Faculty

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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.Learn More

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.Learn More
Website: http://www.math.ucsd.edu/~eariasca/

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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Vineet Bafna

Professor

Vineet Bafna, Ph.D., is a Bioinformatics researcher and a Professor of Computer Science and the Halicioglou Data Science Institute. He received his Ph.D. in computer science from The Pennsylvania State University in 1994 working on theoretical problems in genome rearrangements. His introduction of the breakpoint graph as an analytical tool has been an important driver of the field. After an NSF-funded postdoctoral research at the Center for Discrete Mathematics and Theoretical Computer Science, Bafna was a senior investigator at SmithKline Beecham, conducting research on DNA signaling, target discovery, and EST assembly. From 1999 to 2002, he worked at Celera Genomics, ultimately as director of Informatics Research, participating in the assembly and annotation of the human genome. Vineet Bafna’s research focuses on the identification and characterization of complex structural variation in tumor genomes. He has made important contributions in the analysis of breakage fusion bridge cycles and extrachromosomal DNA, in identifying the genetic signals of adaptation, experimental evolution, and proteogenomics. He has co-authored over 150 research articles in the leading journals in the field. He served as co-Director of the Bioinformatics and Systems Biology Ph.D.…Learn More

Vineet Bafna, Ph.D., is a Bioinformatics researcher and a Professor of Computer Science and the Halicioglou Data Science Institute. He received his Ph.D. in computer science from The Pennsylvania State University in 1994 working on theoretical problems in genome rearrangements. His introduction of the breakpoint graph as an analytical tool has been an important driver of the field. After an NSF-funded postdoctoral research at the Center for Discrete Mathematics and Theoretical Computer Science, Bafna was a senior investigator at SmithKline Beecham, conducting research on DNA signaling, target discovery, and EST assembly. From 1999 to 2002, he worked at Celera Genomics, ultimately as director of Informatics Research, participating in the assembly and annotation of the human genome. Vineet Bafna’s research focuses on the identification and characterization of complex structural variation in tumor genomes. He has made important contributions in the analysis of breakage fusion bridge cycles and extrachromosomal DNA, in identifying the genetic signals of adaptation, experimental evolution, and proteogenomics. He has co-authored over 150 research articles in the leading journals in the field. He served as co-Director of the Bioinformatics and Systems Biology Ph.D. program from 2013 to 19, and was founding faculty of the Halicioglou Data Science Institute at UCSD. He has co-founded two companies: Abterra, LLC, which focuses on services and products relating to proteogenomic data, and Boundless Bio, Inc., which is targeting extrachromosomal DNA in cancer. In 2019, he was selected as a fellow of the International Society of Computational Biology.

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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. Post-Doctoral Fellow: Preetum NakkiranLearn More
Website: misha.belkin-wang.org

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.

Post-Doctoral Fellow: Preetum Nakkiran

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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.Learn More

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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Alex Cloninger

Assistant Professor

Alex Cloninger is an Assistant Professor in Mathematics and the Halıcıoğlu Data Science Institute at UC San Diego. He received his PhD in Applied Mathematics and Scientific Computation from the University of Maryland in 2014, and was then an NSF Postdoc and Gibbs Assistant Professor of Mathematics at Yale University until 2017, when he joined UCSD.  Alex researches problems in the area of geometric data analysis and applied harmonic analysis.  He focuses on approaches that model the data as being locally lower dimensional, including data concentrated near manifolds or subspaces.    The techniques developed have led to research in a number of machine learning and statistical algorithms, including deep learning, network analysis, signal processing, and measuring distances between probability distributions.  This has also led to collaborations on problems in several scientific disciplines, including imaging, medicine, and artificial intelligence.Learn More

Alex Cloninger is an Assistant Professor in Mathematics and the Halıcıoğlu Data Science Institute at UC San Diego. He received his PhD in Applied Mathematics and Scientific Computation from the University of Maryland in 2014, and was then an NSF Postdoc and Gibbs Assistant Professor of Mathematics at Yale University until 2017, when he joined UCSD.  Alex researches problems in the area of geometric data analysis and applied harmonic analysis.  He focuses on approaches that model the data as being locally lower dimensional, including data concentrated near manifolds or subspaces.    The techniques developed have led to research in a number of machine learning and statistical algorithms, including deep learning, network analysis, signal processing, and measuring distances between probability distributions.  This has also led to collaborations on problems in several scientific disciplines, including imaging, medicine, and artificial intelligence.

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David Danks

Professor Danks conducts research at the intersection of machine learning, philosophy, and cognitive science. He examines the ethical, psychological, and policy issues around AI and robotics across a range of sectors. He has also developed multiple novel causal discovery algorithms for complex types of observational and experimental data, and has done significant research in computational cognitive science. Danks received an A.B. in Philosophy from Princeton University, and a Ph.D. in Philosophy from the University of California, San Diego. He is the recipient of a James S. McDonnell Foundation Scholar Award, as well as an Andrew Carnegie Fellowship. Visit David's webpage here at: www.daviddanks.orgLearn More

Professor Danks conducts research at the intersection of machine learning, philosophy, and cognitive science. He examines the ethical, psychological, and policy issues around AI and robotics across a range of sectors. He has also developed multiple novel causal discovery algorithms for complex types of observational and experimental data, and has done significant research in computational cognitive science.

Danks received an A.B. in Philosophy from Princeton University, and a Ph.D. in Philosophy from the University of California, San Diego. He is the recipient of a James S. McDonnell Foundation Scholar Award, as well as an Andrew Carnegie Fellowship.

Visit David’s webpage here at: http://www.daviddanks.org

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Virginia de Sa

Professor, HDSI Associate Director

Associate Director de Sa is a leader in the fields of cognitive science, neuroscience, computer science, engineering, and data science. Her research utilizes multiple approaches to increase our understanding of  how humans and machines 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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Associate Director de Sa is a leader in the fields of cognitive science, neuroscience, computer science, engineering, and data science. Her research utilizes multiple approaches to increase our understanding of  how humans and machines 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

Justin Eldridge is an assistant teaching professor in HDSI. He obtained his PhD in computer science at The Ohio State University as a Presidential Fellow, along with BS degrees in physics and applied math. His research focus lies in statistical machine learning theory, with an emphasis on unsupervised learning and clustering in particular. His research while a PhD student received the best student paper award at COLT 2015 and a full oral presentation at NeurIPS 2016. Justin joined HDSI in 2018, where he develops and teaches courses in both the theoretical and practical foundations of data science and machine learning.Learn More

Justin Eldridge is an assistant teaching professor in HDSI. He obtained his PhD in computer science at The Ohio State University as a Presidential Fellow, along with BS degrees in physics and applied math. His research focus lies in statistical machine learning theory, with an emphasis on unsupervised learning and clustering in particular. His research while a PhD student received the best student paper award at COLT 2015 and a full oral presentation at NeurIPS 2016. Justin joined HDSI in 2018, where he develops and teaches courses in both the theoretical and practical foundations of data science and machine learning.

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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.Learn More

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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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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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. They study issues of fairness, accountability, transparency, responsibility, and contestability in machine learning, particularly in online content moderation. They have 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 their 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. They 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 their 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.Learn More

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. They study issues of fairness, accountability, transparency, responsibility, and contestability in machine learning, particularly in online content moderation. They have 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 their 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. They 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 their 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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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.Learn More

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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Albert Hsiao

Associate Professor

Albert Hsiao, MD, PhD is a cardiothoracic radiologist trained in engineering at Caltech and bioengineering and bioinformatics in the UC San Diego Medical Scientist Training Program (MSTP). He completed his residency and fellowships in Interventional Radiology and Cardiovascular Imaging at Stanford before returning to UC San Diego as faculty in Radiology, where he leads advanced cardiovascular imaging and the Augmented Imaging and Data Analytics (AiDA) research laboratory.  He also serves as co-director of the T32 clinician-scientist radiology research residency program and co-director of the MSTP SURF program. While a radiology resident himself at Stanford, he co-founded Arterys, a cloud-native software company to bring 4D Flow MRI and artificial intelligence technologies to market. He continues to partner with industry to develop and bring new imaging technologies to market to improve diagnosis and management of disease. Post-doctoral Fellow: Samira Masoudi Website: https://profiles.ucsd.edu/albert.hsiaoLearn More

Albert Hsiao, MD, PhD is a cardiothoracic radiologist trained in engineering at Caltech and bioengineering and bioinformatics in the UC San Diego Medical Scientist Training Program (MSTP). He completed his residency and fellowships in Interventional Radiology and Cardiovascular Imaging at Stanford before returning to UC San Diego as faculty in Radiology, where he leads advanced cardiovascular imaging and the Augmented Imaging and Data Analytics (AiDA) research laboratory.  He also serves as co-director of the T32 clinician-scientist radiology research residency program and co-director of the MSTP SURF program. While a radiology resident himself at Stanford, he co-founded Arterys, a cloud-native software company to bring 4D Flow MRI and artificial intelligence technologies to market. He continues to partner with industry to develop and bring new imaging technologies to market to improve diagnosis and management of disease.

Post-doctoral Fellow: Samira Masoudi

Website: https://profiles.ucsd.edu/albert.hsiao

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Zhiting Hu

Assistant Professor

Zhiting Hu is an Assistant Professor in Halicioglu Data Science Institute at UC San Diego. He received his Bachelor's degree in Computer Science from Peking University in 2014, and his Ph.D. in Machine Learning from Carnegie Mellon University in 2020. His research interests lie in the broad area of machine learning, natural language processing, ML systems, healthcare and other application domains. In particular, He is interested in principles, methodologies, and systems of training AI agents with all types of experiences (data, knowledge, rewards, adversaries, lifelong interplay, etc). His research was recognized with best demo nomination at ACL2019 and outstanding paper award at ACL2016.Learn More

Zhiting Hu is an Assistant Professor in Halicioglu Data Science Institute at UC San Diego. He received his Bachelor’s degree in Computer Science from Peking University in 2014, and his Ph.D. in Machine Learning from Carnegie Mellon University in 2020. His research interests lie in the broad area of machine learning, natural language processing, ML systems, healthcare and other application domains. In particular, He is interested in principles, methodologies, and systems of training AI agents with all types of experiences (data, knowledge, rewards, adversaries, lifelong interplay, etc). His research was recognized with best demo nomination at ACL2019 and outstanding paper award at ACL2016.

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Rob Knight

Founding Director, Center for Microbiome Innovation; Professor, Pediatrics, Bioengineering, and Computer Science & Engineering

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 is the Wolfe Family Endowed Chair in Microbiome Research.  His work is at the intersection of data science, bioinformatics, and microbiology, and has linked microbes to a range of health conditions including obesity, inflammatory bowel disease, Parkinson’s Disease and multiple sclerosis, has enhanced our understanding of microbes in environments ranging from the oceans to the tundra, and has made high-throughput DNA sequencing and data analysis techniques accessible to thousands of researchers around the world. This work resulted in the 2019 NIH Director’s Pioneer Award and 2017 Massry Prize. Dr. Knight can be followed on Twitter (@knightlabnews) or on his web site: knightlab.ucsd.edu.  Learn More

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 is the Wolfe Family Endowed Chair in Microbiome Research.  His work is at the intersection of data science, bioinformatics, and microbiology, and has linked microbes to a range of health conditions including obesity, inflammatory bowel disease, Parkinson’s Disease and multiple sclerosis, has enhanced our understanding of microbes in environments ranging from the oceans to the tundra, and has made high-throughput DNA sequencing and data analysis techniques accessible to thousands of researchers around the world. This work resulted in the 2019 NIH Director’s Pioneer Award and 2017 Massry Prize. Dr. Knight can be followed on Twitter (@knightlabnews) or on his web site: knightlab.ucsd.edu.

 

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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.Learn More

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. Post-Doctoral Fellow: Avishek Ghosh  Learn More
Website: http://mazumdar.ucsd.edu/

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.

Post-Doctoral Fellow: Avishek Ghosh

 

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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.Learn More

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. Post-doctoral Fellow: Zhengchao WanLearn More

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.

Post-doctoral Fellow: Zhengchao Wan

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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.Learn More

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.Learn More

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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Suraj Rampure

Lecturer

Suraj Rampure is a Lecturer in the Halıcıoğlu Data Science Institute at UC San Diego. He received his BS and MS in Electrical Engineering and Computer Sciences from UC Berkeley in 2020 and 2021, respectively. While at Berkeley, he helped create and teach multiple undergraduate computer science and data science courses, including the core upper-division course for data science majors and an introductory programming course for non-majors. There, he was the recipient of multiple teaching awards, including the departmental Distinguished Graduate Student Instructor Award in 2020 and the campus Extraordinary Teaching in Extraordinary Times award in 2021. At UC San Diego, he will focus on the undergraduate experience for data science students and will be involved in data science education research. For more information, see http://rampure.org.Learn More
Website: http://rampure.org

Suraj Rampure is a Lecturer in the Halıcıoğlu Data Science Institute at UC San Diego. He received his BS and MS in Electrical Engineering and Computer Sciences from UC Berkeley in 2020 and 2021, respectively.

While at Berkeley, he helped create and teach multiple undergraduate computer science and data science courses, including the core upper-division course for data science majors and an introductory programming course for non-majors. There, he was the recipient of multiple teaching awards, including the departmental Distinguished Graduate Student Instructor Award in 2020 and the campus Extraordinary Teaching in Extraordinary Times award in 2021.

At UC San Diego, he will focus on the undergraduate experience for data science students and will be involved in data science education research.

For more information, see http://rampure.org.

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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. Post-Doctoral Fellow: Rebecca FraenkelLearn More

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.

Post-Doctoral Fellow: Rebecca Fraenkel

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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.Learn More

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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Barna Saha

Associate Professor

Barna Saha is an Associate Professor of UCSD CSE and HDSI. Before joining UCSD, she was an Associate Professor at UC Berkeley. Saha's primary research focus is on Theoretical Computer Science, specifically Algorithm Design. She is passionate about diversity and teaching, and seeing students succeed from all backgrounds. She is a recipient of the Presidential Early Career Award (PECASE)- the highest honor given by the White House to early career scientists, a Sloan fellowship, an NSF CAREER Award, and multiple paper awards.Learn More

Barna Saha is an Associate Professor of UCSD CSE and HDSI. Before joining UCSD, she was an Associate Professor at UC Berkeley. Saha’s primary research focus is on Theoretical Computer Science, specifically Algorithm Design. She is passionate about diversity and teaching, and seeing students succeed from all backgrounds. She is a recipient of the Presidential Early Career Award (PECASE)- the highest honor given by the White House to early career scientists, a Sloan fellowship, an NSF CAREER Award, and multiple paper awards.

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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.Learn More

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.Learn More

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.Learn More

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.Learn More

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.Learn More

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.Learn More

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.Learn More

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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Janine Tiefenbruck

Lecturer

Specializing in combinatorics, algorithms, and the mathematical foundations of data scienceLearn More

Tiefenbruck focuses her mathematics background on developing strong foundations in the teaching of Data Science. She is a lecturer who serves as a core educator in the Data Science major and minor undergraduate education program through the Halıcıoğlu Data Science Institute. Her goal is educating students in the theoretical basis of math that will help them build a solid foundation for whatever direction they choose to take in data science, and other technology specializations. “What I like about the field of Data Science is that it’s so customizable, it’s not just: ‘Here’s a formula, plug it in.’ It’s such a broad field, it covers areas from sports to politics,” said Tiefenbruck. Her educational approach is to highlight the creative side of the academic discipline, and present students with lively projects to spark their imagination, like analyzing trends in the TV quiz show game “Jeopardy!” or predicting the genre of a song based on its lyrics.

Tiefenbruck earned her Ph.D. and master’s degrees in mathematics from UC San Diego, specializing in algebraic and enumerative combinatorics, and her bachelor’s from Loyola University, Maryland in both math and computer science. Among her academic awards include a GAANN Fellowship (federal support for scholars fulfilling a national need), a Goldwater Scholarship and a Best Teacher award from Jacobs School of Engineering. Contact: jlobue@ucsd.edu.

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Berk Ustun

Assistant Professor

Berk Ustun is an Assistant Professor at the Halıcıoğlu Data Science Institute at UCSD. His research lies at the intersection of machine learning, optimization, and human-centered design. In particular, he is interested in developing methods to promote the responsible use of machine learning in medicine, consumer finance, criminal justice, and the physical sciences. Prior to his appointment at UCSD, Ustun held research positions at Google AI and the Harvard Center for Research on Computation and Society. He also co-founded Petal, a financial services company that uses machine learning to broaden access to credit in the United States. He received his PhD in Electrical Engineering and Computer Science from MIT and Bachelors in Operations Research and Economics from UC Berkeley.  Learn More

Berk Ustun is an Assistant Professor at the Halıcıoğlu Data Science Institute at UCSD. His research lies at the intersection of machine learning, optimization, and human-centered design. In particular, he is interested in developing methods to promote the responsible use of machine learning in medicine, consumer finance, criminal justice, and the physical sciences.

Prior to his appointment at UCSD, Ustun held research positions at Google AI and the Harvard Center for Research on Computation and Society. He also co-founded Petal, a financial services company that uses machine learning to broaden access to credit in the United States. He received his PhD in Electrical Engineering and Computer Science from MIT and Bachelors in Operations Research and Economics from UC Berkeley.

 

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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).…Learn More

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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Lily Weng

Assistant Professor

Lily Weng is an Assistant Professor in the Halıcıoğlu Data Science Institute at UC San Diego. Her research interests focus on the intersection between machine learning, optimization and reinforcement learning, with applications in cybersecurity and healthcare. Specifically, her vision is to make the next generation AI systems and deep learning algorithms more robust, reliable, trustworthy and safer. She has worked on developing efficient algorithms as well as theoretical analysis to quantify robustness of deep neural networks. She received her PhD in Electrical Engineering and Computer Sciences (EECS) from MIT in August 2020, and her Bachelor and Master degree both in Electrical Engineering at National Taiwan University in 2011 and 2013. More details please see https://lilyweng.github.io/Learn More

Lily Weng is an Assistant Professor in the Halıcıoğlu Data Science Institute at UC San Diego. Her research interests focus on the intersection between machine learning, optimization and reinforcement learning, with applications in cybersecurity and healthcare. Specifically, her vision is to make the next generation AI systems and deep learning algorithms more robust, reliable, trustworthy and safer. She has worked on developing efficient algorithms as well as theoretical analysis to quantify robustness of deep neural networks. She received her PhD in Electrical Engineering and Computer Sciences (EECS) from MIT in August 2020, and her Bachelor and Master degree both in Electrical Engineering at National Taiwan University in 2011 and 2013. More details please see https://lilyweng.github.io/

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Frank Wuerthwein

Professor

Frank W. Wuerthwein is the Director of the San Diego Supercomputer Center and executive director of the Open Science Grid (OSG), a national cyber infrastructure to advance the sharing of resources, software, and knowledge.Learn More

Frank W. Wuerthwein is the Director of the San Diego Supercomputer Center and executive director of the Open Science Grid (OSG), a national cyber infrastructure to advance the sharing of resources, software, and knowledge.

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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. After a year as Postgraduate Researcher at UC San Diego, she took up Assistant Professorship in Biostatistics at Harvard School of Public Health and Dana-Farber Cancer Institute in Boston, MA. She returned to UC San Diego in 2004 with a joint appointment from the main campus (Mathematics) and health sciences (Biostatistics and Bioinformatics). Among her academic honors are as a Fellow of the American Statistical Association (ASA), and a recipient of the ASA's David P. Byar Young Investigator Award. Her current research interests include causal inference, survival analysis, and machine learning methods as applied to biomedicine. Website: http://www.math.ucsd.edu/~rxu/Learn More

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.

After a year as Postgraduate Researcher at UC San Diego, she took up Assistant Professorship in Biostatistics at Harvard School of Public Health and Dana-Farber Cancer Institute in Boston, MA. She returned to UC San Diego in 2004 with a joint appointment from the main campus (Mathematics) and health sciences (Biostatistics and Bioinformatics). Among her academic honors are as a Fellow of the American Statistical Association (ASA), and a recipient of the ASA’s David P. Byar Young Investigator Award. Her current research interests include causal inference, survival analysis, and machine learning methods as applied to biomedicine.

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.Learn More
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.
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Rose Yu

Assistant Professor

Dr. Rose Yu is an Assistant Professor at the UC San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at the University of Southern California in 2017. She was subsequently a Postdoctoral Fellow at the California Institute of Technology. She was an assistant professor at Northeastern University prior to her appointment at UC San Diego. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first-principles with data-driven models. Among her awards, she has won Google Faculty Research Award, Adobe Data Science Research Award, NSF CRII Award, Best Dissertation Award in USC, and was nominated as one of the ’MIT Rising Stars in EECS’. Website: http://roseyu.comLearn More

Dr. Rose Yu is an Assistant Professor at the UC San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at the University of Southern California in 2017. She was subsequently a Postdoctoral Fellow at the California Institute of Technology. She was an assistant professor at Northeastern University prior to her appointment at UC San Diego.

Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first-principles with data-driven models. Among her awards, she has won Google Faculty Research Award, Adobe Data Science Research Award, NSF CRII Award, Best Dissertation Award in USC, and was nominated as one of the ’MIT Rising Stars in EECS’.

Website: http://roseyu.com

HDSI Faculty Affiliates & Researchers

HDSI brings together a large number of faculty and researchers across many departments and divisions at UC San Diego with overlapping interests in the discipline of Data Science.

Organized into “clusters” of shared interests and domain knowledge, the list of our researchers provides a snapshot of the breadth and diversity of ongoing research at UC San Diego.

We envision launching new research efforts within the Institute that specifically bring together researchers with complementary skills, for instance, method innovators working with application domain experts, to advance the field of Data Science. While we anticipate teaching activities to be integral to all our research, we have created a focus group on Experiential Education spanning education infrastructure support as well as online education.

To get to know our Faculty, please see our Faculty Page. Our New Faculty for Winter 2021 can be found here.

Faculty who wish to join any of the below clusters are encouraged to email datascience@ucsd.edu.