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Photo of Jelena Bradic
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.

Photo 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.

Photo of Arya Mazumdar
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

 

Photo of Yusu Wang
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