Faculty

Photo of Rod Albuyeh
Rod Albuyeh
Lecturer

Albuyeh is Principal Data Scientist at Figure and part-time lecturer at the Halıcıoğlu Data Science Institute at UC San Diego.  He received his Ph.D. in Political Science at USC in 2016.  His specialties lie in anomaly detection for tabular time-series data and machine learning systems–with applications in marketing, fraud, and credit risk.  He is also interested in applying enterprise machine learning approaches to solve problems in the social sciences.

Research Interests: Machine Learning, Deployment, Scalable Systems

Categories: Faculty, Lecturers
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Ilkay Altintas
Chief Data Science Officer, HDSI Founding Faculty Fellow
CHIEF DATA SCIENCE OFFICER, SDSC

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

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

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

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

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

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Ery Arias-Castro
Professor

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

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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. 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 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.

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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.

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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: http://www.daviddanks.org

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Virginia de Sa
Professor, HDSI Associate Director Cognitive Science

Associate Director de Sa is a leader in the fields of cognitive science, neuroscience, computer science, engineering, and data science. Her research utilizes 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.

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Shannon Ellis
Assistant Teaching Professor

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

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Aaron Fraenkel
Assistant Teaching Professor

Fraenkel uses machine learning and experimental design to study large-scale abusive behaviors on the internet, particularly events driven by robots (known as bots). Fraenkel’s 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. Fraenkel 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, Fraenkel chose to return to academia and work at the root of instruction, helping shape student learning and critical thinking.

Fraenkel earned a 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, Fraenkel’s curriculum development of the path-breaking data science program includes creating projects using real-world datasets and challenges.

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Yoav Freund
Professor

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

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

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R Stuart Geiger
Assistant Professor

Geiger studies the relationships between science, technology, and society — not only how science and technology have substantial impacts on society, but also how they are social institutions in themselves. 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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Mike Holst
Professor

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

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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.hsiao

Categories: Faculty Council
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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 applicatio