Core Faculty

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.

Contact

Phone: 858.534.3992

Email: jbradic@ucsd.edu

Headshot of Yian Ma

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.

Contact

Email: yianma.ucsd@gmail.com

Headshot of Ronghui Lily Xu

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/

Contact

Email: rxu@ucsd.edu

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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.

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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.

Contact

Phone: 858.246.5259

Email: jeldridge@eng.ucsd.edu

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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.

Contact

Phone: 858.534.5213

Email: afraenkel@eng.ucsd.edu

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Zhiting Hu

Assistant Professor

Zhiting Hu’s research interests lie in the broad area of machine learning, including learning with all forms of experiences such as structured knowledge, deep learning, generative modelling, natural language processing, healthcare, and ML systems. His work was recognized at ACL2019 and ACL2016 and with IBM fellowship. He is now a Ph.D. candidate in the Machine Learning Department at Carnegie Mellon University.
Headshot of Yian Ma

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.

Contact

Email: yianma.ucsd@gmail.com

Headshot of Gal Mishne

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.

Contact

Email: gmishne@ucsd.edu

Headshot of Berk Ustun

Berk Ustun

Assistant Professor

Berk Ustun is a postdoc at the Harvard Center for Research on Computation and Society. His research interests are in machine learning, optimization, and human-centered design. He develops methods to promote the adoption and responsible use of machine learning in domains such as medicine, consumer finance, and criminal justice.

Berk has built machine learning systems that are now used by major healthcare providers for hospital readmissions prediction, ICU seizure prediction, and adult ADHD screening. His work has been covered by various media outlets, including NPR and Wired, and has won major awards, including the INFORMS Innovative Applications in Analytics Award in 2016 and 2019, and the INFORMS Computing Society Best Student Paper.

Berk holds a PhD in Electrical Engineering and Computer Science from MIT, an MS in Computation for Design and Optimization from MIT, and BS degrees in Operations Research and Economics from UC Berkeley.

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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/

Headshot of Arun Kumar

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.

Contact

Email: arunkk@eng.ucsd.edu

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.

Contact

Email: jshang@ucsd.edu

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).

Contact

Email: yfreund@ucsd.edu

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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.

Contact

Email: meroberts@ucsd.edu

Headshot of Armin Schwartzman

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.

Contact

Email: armins@ucsd.edu

Benjamin Smarr

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.

Headshot of Alex Cloninger

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.

Contact

Phone: 858.534.3992

Email: acloninger@ucsd.edu

Headshot of Gal Mishne

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.

Contact

Email: gmishne@ucsd.edu

Headshot of Rayan Saab

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.

Contact

Phone: 858.534.3992

Email: rsaab@ucsd.edu