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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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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 done significant research in computational cognitive science

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

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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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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.

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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.

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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.

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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.

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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.

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Berk Ustun
Assistant Professor

Berk Ustun is an incoming Assistant Professor at the Halıcıoğlu Data Science Institute at UC San Diego. His research lies at the intersection of machine learning, optimization, and human-centered design. Specifically, he is interested in developing methods to promote the adoption and responsible use of machine learning in medicine, consumer finance, and criminal justice.

Prior to his appointment at UCSD, Ustun held research positions at Google AI and the Center for Research on Computation and Society at Harvard. He received 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. For more details, please see https://www.berkustun.com

 

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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/