New Faculty Summer 2020

Headshot of Yusu Wang

Yusu Wang


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.

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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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Mikhail Belkin


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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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.

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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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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

Mikio Aoi in front of a window with equations written on it

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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Stuart Geiger

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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Konrad Karczewski

Assistant Professor

Dr. Karczewski obtained a Ph.D. in Biomedical Informatics at Stanford University in 2013, advised by Professor Michael Snyder. His doctoral dissertation was titled “Methods for Unraveling the Phenotypic Consequences of Regulatory Variation”. He received an MS degree in Biomedical Informatics and a BA in Molecular Biology from Princeton University. He was a research fellow at the Massachusetts General Hospital from 2013- 2019, and is currently a computational biologist at the Broad Institute. Dr. Karczewski is an expert in biomedical informatics who works on the analysis of rare human genetic variants, and transcriptional regulatory programs.  His work on rare variant analysis stems from the basic conundrum that there are (tens of) millions of variant sites, and a vast majority of these are benign and drifting in the population. Identifying the few that are mildly or severely deleterious and contribute to or cause human disease is a key challenge. His research focuses on genes with systematic depletion of variation which suggests the action of natural selection to protect key genes from deleterious changes. His focus is on the large data problem of assembling and analyzing massive datasets of genetic variation, and developing novel strategies to make use of these datasets for improved interpretation of individual putative disease variants and to improve our global understanding of human biology. Dr, Karczewski led the creation of the world’s largest public dataset of natural human genetic variation (gnomAD), which contained 36 terabytes of data, with 230 million variants. This public resource has been accessed over 20 million times and has been deployed in the successful diagnosis of over 50,000 rare disease patients in clinical laboratories around the world. A couple of weeks ago, Nature group published a package of 7 papers on gnomAD highlighting the importance and impact of his work documented in its flagship paper on which he is the corresponding co-author. He was recognized as a finalist for the ASHG/Charles J. Epstein Trainee Award for Excellence in Human Genetics Research in 2014, and then won the award in 2018, and won an NSF graduate fellowship and NIGMS (NIH) postdoctoral fellowship awards.

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Alicia Martin

Assistant Professor

Dr. Martin obtained her Ph.D. in Genetics from Stanford in 2015, advised by Professor Carlos Bustamante. Her doctoral dissertation was titled “Genetic and Regulatory Variation Across Diverse Human Populations.” She received an MS degree in Biomedical Informatics from Stanford in 2013 and a BS degree in Bioengineering from the University of Washington, Seattle. Since 2015, she has been a Post-doctoral Research Fellow and an Instructor in Investigation at the Massachusetts General Hospital & Broad Institute of MIT/Harvard. Dr. Martin’s current research focuses on the development of “novel statistical genetics methods to improve the generalizability of findings from Eurocentric genetic studies to globally diverse populations.” Simply stated, her work directly addresses and tries to mitigate an inconvenient truth, which is that 80% of the genetics research is conducted, and disproportionately benefits, a Eurocentric population, while Europeans represent, at best, 18% of the human population. This idea of population diversity in medical research has been a consistent theme of her work. In her doctoral work, she pointed out that skin pigmentation is highly heritable, but known pigmentation loci found through analysis of Eurocentric populations explained only a small fraction of the variance. Using the KhoiSan African populations, she helped identify many canonical and non-canonical skin pigmentation loci using statistical association analysis on genome sequences, and greatly improved our understanding of this trait (Cell, 2017). In a recent paper, (Nature Genetics, 2019), Martin points to the use of polygenic risk scores as an example. Her research found that the predictive quality of these scores in African populations is only a fifth of its prediction for European populations. To mitigate these disparities, she is one of the leaders of a consortium that is sampling more African populations and computing polygenic risk scores based on the new genetic data. She received the Cotterman award in 2017 for the best American Journal of Human Genetics paper and was a runner up for the Ommen prize given to the best evolutionary medicine paper of the year in any journal.