 Privacy, Policy, Law and Technology 08-781 Mobile and Pervasive Computing Services 08-801 Dynamic Network Analysis 08-810 Computational Modeling of Complex Socio- Technical Systems 70-45 Web Business Engineering 15-30 Entrepreneurship for Computer Science 15-421 Information Security and Privacy 1-402 Telecommunications Technology, Policy & Management 1-403 Policies of Wireless Systems and the Internet 70-311 Organizational Behavior 70-414 Entrepreneurship for Engineers 70-421 Entrepreneurship for Computer Scientists 70-471 Supply Chain Management 88-260 Organizations 88-341 Organizational Communication 88-343 Economics of Technological Change 88-31 Technology and Economic Growth Other courses may be acceptable, with prior approval from the director of the minor. Required Internship and Reflection Course A software engineering internship of a minimum of 8 full-time weeks in an industrial setting is required. The student must be integrated into a team and exposed to industry pressures. The intern may work in development, management, quality assurance, or other relevant positions. The director of the SE minor program has sole discretion in approving an internship experience based on these criteria. Students should confirm that an internship position is appropriate before accepting it, but internships that fulfill the criteria will also be accepted after the fact.

Carnegie Mellon University 11 17-413 Software Engineering Reflection Each student will write an issue-focused reflection and analysis of some personal software engineering experience, typically (but not always) based on the engineering internship above. This report must be passed by one SCS faculty member and one SE Ph.D. student, for both technical content and effective written communication. Initial course meetings will cover the reflective, writing, and speaking process. In later meetings, each student will present his or her experience through a 30-45 minute talk, which will be evaluated for communication skills and critical reflective content. This course is limited to enrollment of 16, and students who are admitted to the minor program are given first priority. Double Counting Rule At most 2 of the courses used to fulfill the minor requirements may be counted towards any other major or minor program. SCS Policies & Procedures School of Computer Science (SCS) Academic Standards and Actions Grading Practices Grades given to record academic performance in SCS are detailed under Grading Practices at http://coursecatalog.web.cmu.edu/servicesandoptions/ undergraduateacademicregulations/ Dean's List SCS recognizes each semester those undergraduates who have earned outstanding academic records by naming them to the Dean's List. The criterion for such recognition is a quality point average of at least 3.75 while completing a minimum of 36 factorable units and earning no incomplete grades. Academic Actions In the first year, quality point averages below 1.75 in either semester invoke an academic action. For all subsequent semesters an academic action will be taken if the semester quality point average or the cumulative quality point average (excluding the first year) is below 2.00. Probation: The action of probation will be taken in the following cases: 1. One semester of the first year is below 1.75 QPA; 2. The semester QPA of a student in good standing beyond the first year falls below 2.00. The term of probation is one semester as a full-time student. First year students are no longer on probation at the end of the second semester if the second semester's QPA is 1.75 or above. Students in the third or subsequent semester of study are no longer on probation at the end of one semester if the semester QPA and cumulative QPA (excluding the first year) are 2.00 or above. Probation Continued: A student who has had one semester on probation and is not yet meeting minimum requirements but whose record indicates that the standards are likely to be met at the end of the next semester of study is occasionally continued on probation. This action is normally taken only when a student's semester QPA is above 2.0 but their cumulative QPA is not yet above 2.0. Suspension: A student who does not meet minimum standards at the end of one semester of probation will be suspended: A first year student will be suspended if the QPA from each semester is below 1.75. A student on probation in the third or subsequent semester of study will be suspended if the semester QPA is below 2.00. The minimum period of suspension is one academic year (two semesters). At the end of that period a student may return to school (on probation) by: 1. receiving permission in writing from the Assistant Dean for Undergraduate Education, or the student's academic advisor, 2. completing a Return from Leave form from the HUB 6 Students who have been suspended or have withdrawn are required to absent themselves from the campus (including residence halls and Greek houses) within a maximum of two days after the action and to remain off the campus for the duration of the time specified. This action includes debarment from part-time or summer courses at the university for the duration of the period of the action. Although suspended students may not hold student jobs, students on academic suspension may, under certain circumstances, have a non-student job with the university. Students on disciplinary or administrative suspension may not. Drop: This is a permanent severance. Students who have been suspended and who fail to meet minimum standards in the semester that they return to school will be dropped. Students who have been dropped are required to absent themselves from the campus (including residence halls and Greek houses) within a maximum of two days after the action. The relation indicated above between probation, suspension and drop is nominal. In unusual circumstances, College Council may suspend or drop a student without prior probation. Return from Leave of Absence SCS undergraduate students returning from a leave of absence are required to submit a Return from Leave of Absence form to the CS Undergraduate Office for approval by the student's academic advisor and assistant dean. In addition, the student must also supply a letter that explains the reason for the leave, the actions that were performed during the leave to prepare the student for a successful return, and a description of the on-campus resources, if required, that would be used by the student in order to increase the likelihood of success. Students returning from a leave are also encouraged to provide two letters of support from people close to the student (e.g. family, friends, clergy, teachers, coaches, others as appropriate). Requests to return are reviewed by the student's academic advisor, assistant dean and student affairs liaison to determine eligibility and any resources that need to be put into place to assist the student upon return. Contact the CS Undergraduate Office for more information. Transfer into SCS Undergraduate students admitted to colleges at CMU other than SCS and wishing to transfer into SCS should consult with the Assistant Dean for Undergraduate Education during their first year. In general, no undergraduate student will be considered for transfer until after having completed 15-2, 15-150 and at least one 200-level core Computer Science course (15-210, 15-213, or 15-251) with an exceptional grade point average. Additionally, students are expected to have performed well in 21-7. The decision to allow transfer will be made based on availability of space in the student's class and the student's academic performance (in the specified courses and in their courses overall) at the discretion of the Assistant Dean for Undergraduate Education. Students should consult the CS Undergraduate Program office for minimum requirements, transfer request instructions and deadlines. Procedure for transfer of students from another university into SCS: A student should first apply through the Office of Admission. If the Office of Admission believes the applicant is acceptable, the student's record is sent to SCS for evaluation by the Assistant Dean for Undergraduate Education. Admission is based on seat availability, overall academic performance from the student's current institution, and the application material. It is important to note that extremely few external transfers are admitted to the SCS program at Carnegie Mellon University. Graduation Requirements 1. A requirement for graduation is the completion of the program specified for a degree with a cumulative quality point average of 2.00 or higher for all courses taken after the first year. 2. Students must be recommended for a degree by the faculty of SCS. 3. A candidate for the bachelor's degree must complete at the University a minimum of four semesters of full-time study, or the equivalent of parttime study, comprising at least 180 units of course work. 4. Students will be required to have met all financial obligations to the university before being awarded a degree. Modification of Graduation Requirements: A student may seek permission to modify graduation requirements by petition to the SCS College Council.

School of Computer Science Faculty UMUT ACAR, Assistant Professor, Computer Science Department Ph.D., Carnegie Mellon University; Carnegie Mellon, 20. ANIL ADA, Assistant Teaching Professor, Computer Science Department Ph.D., McGill University; Carnegie Mellon, 2014. VICTOR ADAMCHIK, Associate Teaching Professor, Computer Science Department Ph.D., Byelorussian State University; Carnegie Mellon, 2000. YUVRAJ AGARWAL, Assistant Professor, Institute for Software Research Ph.D., University of California, San Diego; Carnegie Mellon, 2013. JONATHAN ALDRICH, Associate Professor, Institute for Software Research Ph.D., University Of Washington; Carnegie Mellon, 2003. VINCENT ALEVEN, Associate Professor, Human-Computer Interaction Institute Ph.D., University Of Pittsburgh; Carnegie Mellon, 2000. OMEAD AMIDI, Senior Systems Scientist, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 14. DAVID ANDERSEN, Associate Professor, Computer Science Department Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 2005. JOHN ANDERSON, R.K. Mellon University Professor Ph.D., Stanford University; Carnegie Mellon, 178. DIMITRIOS APOSTOLOPOULOS, Senior Systems Scientist, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 18. CHRISTOPHER ATKESON, Professor, Robotics Institute Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 2000. JAMES BAGNELL, Associate Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2004. MARIA FLORINA BALCAN, Associate Professor, Machine Learning Department Ph.D., Carnegie Mellon University; Carnegie Mellon, 2014. JOHN BARES, Research Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 13. ZIV BAR-JOSEPH, Associate Professor, Lane Center for Computational Biology Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 2003. MATTHEW BASS, Assistant Teaching Professor, Institute for Software Research M.S., Carnegie Mellon University; Carnegie Mellon, 20. MARCEL BERGERMAN, Systems Scientist, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2005. KAREN BERNTSEN, Associate Teaching Professor, Human Computer Interaction Institute M.S., Duquesne University; Carnegie Mellon, 2005. JEFFREY BIGHAM, Associate Professor, Human-Computer Interaction Institute Ph.D., University of Washington; Carnegie Mellon, 2013. ALAN BLACK, Professor, Language Technologies Institute Ph.D., University Of Edinburgh; Carnegie Mellon, 1. GUY BLELLOCH, Professor, Computer Science Department Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 188. AVRIM BLUM, Professor, Computer Science Department Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 11. LENORE BLUM, Distinguished Career Professor, Computer Science Department Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 1. MANUEL BLUM, University Professor, Computer Science Department Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 1. DAVID BOURNE, Principal Systems Scientist, Robotics Institute M.S., University Of Pennsylvania; Carnegie Mellon, 180. DANIEL BOYARSKI, Professor M.F.A., Indiana University; Carnegie Mellon, 182. TRAVIS BREAUX, Assistant Professor, Institute for Software Research Ph.D., North Carolina State University; Carnegie Mellon, 2010. STEPHEN BROOKES, Professor, Computer Science Department Ph.D., University College; Carnegie Mellon, 181. RALF BROWN, Senior Systems Scientist, Language Technologies Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 13. BRETT BROWNING, Senior Systems Scientist, Robotics Institute Ph.D., University of Queensland; Carnegie Mellon, 2000. EMMA BRUNSKILL, Assistant Professor, Computer Science Department Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 2011. RANDAL BRYANT, University Professor, Computer Science Department Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 184. JAMES CALLAN, Professor, Language Technologies Institute Ph.D., University Of Massachusetts; Carnegie Mellon, 1. JAIME CARBONELL, University Professor and Director, Language Technologies Institute Ph.D., Yale University; Carnegie Mellon, 17. KATHLEEN CARLEY, Professor, Institute for Software Research Ph.D., Harvard University; Carnegie Mellon, 184. JACOBO CARRASQUEL, Associate Teaching Professor, Computer Science Department M.S., Carnegie Mellon University; Carnegie Mellon, 180. JUSTINE CASSELL, Professor, Human-Computer Interaction Institute Ph.D., University of Chicago; Carnegie Mellon, 2010. HOWARD CHOSET, Professor, Robotics Institute Ph.D., California Institute Of Technology; Carnegie Mellon, 16. MICHAEL CHRISTEL, Teaching Professor, Entertainment Technology Center Ph.D., Georgia Institute Of Technology; Carnegie Mellon, 187. EDMUND CLARKE, University Professor, Computer Science Department Ph.D., Cornell University; Carnegie Mellon, 182. WILLIAM COHEN, Professor, Machine Learning Department Ph.D., Rutgers University; Carnegie Mellon, 2003. ALBERT CORBETT, Associate Research Professor, Human-Computer Interaction Institute Ph.D., University Of Oregon; Carnegie Mellon, 183. THOMAS CORTINA, Associate Teaching Professor and Assistant Dean for Undergraduate Education, Computer Science Department Ph.D., Polytechnic University; Carnegie Mellon, 2004. LORRIE CRANOR, Professor, Institute for Software Research Ph.D., Washington University; Carnegie Mellon, 2003. KARL CRARY, Associate Professor, Computer Science Department Ph.D., Cornell University; Carnegie Mellon, 18. LAURA DABBISH, Associate Professor, Human Computer Interaction Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2007. WANDA DANN, Senior Systems Scientist, Computer Science Department Ph.D., Syracuse University; Carnegie Mellon, 2008. ROGER DANNENBERG, Professor, Computer Science Department Ph.D., Carnegie Mellon University; Carnegie Mellon, 182. JENNA DATE, Associate Teaching Professor, Human-Computer Interaction Institute MHCI, Carnegie Mellon University; Carnegie Mellon, 20. FERNANDO DE LA TORRE FRADE, Associate Research Professor, Robotics Institute Ph.D., La Salle School of Engineering; Carnegie Mellon, 2002. ANIND DEY, Director, Human-Computer Interaction Institute Ph.D., Georgia Institute Of Technology; Carnegie Mellon, 2005. M BERNARDINE DIAS, Associate Research Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2003. JOHN DOLAN, Principal Systems Scientist, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 11. STEVEN DOW, Assistant Professor, Human-Computer Interaction Institute Ph.D., Georgia Institute of Technology; Carnegie Mellon, 2011. ARTUR DUBRAWSKI, Senior Systems Scientist, Robotics Institute Ph.D., Institute of Fundamental Technological Research; Carnegie Mellon, 2003. CHRISTOPHER DYER, Assistant Professor, Language Technologies Institute Ph.D., University of Maryland; Carnegie Mellon, 20. DAVID ECKHARDT, Associate Teaching Professor, Computer Science Department Ph.D., Carnegie Mellon University; Carnegie Mellon, 2003. WILLIAM EDDY, Professor Ph.D., Yale University; Carnegie Mellon, 176. JEFFREY EPPINGER, Professor Of The Practice, Institute for Software Research Ph.D., Carnegie Mellon University; Carnegie Mellon, 2001. MICHAEL ERDMANN, Professor, Robotics Institute Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 18. MAXINE ESKENAZI, Principal Systems Scientist, Language Technologies Institute Ph.D., University Of Paris; Carnegie Mellon, 14. SCOTT FAHLMAN, Research Professor, Language Technologies Institute Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 178.

Carnegie Mellon University 13 CHRISTOS FALOUTSOS, Professor, Computer Science Department Ph.D., University Of Toronto; Carnegie Mellon, 17. KAYVON FATAHALIAN, Assistant Professor, Computer Science Department Ph.D., Stanford University; Carnegie Mellon, 2011. STEPHEN FIENBERG, Maurice Falk University Professor Ph.D., Harvard University; Carnegie Mellon, 180. JODI FORLIZZI, Professor, Human-Computer Interaction Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2000. ROBERT FREDERKING, Principal Systems Scientist, Language Technologies Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 11. DAVID GARLAN, Professor, Institute for Software Research Ph.D., Carnegie Mellon University; Carnegie Mellon, 10. CHARLES GARROD, Assistant Teaching Professor, Institute for Software Research Ph.D., Carnegie Mellon University; Carnegie Mellon, 20. ANATOLE GERSHMAN, Distinguished Service Professor, Language Technologies Institute Ph.D., Yale University; Carnegie Mellon, 2007. HARTMUT GEYER, Assistant Professor, Robotics Institute Ph.D., Friedrich- Schiller University; Carnegie Mellon, 2010. GARTH GIBSON, Professor, Computer Science Department Ph.D., University Of California; Carnegie Mellon, 11. CLARK GLYMOUR, Alumni University Professor Ph.D., Indiana University; Carnegie Mellon, 185. SETH GOLDSTEIN, Associate Professor, Computer Science Department Ph.D., University Of California; Carnegie Mellon, 17. GEOFFREY GORDON, Associate Professor, Machine Learning Department Ph.D., Carnegie Mellon University; Carnegie Mellon, 2001. ABHINAV GUPTA, Assistant Research Professor, Robotics Institute Ph.D., University of Maryland; Carnegie Mellon, 2011. ANUPAM GUPTA, Professor, Computer Science Department Ph.D., University Of California At Berkeley; Carnegie Mellon, 2003. VENKATESAN GURUSWAMI, Professor, Computer Science Department Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 200. BERNARD HAEUPLER, Assistant Professor, Computer Science Department Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 2014. JESSICA HAMMER, Assistant Professor, Human-Computer Interaction Institute Ph.D., Columbia University; Carnegie Mellon, 2014. MOR HARCHOL-BALTER, Professor, Computer Science Department Ph.D., University Of California at Berkeley; Carnegie Mellon, 1. ROBERT HARPER, Professor, Computer Science Department Ph.D., Cornell University; Carnegie Mellon, 188. CHRISTOPHER HARRISON, Assistant Professor, Human-Computer Interaction Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2014. ALEXANDER HAUPTMANN, Principal Systems Scientist, Computer Science Department Ph.D., Carnegie Mellon University; Carnegie Mellon, 14. MARTIAL HEBERT, Professor, Robotics Institute Ph.D., Paris-Xl; Carnegie Mellon, 184. JAMES HERBSLEB, Professor, Institute for Software Research Ph.D., University Of Nebraska; Carnegie Mellon, 2002. JESSICA HODGINS, Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2001. RALPH HOLLIS, Research Professor, Robotics Institute Ph.D., University Of Colorado; Carnegie Mellon, 13. JASON HONG, Associate Professor, Human-Computer Interaction Institute Ph.D., University Of California At Berkeley; Carnegie Mellon, 2004. EDUARD HOVY, Associate Research Professor, Language Technologies Institute Ph.D., Yale University; Carnegie Mellon, 20. DANIEL HUBER, Senior Systems Scientist, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2002. SCOTT HUDSON, Professor, Human-Computer Interaction Institute Ph.D., University Of Colorado; Carnegie Mellon, 17. BRANISLAV JARAMAZ, Associate Research Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 17. ANGEL JORDAN, University Professor Emeritus, Robotics Institute Ph.D., Stanford University; Carnegie Mellon, 185. MICHAEL KAESS, Assistant Research Professor Ph.D., Georgia Institute of Technology; Carnegie Mellon, 2013. TAKEO KANADE, University Professor, Robotics Institute Ph.D., Kyoto University; Carnegie Mellon, 180. GEORGE KANTOR, Senior Systems Scientist, Robotics Institute Ph.D., University of Maryland; Carnegie Mellon, 2002. CHRISTIAN KASTNER, Assistant Professor, Institute for Software Research Ph.D., University of Magdeburg; Carnegie Mellon, 20. DILSUN KAYNUR, Assistant Teaching Professor, Computer Science Department Ph.D., University of Edinburgh; Carnegie Mellon, 20. THOMAS KEATING, Assistant Teaching Professor, Computer Science Department M.S., Duquesne University; Carnegie Mellon, 188. ALONZO KELLY, Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 18. GREGORY KESDEN, Associate Teaching Professor, Computer Science Department M.S., Clemson University; Carnegie Mellon, 1. SARA KIESLER, Professor, Human-Computer Interaction Institute Ph.D., Ohio State University; Carnegie Mellon, 17. SEUNGJUN KIM, Systems Scientist, Human-Computer Interaction Institute Ph.D., Gwangju Institute of Science and Technology; Carnegie Mellon, 2011. SEYOUNG KIM, Assistant Professor, Lane Center for Computational Biology Ph.D., University of California At Irvine; Carnegie Mellon, 2010. CARL KINGSFORD, Associate Professor, Lane Center for Computational Biology Ph.D., Princeton University; Carnegie Mellon, 20. ANIKET KITTUR, Assistant Professor, Human-Computer Interaction Institute Ph.D., University of California At Los Angeles; Carnegie Mellon, 200. KENNETH KOEDINGER, Professor, Human-Computer Interaction Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 11. J. ZICO KOLTER, Assistant Professor, Computer Science Department Ph.D., Stanford University; Carnegie Mellon, 20. DAVID KOSBIE, Assistant Teaching Professor, Computer Science Department ABD, Carnegie Mellon University; Carnegie Mellon, 200. IOANNIS KOUTIS, Adjunct Assistant Professor, Computer Science Department Ph.D., Carnegie Mellon University; Carnegie Mellon, 2008. ROBERT KRAUT, Professor, Human-Computer Interaction Institute Ph.D., Yale University; Carnegie Mellon, 13. CHRISTOPHER LANGMEAD, Associate Professor, Computer Science Department Ph.D., Dartmouth University; Carnegie Mellon, 2004. ANTHONY LATTANZE, Teaching Professor, Institute for Software Research M.S., Carnegie Mellon University; Carnegie Mellon, 1. ALON LAVIE, Research Professor, Language Technologies Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 16. CLAIRE LE GOUES, Assistant Professor, Institute for Software Research Ph.D., University of Virginia; Carnegie Mellon, 2013. CHRISTIAN LEBIERE, Research Psychologist, Psychology Ph.D., Carnegie Mellon University; Carnegie Mellon, 1. TAI-SING LEE, Associate Professor, Computer Science Department Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 16. EUN SUN LEE, Assistant Teaching Professor, Institute for Software Research M.S., Carnegie Mellon University; Carnegie Mellon, 2014. LORRAINE LEVIN, Research Professor, Language Technologies Institute Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 18. MAXIM LIKACHEV, Assistant Research Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2010. SIMON LUCEY, Associate Research Professor, Robotics Institute Ph.D., University of Southern Queensland; Carnegie Mellon, 2002. JENNIFER MANKOFF, Associate Professor, Human-Computer Interaction Institute Ph.D., Georgia Institute Of Technology; Carnegie Mellon, 2004. MATTHEW MASON, Professor, Robotics Institute Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 182. NOBORU MATSUDA, Systems Scientist, Human-Computer Interaction Institute Ph.D., University of Pittsburgh; Carnegie Mellon, 200. ROY MAXION, Research Professor, Computer Science Department Ph.D., University Of Colorado; Carnegie Mellon, 184.

14 School of Computer Science BRUCE MCLAREN, Senior Systems Scientist, Human-Computer Interaction Institute Ph.D., University Of Pittsburgh; Carnegie Mellon, 2003. FLORIAN METZE, Assistant Research Professor, Language Technologies Institute Ph.D., Universität Karlsruhe; Carnegie Mellon, 200. NATHAN MICHAEL, Assistant Research Professor, Robotics Institute Ph.D., University of Pennsylvania; Carnegie Mellon, 20. GARY MILLER, Professor, Computer Science Department Ph.D., University Of California; Carnegie Mellon, 188. EDUARDO MIRANDA, Associate Teaching Professor, Institute for Software Research M.S./M.Eng., University of Linköping/University of Ottawa; Carnegie Mellon, 2008. TERUKO MITAMURA, Research Professor, Language Technologies Institute Ph.D., University Of Pittsburgh; Carnegie Mellon, 10. TOM MITCHELL, University Professor, Director and Head, Machine Learning Department Ph.D., Stanford University; Carnegie Mellon, 186. ALAN MONTGOMERY, Associate Professor of Marketing Ph.D., University Of Chicago; Carnegie Mellon, 1. ANDREW MOORE, Dean and Professor, School of Computer Science Ph.D., University of Cambridge; Carnegie Mellon, 13. JAMES MORRIS, Professor, Human-Computer Interaction Institute Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 182. JACK MOSTOW, Research Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon,. TODD MOWRY, Professor, Computer Science Department Ph.D., Stanford University; Carnegie Mellon, 17. ROBERT MURPHY, Professor, Director and Head, Lane Center for Computational Biology Ph.D., California Institute Of Technology; Carnegie Mellon, 183. BRAD MYERS, Professor, Human-Computer Interaction Institute Ph.D., University Of Toronto; Carnegie Mellon, 187. PRIYA NARASIMHAN, Associate Professor Ph.D., University Of California; Carnegie Mellon, 2001. SRINIVASA NARASIMHAN, Associate Professor, Robotics Institute Ph.D., Columbia University; Carnegie Mellon, 2004. CHRISTINE NEUWIRTH, Professor Ph.D., Carnegie Mellon University; Carnegie Mellon, 2004. ILLAH NOURBAKHSH, Professor, Robotics Institute Ph.D., Stanford University; Carnegie Mellon, 17. ERIC NYBERG, Professor, Language Technologies Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 18. RYAN O'DONNELL, Associate Professor, Computer Science Department Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 2006. AMY OGAN, Assistant Professor, Human-Computer Interaction Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2014. DAVID O'HALLARON, Professor, Computer Science Department Ph.D., University of Virginia; Carnegie Mellon, 18. IRVING OPPENHEIM, Professor Ph.D., Cambridge University; Carnegie Mellon, 173. YOUNG-LAE PARK, Assistant Professor, Robotics Institute Ph.D., Stanford University; Carnegie Mellon, 2013. ANDREW PAVLO, Assistant Professor, Computer Science Department Ph.D., Brown University; Carnegie Mellon, 2013. JUERGEN PFEFFER, Assistant Research Professor Ph.D., Vienna University of Technology; Carnegie Mellon, 20. FRANK PFENNING, Professor and Head, Computer Science Department Ph.D., Carnegie Mellon University; Carnegie Mellon, 186. ANDRE PLATZER, Associate Professor, Computer Science Department Ph.D., University of Oldenburg; Carnegie Mellon, 2008. BARNABAS POCZOS, Assistant Professor, Machine Learning Department Ph.D., Eötvös Loránd University; Carnegie Mellon, 20. NANCY POLLARD, Associate Professor, Robotics Institute Ph.D., Massachusetts Institute Of Technology; Carnegie Mellon, 2002. ARIEL PROCACCIA, Assistant Professor, Computer Science Department Ph.D., The Hebrew University of Jerusalem; Carnegie Mellon, 2011. BHIKSHA RAJ RAMAKRISHNAN, Associate Professor, Language Technologies Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2008. RAJ REDDY, University Professor, Institute for Software Research Ph.D., Stanford University; Carnegie Mellon, 16. MARGARET REID-MILLER, Assistant Teaching Professor, Computer Science Department Ph.D., Carnegie Mellon University; Carnegie Mellon, 2002. CAMERON RIVIERE, Associate Research Professor, Robotics Institute Ph.D., Johns Hopkins University; Carnegie Mellon, 15. ALFRED RIZZI, Associate Research Professor, Robotics Institute Ph.D., Yale University; Carnegie Mellon, 18. DAVID ROOT, Associate Teaching Professor, Institute for Software Research M.P.M., Carnegie Mellon University; Carnegie Mellon, 2002. CAROLYN ROSE, Associate Professor, Language Technologies Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2003. RONALD ROSENFELD, Professor, Language Technologies Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 15. MANUEL ROSSO-LLOPART, Associate Teaching Professor, Institute for Software Research M.S., Software Engineering, Carnegie Mellon University; Carnegie Mellon, 2000. ZACK RUBINSTEIN, Senior Systems Scientist, Robotics Institute Ph.D., University of Massachusetts; Carnegie Mellon, 2005. STEVEN RUDICH, Professor, Computer Science Department Ph.D., University of California; Carnegie Mellon, 18. ALEXANDER RUDNICKY, Research Professor, Computer Science Department Ph.D., Carnegie Mellon University; Carnegie Mellon, 180. PAUL RYBSKI, Senior Systems Scientist, Robotics Institute Ph.D., University of Minnesota; Carnegie Mellon, 2003. NORMAN SADEH-KONIECPOL, Professor, Institute for Software Research Ph.D., Carnegie Mellon University; Carnegie Mellon, 11. MAJD SAKR, Teaching Professor, Computer Science Department Ph.D., University of Pittsburgh; Carnegie Mellon, 2006. TUOMAS SANDHOLM, Professor, Computer Science Department Ph.D., University of Massachusetts; Carnegie Mellon, 2001. MAHADEV SATYANARAYANAN, Professor, Computer Science Department Ph.D., Carnegie Mellon University; Carnegie Mellon, 183. PAUL SCERRI, Associate Research Professor, Robotics Institute Ph.D., Linkoping University; Carnegie Mellon, 2003. RICHARD SCHEINES, Professor and Department Head, Philosophy Ph.D., University of Pittsburgh; Carnegie Mellon, 188. SEBASTIAN SCHERER, Systems Scientist, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 20. WILLIAM SCHERLIS, Professor and Director, Institute for Software Research Ph.D., Stanford University; Carnegie Mellon, 18. BRADLEY SCHMERL, Senior Systems Scientist, Computer Science Department Ph.D., Flinders University of South Australia; Carnegie Mellon, 2000. JEFF SCHNEIDER, Research Professor, Robotics Institute Ph.D., University of Rochester; Carnegie Mellon, 15. DANA SCOTT, University Professor Emeritus, Computer Science Department Ph.D., Princeton University; Carnegie Mellon, 181. TEDDY SEIDENFELD, Herbert A. Simon Professor Ph.D., Columbia University; Carnegie Mellon, 185. SRINIVASAN SESHAN, Professor, Computer Science Department Ph.D., University of California; Carnegie Mellon, 2000. MICHAEL SHAMOS, Teaching Professor Ph.D., Yale University; Carnegie Mellon, 175. MARY SHAW, University Professor, Institute for Software Research Ph.D., Carnegie Mellon University; Carnegie Mellon, 165. YASER SHEIKH, Associate Research Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2008. MEL SIEGEL, Associate Research Professor, Robotics Institute Ph.D., University of Colorado; Carnegie Mellon, 182. DANIEL SIEWIOREK, University Professor, Human-Computer Interaction Institute Ph.D., Stanford University; Carnegie Mellon, 172.

Carnegie Mellon University 15 REID SIMMONS, Research Professor, Robotics Institute Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 188. ROBERT SIMMONS, Assistant Teaching Professor, Computer Science Department Ph.D., Carnegie Mellon University; Carnegie Mellon, 2013. AARTI SINGH, Assistant Professor, Machine Learning Department Ph.D., University of Wisconsin At Madison; Carnegie Mellon, 200. RITA SINGH, Systems Scientist, Language Technologies Institute Ph.D., Tata Institute of Fundamental Research; Carnegie Mellon, 2010. SANJIV SINGH, Research Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 14. DONALD SLATER, Systems Scientist, Computer Science Department B.S., Pennsylvania State University; Carnegie Mellon, 2000. DANIEL SLEATOR, Professor, Computer Science Department Ph.D., Stanford University; Carnegie Mellon, 185. NOAH SMITH, Associate Professor, Language Technologies Institute Ph.D., Johns Hopkins University; Carnegie Mellon, 2006. STEPHEN SMITH, Research Professor, Robotics Institute Ph.D., University of Pittsburgh; Carnegie Mellon, 182. ALEX SMOLA, Professor, Machine Learning Department Ph.D., University of Techonology, Berlin; Carnegie Mellon, 20. PETER SPIRTES, Professor and Associate Head, Philosophy Ph.D., University of Pittsburgh; Carnegie Mellon, 183. SIDDHARTHA SRINIVASA, Associate Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2011. JOHN STAMPER, Systems Scientist, Human-Computer Interaction Institute Ph.D., University of North Carolina At Charlotte; Carnegie Mellon, 200. PETER STEENKISTE, Professor, Computer Science Department Ph.D., Stanford University; Carnegie Mellon, 187. MARK STEHLIK, Teaching Professor and Associate Dean for Education, Carnegie Mellon-Qatar B.S., Pace University; Carnegie Mellon, 181. AARON STEINFELD, Associate Research Professor, Robotics Institute Ph.D., University of Michigan; Carnegie Mellon, 2001. ANTHONY STENTZ, Research Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 18. GEORGE STETTEN, Associate Research Professor, Robotics Institute Ph.D., University of North Carolina; Carnegie Mellon, 1. SCOTT STEVENS, Teaching Professor, Entertainment Technology Center Ph.D., University of Nebraska; Carnegie Mellon, 187. KLAUS SUTNER, Teaching Professor and Associate Dean for Undergraduate Education, Computer Science Ph.D., University of Munich; Carnegie Mellon, 15. KATIA SYCARA, Research Professor, Robotics Institute Ph.D., Georgia Institute of Technology; Carnegie Mellon, 187. SUJATA TELANG, Associate Teaching Professor, Institute for Software Research Ph.D., Carnegie Mellon University; Carnegie Mellon, 2004. KAREN THICKMAN, Assistant Teaching Professor, Lane Center for Computational Biology Ph.D., Johns Hopkins University; Carnegie Mellon, 2010. ANTHONY TOMASIC, Instructor, Institute for Software Research Ph.D., Princeton University; Carnegie Mellon, 2003. DAVID TOURETZKY, Research Professor, Computer Science Department Ph.D., Carnegie Mellon University; Carnegie Mellon, 184. ADRIEN TREUILLE, Assistant Professor, Robotics Institute Ph.D., University Of Washington; Carnegie Mellon, 2008. CHRISTOPHER URMSON, Adjunct Faculty, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2008. MANUELA VELOSO, University Professor, Computer Science Department Ph.D., Carnegie Mellon University; Carnegie Mellon,. LUIS VON AHN, Associate Professor, Computer Science Department Ph.D., Carnegie Mellon University; Carnegie Mellon, 2005. JOHN VU, Distinguished Career Professor, Lane Center for Computational Biology M.S., Carnegie Mellon University; Carnegie Mellon, 2011. HOWARD WACTLAR, Research Professor, Computer Science Department M.S., University of Maryland; Carnegie Mellon, 167. ALEXANDER WAIBEL, Professor, Language Technologies Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 188. LARRY WASSERMAN, Professor Ph.D., University of Toronto; Carnegie Mellon, 188. LEE WEISS, Research Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 183. KURT WESCOE, ebusiness Research Fellow, Institute for Software Research M.S., Carnegie Mellon University; Carnegie Mellon, 2004. DAVID WETTERGREEN, Research Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 2000. WILLIAM RED WHITTAKER, Fredkin University Research Professor, Robotics Institute Ph.D., Carnegie Mellon University; Carnegie Mellon, 173. JEANNETTE WING, Professor, Computer Science Department Ph.D., Massachusetts Institute of Technology; Carnegie Mellon, 185. WEI WU, Associate Research Professor, Lane Center for Computational Biology Ph.D., Rutgers University; Carnegie Mellon, 2011. POE ERIC XING, Professor, Machine Learning Department Ph.D., University Of California At Berkeley; Carnegie Mellon, 2004. YIMING YANG, Professor, Language Technologies Institute Ph.D., Kyoto University; Carnegie Mellon, 16. HUI ZHANG, Professor, Computer Science Department Ph.D., University of California; Carnegie Mellon, 15. JOHN ZIMMERMAN, Associate Professor, Human-Computer Interaction Institute M.Des., Carnegie Mellon University; Carnegie Mellon, 2002.