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  • Rising Stars in EECS: 2015
    Engineering MIT We are Rising Stars HOME Suzana Lisanti 2015 07 24T13 18 52 00 00 Welcome to Rising Stars An Academic Career Workshop for Women Rising Stars is a career building workshop for women electrical engineers and computer scientists interested in careers in academia This year s workshop will bring together over 60 top graduates in the fields of electrical engineering and computer science for two days of scientific

    Original URL path: https://risingstars15-eecs.mit.edu/ (2015-12-05)
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  • Rising Stars in EECS: 2015 - ABOUT
    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

    Original URL path: https://risingstars15-eecs.mit.edu/about/ (2015-12-05)
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  • Rising Stars in EECS: 2015 - SCHEDULE
    Break 1 30 2 30 P M Oral Presentation Session III 34 401 Communications and EE Biology 2 30 3 00 P M Break 3 00 4 30 P M How to Develop your Research Statement 36 428 Charles Leiserson Professor Mac Vicar Faculty Fellow Department of Electrical Engineering and Computer Science MIT 4 30 5 00 P M Break 5 00 6 15 P M Junior Faculty Panel 34 401 Moderator Yury Polyanskiy Robert J Shillman 1974 CD Associate Professor MIT Panelist Dirk Englund Jamieson Career Development Assistant Professor MIT Ruonan Han EE Landsman 1958 CD Assistant Professor MIT Thomas Heldt Hermann L F von Helmholtz Career Development Assistant Professor MIT Stefanie Jegelka Assistant Professor MIT Vivienne Sze Emanuel E Landsman 1958 Career Development Assistant Professor MIT 6 30 8 30 P M Group Dinner with Panelists MIT Museum Anantha Chandrakasan Department Head of EECS and the Vannevar Bush Professor of Electrical Engineering and Computer Science MIT Introduction by Christine Ortiz Dean for Graduate Education MIT Speaker Mildred Dresselhaus Institute Professor MIT Tuesday 11 10 34 401 and 36 428 50 Vassar St Cambridge MA 8 30 9 00 A M Breakfast 34 401 9 00 10 00 A M Oral Presentation Session IV 34 401 Materials and Devices and Circuits 10 00 10 30 A M Break 10 30 11 30 A M How to Give a Job Talk and Get a Faculty Job 34 401 John Guttag Dugald C Jackson Professor of Computer Science and Engineering MIT 11 30 A M 12 45 P M Lunch with Senior Women 36 428 12 45 1 00 P M Women s Technology Program Presentation 36 428 Cynthia Skier Director of Women s Technology Program and Industrial Connection Program Department of Electrical Engineering and Computer Science MIT 1 00 1

    Original URL path: https://risingstars15-eecs.mit.edu/schedule/ (2015-12-05)
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  • Rising Stars in EECS: 2015 - PARTICIPANTS
    MIT Systems for Teaching Programming and Hardware Design at Scale Basak Guler Pennsylvania State University Interaction Communication and Computation in Information and Social Networks Divya Gupta UCLA Hosting Services on an Untrusted Cloud Judy Hoffman UC Berkeley Adapting Deep Visual Models for Visual Recognition in the Wild Hui Lin Hsu University of Toronto Reduction in the Photoluminescence Quenching for Erbium Doped Amorphous Carbon Photonic Materials by Deuteration and Fluorination Carlee Joe Wong Princeton University Smart er Data Pricing Gauri Joshi MIT Using Redundancy to Reduce Delay in Cloud Systems Ankita Arvind Kejriwal Stanford University Scalable Low Latency Indexes for a Key Value Store Hana Khamfroush Penn State University On Propagation of Phenomena in Interdependent Networks Hyeji Kim Stanford University Superposition coding is almost always optimal for the Poisson broadcast channel Jung Eun Kim UIUC A New Real Time Scheduling Paradigm for Safety Critical Multicore Systems Varada Kolhatkar Privacy Analytics Inc Resolving Shell Nouns Parisa Kordjamshidi UIUC Saul Towards Declarative Learning Based Programming Ramya Korlakai Vinayak California Institute of Technology Convex Optimization Based Graph Clustering Theoretical Guarantees and Practical Applications Karla Kvaternik Princeton University Consensus Optimization Based Coordination Control Strategies Min Kyung Lee Carnegie Mellon University Designing human centered algorithmic technologies Kun Linda Li UC Berkeley III V compound semiconductor lasers for optical communication and imaging Hongjin Liang University of Science and Technology of China A Program Logic for Concurrent Objects under Fair Scheduling Xi Ling MIT Seeding Promoter Assisted Chemical Vapor Deposition of MoS2 Monolayer Fei Liu Carnegie Mellon University Natural Language Processing Machine Learning Social Media Summarization Yu Hsin Liu UCSD Silicon p n junction photodetectors Kristen Lurie Stanford University New optical imaging tools and visualization techniques for bladder cancer Jelena Marasevic Columbia University Links between systems and theory Full duplex wireless and beyond Ghita Mezzour International University of

    Original URL path: https://risingstars15-eecs.mit.edu/participants/ (2015-12-05)
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  • Rising Stars in EECS: 2015 - All Participants
    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 Bio Carlee Joe Wong is a Ph D 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 2014 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 Gauri Joshi MIT Using Redundancy to Reduce Delay in Cloud Systems Email gauri mit edu http www mit edu gauri Position PhD Candidate Current Institution MIT Abstract 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 Bio 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 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 2012 15 and the Claude E Shannon Research Assistantship 2015 16 She has had summer internships at Bell Labs 2012 and Google 2013 14 Ankita Arvind Kejriwal Stanford University Scalable Low Latency Indexes for a Key Value Store Email ankitak cs stanford edu Website http stanford edu ankitak Position Student Current Institution Stanford University Abstract 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 Bio 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 MSR SVC 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 Hana Khamfroush Penn State University On Propagation of Phenomena in Interdependent Networks Email hkham cse psu edu Website https scholar google com citations user qNAcUB8AAAAJ hl en Position Postdoctoral Scholar Current Institution Penn State University Abstract 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 Bio 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 2014 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 Hyeji Kim Stanford University Superposition coding is almost always optimal for the Poisson broadcast channel Email hyejikim stanford edu Website http stanford edu hyejikim Position PhD Candidate Current Institution Stanford University Abstract 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 Bio Hyeji Kim is a Ph D 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 2013 Her research interest include information theory communication systems and statistical learning She is a recipient of the Stanford Graduate Fellowship Jung Eun Kim UIUC A New Real Time Scheduling Paradigm for Safety Critical Multicore Systems Email jekim314 illinois edu Website http publish illinois edu cpsintegrationlab people jung eun kim Position PhD Candidate Current Institution Department of Computer Science at the University of Illinois at Urbana Champaign Abstract 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 Bio 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 Chang Gun 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 2015 2016 Varada Kolhatkar Privacy Analytics Inc Resolving Shell Nouns Email varada kolhatkar gmail com Position Postdoc Current Institution Privacy Analytics Inc Abstract 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 Bio Varada Kolhatkar s broad research area in the past eight years has been natural language processing and computational linguistics She recently completed her Ph D 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 Ph D 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 Parisa Kordjamshidi UIUC Saul Towards Declarative Learning Based Programming Email kordjam illinois edu Website http cogcomp cs illinois edu kordjam Position Postdoctoral research associate Current Institution University of Illinois at Urbana Champaign Abstract Saul Towards Declarative Learning Based Programming Developing intelligent problem solving systems for real world applications requires addressing a range of scientific and engineering challenges I will present Saul a learning based programming language designed to address some of the shortcomings of programming languages that aim at advancing and simplifying the development of intelligent systems Such languages need to interact with messy naturally occurring data to allow a programmer to specify what needs to be done at an appropriate level of abstraction rather than at the data level to be developed on a solid theory that supports moving to and reasoning at this level of abstraction and finally to support flexible integration of these learning and inference models within an application program Saul is an object functional programming language written in Scala that facilitates these by 1 allowing a programmer to learn name and manipulate named abstractions over relational data 2 supporting seamless incorporation of trainable probabilistic or discriminative components into the program and 3 providing a level of inference over trainable models to support composition and make decisions that respect domain and application constraints Saul is developed over a declaratively defined relational data model can use piecewise learned factor graphs with declaratively specified learning and inference objectives and it supports inference over probabilistic models augmented with declarative knowledge based constraints I will describe the key constructs of Saul and exemplify its use in case studies of developing intelligent applications in the domains of natural language processing and computational biology I will also argue that apart from simplifying programming for complex models one main advantage of such a language is the reusability of the designed inference learning models and features henceforth increasing the replicability of research results Moreover the models can be extended to use new emerging algorithms new data resources and background knowledge with a minimum effort Bio Parisa Kordjamshidi is a postdoctoral researcher in University of Illinois at Urbana Champaign computer science department in cognitive computation group She obtained her PhD degree from KULeuven in July 2013 During her PhD research she introduced the first Semantic Evaluation task and benchmark for Spatial Role Labeling SpRL She has worked on structured output prediction and relational learning models to map natural language onto formal spatial representations appropriate for spatial reasoning as well as to extract knowledge from biomedical text She is also involved in an NIH National Institute of Health project extending her research experience on structured and relational learning to Declarative Learning Based Programming DeLBP and performing biological data analysis DeLBP is a research paradigm in which the goal is to facilitate programming for building systems that require a number of learning and reasoning components that interact with each other This would help experts in various domains who are not expert in machine learning to design complex intelligent systems The results of her research have been published in several international peer reviewed conferences and journals including ACM TSLP JWS BMC Bioinformatics IJCAI Ramya Korlakai Vinayak California Institute of Technology Convex Optimization Based Graph Clustering Theoretical Guarantees and Practical Applications Email ramya caltech edu Website http www its caltech edu rkorlaka Position Graduate Student Current Institution California Institute of Technology Abstract Convex Optimization Based Graph Clustering Theoretical Guarantees and Practical Applications Today we are collecting huge amounts of data with the aim of extracting useful and relevant information Clustering a widely used technique toward this quest refers to the grouping of data points that are similar to each other In many problems the observed data has a network or graphical structure associated to it as is the case in social networks bioinformatics data mining and other fields When attempting to cluster massive data making pairwise comparisons measurements between all data points is exorbitantly expensive A major challenge therefore has been to identify clusters with only partially observed graphs and to design algorithms with provable guarantees for this task In the case of unweighted graphs we consider two algorithms based on the popular convex optimization approach of the low rank plus sparse decomposition of the adjacency matrix Robust Principal Component Analysis We provide sharp performance guarantees for successfully identifying clusters generated by the commonly used Stochastic Block Model in terms of the size of the clusters the density of edges inside the clusters and the regularization parameter of the convex programs For weighted graphs where each weighted edge represents the similarity between its corresponding pair of points we seek to recover a low rank component of the adjacency matrix also called the similarity matrix We use a convex optimization based algorithm which requires no prior knowledge of the number of clusters and behaves in a robust way in the presence of outliers Using a generative stochastic model for the similarity matrix we obtain sharp bounds on the sizes of clusters strength of similarity compared to noise number of outliers and the regularization parameter We corroborate our theoretical findings with simulated experiments We also apply our algorithms to the problem of crowdsourcing inference using real data Bio Ramya Korlakai Vinayak is a PhD candidate in the Department of Electrical Engineering at Caltech She works with Prof Babak Hassibi Her research interests are broadly in the intersection of Optimization and Machine Learning She received the Schlumberger Foundation Faculty of the Future fellowship for the academic years 2013 15 Prior to joining Caltech Ramya obtained her undergraduate degree in Electrical Engineering from Indian Institute of Technology Madras Karla Kvaternik Princeton University Consensus Optimization Based Coordination Control Strategies Email karlak princeton edu Website http www scg utoronto ca kvaternik Position Postdoctoral Research Associate Current Institution Princeton University Abstract Consensus Optimization Based Coordination Control Strategies Consensus decentralized optimization CDO methods originally studied by Tsitsiklis et al have undergone significant theoretical development within the last decade Much of this attention is motivated by the recognized utility of CDO in large scale machine learning and sensor network applications In contrast we are interested in a distinct class of decentralized coordination control problems DCCPs and we aim to investigate the utility and limitations of CDO based coordination control strategies Unlike prototypical machine learning and sensor network problems DCCPs may involve a number of networked agents with heterogeneous dynamics that couple to those of a CDO based coordination control strategy thereby affecting its performance We find that existing analytic techniques cannot easily accommodate such a problem setting Moreover the final desired agent configuration in general DCCPs does not necessarily involve consensus This nuanced observation requires a re interpretation of the variables updated in a standard CDO scheme and exposes a limitation of CDO based coordination control strategies Starting from this re interpretation we address this limitation by proposing the Reduced Consensus Optimization RCO method which is a streamlined variant of CDO particularly well suited to the DCCP context More importantly we introduce a novel framework for the analysis of general CDO methods which is based on the use of interconnected systems techniques small gain arguments and the concept of semiglobal practical asymptotic stability This framework allows us to seamlessly study the performance of RCO as well as problem settings involving dynamic agents In addition when applied to a general class of CDO methods themselves this analytic viewpoint allows us to relax several standard assumptions Bio Karla Kvaternik obtained her B Sc in Electrical and Computer Engineering at the University of Manitoba her M Sc specializing in control theory at the University of Alberta and her Ph D in control theory at the University of Toronto She was the recipient of the prestigious Vanier Canada Graduate Scholarship in 2010 and the recipient of the Best Student Paper award at the 2009 Multiconference on Systems and Control in St Petersburg Russia Her research interests span nonlinear systems and control theory Lyapunov methods nonlinear programming and extremum seeking control but her main interest is the development and application of decentralized coordination control strategies for dynamic multiagent systems She is currently a Postdoctoral Research Associate at Princeton University where her research focuses on the development of optimal social foraging models Min Kyung Lee Carnegie Mellon University Designing human centered algorithmic technologies Email mklee cs cmu edu Website http www cs cmu edu mklee Position Research Scientist Current Institution Carnegie Mellon University Abstract Designing human centered algorithmic technologies Algorithms are everywhere acting as intelligent mediators between people and the world around them Facebook algorithms decide what people see on their news feeds Uber algorithms assign customers to drivers robots drive cars on our behalves Algorithmic intelligence offers opportunities to transform the ways people live and work for the better Yet their opacity can introduce bias into the worlds that people access through such technologies inadvertently provide unfair choices blur accountability or make the technology seem incomprehensible or untrustworthy My research examines the social and decision making implications of intelligent technologies and facilitates more human centered design I study how intelligent technologies change work practices and devise design principles and interaction techniques that give people appropriate control over intelligent technologies In the process I create novel intelligent products that address critical problems in the areas of on demand work and robotic service In the first line of my research I studied Uber and Lyft ridesharing drivers to understand the impact of algorithms used to manage human workers in on demand work The results suggested that workers do not always cooperate with algorithmic management because of the algorithms limited assumptions about worker behaviors and the opacity of algorithmic mechanisms I further examined people s perceptions of algorithmic decisions through an online experiment and created design principles around how we can use transparency anthropomorphization and visualization to foster trust in algorithmic decisions and help people make better use of them In the second line of my research I studied three service robots deployed in the field over long periods of time a receptionist robot a telepresence robot for distributed teams and an office delivery robot that I helped build from scratch using human centered design methods The studies revealed individual and social factors that robots can personalize in order to be more successfully adopted into a workplace Bio Min Kyung Lee is a research scientist in human computer interaction at the Center for Machine Learning and Health at Carnegie Mellon University Her research examines the social and decision making implications of intelligent systems and supports the development of more human centered machine learning applications Dr Lee is a Siebel Scholar and has received several best paper awards as well as an Allen Newell Award for Research Excellence Her work has been featured in media outlets such as the New York Times New Scientist and CBS She received a PhD in HCI in 2013 and an MDes in Interaction Design from Carnegie Mellon and a BS summa cum laude in Industrial Design from KAIST Kun Linda Li UC Berkeley III V compound semiconductor lasers for optical communication and imaging Email lindakli berkeley edu Website https www linkedin com pub kun linda li 24 231 691 Position PhD candidate Current Institution University of California Berkeley Abstract III V compound semiconductor lasers for optical communication and imaging My research projects focus on III V compound semiconductor lasers to generate and manipulate light with both bottom up and top down approaches for applications in optical communications biological imaging ranging and sensing As microprocessors become progressively faster chip scale data transport becomes progressively more challenging Optical interconnects for inter and intra chip communications are required to reduce power consumption and increase bandwidth Lightwave devices have traditionally relied on III V compound semiconductors due to their capacity for efficient optical processes Growing III V materials from the bottom up opens a pathway to integrating superior optoelectronic properties with the massive existing silicon based infrastructure Our approach of self assembling III V nanostructures on silicon in a novel growth mode has bypassed several roadblocks and achieved excellent single crystalline quality with GaAs and InP based materials I have developed a methodology to evaluate optical properties of InP nanostructures and demonstrated its superior surface quality which are critical for optoelectronic devices I also make another type of micro scale semiconductor lasers from the top down which is called vertical cavity surface emitting lasers VCSELs They are key optical sources in optical communications with the advantages of lower power consumption lower cost packaging and ease of fabrication and testing Our group has demonstrated a revolutionary single layer high index contrast sub wavelength grating HCG and implemented it as a reflection mirror in VCSEL Compared with conventional VCSEL mirrors DBRs the seemingly simple structured HCG provides ultra broadband high reflectivity compact size and light weight high tolerant and cost effective fabrication process I mainly work on the development of wavelength tunable 850nm and 1060nm HCG VCSELs These monolithic continuously tunable HCG VCSELs will present extraordinary performance in applications such as wavelength division multiplexed WDM optical network light detection and ranging Its potential wide reflection band and fast tuning speed will also be highly promising for high resolution real time imaging in optical coherent tomography OCT Bio Kun Linda Li is a PhD candidate in the Department of Electrical Engineering and Computer Sciences at University of California Berkeley advised by Prof Connie Chang Hasnain Prior to joining graduate school she received her B S degree from Optical Engineering of Zhejiang University in China 2006 2010 She had one year of exchange experience in University of Hong Kong 2008 2009 Kun s main research interests focus on III V nanostructures directly grown on silicon for integrated optoelectronics and vertical cavity surface emitting laser VCSEL with high contrast grating HCG structure for optical communication and imaging Her skills include optical characterization semiconductor fabrication and optoelectronic device modeling She received Lam Research Graduate Fellowship 2014 to award her performance in the field of semiconductors Besides research Kun is also active in a variety of education outreach and mentoring programs including Girl Scouts Expanding Your Horizon and Girls in Engineering Kun has won the Outstanding Graduate Student Instructor Award at UC Berkeley 2014 Hongjin Liang University of Science and Technology of China A Program Logic for Concurrent Objects under Fair Scheduling Email lhj1018 ustc edu cn Website http staff ustc edu cn lhj1018 Position Limited term associate researcher Current Institution University of Science and Technology of China Abstract A Program Logic for Concurrent Objects under Fair Scheduling Existing work on verifying concurrent objects is mostly concerned with safety only e g partial correctness or linearizability Although there has been recent work verifying lock freedom of non blocking objects much less efforts are focused on deadlock freedom and starvation freedom progress properties of blocking objects These properties are more challenging to verify than lock freedom because they allow the progress of one thread to depend on the progress of another assuming fair scheduling We propose LiLi a new rely guarantee style program logic for verifying linearizability and progress together for concurrent objects under fair scheduling The rely guarantee style logic unifies thread modular reasoning about both starvation freedom and deadlock freedom in one framework It also establishes progress aware abstraction for concurrent objects which can be applied when verifying safety and liveness of client code We have successfully applied the logic to verify starvation freedom or deadlock freedom of representative algorithms such as ticket locks queue locks lock coupling lists optimistic lists and lazy lists This is joint work with Xinyu Feng at USTC Bio Hongjin Liang is a limited term associate researcher at University of Science and Technology of China USTC She received her Ph D in Computer Science from USTC in 2014 under the joint supervision of Prof Xinyu Feng USTC and Prof Zhong Shao Yale Hongjin is interested in program verification and concurrency theory Her Ph D thesis is about refinement verification of concurrent programs and its applications in which she designed simulations and Hoare style program logics for concurrent program refinement and applied them to verify concurrent garbage collectors and prove linearizability of concurrent objects and algorithms She is currently trying to extend her refinement verification techniques to also reason about liveness properties of concurrent algorithms For more information please visit http staff ustc edu cn lhj1018 Xi Ling MIT Seeding Promoter Assisted Chemical Vapor Deposition of MoS2 Monolayer Email xiling mit edu Website https scholar google com citations hl en user 7EvQ AAAAAJ view op list works sortby pubdate Position Postdoctoral Scholar Current Institution Massachusetts Institute of Technology Abstract Seeding Promoter Assisted Chemical Vapor Deposition of MoS2 Monolayer The synthesis of monolayer MoS2 based dichalcogenides is an attractive topic because of their promising properties in diverse fields especially in electronics and optoelectronic Among the various methods to get the monolayer MoS2 the chemical vapor deposition CVD method is considered as the superlative one because of the high efficient low cost and large area synthesis So far sulfur and MoO3 are the widely used precursors to grow monolayer MoS2 on the SiO2 Si substrate Here by loading the organic aromatic molecule on the SiO2 Si substrate as seed it was found that the large area and high quality MoS2 can grow out under a much soft condition such as atmospheric pressure lowing the temperature from 800 C or higher to 650 C Raman spectra photoluminescence spectra and AFM atomic force microscopy are used to identify the thickness and quality of MoS2 Furthermore other kinds of aromatic molecules are tried to use as a seed to grow MoS2 Towards the applications in integrated circuits we developed a method called selective sowing of seeds to construct the basic building blocks of metal semiconductor e g graphene MoS2 semiconductor semiconductor e g WS2 MoS2 and insulator semiconductor e g hBN MoS2 heterostructures through direct and controllable CVD synthesis in a large scale Bio Xi Ling is currently a Postdoctoral Associate in the Research Laboratory of Electronics at Massachusetts Institute of Technology MIT since September 2012 under the supervision of Professors Mildred Dresselhaus and Jing Kong She obtained her PhD degree in physical chemistry from Peking University in July 2012 under the supervision of Professor Jin Zhang and Zhongfan Liu She has a multidisciplinary background in chemistry materials science electrical engineering and physics with research experience on spectroscopy chemical vapor deposition CVD and optoelectronic devices Fei Liu Carnegie Mellon University Natural Language Processing Machine Learning Social Media Summarization Email feiliu cs cmu edu Website http www cs cmu edu feiliu Position Postdoctoral Fellow Current Institution Carnegie Mellon University Degree PhD 2011 University of Texas at Dallas Degree Advisor s Professor Yang Liu Research Topics Natural Language Processing Machine Learning Social Media Summarization Abstract Summarizing Information in Big Data Algorithms and Applications Information floods the lives of modern people and we find it overwhelming Summarization systems that identify salient pieces of information and present it concisely can help I will discuss both algorithmic and application perspectives of summarization Algorithm wise I will describe keyword extraction sentence extraction and summary generation including a range of techniques from information extraction to semantic representation of data sources application wise I focus on summarizing human conversations social media contents and news articles The data sources span low quality speech recognizer outputs and social media chats to high quality content produced by professional writers A special focus of my work is exploring multiple information sources In addition to better integration across sources this allows abstraction to shared research challenges for broader impact Finally I try to identify the missing links in cross genre summarization studies and discuss future research directions Bio Fei worked as a Senior Research Scientist at Bosch Research Palo Alto California one of the largest German companies providing intelligent car systems and home appliances Fei received her Ph D in Computer Science from the University of Texas at Dallas in 2011 supported by Erik Jonsson Distinguished Research Fellowship Prior to that she obtained her Bachelors and Masters degrees in Computer Science from Fudan University Shanghai China Feihas published over twenty peer reviewed articles and she serves as a referee for leading journals and conferences Yu Hsin

    Original URL path: https://risingstars15-eecs.mit.edu/all-participants/ (2015-12-05)
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  • Rising Stars in EECS: 2015 - SPEAKERS
    Learn more about the speakers participating in the 2015 Rising Stars workshop below To view all speaker information in one list click here Regina Barzilay Anantha Chandrakasan Munther Dahleh Mildred Dresselhaus Dirk Englund William Freeman John Guttag Ruonan Han Thomas

    Original URL path: https://risingstars15-eecs.mit.edu/speakers/ (2015-12-05)
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  • Rising Stars in EECS: 2015 - All Speakers
    at Johannes Gutenberg University Germany at Yale University and MIT He received the PhD degree in Medical Physics from MIT s Division of Health Sciences and Technology and commenced postdoctoral training at MIT s Laboratory for Electromagnetic and Electronics Systems Prior to joining the MIT faculty Thomas was a Principal Research Scientist at RLE Thomas s research interests focus on signal processing mathematical modeling and model identification to support real time clinical decision making monitoring of disease progression and titration of therapy primarily in neurocritical and neonatal critical care In particular he is interested in developing a mechanistic understanding of physiologic systems and in formulating appropriately chosen computational physiologic models for improved patient care His research is conducted in close collaboration with colleagues at MIT and clinicians from Boston Children s Hospital Beth Israel Deaconess Medical Center and Boston Medical Center Stefanie Jegelka Website http people csail mit edu stefje Assistant Professor Bio Stefanie Jegelka joined the faculty of MIT in 2015 Her research interests lie in algorithmic machine learning In particular she is interested in modeling and efficiently solving machine learning problems that involve complex discrete structure She is also interested parallel and distributed machine learning Prior to joining MIT she was a postdoctoral researcher at UC Berkeley Before that she earned a Diplom in Bioinformatics from the University of Tuebingen Germany and a Ph D in Computer Science from ETH Zurich and the Max Planck Institute for Intelligent Systems She has won a number of fellowships and a Best Paper Award at the International Conference on Machine Learning Dina Katabi Website http people csail mit edu dina Workshop Technical Program Co Chair Andrew Erna Viterbi Professor of Electrical Engineering and Computer Science Bio Dina Katabi is the Andrew Erna Viterbi Professor of Electrical Engineering and Computer Science and the director of the MIT s center for wireless networks and mobile computing Wireless MIT Katabi s work focuses on computer networks and wireless systems She received her PhD and MS from MIT in 2003 and 1999 and her Bachelor of Science from Damascus University in 1995 Katabi was named an ACM fellow in 2014 and a MacArthur Fellow in 2013 She received the ACM Grace Murray Hopper Award in 2013 a Faculty Research Innovation Fellowship in 2011 the IEEE William R Bennett prize in 2009 a Sloan Fellowship in 2006 the NBX Career Development chair in 2006 and an NSF CAREER award in 2005 Katabi s doctoral dissertation won an ACM Honorable Mention award and a Sprowls award for academic excellence She also received multiple best paper awards from ACM SIGCOMM and Usenix NSDI the Test Of Time Award from ACM SIGCOMM and the Technology Review TR10 Award Charles Leiserson Website http people csail mit edu cel Professor Mac Vicar Faculty Fellow Department of Electrical Engineering and Computer Science Bio Charles E Leiserson is Professor of Computer Science and Engineering at MIT He holds the position of Edwin Sibley Webster Professor in MIT s Department of Electrical Engineering and Computer Science EECS He is a Margaret MacVicar Faculty Fellow the highest recognition at MIT for undergraduate teaching He is a member of MIT s Computer Science and Artificial Intelligence Laboratory CSAIL a member of the Lab s Theory of Computation Group TOC and head of the Lab s Supertech Research Group Professor Leiserson is an ACM Fellow a AAAS Fellow a SIAM Fellow and a senior member of IEEE Paula Long Website http datagravity com Chief Executive Officer and Co Founder DataGravity inc Bio Paula brings over 30 years of experience to DataGravity in delivering meaningful and game changing high tech innovation Prior to DataGravity Paula served as vice president of product development at Heartland Robotics In 2001 Paula co founded storage provider EqualLogic resetting the bar on how customers managed and purchased data storage EqualLogic was acquired by Dell for 1 4 billion in 2008 and Paula remained at Dell as vice president of storage until 2010 Previous to EqualLogic she served in several engineering management positions at Allaire Corporation and oversaw all aspects of the ClusterCATS product line while at Bright Tiger Technologies Her executive and technical leadership has been extensively recognized including the New Hampshire High Tech Council Entrepreneur of the Year award the Ernst Young 2008 Northeast Regional Entrepreneur of the Year and a national finalist for the same award Her technical awards span systems designs and enterprise software including the EqualLogic and ClusterCATS product lines She is a graduate of Westfield State College Paula is also active in the startup community Outside of high tech she works with charities creating equality for professional women and girls as well as with organizations enabling literacy for all children regardless of economic status Silvio Micali Website https www csail mit edu user 994 Associate Department Head MIT Electrical Engineering and Computer Science Ford Professor of Engineering Bio Silvio Micali has received his Laurea in Mathematics from the University of Rome and his PhD in Computer Science from the University of California at Berkeley Since 1983 he has been on the MIT faculty in Electrical Engineering and Computer Science Department where he is Ford Professor of Engineering Since January 2015 he is the Associate Head of the Department of Electrical Engineering and Computer Science Silvio s research interests are cryptography zero knowledge pseudo random generation secure protocols and mechanism design Silvio is the recipient of the Turing Award in computer science of the Goedel Prize in theoretical computer science and the RSA prize in cryptography He is a member of the National Academy of Sciences the National Academy of Engineering and the American Academy of Arts and Sciences Christine Ortiz Website http web mit edu cortiz www Dean for Graduate Education Morris Cohen Professor of Materials Science and Engineering Bio Christine Ortiz is the Dean for Graduate Education and the Morris Cohen Professor of Materials Science and Engineering at the Massachusetts Institute of Technology Professor Ortiz obtained her BS from Rensselaer Polytechnic Institute and MS and PhD from

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  • Rising Stars in EECS: 2015 - PAST WORKSHOPS
    List View SPEAKERS Grid View List View PREVIOUS WORKSHOPS CONTACT PAST WORKSHOPS Suzana Lisanti 2015 07 22T21 03 16 00 00 2014 Rising Stars University of California Berkeley 2013 Rising

    Original URL path: https://risingstars15-eecs.mit.edu/previous-workshops/ (2015-12-05)
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