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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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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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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). 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.

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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.

Marina Langlois
Teaching Professor
Categories: Teaching Faculty
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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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Dimitris Politis
Distinguished Professor, HDSI Associate Director

Associate Director Politis is an internationally known scholar in mathematics and economics, working on time series, bootstrap methods, and nonparametric estimation, and a researcher with authorship of more than 100 journal papers and monographs.

Politis earned his Ph.D. in statistics from Stanford University, and dual masters’ degrees from Stanford in statistics and mathematics. He also holds a master’s degree from Rensselaer Polytechnic Institute in computer and systems engineering, and his bachelor of science degree in electrical engineering from University of Patras in Greece.

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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.

Photo of Janine Tiefenbruck
Janine Tiefenbruck

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.

Tiefenbruck earned her Ph.D. and master’s degrees in mathematics from UC San Diego, specializing in algebraic and enumerative combinatorics, and her bachelor’s from Loyola University, Maryland in both math and computer science. Among her academic awards include a GAANN Fellowship (federal support for scholars fulfilling a national need), a Goldwater Scholarship and a Best Teacher award from Jacobs School of Engineering. Contact: jlobue@ucsd.edu.

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Brad Voytek
Associate Professor, HDSI Founding Faculty Fellow

Bradley Voytek is an Associate Professor in the Department of Cognitive Science, the Halıcıoğlu Data Science Institute, and the Neurosciences Graduate Program at UC San Diego. He is an Alfred P. Sloan Neuroscience Research Fellow and National Academies Kavli Fellow, as well as a founding faculty member of the UC San Diego Halıcıoğlu Data Science Institute and its undergraduate Data Science program, where he serves as Vice-chair.

His research program uses neural modeling and simulation, along with large-scale data mining and machine learning techniques, to understand the physiological basis of human cognition and age-related cognitive decline.