HALICIOĞLU DATA SCIENCE INSTITUTE FACULTY ADVISORY GROUP
Research has ranged from elementary to particle physics; plasma physics; and nonlinear dynamics of biological and physical systems to the statistical physics of machine learning
Abarbanel studies applications of contemporary developments in dynamical systems and nonlinear dynamics to problems of physical interest in fluid and plasma physics as well as biological physics and machine learning. His focus has included nonlinear stability of fluid flows in geophysical settings. His team has developed a method for separating modes of an inviscid flow (is the flow of an ideal fluid assumed to have no viscosity) with significantly different time scales while retaining the Hamiltonian structure of the dynamics. He has worked on the spatial and temporal scales associated with nonlinear instabilities in a continuum system such as a fluid or plasma, considered a new direction in the study on nonlinear instability and one with potential for experiments seeking characteristics of these instabilities. Abarbanel earned his Ph.D. in physics from Princeton University, in 1966, and the bachelor’s in physics from California Institute of Technology (CalTech), in 1963 respectively.
Makes computational data science more reusable, scalable and reproducible through methods and tools for workflows for problem solving
Specializing in statistics and applied probability, high-dimensional statistics, machine learning, spatial statistics, image processing, applied probability
Arias-Castro joined the mathematics department faculty in 2005. His research interests in in statistics and applied probability, and specializes in detection of problems. His current work includes compressed sensing and statistics in high-dimensions (model selection); machine learning (clustering, dimensionality reduction) and signal/image processing (denoising); and network analysis, inference of geometric characteristics. His faculty appointment followed his work postdoctoral work at the Institute for Pure and Applied Mathematics, in the program on Multiscale Geometry and Analysis in High Dimensions. His subsequent postdoctoral work was for the Mathematical Sciences Research Institute, in the program on Mathematical, Computational and Statistical Aspects of Image Analysis. His research has been supported by the National Science Foundation and the Office of Naval Research.
Specializing in computational molecular biology, bioinformatics, human genome, population genetics, algorithmic and computational problems arising in the analysis of biological data
Bafna is a leading bioinformatics researcher who holds appointments in CSE, Bioengineering, Moores Cancer Center, and the Institute of Genomic Medicine. He leads development of computational tools for metaproteogenomics, following his foundational advancement in proteogenomics, the annotation of coding regions on the genome using mass spectrometry data. He helped develop techniques used to identify novel genes in model organisms, leading to breakthroughs in cancer detection. His research includes computational analysis of peptide mass spectra and applications to protein function, and analyzing data on single nucleotide polymorphisms (SNP), with applications to therapeutics and diagnostics.
Specializing computational modeling, strategic/non-cooperative uses of language, and improving the linguistic accuracy of deep learning systems
Bergen is a computational linguist with a background in brain and cognitive science research. His current research focuses on building computational models to calculate how humans use language, and using insights derived from the study of human cognition to improve engineering systems. He utilizes data science in his computational modeling and experimental methods to research such areas as the development of intelligence. His awards include a National Science Foundation grant, and winner of the computational modeling prize for language from the Cognitive Science Society.
Specializing in machine learning, statistics and applied probability, high dimensional statistics, functional genomics and biostatistics.
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.
Specializing in the foundations and philosophy of physics, environmental ethics, the metaphysics of science, and philosophy of science in general
Callender is a professor of philosophy, and co-director of the university’s Institute for Practical Ethics, a leading voice on the ethics and social impact of cutting-edge science. He leads the Institute emphasis on research on contemporary issues and engaging ethicists, scientists, and policymakers in closing the gap between the rapid pace of innovation and society’s ability to responsibly deal with tough questions. He helped lead efforts at UC San Diego in developing a minor in bioethics in response to the rapid development of technology and data science in health, biotech and medicine. He is president of the Philosophy of Time Society, which promotes the study of the philosophy of time from a broad analytic perspective. He earned his Ph.D. from Rutgers University, and his bachelor’s in philosophy and humanities from Providence College.
Specializing in numerical analysis, deep learning, applied harmonic analysis, machine learning, neural networks, diffusion geometry
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.
Specializing in statistical analysis of data; continuing development and refinement of accurate and automated algorithms for evaluation subjects using multimodality approaches to data collection
Dr. Dale is a professor and Vice Chair for Research in the Department of Radiology, and professor of Neurosciences, Psychiatry, Cognitive Science, and Data Science. He is the founding Director of the UC San Diego Center for Multimodal Imaging and Genetics (CMIG), and the Center for Translational Imaging and Precision Medicine (CTIPM). His research program is focused on the development and application of advanced techniques for acquisition and analysis of multimodal structural and functional imaging data, and the integration of quantitative imaging biomarkers and genetics for Precision Health.
Dr. Dale has played a key role in the development of several methods that are widely used in the neuroscience community, including the FreeSurfer software package, event-related experimental design for functional MRI, advanced diffusion MRI for non-invasive characterization of tissue microstructure, and spatiotemporal imaging of brain activity using MEG, EEG, and MRI. He has authored more than 400 papers, and has been cited more than 110,000 times, with an h-index of 142.
Specializing in use of machine learning, computational modeling, visual perception, brain computer interface, and computational neuroscience to learn more about visual and multi-sensory perception
De Sa seeks to better understand the neural basis of human perception and learning: How we learn, from a neural and computational point of view. She studies the computational properties of machine learning algorithms and also investigates what physiological recordings and the constraints and limitations of human performance tell us about how our brains learn. She is a recipient of more than 10 National Science Foundation awards, and an award for research innovation from the Kavli Institute for Brain and Mind, among many other academic awards.
Specializing in scientific discovery aided by machine learning, artificial intelligence, theoretical foundations of unsupervised learning
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.
He is one of the leading instructors of data science classes in the HDSI-operated program. He has been instrumental in helping shape the foundational curriculum and guiding undergraduates through the program since its first full year of operation.
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.
Specializing in data science, machine learning, large-scale abusive events on the internet
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.
Specializing in machine learning, adaptive algorithms, statistics and bio-informatics, computational learning theory and the related areas in probability theory, information theory, statistics and pattern recognition
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 is a major contributor to the field of machine learning with co-development of the Adaboost algorithm. The algorithm achieves acceleration of even complex tasks like analysis of biomedical microscopy images, allowing hundreds of thousands of images per day. 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).
Specializing in artificial intelligence, robotics, programming systems, embedded systems including self-driving vehicles
Gao develops design automation techniques for complex cyber-physical systems, such as autonomous cars and cardiac pacemakers. His research focus is on automated reasoning systems that enable formal reliability guarantees. Gao leads the development of a nonlinear optimization and automated reasoning tool named dReal. The tool has been used by dozens of research groups in academia and industry for widely differing applications. Gao is the recipient of the 2018 Young Investigator Award from the U.S. Air Force for his research on artificial intelligence. He earned a Ph.D. in Logic from Carnegie Mellon University. His thesis won honorable mention for the CMU School of Computer Science distinguished doctoral dissertation award. He holds bachelor of science degrees in logic and mathematics from Peking University. Gao’s other honors include a silver medal for the Gödel Research Prize Fellowship from the Kurt Gödel Society.
Specializing in inverse problems in ocean acoustics, seismology, and electromagnetics, seismic imaging with noise, origin of microseisms (earth tremors), biodiversity and conservation, earth through space and time, energy and the environment, earthquake physics, electromagnetism in the climate system, seismology and earthquake physics
Gersoft’s acoustic and seismology-based research has taken him as far as the South Pole, where he works on a National Science Foundation Polar Programs to install a seismic array on Antarctica’s Ross Ice Shelf. He joined UC San Diego’s Marine Physical Laboratory in 1997, his teaching subject areas include machine learning for physical applications, and his Noiselab research focuses on physics-based signal processing applied to ocean acoustics, seismology, and radar. He earned his master’s and Ph.D. in structural engineering from the Technical University of Denmark, and also master’s from Canada’s University of Western Ontario, in the Alan G. Davenport Wind Engineering Group. He is a Fellow of the Acoustical Society of America, and an elected member of the International Union of Radio Science.
Specializing in microbiomes, bacteria, biodiversity, microbiota, soil microbiology, microbial consortia
Gilbert is a recognized leader in the field of microbiome research, and is currently serving in a joint appointment with Scripps Institution and the Department of Pediatrics at the UC San Diego School of Medicine as well as Argonne National Laboratory. Working with the University’s Center for Microbiome Innovation, he co-founded the team literally making a microbial map of the world.
His research areas include ecology, evolution, and metabolic dynamics of microbial ecosystems from environments including built environments, oceans, rivers, soils, air, plants, animals and humans.
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. He currently leads NSF project Metro Insight with the goal to organize and use city-scale sensing data for improved services. His past contributions include SystemC modeling and SPARK parallelizing high-level synthesis, both of which have been incorporated into industrial practice. Earlier, Gupta led NSF Expeditions on Variability, and DARPA-sponsored efforts under the Data Intensive Systems (DIS) and Circuit Realization at Faster Timescales (CRAFT) programs. Gupta and his students have received a best demonstration paper award at ACM BuildSys’16 , best paper award at IEEE/ACM DCOSS’08 and a best demonstration award at IEEE/ACM IPSN/SPOTS’05. 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. He currently holds INRIA International Chair at the French international research institute in Rennes, Bretagne Atlantique. Guptais a Fellow of the IEEE, the ACM and the American Association for the Advancement of Science.
Specializing in numerical analysis, applied analysis, partial differential equations, mathematical physics, adaptive finite element methods, biophysics, general relativity.
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.
Specializing in marketing strategy, consumer choice, pricing, econometrics, high-tech industry, retail industry, e-commerce, direct mail
Hansen’s primary research interests are centered on developing, analyzing and testing theories of household/customer behavior and the implications for retail strategy and competition. From 1999 to 2002 Hansen was a postdoctoral fellow and lecturer at the University of Chicago. During his graduate work, he was awarded the Alfred P. Sloan Doctoral Dissertation Fellowship and the Abramson Award for Exceptional Dissertation Progress. Prior to joining UC San Diego, Hansen was an associate professor of marketing at Northwestern University. While at Northwestern, he was recognized by the Marketing Science Institute as part of its Young Scholar program, and served as a Kraft Research Professor. Among his scholarly research publications, in an award-winning paper published recently in the Journal of Econometrics, Hansen found evidence that an additional year of schooling can indeed boost test scores by up to 4 percent. Hansen earned his Ph.D. in economics from Brown University.
Specializing in international trade, international migration and economic geography; researches how international economic integration – through trade, foreign investment and migration – affect where people live and work and how much they earn; labor-market impacts of globalization and its affects the design of public policy and political debates
Hanson holds the Pacific Economic Cooperation Chair in International Economic Relations and directs the Center on Global Transformation. He is also a research associate at the National Bureau of Economic Research, and a member of the Council on Foreign Relations. Hanson earned his Ph.D. in economics from Massachusetts Institute of Technology, and his bachelor’s in economics from Occidental College. Hanson’s current research addresses how expanded trade with China has affected the U.S. labor market, and how U.S. regional economies adjust to immigration. An example of his work has been in new research collaborations with the World Bank, the U.K. International Growth Centre, and the U.S. Federal Emergency Management Agency (FEMA) to evaluate the economic consequences of transportation infrastructure investment in India and the short and long-run impacts of extreme weather events on communities in the U.S. and Southeast Asia.
Specializing in wireless systems and communications networks, stochastic and optimal resource allocation, network design and control, multi-access control, and topology design in ad-hoc systems
Javidi founded and co-directs the Center for Machine-Integrated Computing and Security at UC San Diego. Among her projects is DetecDrone Target Search Technology, what she calls drones that think. Her research focus is on stochastic analysis, design, and control of information collection, processing, and transfer in modern communication and networked systems. Interests include: Information acquisition and utilization (such as active hypothesis testing and variable length coding over channels with feedback); optimal routing in wireless mesh networks; cognitive and delay-sensitive communications and networking; and communications under application and/or secrecy constraints. Her work includes the applications of microeconomic theory and organizational science to the design of wireless networks.
Specializing in statistical signal processing and information theory, with applications in communication, networking, data compression, and information processing
Kim primarily works on two important challenges for high-speed, high-volume information processing systems — how to describe information efficiently, and how to transmit it reliably in the presence of noise and interference. He is a co-author of the textbook, Network Information Theory. Kim earned his bachelor’s degree with honors in electrical engineering from Seoul National University, receiving a GE Foundation Scholarship. He worked in industry as a software architect at Tong Yang Systems in Seoul, on projects such as developing the communication infrastructure for then newly opening Incheon International Airport. He then resumed his graduate studies at Stanford University, and earning his Ph.D. in electrical engineering. Among his awards and honors: the 2008 National Science Foundation Faculty Early Career Development Award; the 2009 U.S.-Israel Binational Science Foundation Bergmann Memorial Award; the 2012 IEEE Information Theory Paper award; and the 2015 James L. Massey Research & Teaching Award for Young Scholars. Kim has served as a distinguished lecturer for the IEEE Information Theory Society, and as Associate Editor for the IEEE Transactions on Information Theory. He is a Fellow of the IEEE.
Specializing in microbiomes, biomolecules, genomes, and communities in different ecosystems
Knight is founding Director of the Center for Microbiome Innovation, and a pioneer in research in human microbes and microbiomes and their role on health and potential to cure disease. He also serves as a professor of pediatrics in the School of Medicine, and a professor of Computer Science & Engineering. His work has linked microbes to a range of health conditions including obesity and inflammatory bowel disease, and has enhanced scientific understanding of microbes in environments ranging from the oceans to the tundra.
Specializing data management and software systems for machine learning/artificial intelligence-based data analytics
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. His awards include: a Hellman Fellowship 2018; Google Faculty Research Award 2016; the Graduate Student Research Award 2016 for the best dissertation research in University of Washington-Madison computer science; and the Best Paper Award 2014 ACM SIGMOD.
Specializes in recommender systems, machine learning, data mining, graphical models
McAuley is an assistant professor in CSE. His research focuses on the linguistic, temporal, and social dimensions of opinions and behavior in social networks and other online communities. He has harnessed data science tools to increase understanding the facets of people’s opinions, the processes that lead people to acquire taste for gourmet foods and beers, and even the visual dimensions of how they make fashion choices. He has gained academic, industry and media attention for his work analyzing massive volumes of user data from online social communities including Amazon, Yelp, Facebook and BeerAdvocate. His work includes using artificial intelligence in fashion choice, and data science in developing models that generate step-charts for the globally popular videogame, Dance Dance Revolution.
Halıcıoğlu Data Science Institute
Specialzing in data-driven geometric analysis methods for complex high-dimensional datasets with applications in remote sensing imagery and computational biology.
Specialzing in neuroscience theory and computational analysis of large-scale neural data, electrophysiology of sleep and general anesthesia, computational epigenomics in brain cells
Mukamel is a computational neuroscientist focusing on modeling and analysis of large-scale data sets to understand complex biological networks of the brain. He uses large-scale genomic and epigenomic datasets to study how brain cells develop, and adapt throughout the lifespan. He serves as principal investigator for numerous National Institutes of Health research grants that include applying data-science tools to answer such questions as: How can we use large-scale, complex biological data sets to discover the structure and function of brain networks? He earned his physics Ph.D. from Stanford University, and his bachelor’s and master’s degrees in physics and mathematics from Harvard University, where he followed with a postdoctoral fellowship in theoretical neuroscience. He served as a UC San Diego fellow in theoretical biological physics before joining the faculty. He has served concurrently as a postdoctoral fellow at The Salk Institute for Biological Studies in computational neuroscience.
Specializing in marketing analytics, competition, market entry, category management, pricing, marketing strategy and branding, with industry focus on retail, high tech and ecommerce
Nijs studies multiple aspects of marketing including on the effectiveness of marketing actions, utilizing the analytical power of data science in his research. His expertise includes the retail, high tech and e-commerce industries. His current studies address issues such as promotion and advertising impact, competitive retaliation, category captains, category management, pass-through of trade-promotions, and price rigidity. Nijs was awarded the Excellence in Teaching Award in 2012, 2015, and 2017 and the Most Valuable Professor award in 2016 and 2018. Before joining UC San Diego, Nijs was an assistant professor of marketing at Northwestern University’s Kellogg School of Management. Notably at Northwestern, he served as the McManus Research Professor, won the Sidney J. Levy Teaching Award for outstanding teaching in an elective course, and was recognized by the Marketing Science Institute as part of its Young Scholars program. Nijs holds a master’s degree in marketing research from the University of Groningen, and a Ph.D. in marketing from the University of Leuven. He won the John D. C. Little Award in 2001, and Frank M. Bass Award in 2002 for the paper “The Category Demand Effects of Price Promotions,” published in Marketing Science. Other publications include academic studies on such marketing areas as outsourcing, information firewalls, and the effect of big-box stores on retail pricing.
Specializing in using big data to enable precision medicine, bioinformatics and advancement of the medical sciences, health care and public health.
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 currently co-leads the California Precision Medicine Consortium, which manages the All of Us Research Program, gathering genetic, biological, environmental, health and lifestyle data from some 1 million volunteer participants. She founded the Biomedical Informatics program at the School of Medicine, which designs, implements and evaluates informatics algorithms and systems that serve biomedical researchers and health care providers. 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.
Specializing In communications and information theory, compression, communication, probability estimation, prediction, machine learning, and speech recognition
Orlitsky examines information-theoretic approaches to speech processing, touching on issues what include speech recognition, compression and synthesis. His work in learning theory is primarily about online learning, and he maintains an interest in investment theory based on the work he did as a quantitative analyst for the leading Wall Street investment firm D.E. Shaw and Co. He is the founding director of the university’s centers of Information Theory and Applications and Wireless Communications. He holds the Qualcomm Endowed Chair in Information Theory and Applications Orlitsky works with interactive communications.
Specializing in high dimensional statistical signal processing and big data analysis, energy efficient sketching and sampling for statistical inference, compressive sensing and sparse estimation, tensor methods, convex and non-convex optimization, optical signal processing and high-resolution imaging, statistical learning
Pal’s research develops techniques for solving inverse problems in signal processing with applications in radar and sonar signal processing, biomedical and molecular imaging, and machine learning. Her focus is on developing new energy-efficient sampling and sensing techniques for acquiring and processing high dimensional signals, and understanding their fundamental performance limits. She works on designing and analyzing new energy-efficient sensing paradigms coupled with computationally efficient robust algorithms for information processing of high-dimensional signals, and understanding their fundamental performance limits. Pal earned her Ph.D. in electrical engineering from California Institute of Technology, where she won an outstanding thesis award. She earned her bachelor’s in electronics and electrical communication engineering from the Indian Institute of Technology, Kharagpur. Before joining UC San Diego, she was an assistant professor of Electrical and Computer Engineering at the University of Maryland, College Park, affiliated with the Institute for Systems Research.
Specializing in computer-intensive statistical methods, quantifying the variability of estimators by means of a standard error or a confidence interval; nonparametric statistics and time series analysis.
Politis offers cross-discipline expertise in mathematics, economics, computer science, statistics and engineering. In addition to serving as Distinguished Professor of Mathematics, he is affiliated with the university’s Department of Economics. His research includes statistical analysis of time series, data collected sequentially over time such as daily stock prices, and random fields, i.e., data collected over a spatial domain such as temperatures recorded across California at all measuring locations on a certain day. He has served on the editorial boards of more than 15 academic journals and book series, including as co-editor of the Journal of Time Series Analysis, and the Journal of Nonparametric Statistics.
Specializing in researching complex biological systems with rigorous application of engineering and physical sciences
Rangamani brings academic strength in both biology and engineering to her research, with expertise in theoretical and computational biophysics focused on the interplay of cell shape, signaling and mechanics. She is the principal investigator of a $7.5 million grant in brain research, leading a team from UC San Diego and Salk Institute for Biological Studies to try to answer key questions: What is the memory capacity of a brain; and how does the brain process information with maximum energy efficiency? The grant was awarded by the Air Force Office of Scientific Research, through a Multidisciplinary University Research Initiative. Rangamani earned her Ph. D. in biological sciences from the Icahn School of Medicine at Mount Sinai, New York. She holds both master’s and bachelor’s degrees in chemical engineering, respectively, from Georgia Institute of Technology, and Osmania University in India.
Specializing in political methodology, political communication, text analysis, Chinese politics, developing software for social-science data analysis
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. Her work has appeared in journals such as Science, the American Journal of Political Science, American Political Science Review, and Political Analysis. To better analyze political trends she has been active in developing software to analyze text data, including the stm R package, which implements the Structural Topic Model that she developed with colleagues; and the stmBrowser R package for visualizing these models. 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.
Specializing in digitization of data, signal processing and analysis, sparse and low-dimensional representations of high dimensional data, compressed sensing
Saab’s research draws upon and develops tools in information theory, random matrix theory, frame theory, and geometric functional analysis to solve problems motivated by the acquisition, digitization and processing of signals. His research covers areas of signal processing and analysis, including work on blind source separation and its application to what is popularly known as “the cocktail party problem,” referring to the ability of humans over machines to be able to focus on a specific human voice while filtering out background noise like at a noisy party.
Specializing in signal and image analysis, spatio-temporal and high-dimensional data, and geometric statistics, with applications in biomedicine and the environment.
Schwartzman’s research aims to bring the strength of statistical signal and image analysis to solve important scientific problems that impact society. His methodological work has largely, but not exclusively, centered on the question of how to compare images and find differences between them in a statistically rigorous manner. This work has been developed with collaborations in medical imaging (particularly cognitive, anatomical and oncological neuroimaging), genomics, environmental remote sensing, climate modeling and cosmology. The solutions to these problems involve many areas of mathematics, probability and statistics such as theory of Gaussian random fields, differential geometry, nonparametric regression and high dimensional inference.
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. After working as a communications engineer at Rockwell Semiconductor and Algorithm Developer at Biosense Webster, he went on to obtain his PhD in Statistics from Stanford University. He has since then held faculty positions at Harvard University and North Carolina State University, and joined UCSD in 2016.
Specializing in uncovering linking principles from brain to behavior using computational models, biophysical properties of synapses and neurons, population dynamics of large networks of neurons
Sejnowski is co-director of the UC San Diego Institute for Neural Computation. His research pioneered learning algorithms for neural networks. His current work on brain and behavior links includes addressing such fundamental questions as: Why do we sleep? How do neurons communicate at synapses? As co-inventor of the Boltzmann machine, he helped create the first learning algorithm for multilayer neural networks and laid the foundation for deep learning. He holds the Francis Crick Chair at The Salk Institute for Biological Studies. His latest book, “The Deep Learning Revolution,” was published October 2018 by MIT Press.
Specializing in advanced cyberinfrastructure, quantified self-movement, science advisory leadership
Professor Smarr is founding Director of the California Institute for Telecommunications and Information Technology (Calit2), a UC San Diego/UC Irvine partnership. His research includes pioneering work in advanced cyberinfrastructure and the quantified self movement. He currently serves as principal investigator on 3 National Science Foundation (NSF) grants: Pacific Research Platform, Toward a National Research Platform, and Cognitive Hardware and Software Ecosystem Community Infrastructure. His data science and cross-disciplinary scholarship and leadership are extensive, and he has held advisory positions with NASA, NSF and the National Institutes of Health. He is frequently interviewed by scientific and mainstream media, including Science, Nature, the New York Times, BBC and the Wall Street Journal
Specializing in security & privacy, programming languages, and systems.
Stefan is an assistant professor in the Computer Science & Engineering. His research focuses on building principled and practical secure systems, along with programming languages and a longtime interest in hardware architectures, especially in the context of security. He leads students in working on several secure systems, spanning from new web server frameworks, to web browser architectures to constant-time programming languages (FaCT and CT-Wasm), to package managers (SPAM), to multi-core language runtime systems and garbage collectors (LIO, SaferNode.js, and Physis). In addition to his teaching and research, he has extensive industry experience, currently serving as the Chief Scientist at Intrinsic (formerly GitStar), a web security start-up he co-founded. He earned his Ph.D. in computer science from Stanford University, and both his bachelor’s and master’s degrees in electrical engineering from Cooper Union in New York.
Specializing in artificial intelligence, machine learning, computer vision and graphics, robotics, and deep learning
Su works on advancing artificial intelligence disciplines, including machine learning, computer vision, computer graphics, robotics and smart manufacturing. He focuses on deep learning for 3D data understanding, on interconnecting 3D data with images and text, and efficient planning leveraging the deep understanding of environments. Applications of his work include robotics, autonomous driving, virtual reality and augmented reality. He is a leader in the new field of 3D Deep Learning, working at the intersection of computer vision and computer graphics, he explains, “My thrust will be to push the frontier in both fields to become a bridge that connects intellectuals across disciplinary boundaries.”
Specializing in bioinformatics, systems biology and medicine.
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 played a key role in raising national awareness for training and research in bioinformatics. Current research includes developing novel strategies for identifying protein interaction networks, intracellular localization of proteins and identification of functional networks in cells. In systems medicine, he collaborates with biomedical scientists to deepen understanding of diseases associated with insulin resistance. He has served on advisory boards for several biotech and bioinformatics companies, universities, international governmental organizations, and the NIH.
Specializing in inductive theory for understanding nature from observational data. Key terms: equation-free mathematics, chaos, forecasting, detecting causality
Sugihara is a theoretical ecologist who has performed foundational work in the data analysis of complex systems from fisheries to medicine to finance. He gained renown for developing, with Lord Robert May, methods for forecasting chaotic systems, providing the first example of chaos in nature with the diatom populations at Scripps Pier. He has worked with the major institutions on questions of systemic risk and on detecting early warning signs of critical transitions, including the Federal Reserve Bank of New York and The Bank of England. He also worked with fisheries to develop a currently enacted market-based incentive plan for reducing wasteful bycatch and to improve forecasting of wild fish stocks. His current interest in neurobiology and genomics includes collaborations with the Salk Institute for Biological Studies to apply EDM to neurobiology and to problems in gene expression in cancer.
Computer Science & Engineering | HDSI Data Science
Specializing in combinatorics, algorithms, and the mathematical foundations of data science
Tiefenbruck focuses her mathematics background on developing strong foundations in the teaching of Data Science. She is a lecturer who serves as a core educator in the Data Science major and minor undergraduate education program through the Halıcıoğlu Data Science Institute. Her goal is educating students in the theoretical basis of math that will help them build a solid foundation for whatever direction they choose to take in data science, and other technology specializations. “What I like about the field of Data Science is that it’s so customizable, it’s not just: ‘Here’s a formula, plug it in.’ It’s such a broad field, it covers areas from sports to politics,” said Tiefenbruck. Her educational approach is to highlight the creative side of the academic discipline, and present students with lively projects to spark their imagination, like analyzing trends in the TV quiz show game “Jeopardy!” or predicting the genre of a song based on its lyrics.
Specializing in understanding relationships between neural oscillations, cognition, and disease by combining large-scale data-mining and machine-learning techniques with hypothesis-driven experimental research
Voytek works with students in Cognitive Science, Neuroscience and Data Science, and is a leader of both the neuroscience graduate program and the HDSI Undergraduate Fellowship program. His research focuses on automated science, aging, attention, working memory, and brain/cognition/society interactions His goal is to construct an understanding of cognition built on the first principles of neurophysiology: How can neural systems interact to give rise to cognitive phenomena normally equated with “attention” and “working memory,” and what are the behavioral and cognitive limitations and consequences of these biological constraints? He advocates for stronger communications in science, and focuses on what he calls “the human side of data science.”
Specializing in probability theory and stochastic models of complex networks such as in systems biology and in Internet congestion control
Specializing in probability theory and stochastic models of complex networks such as in systems biology and in Internet congestion control
Williams holds the Charles Lee Powell Chair in Mathematics. She is a leader in the field of probability theory, renowned for her work analyzing traffic congestion within stochastic networks, involving real-world systems running at near-maximum capacity such as congested Internet traffic, assembly line glitches, and roadways at rush hour. She has long applied data science and interdisciplinary approach to research, having worked with colleagues in electrical engineering, mechanical engineering, and other fields to tackle a variety of problems related to heavy traffic in systems such as wireless communications networks. She likes to say, “I eat problems for breakfast.”
Specializing in search for dark matter, supersymmetry, electroweak symmetry breaking, experimental particle physics, distributed high-throughput computing
Wuerthwein is the Distributed High-Throughput Computing Lead at San Diego Supercomputer Center and executive director of the Open Science Grid (OSG), a national cyberinfrastructure to advance the sharing of resources, software and knowledge. His research focuses on distributed high-throughput computing with large data volumes. His research in experimental particle physics research includes searching for new phenomena at the high-energy frontier with the CMS detector (Compact Muon Solenoid) at the Large Hadron Collider at CERN in Geneva. He worked on the team that discovered the Higgs Boson, popularly known as the God particle.
Specializing in statistics and biostatistics, causal inference, clinical trials, machine learning methods, random effects models, survival analysis, interdisciplinary work in bioinformatics and epidemiology
Applying her data science and mathematical expertise to the emergence of biomedical Big Data, Xu utilizes machine learning methods to develop predictions, as well as statistical inference for complex data types. Her interdisciplinary work includes investigating such data analysis as competing risks of cancer versus non-cancer mortality, in the presence of high dimensional covariates. Her research also includes focus on causal inference methodology using propensity scores or instrumental variables, for complex data types and for rare events such as in pregnancy safety data.
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.