HDSI Faculty Council

Photo of Ilkay Altintas
Ilkay Altintas
Chief Data Science Officer
CHIEF DATA SCIENCE OFFICER, SDSC

Ilkay Altintas is a Fellow of Halıcıoğlu Data Science Institute (HDSI) as well as San Diego Supercomputer Center’s Chief Data Science Officer. She has played a huge role in the stewardship of HDSI cyberinfrastructure (CI) resources and services.

She will continue to work with UC San Diego faculty, industry partners, and students at all levels. In addition to her CDSO responsibilities at SDSC, an Organized Research Unit of UC San Diego, Altintas is an associate research scientist and director of the Workflows for Data Science (WoRDS) Center of Excellence at SDSC. WoRDS specializes in developing scientific workflows and solution architectures used throughout data and computational science.
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Mikio Aoi
Assistant Professor

Dr. Aoi is a computational neuroscientist interested in studying how populations of neurons coordinate their activity to perform computations. In particular, his interests are in understanding how the dynamics of neural computations impact behavior and in developing principled approaches to data analysis in close collaboration with experimentalists.

Before pursuing an interest in neuroscience he earned a bachelor’s degree in Kinesiology from California State University, Long Beach and a PhD in Mathematical Biology from North Carolina State University studying the dynamics of cerebrovascular function in stroke patients.  As postdoctoral associate in the Department of Mathematics at Boston University he developed statistical methods for characterizing rhythmic synchrony in neuronal populations. He then moved to Princeton University, where he continued his postdoctoral training with Jonathan Pillow, developing scalable methods for analyzing high dimensional datasets of neuronal activity in animals performing perceptual decision making tasks.

As a native of Southern California, Dr. Aoi is thrilled to return to California to join the outstanding students and faculty at UCSD in the Halıcıoğlu Data Science Institute and The Department of Neurobiology – Division of Biology.

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Ery Arias-Castro
Professor

Ery Arias-Castro received his Ph.D. in Statistics from Stanford University in 2004. He then took a postdoctoral position at the Institute for Pure and Applied Mathematics (IPAM), where he participated in the program on Multiscale Geometry and Analysis in High Dimensions. After that, he took a postdoctoral position at the Mathematical Sciences Research Institute (MSRI), where he participated in the program on  Mathematical, Computational and Statistical Aspects of Image Analysis. He joined the faculty in the mathematics department at UCSD in 2005.  His research interests are in high-dimensional statistics, machine learning, spatial statistics, image processing, and applied probability.

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Mikhail Belkin
Professor

Mikhail Belkin received his Ph.D. in 2003 from the Department of Mathematics at the University of Chicago. His research interests are in theory and  applications of machine learning and data analysis. Some of his well-known work includes widely used Laplacian Eigenmaps, Graph Regularization and Manifold Regularization algorithms, which brought ideas from classical differential geometry and spectral analysis to data science. His recent work has been concerned with understanding remarkable mathematical and statistical phenomena observed in deep learning. This empirical evidence necessitated revisiting some of the basic concepts in statistics and optimization.  One of his key recent findings is the “double descent” risk curve that extends the textbook U-shaped bias-variance trade-off curve beyond the point of interpolation.

Mikhail Belkin is a recipient of a NSF Career Award and a number of best paper and other awards. He has served on the editorial boards of the Journal of Machine Learning Research, IEEE Pattern Analysis and Machine Intelligence and SIAM Journal on Mathematics of Data Science.

Website: misha.belkin-wang.org

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Jelena Bradic
Professor

Bradic is an Associate Professor of Statistics, and winner of multiple teaching awards. She directs the Statistical Lab for Learning Large-Scale and Complex Data. Her interests include ensemble learning, robust statistics and survival analysis. Her application areas include gene-knockout experiments, understanding cell cycles, developing new policies or detecting effects of treatments onto survival, Her research also reaches into the area of causal inference and developing new learning algorithms that can make new scientific discoveries but also quantify uncertainty with which these discoveries are being made. Her multidisciplinary expertise in handling data has expanded her research into multidisciplinary fields that include political science, marketing, engineering, public health as well as biomedical sciences.

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Henrik Christensen
Director/CRI

Henrik Christensen is the director of the UC San Diego Contextual Robotics Institute and a professor in the Department of Computer Science and Engineering at the Jacobs School of Engineering. Christensen came to UC San Diego after serving, most recently, as executive director of the Institute for Robotics and Intelligent Machines at the Georgia Institute of Technology.

Christensen was initially trained in mechanical engineering and worked subsequently with MAN/BW Diesel. He earned a master’s and Ph.D. in electrical engineering from Aalborg University in Denmark, in 1987 and 1990, respectively.

 

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Alex Cloninger
Assistant Professor

Cloninger’s research interests are in applied harmonic analysis, machine learning and neural networks, analysis of graphs and data sets sampled from continuous geometric structures embedded in high-dimensional spaces, and applications in various scientific domains.

Cloninger researches problems around the analysis of high dimensional data. He focuses on approaches that model the data as being locally lower dimensional, including data concentrated near manifolds or subspaces. These types of problems arise in many key scientific disciplines, including medicine, imaging, and artificial intelligence. And the techniques developed relate to a number of machine learning and statistical algorithms, including deep learning, network analysis, and measuring distances between probability distributions.

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Virginia de Sa
Professor, HDSI Associate Director Cognitive Science

Associate Director de Sa is a leader in the fields of cognitive science, neuroscience, computer science, engineering, and data science. Her research utilizes a numerous increase understanding of how humans learn to perceive the world around them.

She earned her Ph.D. and master’s in Computer Science from the University of Rochester, and a bachelor’s degree in Mathematics and Engineering from Canada’s Queen’s University.

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Justin Eldridge
Assistant Teaching Professor

Eldridge’s research interests include machine learning theory, and improving the correctness of learning algorithms to improve accuracy of output. His work has been in the theory of clustering as well as understanding the process of learning.

Previously, Eldridge taught foundational computer science and algorithms at Ohio State University, and worked as a post-doctoral researcher at OSU, where he was also honored as a Presidential Fellow. He served as a graduate visitor at Simons Institute for the Theory of Computing, at UC Berkeley. He earned his Ph.D. and master’s degrees in computer science from Ohio State University, as well as dual masters’ degrees in physics and mathematics from OSU.

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Shannon Ellis
Assistant Teaching Professor

Shannon Ellis is an Assistant Teaching Professor in Cognitive Science and HDSI at UC San Diego. She obtained her Ph.D. in Human Genetics from Johns Hopkins School of Medicine. Her primary focus at UC San Diego is to foster and promote data science education. To this end, she teaches undergraduate programming and data science courses and pursues projects that help provide access and educational materials to individuals who have historically lacked access to an education in data science.

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Aaron Fraenkel
Assistant Teaching Professor

Fraenkel uses machine learning and experimental design to study large-scale abusive behaviors on the internet, particularly events driven by robots (known as bots). His teaching expertise is in the end-to-end practice of data science, drawing from his industry experience with cybersecurity, anti-fraud, and anti-abuse systems. He is one of the leaders developing and teaching the university’s foundational data science curriculum and major program overseen the Halıcıoğlu Data Science Institute.

Before joining UC San Diego in 2018, Fraenkel worked as a senior scientist at Amazon, with a focus on machine learning. Having worked as a data scientist at work in industry, he chose to return to academia and work at the root of instruction, helping shape student learning and critical thinking.

He earned his Ph.D. and undergraduate degrees in mathematics from UC Berkeley, and worked in postdoctoral faculty appointments in mathematics at Boston College and Pennsylvania State University. At HDSI, his curriculum development of the path-breaking data science program includes creating projects using real-world datasets and challenges.

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Yoav Freund
Professor

Freund works on applications of machine learning algorithms in bioinformatics, computer vision, finance, network routing and high-performance computing. His current research focuses on machine learning to develop and analyze adaptive algorithms that change their behavior by learning from examples, rather than by re-programming.

He served as a senior research scientist at Columbia University in computational learning systems, and in machine learning development for AT&T Labs (formerly Bell Labs).

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R Stuart Geiger
Assistant Professor

Geiger studies the relationships between science, technology, and society — not only how science and technology have substantial impacts on society, but also how they are social institutions in themselves. He studies issues of fairness, accountability, transparency, responsibility, and contestability in machine learning, particularly in online content moderation. He has examined how values and biases are embedded in these technologies and how communities make decisions about how to use or not use them. Geiger also studies the development of data science as an academic and professional field, as well as the sustainability of free/open-source software and scientific cyberinfrastructure projects.

Geiger earned his Ph.D in 2015 at the UC Berkeley School of Information and the Berkeley Center for New Media, then was the staff ethnographer at the UC Berkeley Institute for Data Science. He joined UCSD in 2020, jointly appointed as faculty in the Department of Communication. Geiger is a methodological and disciplinary pluralist who collaborates across many different ways of knowing, but his work is often grounded in the fields of communication & media studies, science & technology studies, cultural anthropology, organizational sociology, human-computer interaction, and history and philosophy of science.

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Rajesh Gupta
Distinguished Professor, HDSI Founding Director

Professor Gupta’s research interests span topics in embedded and cyber-physical systems with a focus on energy efficiency from algorithms, devices to systems that scale from IC chips, and data centers to built environments such as commercial buildings.

Gupta received a Bachelor of Technology in electrical engineering from IIT Kanpur, India; a Master of Science in EECS from University of California, Berkeley; and a PhD in electrical engineering from Stanford University, US. Gupta is a Fellow of the IEEE, the ACM and the American Association for the Advancement of Science.

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Mike Holst
Professor

Holst is a leader in the Mathematical and Computational Physics Research Group, the Center for Computational Mathematics, and the Center for Astrophysics and Space Sciences. His interdisciplinary work at the university reaches into the fields of biochemistry and biophysics, computational fluid dynamics, computer graphics, materials science, and numerical algorithms relativity. A century after Einstein predicted the existence of gravitational waves, he has been part of a $600 million National Science Foundation collaboration working on detecting them.

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Tara Javidi
Professor

Javidi founded and co-directs the Center for Machine-Integrated Computing and Security at UC San Diego. Among her projects is DetecDrone Target Search Technology, what she calls drones that think. Her research focus is on stochastic analysis, design, and control of information collection, processing, and transfer in modern communication and networked systems. Interests include: Information acquisition and utilization (such as active hypothesis testing and variable length coding over channels with feedback); optimal routing in wireless mesh networks; cognitive and delay-sensitive communications and networking; and communications under application and/or secrecy constraints. Her work includes the applications of microeconomic theory and organizational science to the design of wireless networks.

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Young-Han Kim
Professor

Kim primarily works on two important challenges for high-speed, high-volume information processing systems — how to describe information efficiently, and how to transmit it reliably in the presence of noise and interference. He is a co-author of the textbook, Network Information Theory. Kim earned his bachelor’s degree with honors in electrical engineering from Seoul National University, receiving a GE Foundation Scholarship. Kim has served as a distinguished lecturer for the IEEE Information Theory Society, and as Associate Editor for the IEEE Transactions on Information Theory. He is a Fellow of the IEEE.

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Robin (Rob) Knight
Prof-BIOENGR/CSE/Dir-CMI

Rob Knight is the founding Director of the Center for Microbiome Innovation and Professor of Pediatrics, Bioengineering, and Computer Science & Engineering at UC San Diego. He was honored with the 2019 NIH Director’s Pioneer Award for his microbiome research and received the 2017 Massry Prize, often considered a predictor of the Nobel. His work has linked microbes to a range of health conditions including obesity and inflammatory bowel disease, has enhanced our understanding of microbes in environments ranging from the oceans to the tundra, and made high-throughput sequencing techniques accessible to thousands of researchers around the world. Dr. Knight can be followed on Twitter (@knightlabnews) or on his web site http://knightlab.ucsd.edu/.

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Arun Kumar
Assistant Professor

Kumar is a member of the Database Lab and Center for Networked Systems and an affiliate member of the AI Group, specializing in artificial intelligence. Systems and ideas based on his research have been released as part of the MADlib open-source library, shipped as part of products from EMC, Oracle, Cloudera, and IBM, and used internally by Facebook, LogicBlox, Microsoft, and other companies. His current work focuses on simplifying and accelerating the processes of data preparation, model selection, and model deployment – complementing his primary research interests in data management and software systems for machine learning/artificial intelligence-based data analytics.

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Yian Ma
Assistant Professor

Yian Ma works on scalable inference methods and their theoretical guarantees, with a focus on time series data and sequential decision making. He has been developing new Bayesian inference algorithms for uncertainty quantification as well as deriving computational and statistical guarantees for them.

Prior to his appointment at UCSD, he worked as a post-doctoral fellow at UC Berkeley. He obtained his Ph.D. degree at University of Washington and his bachelor’s degree at Shanghai Jiao Tong University.

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Arya Mazumdar
Associate Professor

Arya Mazumdar obtained his Ph.D. degree from University of Maryland, College Park (2011) specializing in information theory. Subsequently Arya was a postdoctoral scholar at Massachusetts Institute of Technology (2011-2012), an assistant professor in University of Minnesota (2013-2015), and an assistant followed by associate professor in University of Massachusetts Amherst (2015-2021). Arya is a recipient of multiple awards, including a Distinguished Dissertation Award for his Ph.D. thesis (2011), the NSF CAREER award (2015), an EURASIP JSAP Best Paper Award (2020), and the IEEE ISIT Jack K. Wolf Student Paper Award (2010). He is currently serving as an Associate Editor for the IEEE Transactions on Information Theory and as an Area editor for Now Publishers Foundation and Trends in Communication and Information Theory series. Arya’s research interests include coding theory (error-correcting codes and related combinatorics), information theory, statistical learning and distributed optimization.

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Julian McAuley
Assistant Professor

McAuley is an assistant professor in CSE. His research focuses on the linguistic, temporal, and social dimensions of opinions and behavior in social networks and other online communities. He has harnessed data science tools to increase understanding the facets of people’s opinions, the processes that lead people to acquire taste for gourmet foods and beers, and even the visual dimensions of how they make fashion choices. He has gained academic, industry and media attention for his work analyzing massive volumes of user data from online social communities including Amazon, Yelp, Facebook and BeerAdvocate. His work includes using artificial intelligence in fashion choice, and data science in developing models that generate step-charts for the globally popular videogame, Dance Dance Revolution.

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Gal Mishne
Assistant Professor

Mishne’s research is at the intersection of signal processing and machine learning for graph-based modeling, processing and analysis of large-scale high-dimensional real-world data. She develops unsupervised and generalizable methods that allow the data to reveal its own story in an unbiased manner. Her research includes anomaly detection and clustering in remote sensing imagery, manifold learning on multiway data tensors with biomedical applications, and computationally efficient application of spectral methods. Most recently her research has focused on unsupervised data analysis in neuroscience, from processing of raw neuroimaging data through discovery of neural manifolds to visualization of learning in neural networks.

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Eran Mukamel
Assistant Professor

Mukamel is a computational neuroscientist focusing on modeling and analysis of large-scale data sets to understand complex biological networks of the brain. He uses large-scale genomic and epigenomic datasets to study how brain cells develop, and adapt throughout the lifespan.

He earned his physics Ph.D. from Stanford University, and his bachelor’s and master’s degrees in physics and mathematics from Harvard University, where he followed with a postdoctoral fellowship in theoretical neuroscience.

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Lucila Ohno-Machado
Professor

Ohno-Machado, MD, PhD, focuses on making health data more accessible and usable, so patients and clinicians can make better informed, evidenced-based health decisions together. She is a founding faculty member of HDSI. She was the founding chair UC-wide initiative that allows researchers to search more than 15 million de-identified patient records from the five largest UC health systems with one query.

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Michael Pazzani
Distinguished Researcher

Michael Pazzani is a Distinguished Scientist at the Halıcıoğlu Data Science Institute at University of California, San Diego.  He conducts research in machine learning, explainable artificial intelligence, personalization, internet search, and recommendation systems.

Prior to UCSD, Dr. Pazzani was the Vice Chancellor for Research and Economic Development at University of California, Riverside where he was also a professor of computer science with additional appointments in statistics and psychology.

He received his Ph.D. in Computer Science from UCLA and was an assistant, associate and full professor at the University of California, Irvine, where he also served as Chair of Information and Computer Science.

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Dimitris Politis
Distinguished Professor, HDSI Associate Director

Associate Director Politis is an internationally known scholar in mathematics and economics, working on time series, bootstrap methods, and nonparametric estimation, and a researcher with authorship of more than 100 journal papers and monographs.

Politis earned his Ph.D. in statistics from Stanford University, and dual masters’ degrees from Stanford in statistics and mathematics. He also holds a master’s degree from Rensselaer Polytechnic Institute in computer and systems engineering, and his bachelor of science degree in electrical engineering from University of Patras in Greece.

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Margaret “Molly” Roberts
Associate Professor

Roberts research lies at the intersection of political methodology and the politics of information, with a focus on methods of automated content analysis and the politics of censorship in China.  Roberts co-directs the China Data Lab, which is part of the 21st Century China Center at the School for Global Policy and Strategy.

She earned her Ph.D. in government from Harvard, and both her master’s in statistics and bachelor’s in international relations and economics from Stanford. She joined UC San Diego in 2014. Her recent book, Censored: Distraction and Diversion Inside China’s Great Firewall, was published in 2018 by Princeton University Press.

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Rayan Saab
Associate Professor

Rayan’s work is on the mathematics of information, data, and signals. His research is broadly motivated by problems in the acquisition, digitization, and processing of data. For example, he is interested in sampling and quantization, compressed sensing, sparse  and low-dimensional representations of data, as well as inverse problems like phase retrieval and blind source separation.

Before joining UCSD as an assistant professor in 2013, he was a visiting assistant professor and a Banting postdoctoral fellow at Duke University (2011-2013). Before that, he completed his PhD at the University of British Columbia in 2010, where he was a member of the Institute of Applied Mathematics.

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Babak Salimi
Assistant Professor

Babak Salimi is an assistant professor in HDSI at UC San Diego. Before joining UC San Diego, he was a postdoctoral research associate in the Department of Computer Science and Engineering, University of Washington where he worked with Prof. Dan Suciu and the database group. He received his Ph.D. from the School of Computer Science at Carleton University, advised by Prof. Leopoldo Bertossi.  His research seeks to unify techniques from theoretical data management, causal inference and machine learning to develop a new generation of decision-support systems that help people with heterogeneous background to interpret data. His ongoing work in causal relational learning aims to develop the necessary conceptual foundations to make causal inference from complex relational data. Further, his research in the area of responsible data science develops needed foundations for ensuring fairness and accountability in the era of data-driven decisions. His research contributions have been recognized with a Research Highlight Award in ACM SIGMOD, a Best Demonstration Paper Award at VLDB and a Best Paper Award in ACM SIGMOD.

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Armin Schwartzman
Professor

Armin Schwartzman’s research encompasses theoretical and practical aspects of statistical signal and image analysis in a variety of scientific applications. These include spatio-temporal and high-dimensional data analysis, geometric statistics and smooth Gaussian random fields, with applications in biomedicine, the environment, neuroscience, genetics and cosmology.

Armin Schwartzman received his bachelor’s and master’s degrees in electrical engineering from the Technion – Israel Institute of Technology and the California Institute of Technology; and his PhD in Statistics from Stanford University. He was an R&D engineer at Rockwell Semiconductor and Biosense Webster, and has held faculty positions in Biostatistics at Harvard University and Statistics at North Carolina State University.

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Terry Sejnowski
Co Dir/INC

Terry Sejnowski received his Ph.D. in physics from Princeton University. He was a postdoctoral fellow in the Department of Neurobiology at Harvard Medical School before joining the faculty at Johns Hopkins, where he was a Professor of Biophysics.   He currently holds the Francis Crick Chair at The Salk Institute for Biological Studies, and is a Distinguished Professor of Biology and Computer Science and Engineering at the University of California, San Diego, where he is co-director of the Institute for Neural Computation.  He also is the founding editor-in-chief of Neural Computation.  His book on “The Computational Brain” introduced distributed representations in neural networks to a generation of neuroscientists.

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Jingbo Shang
Assistant Professor

Jingbo Shang is an Assistant Professor in CSE and HDSI at UC San Diego. He obtained his Ph.D. from CS@UIUC. He received his B.E. from Shanghai Jiao Tong University, China. He is broadly interested in data mining, natural language processing, and machine learning. His research about mining and constructing structured knowledge from massive text corpora with minimum human effort has been recognized by many prestigious awards, including the Grand Prize of Yelp Dataset Challenge in 2015 and Google Ph.D. Fellowship in Structured Data and Database Management in 2017.

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Benjamin Smarr
Assistant Professor

Benjamin Smarr is an assistant professor at the Halicioğlu Data Science Institute and the Department of Bioengineering at the University of California, San Diego. As an NIH fellow at UC Berkeley he developed techniques for extracting health and performance predictors from repeated, longitudinal physiological measurements. Historically his work has focused on neuroendocrine control and women’s health, including demonstrations of pregnancy detection and outcome prediction, neural control of ovulation, and the importance of circadian rhythms in healthy in utero development. Pursuing these and other projects he has won many awards from NSF, NIH, and private organizations, and has founded relationships with patient communities such as Quantified Self. With the COVID-19 pandemic, he became the technical lead on TemPredict, a global collaboration combining physiological data, symptom reports, and diagnostic testing, seeking to build data models capable of early-onset detection, severity prediction, and recovery monitoring.

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Shankar Subramaniam
Professor

Subramaniam is the Joan and Irwin Jacobs Endowed Chair in Bioengineering and Systems Biology, and a Distinguished Scientist at the San Diego Supercomputer Center. He has served on advisory boards for several biotech and bioinformatics companies, universities, international governmental organizations, and the NIH.

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George Sugihara
Professor

Sugihara is a theoretical ecologist who has performed foundational work in the data analysis of complex systems from fisheries to medicine to finance. He gained renown for developing, with Lord Robert May, methods for forecasting chaotic systems, providing the first example of chaos in nature with the diatom populations at Scripps Pier. He has worked with the major institutions on questions of systemic risk and on detecting early warning signs of critical transitions, including the Federal Reserve Bank of New York and The Bank of England. He also worked with fisheries to develop a currently enacted market-based incentive plan for reducing wasteful bycatch and to improve forecasting of wild fish stocks. His current interest in neurobiology and genomics includes collaborations with the Salk Institute for Biological Studies to apply EDM to neurobiology and to problems in gene expression in cancer.

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Brad Voytek
Associate Professor
DEPARTMENT OF COGNITIVE SCIENCE

Bradley Voytek is an Associate Professor in the Department of Cognitive Science, the Halıcıoğlu Data Science Institute, and the Neurosciences Graduate Program at UC San Diego. He is an Alfred P. Sloan Neuroscience Research Fellow and National Academies Kavli Fellow, as well as a founding faculty member of the UC San Diego Halıcıoğlu Data Science Institute and its undergraduate Data Science program, where he serves as Vice-chair.

His research program uses neural modeling and simulation, along with large-scale data mining and machine learning techniques, to understand the physiological basis of human cognition and age-related cognitive decline.

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Yusu Wang
Professor

Yusu Wang obtained her PhD degree from Duke University in 2004, and from 2004 – 2005, she was a was a post-doctoral fellow at Stanford University. Prior to joining UC San Diego, Yusu Wang is Professor of Computer Science and Engineering Department at the Ohio State University, where she also co-directed the Foundations of Data Science Research CoP (Community of Practice) at Translational Data Analytics Institute (TDAI@OSU) from 2018–2020.

Yusu Wang primarily works in the field of geometric and topological data analysis. She is particularly interested in developing effective and theoretically justified algorithms for data analysis using geometric and topological ideas and methods, as well as in applying them to practical domains. Very recently she has been exploring how to combine geometric and topological ideas with machine learning frameworks for modern data science.

Yusu Wang received the Best PhD Dissertation Award from the Computer Science Department at Duke University. She also received DOE Early Career Principal Investigator Award in 2006, and NSF Career Award in 2008. Her work received several best paper awards. She is currently on the editorial boards for SIAM Journal on Computing (SICOMP), Computational Geometry: Theory and Applications (CGTA), and Journal of Computational Geometry (JoCG). She is elected to serve on the Computational Geometry Steering Committee in 2020.

Website: yusu.belkin-wang.org

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Frank Wuerthwein
Professor

Wuerthwein is the Distributed High-Throughput Computing Lead at San Diego Supercomputer Center and executive director of the Open Science Grid (OSG), a national cyberinfrastructure to advance the sharing of resources, software and knowledge. His research focuses on distributed high-throughput computing with large data volumes. His research in experimental particle physics research includes searching for new phenomena at the high-energy frontier with the CMS detector (Compact Muon Solenoid) at the Large Hadron Collider at CERN in Geneva. He worked on the team that discovered the Higgs Boson, popularly known as the God particle.

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Ronghui (Lily) Xu
Professor

Xu earned her Ph.D. in mathematics and a master’s in applied mathematics from UC San Diego, and her bachelor’s in math from Nankai University, China.

She was a postgraduate researcher at the University’s Moores Cancer Center and in the Department of Mathematics before becoming an assistant professor at Harvard T.H. Chan School of Public Health in the Department of Biostatistics, and at the Dana-Farber Cancer Institute’s Department of Biostatistical Science.

Website: http://www.math.ucsd.edu/~rxu/

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Angela Yu
Associate Professor
Since 2008, Dr. Yu has directed the Computational Cognitive Neuroscience Laboratory at UCSD, which studies the computational problems faced by intelligent agents, as well as their algorithmic solutions in natural and artificial intelligence systems. Previously, Dr. Yu studied at MIT (B.S. in Brain & Cognitive Sciences, Mathematics, Computer Science), UCL Gatsby Unit (PhD in Computational Neuroscience), and received postdoctoral training at Princeton University (Center for the Brain, Mind, and Behavior). Currently, Dr. Yu is working on learning and decision-making under uncertainty, the contribution of volitional movement to representation learning, as well as social cognition.