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- Rising Stars in EECS: 2015 - Idoia Ochoa, Stanford University. “Genomic data compression and processing”

due to imperfections on the data and the lack of theoretical models that describe it the analysis tools do not generally have theoretical guarantees and different approaches exist for the same task Thus it is important to develop new tools and algorithms to facilitate the transmission and storage of the data and to improve the inference performed on it This is exactly the focus of my research Part of it consists on developing compression schemes which range from compression of single genomes to compression of the raw data outputted by the sequencing machines Part of this data the reliability of the outputted nucleotides is normally lossy compressed as it is inherently noisy and therefore difficult to compress Moreover it has been shown that lossy compression can potentially reduce the storage requirements while improving the inference performed on the data Further understanding this effect is part of my ongoing research together with characterizing the statistics of the noise such that denoisers tailored to them can be designed I have also worked on developing compression schemes for databases such that similarity queries can still be performed on the compressed domain This is of special interest in large biological databases where retrieving genomic sequences similar to others is necessary in several applications Finally I designed a tool for identifying disease driver genes associated with molecular processes in cancer patients Bio Idoia is currently in her 5th year of PhD in the Electrical Engineering department at Stanford University working with Prof Tsachy Weissman She also received her MSc from the same department in 2012 Previous to Stanford she got a BS and MSc from the Telecommunications Engineering Electrical Engineering department at University of Navarra Spain During her time at Stanford she conducted internships at Google and Genapsys and she served as a technical consultant

Original URL path: https://risingstars15-eecs.mit.edu/idoia-ochoa-alvarez/ (2015-12-05)

Open archived version from archive - Rising Stars in EECS: 2015 - Eleanor O’Rourke, University of Washington. “Educational Systems for Maximizing Learning Online and in the Classroom”

the classroom Specifically my dissertation explores the design implementation and evaluation of novel educational systems that increase motivation provide personalized learning experiences and support formative assessment As part of this work I have created an incentive structure that promotes the growth mindset in an educational game developed a framework for automatically generating instructional scaffolding and evaluated a system that visualizes student data in real time to assist classroom teachers In the development of these systems I combine ideas from computer science psychology education and the learning sciences to develop novel technical methods of integrating learning theory into computational tools In addition to evaluating my work through classroom studies with students and teachers I have also conducted large scale online experiments with tens of thousands of students My findings provide new insights into how students learn and how computing systems can support the learning process The ultimate goal of my research is to build personalized data driven systems that transform how we teach assess communicate and collaborate in learning environments Bio Nell is a PhD candidate in Computer Science and Engineering at the University of Washington advised by Zoran Popović in the Center for Game Science She received a B A

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

Open archived version from archive - Rising Stars in EECS: 2015 - Amanda Prorok, University of Pennsylvania. “Heterogeneous Robot Swarms”

lack the foundational theories to help us make the right design choices and understand the implications of heterogeneity My approach to designing swarm robotic systems considers both top down methodologies macroscopic modeling as well as bottom up single robot level algorithmic design My first research thrust targeted the specific problem of indoor localization for large robot teams and employed a fusion of ultra wideband and infrared signals to produce high accuracy I developed the first ultra wideband time difference of arrival sensor model for mobile robot localization which when used collaboratively achieved centimeter level accuracy Experiments with ten robots illustrated the effect of distributing the sensing capabilities heterogeneously throughout the team This bottom up approach highlighted the compromise between homogenous teams that are very efficient yet expensive and heterogeneous teams that are low cost My second research thrust which aims at formally understanding this compromise targets the general problem of distributing a heterogeneous swarm of robots among a set of tasks My strategy is to model the swarm macroscopically and subsequently extract decentralized control algorithms that are optimal given the heterogeneous swarm composition and underlying task requirements I developed a dedicated diversity metric that identifies the relationship between performance and heterogeneity and that provides a means with which to control the composition of the swarm so that performance is maximized This top down approach complements the bottom up method by providing high level abstraction and foundational analyses thus shaping a new way of exploiting heterogeneity as a design paradigm Bio Amanda Prorok is a Postdoc in the General Robotics Automation Sensing and Perception GRASP Lab at the University of Pennsylvania where she works with Prof Vijay Kumar on multi robot systems Prior to moving to UPenn she spent one year working on cutting edge sensor technologies at Sensirion during which

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

Open archived version from archive - Rising Stars in EECS: 2015 - Elina Robeva, UC Berkeley. “Super-resolution without Separation.”

by the imaging device telescope microscope camera or others every point source of light is blurred by a given point spread function We assume that the incoming signal is a linear combination of M shifted copies centered at each of the M point sources of a known point spread function with unknown shifts the locations of the point sources and intensities and one only observes a finite collection of evaluations of this signal To recover the locations and intensities practitioners solve a convex program which is a weighted version of basis pursuit over a continuous dictionary Despite the recent success in many empirical disciplines the theory of super resolution imaging remains limited More precisely our aim is to show that the true point source locations and intensities are the unique optimal solution to the above mentioned convex program Much of the existing proofs to date rely heavily on the assumption that the point sources are separated by more than some minimum amount Building on polynomial interpolation techniques and tools from compressed sensing we show that under some reasonable conditions on the point spread function arbitrarily close point sources can be resolved by the above convex program from 2M 1 observations Moreover we show that the Gaussian point spread function satisfies these conditions Bio Elina Robeva is a fourth year graduate student in mathematics at UC Berkeley advised by Bernd Sturmfels Originally from Bulgaria Elina s career as a mathematician started in middle school when she took part in many competitions in mathematics and computer science After winning two silver medals from the international mathematical olympiad in high school she started her undergraduate degree at Stanford University in 2007 There she pursued her interests in mathematics and wrote two combinatorics papers with Professor Sam Payne She received the Dean s award

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

Open archived version from archive - Rising Stars in EECS: 2015 - Deblina Sarkar, MIT. “2D Steep Transistor Technology: Overcoming Fundamental Barriers in Low-Power Electronics and Ultra-Sensitive Biosensors”

to transistors with super steep turn on characteristics which is crucial for obtaining high energy efficiency and ultra scalability My research also establishes for the first time that the material and device technology which have evolved mainly with an aim of power reduction in digital electronics can revolutionize a completely diverse arena of bio gas sensor technology The unique advantages of 2D semiconductors for electrical sensors is demonstrated and it is shown that they lead to ultra high sensitivity and also provide an attractive pathway for single molecular detectability the holy grail for all biosensing research Moreover it is theoretically illustrated that steep turn on obtained through novel technology such as BTBT can result in unprecedented performance improvement compared to that of conventional electrical biosensors with around 4 orders of magnitude higher sensitivity and 10x lower detection time With the aim towards building ultra scaled low power electronics as well as highly efficient sensors my research achieves a significant milestone furnishing the first experimental demonstration of TFETs based on 2D channel material to beat the fundamental limitation in subthreshold swing SS This device comprising of an atomically thin channel exhibits record average SS at ultra low supply voltages thus cracking the long standing issue of simultaneous dimensional and power supply scalability and hence can lead to a paradigm shift in information technology as well as healthcare Bio Deblina Sarkar completed her M S and PhD in the ECE department at UCSB in 2010 and 2015 respectively Her doctoral research which combined the interdisciplinary fields of engineering physics and biology included theoretical modeling and experimental demonstration of energy efficient electronic devices and ultra sensitive biosensors She is currently a postdoctoral researcher in the Synthetic Neurobiology group at MIT and is interested in exploring novel technologies for mapping and controlling the brain

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

Open archived version from archive - Rising Stars in EECS: 2015 - Melanie Schmidt, Carnegie Mellon University. “Algorithmic techniques for solving the k-means problem on big data sets”

groups and to find a representative point for each group Clustering is a major tool in machine learning Imagine that the vectors represent songs in a music collection or handwritten letters The clustering can show which objects are similar and the representatives can be used to classify newly arriving objects There are many clustering objectives and the k means objective might be the most popular among them It is based on the Euclidean distance The representative of a group is the centroid i e the sum of the points in the group divided by their number A grouping is evaluated by computing the squared Euclidean distance of every point to its representative and summing these up The k means problem consists of finding a grouping into k groups that minimizes this cost function The algorithmic challenges connected to the k means problem are numerous The problem is NP hard but it can be solved approximately up to a constant factor What is the best possible approximation factor Can we prove lower bounds A different approach is to fix a parameter to lower the complexity If the number of groups k is fixed then the problem can be approximated to an arbitrary precision This assumption also allows us to approximately solve the problem by algorithms that only read the input data once and in a given order a main tool to deal with big data How small can we make the memory need of such a streaming algorithm and will the algorithm be efficient in practice We see different answers to this question Bio Melanie Schmidt obtained a master s degree with distinction in computer science with minor in mathematics from TU Dortmund University in 2009 In her undergraduate studies she focused on network flow theory a topic that lies in

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

Open archived version from archive - Rising Stars in EECS: 2015 - Claudia Schulz, Imperial College London. “Explaining Logic Programming with Argumentation”

human like reasoning thus sometimes neglecting the efficiency of the reasoning procedure whereas the latter is concerned with the efficient computation of solutions to a reasoning problem resulting in a less human understandable process In recent years Logic Programming has been frequently applied for the computation of reasoning problems in Argumentation Theory and has been found an efficient method for determining solutions to those problems My research is concerned with the opposite direction i e with using ideas from Argumentation Theory to improve Logic Programming techniques One of the shortcomings of Logic Programming is that it does not provide any explanation of the solution computed for a given problem For recommendation systems based on Logic Programming this means that there is no explanation for a recommendation made by the system I thus created a mechanism to explain Logic Programming solutions in a human like argumentative style by applying ideas from the field of Argumentation Theory A medical treatment recommendation can thus be automatically explained in the style of two physicians arguing about the best treatment Bio Claudia Schulz received her B Sc in Cognitive Science from the University of Osnabrück in 2011 She then decided to specialise in Artificial Intelligence

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

Open archived version from archive - Rising Stars in EECS: 2015 - Mahsa Shoaran, California Institute of Technology. “Low-Power Circuit and System Design for Epilepsy Diagnosis and Therapy”

brain stimulation holds great promises for improving the quality of life of millions of people with epileptic seizures worldwide In this context low power circuit and system design techniques for data acquisition compression and seizure detection in multichannel cortical implants are presented in the current research work Compressive sensing is utilized as the main data reduction method in the proposed system The existing microelectronic implementations of compressive sensing are applied in a single channel basis Therefore these topologies incur a high power consumption and large silicon area As an alternative a multichannel measurement scheme and an appropriate recovery scheme are proposed which encode the entire array into a single compressed data stream The first fully integrated circuit that addresses the multichannel compressed domain feature extraction for epilepsy diagnosis is proposed This approach enables the real time compact low power and low hardware complexity implementation of the seizure detection algorithm as a part of an implantable neuroprosthetic device for the treatment of epilepsy The developed methods in this research can be employed in other applications than epilepsy diagnosis and neural recording which similarly require data recording and processing from multiple nodes Bio Mahsa received her B Sc and M Sc degrees

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

Open archived version from archive