EECS. Rising Stars. Rising Stars

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1 EECS 2015 Rising Stars Rising Stars

2 MIT is delighted to host such an esteemed group of women in computer science and electrical engineering. The Rising Stars program gives you a terrific opportunity to present your research, network with your peers, and learn about ways to pursue a career in academia. It can serve as a professional launching pad, and I am thrilled you are here to take part! Cynthia Barnhart Chancellor Ford Professor of Engineering Massachusetts Institute of Technology Welcome to MIT! The Rising Stars Workshop has again brought together some of the most talented women in computer science and electrical engineering globally. You will help lead research, education, and the professional community in these fields, and others, in the years to come. We hope this program will provide guidance and inspiration as you launch your careers, and help foster a strong collegial network that will persist long into the future. Ian A. Waitz Dean of Engineering Jerome C. Hunsaker Professor of Aeronautics and Astronautics Massachusetts Institute of Technology 2 Rising Stars 2015

3 From the 2015 Rising Stars Workshop Chairs Welcome to the 2015 Rising Stars in EECS Workshop at MIT. We launched Rising Stars in 2012 to identify and mentor outstanding young women electrical engineers and computer scientists interested in exploring careers in academia. We are pleased that the program has grown substantially since its beginning. This year s workshop will bring together 62 of the world s brightest women PhD students, postdocs, and engineers/scientists working in industry, for two days of scientific interactions and career-oriented discussions aimed at navigating the early stages of careers in academia. This year s program focuses on the academic job search process and how to succeed as a junior faculty member. Our program includes invited presentations targeting the academic search process, how to give an effective job talk, and developing and refining one s research and teaching statement. There will also be panels focused on the early years of an academic career, covering topics such as forming and ramping up a research group, leadership, work-life balance, fundraising, and the promotions process. The workshop this year will also feature 24 oral presentations and 38 poster presentations by participants, covering a wide range of specialties representative of the breadth of EECS research. The presentations span the spectrum from materials, devices and circuits, to signal processing, communications, computer science theory, artificial intelligence and systems. Many attendees from previous workshops have gone on to secure faculty positions at top universities, or research positions in leading industry labs. Toward this end, we are pleased to highlight and feature workshop participants by circulating this brochure to the leadership of EECS departments at top universities and to selected research directors in industry. We hope, in addition, that Rising Stars will give participants the opportunity to network with peers and present their research, opening the door for ongoing collaboration and professional support for years to come. We are very grateful to the supervisors who supported the participation of the rising stars. We would also like to thank MIT s School of Engineering, the Office of the Dean for Graduate Education, and the EECS-affiliated research labs CSAIL, LIDS, MTL, and RLE for their support. We look forward to meeting and interacting with you all. Anantha Chandrakasan, Workshop Chair Vannevar Bush Professor of Electrical Engineering and Computer Science Department Head, MIT Electrical Engineering and Computer Science Regina Barzilay, Workshop Technical Co-Chair Professor of Electrical Engineering and Computer Science, MIT Dina Katabi, Workshop Technical Co-Chair Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science, MIT Asu Ozdaglar, Workshop Technical Co-Chair Professor of Electrical Engineering and Computer Science, MIT Director, Laboratory for Information and Decision Systems Rising Stars

4 The Rising Stars in EECS Workshop provides what today s graduates need, opportunities to take the lead, to present innovative work, to deliver professional communications, and to address global, scientific, and ethical issues. Above all, the conference connects women graduates with a critical network of mentors, colleagues, and faculty who will support their academic and professional success. Christine Ortiz Dean for Graduate Education Morris Cohen Professor of Materials Science and Engineering Massachusetts Institute of Technology 2 Rising Stars 2015

5 2015 EECS Rising Stars Henny Admoni Yale University Ilge Akkaya University of California at Berkeley Sara Alspaugh University of California at Berkeley Elnaz Banan Sadeghian Georgia Institute of Technology Katherine Bouman Massachusetts Institute of Technology Carrie Cai Massachusetts Institute of Technology Precious Cantú École Polytechnique Fédérale de Lausanne Peggy Chi University of California at Berkeley Hannah Clevenson Massachusetts Institute of Technology SeyedehAida (Aida) Ebrahimi Purdue University Motahareh Eslamimehdiabadi University of Illinois at Urbana- Champaign Virginia Estellers University of California at Los Angeles Fei Fang University of Southern California Liyue Fan University of Southern California Giulia Fanti University of California at Berkeley Lu Feng University of Pennsylvania Kathleen Fraser University of Toronto Marzyeh Ghassemi Massachusetts Institute of Technology Elena Leah Glassman Massachusetts Institute of Technology Basak Guler Pennsylvania State University Divya Gupta University of California at Los Angeles Judy Hoffman University of California at Berkeley Hui-Lin Hsu University of Toronto Carlee Joe-Wong Princeton University Gauri Joshi Massachusetts Institute of Technology Ankita Arvind Kejriwal Stanford University Hana Khamfroush Pennsylvania State University Hyeji Kim Stanford University Jung-Eun Kim University of Illinois at Urbana-Champaign Varada Kolhatkar Privacy Analytics Inc. Parisa Kordjamshidi University of Illinois at Urbana-Champaign Ramya Korlakai Vinayak California Institute of Technology Karla Kvaternik Princeton University Min Kyung Lee Carnegie Mellon University Kun (Linda) Li University of California at Berkeley Hongjin Liang University of Science and Technology of China Xi Ling Massachusetts Institute of Technology Fei Liu Carnegie Mellon University Yu-Hsin Liu University of California at San Diego Kristen Lurie Stanford University Jelena Marasevic Columbia University Ghita Mezzour International University of Rabat Jamie Morgenstern University of Pennsylvania Vaishnavi Nattar Ranganathan University of Washington Xiang Ni University of Illinois at Urbana Champaign Dessislava Nikolova Columbia University Farnaz Niroui Massachusetts Institute of Technology Idoia Ochoa Stanford University Eleanor O Rourke University of Washington Amanda Prorok University of Pennsylvania Elina Robeva University of California at Berkeley Deblina Sarkar Massachusetts Institute of Technology Melanie Schmidt Carnegie Mellon University Claudia Schulz Imperial College London Mahsa Shoaran California Institute of Technology Eva Song Princeton University Veronika Strnadova-Neeley University of California at Santa Barbara Huan Sun University of California at Santa Barbara Ewa Syta Yale University Rabia Yazicigil Columbia University Qi (Rose) Yu University of Southern California Zhou Yu Carnegie Mellon University Rising Stars

6 Henny Admoni Yale University Nonverbal Communication in Human-Robot Interaction Robotics has already improved lives by taking over dull, dirty, and dangerous jobs, freeing people for safer, more skillful pursuits. For instance, autonomous mechanical arms weld cars in factories, and autonomous vacuum cleaners keep floors clean in millions of homes. However, most currently deployed robotic devices operate primarily without human interaction, and are typically incapable of understanding natural human communication. My research focuses on enabling human-robot communication in order to develop social robots that interact with people in natural, effective ways. Application areas include social robots that help elderly users with tasks like preparing meals or getting dressed; manufacturing robots that act as intelligent third hands, improving efficiency and safety for workers; and robot tutors that provide students with personalized lessons to augment their classroom time. Nonverbal communication, such as gesture and eye gaze, is an integral part of typical human communication. Nonverbal communication happens bidirectionally in an interaction, so social robots must be able to both recognize and generate nonverbal behaviors. These behaviors are extremely dependent on context, with different types of behaviors accomplishing different communicative goals like directing attention or managing conversational turn-taking. To be effective in the real world, nonverbal behaviors must occur in real time in dynamic, unstructured interactions. My research focuses on developing bidirectional, context aware, real time nonverbal behaviors for personally assistive robots. Developing effective nonverbal communication for robots engages a number of disciplines including autonomous control, machine learning, computer vision, design, and cognitive psychology. My approach to this research is three-fold. First, I conduct well-controlled human-robot interaction studies to understand people s perceptions of robots. Second, I build computational models of nonverbal behavior using data from human-human interactions. Third, I develop robot-agnostic behavior controllers for collaborative human-robot interactions based on my models of human behavior, and test these behavior controllers in real-world human-robot interactions. Henny Admoni is a PhD candidate at the Social Robotics Laboratory in the Department of Computer Science at Yale University, where she works with Professor Brian Scassellati. This winter, Henny will begin as a Postdoctoral Fellow at the Robotics Institute at Carnegie Mellon University, working with Siddhartha Srinivasa. Henny creates and studies intelligent, autonomous robots that improve people s lives by providing assistance in social environments like homes and offices. Her dissertation research investigates how robots can recognize and produce nonverbal behaviors, such as eye gaze and pointing, to make human-robot interactions more natural and effective for people. Her interdisciplinary work spans the fields of artificial intelligence, robotics, and cognitive psychology. Henny holds an MS in Computer Science from Yale University, and a BA/ MA joint degree in Computer Science from Wesleyan University. Henny s scholarship has been recognized with awards such as the NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, and the Palantir Women in Technology Scholarship. Ilge Akkaya University of California at Berkeley Compositional Actor- Oriented Learning and Optimization for Swarm Applications Rapid growth of networked smart sensors today offer unprecedented volumes of continually streaming data, which renders many traditional control and optimization techniques ineffective for designing large-scale applications. The overarching goal of my graduate studies has been enabling seamless composition of distributed dynamic swarm applications. In this regard, I work on developing actor-oriented frameworks for deterministic and compositional heterogeneous system design. A primary goal of my graduate work is to mitigate the heterogeneity within Internet-of-Things applications by presenting an actor-oriented framework, which enables developing compositional learning and optimization applications that operate on streaming data. Ptolemy Learning, Inference, and Optimization Toolkit (PILOT) achieves this by presenting a library of reusable interfaces to machine learning, control and optimization tasks for distributed systems. A key goal of PILOT is to enable system engineers who are not experts in statistics and machine learning to use the toolkit in order to develop applications that rely on on-line estimation and inference. In this context, we provide domain-specific specializations of general learning and control techniques, including parameter estimation and decoding on Bayesian networks, model-predictive control, and state estimation. Recent and ongoing applications of the framework include cooperative robot control, real-time audio event detection, and constrained reactive machine improvisation. A second branch of my research aims at maintaining separation-of-concerns in model-based design. In industrial cyber-physical systems, composition of sensors, middleware, computation and communication fabrics yields a highly complex and heterogeneous design flow. Separation-of-concerns becomes a crucial quality in model-based design of such systems. We introduce the aspect-oriented modeling (AOM) paradigm, which addresses this challenge by bridging actor-oriented modeling with aspect-oriented abstractions. AOM specifically enables learning and optimization tasks to become aspects within a complex design flow, while greatly improving scalability and modularity of heterogeneous applications. Ilge Akkaya is a PhD candidate in the Electrical Engineering and Computer Science department at UC Berkeley, working with Prof. Edward A. Lee. She received the BS degree in Electrical and Electronics Engineering from Bilkent University, Ankara, Turkey in During her graduate studies, she explored systems engineering for distributed cyber-physical systems, with a focus on distributed smart grid applications and cooperative mobile robotic control. Her thesis work centers around actor-oriented machine learning interfaces for distributed swarm applications. 4 Rising Stars

7 Sara Alspaugh University of California at Berkeley Characterizing Data Exploration Behavior to Identify Opportunities for Automation Exploratory analysis is undertaken to familiarize oneself with a dataset. Despite being a necessary part of any analysis, it remains a nebulous art defined by an attitude and a collection of techniques, rather than a systematic methodology. It typically involves manually making hundreds to thousands of individual function calls or small interactions with a GUI in order to obtain different views of the data. It is not always clear which views will be effective for a given dataset or question, how to be systematic about which views to examine, or how to map a high-level question into a series of low-level actions to answer it. This results in unnecessary repetition, disrupted mental flow, ad hoc and hard-to-repeat workflows, and inconsistent exploratory coverage. Identifying useful, repeatable exploration workflows, opportunities for automation of tedious tasks, and intelligent interfaces better suited for expressing exploratory questions, all require a better understanding of data exploration behavior. We seek this through three means: We analyze interaction records logged from data analysis tools to identify behavioral patterns and assess the utility of log data for building intelligent assistance and recommendation algorithms that learn from user behavior. Preliminary results reveal that while logs can say which functions are used in which contexts, more comprehensive instrumentation and collection is likely needed to train intelligent exploration assistants. We interview experts about their data exploration habits and frustrations to identify good exploratory workflows and ascertain important features not provided by existing tools. Preliminary results reveal opportunities to make data exploration more thorough and efficient. We design and evaluate a prototype for obtaining quick data overviews to assess new interface elements designed to better match data exploration needs. Preliminary results suggest that small simple automation in existing tools would decrease user effort, increase exploratory coverage, and help users identify erroneous assumptions more readily. Elnaz Banan Sadeghian Georgia Institute of Technology Detector for Two- Dimensional Magnetic Recording The data industry such as Google, Facebook, Yahoo, and also many other organizations, rely heavily on data storage facilities to store their valuable data. Hard disk drives, due to their reliability and extremely cheap price, form a main part of these data storage facilities. The disk drive industry is currently pursuing a huge increase in the recorded data density up to 10 Terabits per square inch of the medium through two-dimensional magnetic recording (TDMR). I work toward realization of this technology, specifically, to design a detector which can recover the data from extremely dense hard drives. This is a challenge, in part because this novel technology shrinks the widths of the data tracks to such an extent that an attempt to read data from one track will inevitably lead to interference from neighboring tracks, and in part because of the challenging nature of the magnetic medium itself. The combination of interference between different tracks and along adjacent bits on each track is a key challenge for TDMR and motivates the development of two-dimensional signal processing strategies of manageable complexity to mitigate this two-dimensional interference. To address this issue, we have designed a novel detection strategy for TDMR recording channel with multiple read heads. Our method suppresses the intertrack interference and thereby reduces the detection problem to a traditional one-dimensional problem, so that we may leverage existing one-dimensional iterative detection strategies. Simulation results show that our proposed detector is able to reliably recover five tracks from an array of five read heads at an acceptable signalto-noise ratio. Further, we are working on a detector which also performs the task of synchronizing the reader and the writer clock speeds so that the data can be extracted more accurately. Obtained results from this research can help greatly increase hard disk capacities through TDMR. Sara Alspaugh is a computer scientist and PhD candidate at the UC Berkeley. In her research, she mines user interaction records logged from data analysis tools to better characterize data exploration behavior, identify challenges and opportunities for automation, and improve system and interface design. She also conducts qualitative research through interview studies with expert analysts and usability evaluations of data exploration tools; and has prototyped new interfaces to help users get an overview of their data. More broadly, her research interests include data science, data mining, visualization, and user interaction with data analysis tools. She is a member of the AMPLab and is advised by Randy Katz and Marti Hearst. She received her MS in Computer Science from UC Berkeley in 2012 and her BA in Computer Science from the University of Virginia in She is the recipient of an NSF Graduate Fellowship, a Tableau Fellowship, and a Department Chair scholarship. Rising Stars Elnaz Banan Sadeghian received the BS degree in electrical engineering from Shahid Beheshti University, Tehran, Iran, in 2005, and the M.S. degree in medical Engineering from Amirkabir University of Technology, Tehran, Iran, in She is currently pursuing her PhD degree in electrical engineering at the Georgia Institute of Technology, Atlanta, Georgia, USA. Her current research interests are in the area of signal processing and communication theory, including synchronization, equalization, and coding as applied to magnetic recording channels.

8 Katherine Bouman Massachusetts Institute of Technology Visual Vibrometry: Estimating Material Properties from Small Motions in Video The estimation of material properties is important for scene understanding, with many applications in vision, robotics, and structural engineering. We have connected fundamentals of vibration mechanics with computer vision techniques in order to infer material properties from small, often imperceptible motion in video. Objects tend to vibrate in a set of preferred modes. The shapes and frequencies of these modes depend on the structure and material properties of an object. Focusing on the case where geometry is known or fixed, we have shown how information about an object s modes of vibration can be extracted from video and used to make inferences about that object s material properties. We demonstrate our approach by estimating material properties for a variety of rods and fabrics by passively observing their motion in high-speed and regular framerate video. Katherine Bouman received a BSE in Electrical Engineering from University of Michigan, Ann Arbor, MI in 2011 respectively and an S.M. degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT), Cambridge, MA in She is currently a PhD candidate in the Computer Vision group at MIT, working under the supervision of Prof. William Freeman. Katherine is the recipient of the NSF Graduate Fellowship, the Irwin Mark Jacobs and Joan Klein Jacobs Presidential Fellowship, and is a Goldwater Scholar. Her research interests include computer vision, computational photography, and inverse imaging algorithms. Carrie Cai Massachusetts Institute of Technology Wait-Learning: Leveraging Wait Time for Education The busyness of daily life makes it hard to find time for informal learning. Yet, learning typically requires significant time and effort, with repeated exposures to educational content on a recurring basis. My work introduces the concept of wait-learning: leveraging wait time for education. Despite the struggle to find time for learning, there are numerous times in a day that are wasted due to brief moments of waiting, such as waiting for the elevator, waiting for wifi to connect, or waiting for an instant message reply. Combining wait time with productive work opens up a new class of software systems that overcomes the problem of limited time while addressing the frustration often associated with waiting. My goal is to understand how to detect and manage these waiting moments, and to discover essential design principles for wait-learning systems. I have designed and built several systems that enable wait-learning: WaitChatter delivers second-language vocabulary exercises while users wait for instant message replies, and Flash- Suite integrates learning across diverse kinds of waiting, including elevators, wifi, and loading. Through developing and evaluating these systems, we identify waiting moments to use for learning, and ways to encourage learning unobtrusively while maximizing engagement. A study of WaitChatter with 20 participants found that wait-learning can be an effective and engaging way to learn. During two weeks of casual instant messaging, participants learned and retained an average of 57 Spanish and French words, or about four new words per day. Carrie is a PhD student in Computer Science at MIT CSAIL. Her dissertation project focuses on wait-learning: leveraging wait time for education. Broadly, she is interested in developing systems that help humans learn and improve productivity in environments with limited time. Her research brings together disciplines in human-computer interaction, education, attention management, and productivity. Carrie holds a B.A. in Human logy and M.A. in Education from Stanford University. 6 Rising Stars

9 Precious Cantú Peggy Chi Patterning via Optical Saturable Transitions Designing Video-Based Interactive Instructions Fulbright Postdoctoral Fellow École Polytechnique Fédérale de Lausanne For the past 40 years, optical lithography has been the patterning workhorse for the semiconductor industry. However, as integrated circuits have become more and more complex, and as device geometries shrink, more innovative methods are required to meet these needs. In the farfield, the smallest feature that can be generated with light is limited to approximately half the wavelength. This, so called far-field diffraction limit or the Abbe limit (after Prof. Ernst Abbe who first recognized this), effectively prevents the use of long-wavelength photons >300nm from patterning nanostructures <100nm. Even with a 193nm laser source and extremely complicated processing, patterns below ~20nm are incredibly challenging to create. Sources with even shorter wavelengths can potentially be used. However, these tend be much more expensive and of much lower brightness, which in turn limits their patterning speed. Multi-photon reactions have been proposed to overcome the diffraction limit. However, these require very large intensities for modest gain in resolution. Moreover, the large intensities make it difficult to parallelize, thus limiting the patterning speed. In this dissertation, a novel nanopatterning technique using wavelength-selective small molecules that undergo single-photon reactions, enabling rapid top-down nanopatterning over large areas at low-light intensities, thereby allowing for the circumvention of the far-field diffraction barrier is developed and experimentally verified. This approach, which I refer to as Patterning via Optical Saturable Transitions (POST) has the potential for massive parallelism, enabling the creation of nanostructures and devices at a speed far surpassing what is currently possible with conventional optical lithographic techniques. The fundamental understanding of this technique goes beyond optical lithography in the semiconductor industry and is applicable to any area that requires the rapid patterning of large-area two or three-dimensional complex geometries. Dr. Precious Cantú is a Postdoctoral Researcher in the Materials Science and Engineering Department at École Polytechnique Fédérale de Lausanne (EPFL), where she works with Professor Francesco Stellacci in the Supramolecular Nanomaterials and Interfaces Laboratory. She recently received her PhD in Electrical Engineering from the University of Utah, advised by Prof. Rajesh Menon. Her research area of interest is Optics and Nanofabrication, with a specific focus on extending the spatial resolution of optics to the nanoscale. Her PhD dissertation focused on developing a novel nanopatterning technique using wavelength-selective small molecules. She is the recipient of the National Science Foundation Graduate Research Fellowship (NSF GRFP), University of Utah Nanotechnology Training Fellowship, Global Entrepreneurship Monitor Consortium (GEM) Fellowship, More Graduate Education at Mountain States Alliance (MGE/MSA) Fellowship, and The Fulbright U.S. Scholars Fellowship. University of California at Berkeley When aiming to accomplish unfamiliar, complicated tasks, people often search for online helps to follow instructions shared by experts or hobbyists. Although the availability of content sharing sites such as YouTube and Blogger has led to an explosion in user-generated tutorials, it remains a challenge for tutorial creators to offer concise and effective content for learners to put into actions. From using software applications, performing physical tasks such as machine repair and cooking, to giving a lecture, each domain involves specific how-to knowledge with certain degree of complexity. Authors therefore need to carefully design what and when to introduce an important concept in addition to accurately performing the tasks. My research introduces video editing, recording, and playback tools optimized for producing and consuming instructional demonstrations. We focus on videos as they are commonly used to capture a demonstration contained with visual and auditory details. Using video and audio analysis techniques, our goal is to dramatically increase the quality of amateur-produced instructions, which in turn improves learning for viewers to interactively navigate. We show a series of proposed systems that create effective tutorials to support this vision, including MixT that automatically generates mixed-media software instructions, DemoCut that automatically applies video editing effects to a recording of a physical demonstration, and DemoWiz that provides an increased awareness of upcoming actions through glanceable visualizations. Pei-yu (Peggy) Chi designs intelligent systems that enhance and improve everyday experiences. She is currently a fifth-year PhD student in Computer Science at UC Berkeley, working with Prof. Bjoern Hartmann on computer-generated interactive tutorials. She received the Google PhD Fellowship in Human Computer Interaction ( ) and the Berkeley Fellowship for Graduate Study ( ). Peggy earned her MS in Media Arts and Sciences in 2010 from the MIT Media Lab, where she was awarded as a lab fellow and worked with Henry Lieberman at the Software Agents Group. She also holds a MS in Computer Science in 2008 from National Taiwan University, where she worked with Hao-hua Chu at the UbiComp Lab. Peggy s research in Human-Computer Interaction focuses on novel authoring tools for content creation. Her recent work published at top HCI conferences includes: tutorial generation for software applications and physical tasks, designing and scripting cross-device interactions, and interactive storytelling for sharing personal media. Rising Stars

10 Hannah Clevenson Massachusetts Institute of Technology Sensing and Timekeeping using a Light-Trapping Diamond Waveguide Solid-state quantum sensors are attracting wide interest because of their sensitivity at room temperature. In particular, the spin properties of individual nitrogen vacancy (NV) color centers in diamond make them outstanding nanoscale sensors of magnetic fields, electric fields, and temperature under ambient conditions. Recent work on NV ensemble-based magnetometers, inertial sensors, and clocks has employed unentangled color centers to realize significant improvements in sensitivity. However, to achieve this potential sensitivity enhancement in practice, new techniques are required to excite efficiently and to collect the optical signal from large NV ensembles. Here, we introduce a light-trapping diamond waveguide geometry with an excitation efficiency and signal collection that enables in excess of 5% conversion efficiency of pump photons into optically detected magnetic resonance (ODMR) fluorescence an improvement over previous single-pass geometries of more than three orders of magnitude. This marked enhancement of the ODMR signal enables precision broadband measurements of magnetic field and temperature in the low-frequency range, otherwise inaccessible by dynamical decoupling techniques. We also use this device architecture to explore other precision sensing and timekeeping applications. Hannah earned her BE (cum laude) in electrical engineering from Cooper Union in She was a NASA MUST scholar and spent four summers working in the nanotechnology division at NASA Ames Research Center on the Microcolumn Scanning Electron Microscope (MSEMS) project and led a microgravity flight experiment. She finished her masters degree at Columbia University in She is a NASA Space Technology Research Fellow and spent a summer as a visiting technologist in the Quantum Sciences and Technology group at JPL. She is currently a PhD candidate at MIT, splitting her time between Dirk Englund s lab on campus and Danielle Braje s lab in group 89 at MIT Lincoln Laboratory. Her current research focuses on precision sensing and timekeeping based on large ensembles of NV centers in diamond. SeyedehAida (Aida) Ebrahimi Purdue University Droplet-Based Impedance Spectroscopy for Highly- Sensitive sensing within Minutes Rapid detection of biomolecules in small volumes of highly diluted solutions is of essential interest in various applications, such as food safety, homeland security, fast drug screening, and addressing the global issue of antibiotic resistance. Toward this goal, we developed a label-free, electrical approach which is based on (i) evaporation-induced beating of diffusion limit for reducing the sensor response time and (ii) continuous monitoring of non-faradic impedance of an evaporating droplet containing the analytes. Small droplets are deposited and pinned on a multifunctional, specially designed superhydrophobic sensor which results in highly-controlled evaporation rate, essential for highly-precise data acquisition. Our method is based on the change of the droplet s impedance due to ionic modulation caused by evaporation. The time-multiplexing feature of the developed platform results in a remarkably reduced data variation, which is necessary for a reliable biosensing assay. Furthermore, we examined applicability of the developed technique as a fast, label-free platform for: improving the detection limit of classical methods by five orders of magnitude (detection of attomolar concentration of biomolecules), selective identification of DNA hybridization (down to nm concentration, without any probe immobilization), and bacterial viability (detection is achieved within minutes, as opposed to hours in conventional methods). More specifically, the proposed viability assay relies on a basis fundamentally different from most bacterial viability assays which rely on cell multiplication. Instead, our method is based on modulation of the osmotic pressure to trigger cells to modify their surroundings. The developed paradigm eliminates the need for bulky reference electrodes (which impose integration challenges), requires only a few microliter sample volume, and is cost-effective and integrable with the microfabrication processes. It has therefore the potential for integration in portable, array-formatted, point-of-care applications. Aida Ebrahimi received her BSc and MSc degrees both in Electrical and Computer Engineering from University of Tehran, Iran. Her Master s project was on fabrication and characterization of highly sensitive capacitive sensors and actuators based on Branched Carbon Nanotubes (BCNTs). In 2012, she joined CEED group, under supervision of Prof. M. A. Alam at Purdue University, West Lafayette, IN, USA. She is currently pursuing a PhD degree in ECE. The title of her dissertation is Droplet-based non-faradaic Impedance Sensing for Combating Antibiotic Resistance. During her academic life, Aida has developed the required skills to approach scientific problems. She has been involved in various, yet connected, projects whose outcome has been published in 15 peer-reviewed journal articles and more than 10 conference proceedings. She enjoys diversity in scientific thinking and intertwining various disciplines to advance the state of the art of a specific problem, especially in health-related applications. Aida is a recipient of Meissner Fellowship Award (Purdue University, 2011) and Bilsland Dissertation Fellowship Award (Purdue University, 2015). 8 Rising Stars

11 Motahareh Eslamimehdiabadi University of Illinois at Urbana- Champaign Reasoning about Invisible Algorithms in News Feeds Our daily digital life is full of algorithmically selected content such as social media feeds, recommendations and personalized search results. These algorithms have great power to shape users experiences, yet users are often unaware of their presence. Whether it is useful to give users insight into these algorithms existence or functionality and how such insight might affect their experience are open questions. To address them, we conducted a user study with 40 Facebook users to examine their perceptions of the Facebook News Feed curation algorithm. Surprisingly, more than half of the participants (62.5%) were not aware of the News Feed curation algorithm s existence at all. Initial reactions for these previously unaware participants were surprise and anger. We developed a system, FeedVis, to reveal the difference between the algorithmically curated and an unadulterated News Feed to users, and used it to study how users perceive this difference. Participants were most upset when close friends and family were not shown in their feeds. We also found participants often attributed missing stories to their friends decisions to exclude them rather than to Facebook News Feed algorithm. By the end of the study, however, participants were mostly satisfied with the content on their feeds. Following up with participants two to six months after the study, we found that for most, satisfaction levels remained similar before and after becoming aware of the algorithm s presence, however, algorithmic awareness led to more active engagement with Facebook and bolstered overall feelings of control on the site. Motahhare Eslami is a 4th year PhD candidate at Computer Science department, University of Illinois at Urbana-Champaign. Her research interests are in social computing, human computer interaction and data mining areas. She is interested in performing research to analyze and understand people s behavior in online social networks. Her recent work has focused on the effects of feed personalization in social media and how the awareness of filtering algorithm s existence affects users perception and behavior. Her work has published at prestigious conferences and also appeared internationally in the press-in the Washington Post, TIME, MIT Technology Review, New Scientist, the BBC, CBC Radio, Oglobo (a prominent Brazilian newspaper), numerous biogs, Fortune, and more. Motahhare has been nominated as a Google PhD Fellowship Nominee (2015) by University of Illinois as one of the two students from the entire College of Engineering. Her research has received honorable mention award at Facebook Midwest Regional Hackathon 2013 and the best paper award at CHI Virginia Estellers Postdoctoral Fellow University of California at Los Angeles Robust Models and Efficient Algorithms for Imaging I work on mathematical modeling and computational techniques for imaging. I am interested in the theoretical and physical aspects of the acquisition of images, their mathematical representations, and the development of efficient algorithms to extract information from them. To this purpose, I focus on three lines of research. Better Models in Image Processing: My dissertation focused on variational models for inverse problems in imaging, that is, the design of minimization problems that reconstruct or analyze an image from incomplete and corrupted measurements. To overcome the ill-posed nature of these problems, prior knowledge about the solution its geometry, shape, or smoothness is incorporated into a mathematical model that both matches the measurements and is physically meaningful. Efficient Algorithms: In the same way that simplifying an algebraic expression speeds its computation and reduces numerical errors, developing an efficient algorithm reduces the computational cost and errors of the numerical minimization. For this reason, my work focuses also on developing algorithms tailored to each problem to overcome the limitations of non-differentiable functionals, high-order derivatives, and non-convex problems. Stepping out of the Image plane: Computer Vision analyzes 3D scenes from 2D images or videos and therefore requires to step out of the image plane and develop models that account for the 3D nature of the scene, modeling their geometry and topology to account for the occlusions and shadows observable in videos and images. My research, in a nutshell, brings together models and algorithms into solid mathematical grounds to designs techniques that only extract the information that is meaningful for the problem at hand. It incorporates the knowledge available on the solution into the mathematical model of the problem, chooses a discretization suited to the object being imaged, and designs optimization strategies that scale well and are easy to parallelize. Dr. Estellers received her PhD in image processing from Ecole Polythechnique Federale de Lausanne in 2013, and joined the UCLA Vision Lab as a postdoctoral fellow with an SNSF fellowship. Previous to that, she completed Bachelor and Master studies at the Polytechnic University of Catalonia in both Mathematics and Electrical Engineering. Rising Stars

12 Fei Fang University of Southern California Towards Addressing Spatio-Temporal Aspects in Security Games My research aims to provide game-theoretic solutions for fundamental challenges of security resource optimization in the real-world, in domains ranging from infrastructure protection to sustainable development. Whereas first generation of security games research provided algorithms for optimizing security resources in mostly static settings, my thesis advances the state-of-the-art to a new generation of security games, handling massive games with complex spatio-temporal settings and leading to real-world applications that have fundamentally altered current practices of security resource allocation. My work provides the first algorithms and models for advancing three key aspects of spatio-temporal challenges in security games. First, focusing on games where actions are taken over continuous time (for example games with moving targets such as ferries and refugee supply lines), I provide an efficient linear-programming-based solution while accurately modeling the attacker s continuous strategy. This work has been deployed by the US Coast Guard for protecting the Staten Island Ferry in New York City in past few years and fundamentally altering previously used tactics. Second, for games where actions are taken over continuous space (for example games with forest land as target), I provide an algorithm computing the optimal distribution of patrol effort. Third, my work addresses challenges with one key dimension of complexity the temporal change of strategy. Motivated by the repeated interaction of players in domains such as preventing poaching and illegal fishing, I introduce a novel game model that accounts for temporal behavior change of opponents and provide algorithms to plan effective sequential defender strategies. Furthermore, I incorporate complex terrain information and design the PAWS application to combat illegal poaching, which generates patrol plans with detailed patrol routes for local patrollers. PAWS has been deployed in a protected area in Southeast Asia, with plans for worldwide deployment. Liyue Fan Postdoctoral Research Associate University of Southern California Preserving Individual Privacy in Big Data Analytics We live in the age of big data. With an increasing number of people, devices, and sensors connected with digital networks, individual data now can be largely collected and analyzed by data mining applications for social good as well as for commercial interests. However, the data generated by individual users exhibit unique behavioral patterns and sensitive information, and therefore must be transformed prior to the release for analysis. The AOL search log release in 2006 is an example of privacy catastrophe, where the searches of an innocent citizen were quickly re-identified by a newspaper journalist. In this talk, I present a novel framework to release continuous aggregation of private data for an important class of real-time data mining tasks, such as disease outbreak detection and web mining, to name a few. The key innovation is that the proposed framework captures the underlying dynamics of the continual aggregate statistics with time series state-space models, and simultaneously adopts filtering techniques to correct the observed, noisy data. It can be shown that the new framework provides a rigorous, provable privacy guarantee to individual data contributors without compromising the output analysis results. I will also talk about my current research, including the extension of the framework to spatial crowd-sourcing and privacy-preserving machine learning in a distributed research network. Liyue Fan is a postdoctoral research associate at the Integrated Media Systems Center at USC. She holds a PhD in Computer Science and Informatics from Emory University and a BSc in Mathematics from Zhejiang University in China. Her PhD dissertation research centers around the development of data publication algorithms which provide rigorous guarantee for individual privacy without compromising output utility. After joining USC, she also works on spatial crowd-sourcing, transportation, and healthcare informatics. Fei Fang is a PhD candidate in Department of Computer Science at University of Southern California. She is working with Professor Milind Tambe at Teamcore Research group. She received her bachelor degree from the department of Electronic Engineering, Tsinghua Unviersity in July, Her research lies in the field of artificial intelligence and multi-agent systems, focusing on computational game theory with applications to security and sustainability domains. Her work on Protecting Moving Targets with Mobile Resources has been deployed by the US Coast Guard for protecting the Staten Island Ferry in New York City since April This work has led to her receiving the Meritorious Team Commendation from Commandant of the US Coast Guard and Flag Letter of Appreciation from Vice Admiral and she is named a poster competition finalist in the First Conference on Validating Models of Adversary Behaviors (2013). Her work on When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing won the Outstanding Paper Award in IJCAI-15 Computational Sustainability Track. She is the chair of the AAAI Spring Symposium 2015 on Applied Computational Game Theory and the recipient of WiSE Merit Fellowship (2014). 10 Rising Stars

13 Giulia Fanti University of California at Berkeley Spy vs. Spy: Anonymous Messaging Anonymous microblogging platforms, such as Secret, Yik Yak, and Whisper have emerged as important tools for sharing one s thoughts without fear of judgment by friends, the public, or authority figures. These platforms provide anonymity by allowing users to share content (e.g., short messages) with their peers without revealing authorship information to users. However, recent advances in rumor source detection show that existing messaging protocols, including those used in the mentioned anonymous microblogging applications, leak authorship information when the adversary has global access to metadata. For example, if an adversary can see which users of a messaging service received a particular message, or the timestamps at which a subset of users received a given message, the adversary can infer the message author s identity with high probability. We introduce a novel anonymous messaging protocol, which we call adaptive diffusion, that is designed to resist such adversaries. We show that adaptive diffusion spreads messages quickly while achieving provably-optimal anonymity guarantees when the underlying messaging network is an infinite regular tree. Simulations on real social network data show that adaptive diffusion effectively hides the location of the source even when the graph is finite, irregular and has cycles. Giulia Fanti is a 6th year PhD student at the University of California-Berkeley, studying privacy-preserving algorithms under Professor Kannan Ramchandran. She received her M.S. in EECS from the University of California-Berkeley in 2012 and her B.S. in Electrical and Computer Engineering from Olin College of Engineering in She is a recipient of the National Science Foundation Graduate Research Fellowship, as well as a Best Paper Award at ACM Sigmetrics 2015 for her work on anonymous rumor spreading, in collaboration with Peter Kairouz, Professor Sewoong Oh and Professor Pramod Viswanath of the University of Illinois at Urbana-Champaign. Lu Feng Postdoctoral Fellow University of Pennsylvania Assuring the Safety and Security of Cyber-Physical Systems Cyber-Physical Systems (CPS) also called the Safety-Critical Internet of Things are smart systems that include co-engineered interacting networks of physical and computational components. These highly interconnected and integrated systems provide new functionalities to improve quality of life and enable technological advances in critical areas, such as smart healthcare, transportation, manufacturing, and energy. The increasing complexity and scale of CPS, with high-level expectations of autonomous operation, predictability and robustness, in the presence of environmental uncertainty and resource limitations, pose significant challenges for assuring the safety and security of CPS. My research is focused on assuring the safety, security and dependability of CPS, through formal methods and data-driven approaches, with particular emphasis on probabilistic modeling and quantitative verification. My doctoral thesis work improves the scalability of probabilistic model checking a powerful formal verification method that focuses on analyzing quantitative properties of stochastic systems by developing, for the first time, fully automated compositional verification techniques for probabilistic systems. My current postdoctoral research includes two themes. One theme is medical CPS, which are life-critical, context-aware, networked systems of medical devices. For example, I have worked on assuring the interoperability of on-demand plug & play medical devices, and model-based development of high-confidence medical devices. Another theme of my current work is human-in-the-loop CPS. I collaborate with clinicians and develop data-driven modeling framework for studying the behavior of Diabetic patients who depend on insulin pumps. The research outcome could potentially assist in developing safer, more effective, and even personalized treatment devices. In another project, with my collaborators at the Air Force Research Lab, I develop approaches for synthesizing provably correct human-in-the-loop control protocols for unmanned aerial vehicles (UAV). My other on-going projects include human factors in CPS security assurance, and operator behavior signatures for the haptic authentication of surgical robots. Lu Feng is postdoctoral fellow at the PRECISE Center and Department of Computer & Information Science at the University of Pennsylvania, advised by Professor Insup Lee. She received her DPhil (PhD) in Computer Science from the University of Oxford in 2014, under the supervision of Professor Marta Kwiatkowska. She also holds a B.Eng. in Information Engineering from the Beijing University of Posts and Telecommunications and a M.Phil. in Computer Speech, Text and Internet Technology from the University of Cambridge. Lu is a recipient of the prestigious James S. McDonnell Foundation Postdoctoral Fellowship, which only selects 10 fellows internationally and trans-disciplinary each year. She has also received various other awards, including the ACM SIGMOBILE N2 Women Young Researcher Fellowship, UK Engineering and Physical Sciences Research Council Graduate Scholarship, and Cambridge Trust Scholarship. Rising Stars

14 Kathleen Fraser University of Toronto Text and Speech Processing for the Detection of Dementia It has been shown that language can be a sensitive barometer of cognitive health. However, current approaches to screening and diagnosis for dementia do not typically include a detailed analysis of spontaneous speech because the manual annotation of language samples is far too time-consuming. Using methods from natural language processing and machine learning, we have been able to extract relevant linguistic and acoustic features from short speech samples and their transcripts to predict whether the speaker has Alzheimer s Disease with 92% accuracy. We have also investigated a type of dementia called primary progressive aphasia (PPA), in which language ability is the primary impairment. In addition to determining whether participants had PPA or not, we were able to distinguish between semantic-variant PPA and agrammatic-variant PPA by incorporating features to detect signs of empty speech and syntactic simplification. Another component of my current work involves improving automatic speech recognition for cognitive assessment. By developing computational tools to collect, analyze, and interpret language data from cognitively impaired speakers, I hope to provide the groundwork for numerous potential applications, including remote screening, support for diagnosis, assistive technologies for community living, and the quantitative evaluation of therapeutic interventions. Katie Fraser is a PhD candidate at the University of Toronto in the Computational Linguistics group, where her main research interests are text processing, automatic speech recognition, and machine learning. She is particularly interested in how these techniques can be used to assess potential cognitive impairment. She received a Master of Computer Science degree from Dalhousie University in Halifax, Nova Scotia, where she developed techniques for reducing noise and blur in microscope images. Before that, she researched the structure and dynamics of glass-forming liquids as part of her Bachelor of Science in Physics at St. Francis Xavier University in Antigonish, Nova Scotia. Marzyeh Ghassemi Massachusetts Institute of Technology Estimating the Response and Effect of Clinical Interventions Much prior work in clinical modeling has focused on building discriminative models to detect specific easily coded outcomes with little clinical utility (e.g., hospital mortality) under specific ICU settings, or understanding the predictive value of various types of clinical information without taking interventions into account. In this work, we focus on understanding the impact of interventions on the underlying physiological reserve of patients in different clinical settings. Reserve can be thought of as the latent variability in patient response to treatment after accounting for their observed state. Understanding reserve is therefore important to performing successful interventions, and can be used in many clinical settings. I attempt to understand reserve in response to intervention in two settings: 1) the response of intensive care unit (ICU) patients to common clinical interventions like vassopressor and ventilation administration in the ICU, and 2) the response of voice patients to behavioral and surgical treatments in an ambulatory outpatient setting. In both settings, we use large sets of clinical data to investigate whether specific interventions are meaningful to patients in an empirically sound way. Marzyeh Ghassemi is a PhD student in the Clinical Decision Making Group (MEDG) in MIT s Computer Science and Artificial Intelligence Lab (CSAIL) supervised by Prof. Peter Szolovits. Her research uses machine learning techniques and statistical modeling to predict and stratify relevant human risks. Marzyeh is interested in creating probabilistic latent variable models to estimate the underlying physiological state of patients during critical illnesses. She is also interested in understanding the development and progression of conditions like hearing loss and vocal hyperfunction using a combination of sensor data, clinical observations, and other physiological measurements. While at MIT, Marzyeh has served on MIT s Women s Advisory Group Presidential Committee, as Connection Chair to the Women in Machine Learning Workshop, on MIT s Corporation Joint Advisory Committee on Institute-wide Affairs, and on MIT s Committee on Foreign Scholarships. Prior to MIT, Marzyeh received two B.S. degrees in computer science and electrical engineering with a minor in applied mathematics from New Mexico State University as a Goldwater Scholar, and a MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar. She also worked at Intel Corporation in the Rotation Engineering Program, and then as a Market Development Manager for the Emerging Markets Platform Group. 12 Rising Stars

15 Elena Leah Glassman Massachusetts Institute of Technology Systems for Teaching Programming and Hardware Design at Scale In a massive open online course (MOOC), a single programming exercise may yield thousands of student solutions that vary in many ways, some superficial and some fundamental. Understanding large-scale variation in programs is a hard but important problem. For teachers, this variation can be a source of pedagogically valuable examples and expose corner cases not yet covered by autograding. For students, the variation in a large class means that other students may have struggled along a similar solution path, hit the same bugs, and can offer hints based on that earned expertise. I have developed three systems to explore solution variation in large-scale programming and computer architecture classes. (1) OverCode visualizes thousands of programming solutions using static and dynamic analysis to cluster similar solutions. It lets teachers quickly develop a high-level view of student understanding and misconceptions and provide feedback that is relevant to many student solutions. (2) Foobaz clusters variables in student programs by their names and behavior so that teachers can give feedback on variable naming. Rather than requiring the teacher to comment on thousands of students individually, Foobaz generates personalized quizzes that help students evaluate their own names by comparing them with good and bad names from other students. (3) ClassOverflow collects and organizes solution hints indexed by the autograder test that failed or a performance characteristic like size or speed. It helps students reflect on their debugging or optimization process, generates hints that can help other students with the same problem, and could potentially bootstrap an intelligent tutor tailored to the problem. All three systems have been evaluated using data or live deployments in on-campus or edx courses with thousands of students. Elena Glassman is an EECS PhD candidate at MIT Computer Science and Artificial Intelligence Lab, where she specializes in human-computer interaction. For her dissertation, Elena has created tools that help teach programming and hardware design to thousands of students at once. She uses theories from the learning sciences, as well as the pain points of students and teachers, to guide the creation of new systems for teaching and learning online and at scale. Elena earned both her MIT EECS BS and MEng degrees in 08 and 10, respectively, with a Ph.D. expected in 16. She has been a visiting researcher at Stanford and an intern at Google and Microsoft Research. She earned the NSF and NDSEG fellowships and MIT s Amar Bose Teaching Fellowship. She also leads the MIT chapter of MEET, which helps teach gifted Palestinians and Israelis computer science and teamwork in Jerusalem. Basak Guler Pennsylvania State University Interaction, Communication, and Computation in Information and Social Networks Modern networks are designed to facilitate the interaction of humans with computers. These networks consist of actors with possibly different characteristics, goals, and interests. My research takes a mathematical approach to modeling semantic and social networks. I study the fundamental limits of the information transferred in real-world networks, and develop algorithms to make network applications human-centric. Unlike conventional communication networks, this necessitates taking into account the semantic relationships between words, phrases, or clauses, as well as the personal background, characteristics, and knowledge bases of the interacting parties. These differences can in turn lead to various interpretations of the received information in a communication system. Modern network systems should be able to operate under such ambiguous environments, and adapt to the interpretation differences of the communicating parties. My goal is to incorporate these individual characteristics for designing effective network models that can leverage and adapt to the semantic and social features of the interacting parties. To do this, my research takes an interdisciplinary approach, rooted in information theory and optimization, and incorporates social networks, and mathematical logic. As such, we consider a diverse set of problems ranging from lossless and lossy source coding to reliable communication with social structures. We identify the optimal strategies to represent a remotely observed phenomenon when the communicating parties have individual and common backgrounds, as well as optimal interaction protocols for exchanging messages with semantic relationships. Basak Guler received her BSc degree in electrical and electronics engineering from Middle East Technical University (METU), Ankara, Turkey in 2009 and her M.Sc. degree in electrical engineering from Wireless Communications and Networking Laboratory, Pennsylvania State University, University Park, PA, in She is currently pursuing the PhD degree and is a graduate research assistant with the Department of Electrical Engineering, Pennsylvania State University, University Park, PA. Her research interests include information theory, social networks, semantic communications, source coding, data compression, interactive communication, and heterogeneous wireless networks. Rising Stars

16 Divya Gupta Judy Hoffman Hosting Services on an Untrusted Cloud Adapting Deep Visual Models for Visual Recognition in the Wild University of California at Los Angeles Outsourcing computation from a weak client to a more powerful server has received a lot of attention in recent years. This is partly due to the increasing interest in cloud computing, where the goal is to outsource all the computations to a (possibly untrusted) cloud. Though this is quickly becoming the predominant mode of day-today computation, it brings with it many security challenges, and there has been large numbers papers which address them. In our work, we expand the realm of outsourcing computation to more challenging security and privacy settings. We consider a scenario where a service provider has created a software service and desires to outsource the execution of this service to an untrusted cloud. The software service contains secrets that the provider would like to keep hidden from the cloud. For example, the software might contain a secret database, and the service could allow users to make queries to different slices of this database depending on the user s identity. This setting presents significant challenges not present in previous works on outsourcing or secure computation because secrets in the software itself must be protected against an adversary that has full control over the cloud that is executing this software. Furthermore, we seek to protect knowledge of the software to the maximum extent possible even if the cloud can collude with several corrupted users of this service. In this work, we provide the first formalizations of security for this setting, yielding our definition of a secure cloud service scheme. We also provide constructions of secure cloud service schemes using cryptographic tools. Divya Gupta is a doctoral candidate in the Department of Computer Science at University of California at Los Angeles, where she started in the Fall of 2011 under the supervision of Prof. Amit Sahai. Her research interests include cryptography, security, and theoretical computer science. Before coming to UCLA, she graduated with a B.Tech. and M.Tech from IIT Delhi. 14 Rising Stars 2015 Postdoctoral Research Associate University of California at Berkeley Understanding visual scenes is a crucial piece in many artificial intelligence applications ranging from autonomous vehicles and household robotic navigation to automatic image captioning for the blind. Reliably extracting high-level semantic information from the visual world in real-time is key to solving these critical tasks safely and correctly. Existing approaches based on specialized recognition models are prohibitively expensive or intractable due to limitations in dataset collection and annotation. By facilitating learned information sharing between recognition models these applications can be solved; multiple tasks can regularize one another, redundant information can be reused, and the learning of novel tasks is both faster and easier. My work focuses on transferring learned information quickly and reliably between visual data sources and across visual tasks all with limited human supervision. I aim to both formally understand and empirically quantify the degree to which visual models can be adapted and provide algorithms to facilitate information transfer. Most visual recognition systems learn concepts directly from a large collection of manually annotated images/videos. A model which detects pedestrians requires a human to manually go through thousands or millions of images and indicate all instances of pedestrians. However, this model is susceptible to biases in the labeled data and often fails to generalize to new scenarios a detector trained in Palo Alto may have degraded performance in Rome, or a detector trained in sunny weather may fail in the snow. Rather than require human supervision for each new task or scenario, my work draws on deep learning, transformation learning, and convex-concave optimization to produce novel optimization frameworks which transfer information from the large curated databases to real world scenarios. This results in strong recognition models for novel tasks and paves the way towards scalable visual understanding. Judy Hoffman is a PhD candidate at UC Berkeley s Computer Vision Group. She received her B.Sc. in Electrical Engineering and Computer Science from UC Berkeley in Her research lies at the intersection of computer vision, transfer learning, and machine learning: she is interested in minimizing the amount of human supervision needed to learn new visual recognition models. Judy was awarded the NSF Graduate Research Fellowship in 2010 and the Rosalie M. Stern Fellowship She was the co-president of the Women in Computer Science and Engineering at UC Berkeley , the outreach and diversity officer for the Computer Science Graduate Association , and organized the first workshop for Women in Computer Vision located at CVPR

17 Hui-Lin Hsu Research Assistant University of Toronto Reduction in the Photoluminescence Quenching for Erbium- Doped Amorphous Carbon Photonic Materials by Deuteration and Fluorination The integration of photonic. materials into CMOS processing involves the use of new materials. A simple one-step metal-organic radio frequency plasma enhanced chemical vapor deposition system (RF-PEMOCVD) was deployed to grow erbium-doped amorphous carbon thin films (a-c:(er)) on Si substrates at low temperatures (<200 C). A partially fluorinated metal-organic compound, tris(6,6,7,7,8,8,8-heptafluoro-2,2-dimethyl-3,5-octanedionate) Erbium(+III) or abbreviated Er(fod)3, was incorporated in situ into a-c based host. It was found that the prominent room-temperature photoluminescence (PL) signal at 1.54 µm observed from the a-c:h:f(er) film is attributed to several factors including a high Er concentration, the large optical bandgap of the a-c:h host, and the decrease in the C-H quenching by partial C-F substitution of metal-organic ligand. In addition, six-fold enhancement of Er PL was demonstrated by deuteration of the a-c host. Also, the effect of RF power and substrate temperature on the PL of a-c:d:f(er) films was investigated and analyzed in terms of the film structure. PL signal increases with increasing RF power, which is the result of an increase in [O]/[Er] ratio and the respective erbium-oxygen coordination number. Moreover, PL intensity decreases with increasing substrate temperature, which is attributed to an increased desorption rate or a lower sticking coefficient of the fluorinated fragments during film growth and hence [Er] decreases. In addition, it is observed that Er concentration quenching begins at ~2.2 at% and continues to increase until 5.5 at% in the studied a-c:d:f(er) matrix. This technique provides the capability of doping Er in a vertically uniform profile. Hui-Lin Hsu is a PhD graduate in Electrical Engineering (Photonics) from the University of Toronto, with M.S. and B.S. degrees in Materials Science and Engineering from National Tsing Hua University, Taiwan. Her research interest is in the areas of thin film and nano-material processing, material characterizations, and microelectronic and photonic devices fabrication. Hui-Lin has completed four different research projects (Organic Thin Film Transistors (OTFTs), Flexible Carbon Nanotubes Electrodes for Neuronal Recording, Si Nanowire for Optical Waveguide Interconnection Application, and Rare Earth doped Amorphous Carbon Based Thin Films for Light Guiding/Amplifying Applications). Hui-Lin has also first authored 3 patents (1 in USA, 2 in Taiwan) and 5 SCI journal articles, co-authored 9 SCI journal articles, and 15 international conference presentations. She did internships at Taiwan Semiconductor Manufacturing Company (TSMC) and Industrial Technology Research Institute (ITRI). She is also a recipient of the 2008 scholarship for studying abroad from Taiwan government, and an invited participant for the 2007 Taiwan Semiconductor Young Talent Camp held by Applied Materials and 2015 ASML PhD master class. Carlee Joe-Wong Princeton University Smart(er) Data Pricing Over the past decade, many more people have begun to use the Internet regularly, and the proliferation of mobile apps allows them to use the Internet for more and more tasks. As a result, data traffic is growing nearly exponentially. Yet network capacity is not expanding fast enough to handle this growth in traffic, creating a problem of network congestion. My research argues that the very diversity in usage that is driving growth in data traffic points to a viable solution for this fundamental capacity problem. Smart data pricing reduces network congestion by looking at the users who drive demand for data. In particular, we ask what incentives will alter user demand so as to reduce congestion, and perhaps more importantly, what incentives should we offer users in practice? For instance, simply raising data prices or throttling data throughput rates will likely drive down demand, but also lead to vast user dissatisfaction. More sophisticated pricing schemes may not work in practice, as they require users to understand the prices offered and algorithms to predict user responses. We demonstrate the feasibility and benefits of a smart data pricing approach through end-to-end investigations of what prices to charge users, when to charge which prices, and how to price supplementary network technologies. Creating viable pricing solutions requires not only mathematical models of users reactions to the prices offered, but also knowledge of systems-building and human-computer interaction. My work develops a feedback loop between optimizing the prices, offering them to users, and measuring users reactions to the prices so as to re-calibrate the prices over time. My current research expands on this pricing work by studying users incentives to contribute towards crowd-sourced data. Without properly designed incentive mechanisms, users might free-ride on others measurements or collect redundant measurements at a high cost to themselves. Carlee Joe-Wong is a PhD candidate and Jacobus Fellow at Princeton University s Program in Applied and Computational Mathematics. Her research interests include network economics, distributed systems, and optimal control. She received her A.B. in mathematics in 2011 and her M.A. in applied mathematics in 2013, both from Princeton University. In 2013, she was the Director of Advanced Research at DataMi, a startup she co-founded in 2012 that commercializes new ways of charging for mobile data. DataMi was named a startup to watch by Forbes in Carlee received the INFORMS ISS Design Science Award in 2014 for her research on smart data pricing, and the Best Paper Award at IEEE INFOCOM 2012 for her work on the fairness of multi-resource allocations. In 2011, she received the National Defense Science and Engineering Graduate Fellowship (NDSEG). Rising Stars

18 Gauri Joshi Ankita Arvind Kejriwal Massachusetts Institute of Technology Using Redundancy to Reduce Delay in Cloud Systems It is estimated that by 2018, more than thirty percent of all digital content will be stored and processed on the cloud. The term cloud refers to a shared pool of a large number of connected servers, used to host services such as Dropbox, Amazon EC2, Netflix etc. The sharing of resources provides scalability and flexibility to cloud systems, but it also causes randomness in the response time of individual servers, which can result in large and unpredictable delays experienced by users. My research develops techniques to use redundancy to reduce delay, while using the available resources efficiently. In cloud storage and computing systems, a task (for e.g. searching for a term on Google, or accessing a file from Dropbox) experiences random queuing and service delays at the machine it is assigned to. To reduce the overall latency, we can launch replicas of the task on multiple machines and wait for the earliest copy to finish, albeit at the expense of extra computing and network resources. We develop a fundamental understanding how the randomness in the response time of a server affects latency and cost of computing resources. This helps us find cost-efficient strategies of launching and canceling redundant tasks to minimize latency. Achieving low latency is even more challenging in streaming services such as Netflix and Youtube because they require fast, in-order playback of packets. Another focus of my research is to develop erasure codes to transmit redundant combinations of packets, and minimize the number of interruptions in playback. Gauri Joshi is a PhD candidate at MIT, advised by Prof. Gregory Wornell. She works on applying probability and coding theory to improve today s cloud infrastructure. She received an S.M. in EECS from MIT in 2012, for which she received the William Martin memorial award for best thesis in Computer Science at MIT. Stanford University Scalable Low-Latency Indexes for a Key-Value Store Many large-scale key-value storage systems sacrifice features like secondary indexing and/or consistency in favor of scalability or performance. This limits the ease and efficiency of application development on these systems. My work shows how a large-scale key-value storage system can be extended to provide secondary indexes in a fashion that is highly scalable and offers ultra low latency access. The architecture, called SLIK, enables multiple keys for each object, and allows indexes to be partitioned and distributed independently of their objects. SLIK represents index B+ trees using objects in the underlying key-value store. It uses an ordered write approach for object updates, which allows temporary inconsistencies between indexes and their objects but masks those inconsistencies from applications. When implemented using RAMCloud as the underlying key-value store, SLIK performs indexed reads in 11 μs and writes in 30 μs; it supports indexes spanning thousands of nodes, and provides linear scalability for throughput. SLIK is also an order of magnitude faster than other state-of-the-art systems. Ankita Kejriwal is a PhD candidate in the Computer Science department at Stanford University working with Prof. John Ousterhout. She enjoys working on problems in distributed systems. She is building RAMCloud, a low-latency datacenter storage system, along with the rest of her lab. Her recent project, called SLIK, extends a key-value store to enable scalable, low-latency indexes. She interned at MSRSVC in 2013 with Marcos Aguilera and designed an algorithm for low-latency distributed transactions. Prior to graduate school, she completed her Bachelor in Computer Science at Birla Institute of Technology and Science - Pilani, Goa Campus. Before coming to MIT in 2010, she completed a B.Tech and M. Tech in Electrical Engineering from the Indian Institute of Technology (IIT) Bombay. She was awarded the Institute Gold Medal of IIT Bombay, for highest GPA across all majors. Gauri has received several other awards and honors including the Schlumberger Faculty for the Future fellowship ( ) and the Claude E. Shannon Research Assistantship ( ). She has had summer internships at Bell Labs (2012) and Google (2013, 14). 16 Rising Stars

19 Hana Khamfroush Postdoctoral Scholar Pennsylvania State University On Propagation of Phenomena in Interdependent Networks Operational networks of different types are often interdependent and interconnected. Many of today s infrastructures are organized in the form of interdependent networks. For example, the smart grid is controlled via the Internet, and the Internet is powered by the smart grid. A failure in one may lead to service degradation and possibly failure in the other. This failure procedure can cascade multiple times between the two interdependent networks and therefore, results in catastrophic widespread failures. Previous works that are modeling the interdependency between two networks are generally based on strong assumptions and specific applications, thus fail to capture important aspects of real networks. Furthermore, most of the previous works only address the asymptotic behavior of the networks. To fill this gap, we focused on the temporal evolution of the phenomena propagation in interdependent networks. The goal is to identify the importance of the nodes in terms of their influence on the propagation phenomenon, and to design more efficient interdependent networks. We proposed a general theoretical model for such a propagation, which captures several possible models of interaction among affected nodes. Our model is general in the sense that there is no assumption on the network topology, propagation model, or the capability of the network nodes (heterogeneity of the networks). The theoretical model allows us to evaluate small-scale networks. On the other hand, we implemented a simulator, which allows for the evaluation of larger scale networks for different types of random graphs, different models of coupling between networks, and different initial spreaders. Based on our analysis, we propose a new centrality metric designed for the interdependent networks that is shown to be more precise in identifying the importance of the nodes compared to the traditional centrality metrics. Our next step would be analyzing the phenomena propagation in time-varying interdependent networks. Hyeji Kim Stanford University Superposition Coding is Almost Always Optimal for the Poisson Broadcast Channel The two fundamental building blocks of wireless networks is the multiple access channel (multiple transmitters and one receiver) and the broadcast channel (one transmitter and multiple receivers). While the capacity region for multiple access channel is known, the capacity region for broadcast channels has been an open problem for 40 years. A continuous-time Poisson channel is a canonical model for optical communications that is widely used to transmit telephone signals, internet communication, and cable television signals. The 2-receiver continuous-time Poisson broadcast channel is a 2-receiver broadcast channel for which the channel to each receiver is a continuous-time Poisson channel. We show that superposition coding is optimal for this channel for almost all channel parameter values. Interestingly, the channel in some subset of these parameter values does not belong to any of the existing classes of broadcast channels for which superposition coding is known to be optimal. For the rest of the channel parameter values, we show that there is a gap between the best known inner bound and the best known outer bound Marton s inner bound and the UV outer bound. Hyeji Kim is a PhD candidate in the Department of Electrical Engineering at Stanford University advised by Prof. Abbas El Gamal. She received the B.S. degree with honors in Electrical Engineering from the Korea Advanced Institute of Science and Technology (KAIST) in 2011 and the M.S. degree in Electrical Engineering from Stanford University in Her research interest include information theory, communication systems, and statistical learning. She is a recipient of the Stanford Graduate Fellowship. Hana Khamfroush is a postdoctoral scholar in the Electrical Engineering and Computer Science department of Penn State University, working with Prof. Thomas La Porta. She received her PhD with highest distinction from the University of Porto in Portugal and in Collaboration with Aalborg University of Denmark in Nov Her PhD research focused on network coding for cooperation in dynamic wireless networks. Currently at PSU, she is working on interdependent networks, network recovery and network tomography. Her research interests include complex networks, computer networks, wireless communications, and mathematical models. She received a four-year scholarship from the ministry of science of Portugal for her PhD, and was awarded many grants and fellowships from the European Union. Recently, she received the best poster award for her recent work in the basic research technical review meeting of DTRA. Rising Stars

20 Jung-Eun Kim Varada Kolhatkar University of Illinois at UrbanaChampaign A New Real-Time Scheduling Paradigm for Safety-Critical Multicore Systems Over the past decade, multicore processors have become increasingly common for their potential of efficiency, which has made new single-core processors become relatively scarce. As a result, it has created a pressing need to transition to multicore processors. However, existing safety-critical software that has been certified on single-core processors is not allowed to be fielded on a multicore system as is. The issue stems from, namely, serious inter-core interference problems on shared resources in current multicore processors, which create non-deterministic timing behavior. Meeting the timing constraints is the crucial requirement of safety-critical real-time systems as timing violations could have disastrous effects, from loss of human life to damages to machines and/or the environment. This is why Federal Aviation Administration (FAA) does not currently allow the use of more than one core in a multicore chip. Academia has paid little attention to non-determinism due to uncoordinated I/O communications relatively compared to other resources such as cache or memory, although industry considers it as one of the most troublesome challenges. Hence we focuse on I/O synchronization while assuming unknown Worst Case Execution Time (WCET) that can get impacted by other interference sources. Traditionally, a two-level scheduling, such as Integrated Modular Avionics system (IMA), has been used for providing temporal isolation capability. However, such hierarchical approaches introduce significant priority inversions across applications, especially in multicore systems, ultimately leading to lower system utilization. To address these issues, we have proposed a novel scheduling mechanism called budgeted generalized rate monotonic analysis (Budgeted GRMS) in which different applications tasks are globally scheduled for avoiding unnecessary priority inversions, yet the CPU resource is still partitioned for temporal isolation among applications. Incorporating the issues of unknown WCETs and I/O synchronization, this new scheduling paradigm enables the safe use of multicore processors in safety-critical real-time systems. Jung-Eun Kim is a PhD candidate advised by Prof. Lui Sha in the Department of Computer Science at the University of Illinois at Urbana-Champaign. She received her BS and MS (advised by Prof. ChangGun Lee) degrees from the department of Computer Science and Engineering of Seoul National University, Korea in 2007 and 2009, respectively. Her current research interests include real-time scheduling (schedulability analysis, optimization, hierarchical scheduling) and real-time multicore architecture. The main targeted application is safety-critical hard real-time systems such as avionics systems (Integrated modular avionics (IMA) systems). She is a recipient of the Richard T. Cheng Endowed Fellowship for Rising Stars 2015 Postdoc toral Researcher Privacy Analytics Inc. Resolving Shell Nouns Shell nouns are abstract nouns, such as fact, issue, idea, and problem, which, among other functions, facilitate efficiency by avoiding repetition of long stretches of text. Shell nouns encapsulate propositional content, and the process of identifying this content is referred to as shell noun resolution. My research presents the first computational work on resolving shell nouns. The research is guided by three primary questions: first, how an automated process can determine the interpretation of shell nouns; second, the extent to which knowledge derived from the linguistics literature can help in this process; and third, the extent to which speakers of English are able to interpret shell nouns. I start with a pilot study to annotate and resolve occurrences of this issue in the Medline abstracts. The results illustrate the feasibility of annotating and resolving shell nouns, at least in this closed domain. Next, I move to developing general algorithms to resolve a variety of shell nouns in the newswire domain. The primary challenge was that each shell noun has its own idiosyncrasies and there was no annotated data available. I developed a number of computational methods for resolving shell nouns that do not rely on manually annotated data. For evaluation, I developed annotated corpora for shell nouns and their content using crowdsourcing. The annotation results showed that the annotators agreed to a large extent on the shell content. The evaluation of resolution methods showed that knowledge derived from the linguistics literature helps in the process of shell noun resolution, at least for shell nouns with strict semantic and syntactic expectations. Varada Kolhatkar s broad research area in the past eight years has been natural language processing and computational linguistics. She recently completed her PhD in computational linguistics from the university of Toronto. Her advisor was Dr. Graeme Hirst. Prior to that, she did her Master s with Dr. Ted Pedersen at the University of Minnesota Duluth. During her PhD she focused primarily on the problem of anaphora resolution. Her Master s thesis explores all-words-sense disambiguation, showing the effect of polysemy, context window size, and sense frequency on disambiguation. At the end of her Ph.D., Varada spent four months at the University of Hamburg, Germany, where she worked with Dr. Heike Zinsmeister on non-nominal anaphora resolution. Currently, Varada is working as a research analyst at a company called Privacy Analytics Inc, where she focuses on the problem of text de-identification, i.e., the process used to protect against inappropriate disclosure of personal information in unstructured data.