Five New Faculty Members Join the Ranks at the Halıcıoğlu Data Science Institute

Article Content

These talented researchers have been hired to support the growing demand for HDSI’s academic programs and to expand upon the robust interdisciplinary data science research happening across UC San Diego.

"HDSI continues to attract highly regarded researchers in all areas of data science," said founding director Rajesh K. Gupta. "The new appointments reflect growth in core areas of Data Science such as Machine Learning, Mobile Health, Privacy, and Statistics but also interdisciplinary areas such as Genetics and Environmental Data Science, which is the subject of an ongoing search." 

The new faculty members are leaders in their respective spheres and will host courses in several areas of research, including machine learning, statistics, biomedical informatics, and applied math. They join roughly 50 colleagues with full or partial appointments with HDSI. For a full list please visit https://datascience.ucsd.edu/about/faculty/faculty/. 

HDSI continues to recruit additional faculty members with several searches presently underway.

Founded in 2018 as an independent academic unit at UC San Diego, the mission of the Halıcıoğlu Data Science Institute is to establish the scientific foundations of data science, develop new methods and infrastructure, and train students and partners to use data science to solve the world’s most pressing problems.  The institute is the administrative home of the undergraduate major and minor in data science, as well as three graduate-level data science programs (MS, Ph.D., online MDS).

AY2022-2023 New Faculty:

Tiffany Amariuta, Assistant Professor  
Ph.D.: Harvard University

Before starting her lab in San Diego, Tiffany earned a B.S. in Biological Engineering at MIT and went on to conduct graduate research with Dr. Soumya Raychaudhuri as part of the Bioinformatics and Integrative Genomics Ph.D. program at Harvard Medical School, where she studied the genetic susceptibility of autoimmune diseases and other polygenic diseases. During graduate school, Tiffany developed machine learning methods to predict the functionality of regulatory variants, which had applications to transcription factor binding prediction, eQTL mapping, heritability enrichment analysis, and trans-ancestry portability of polygenic risk scores. Dr. Amariuta has a joint appointment with the Department of Medicine.

Biwei Huang, Assistant Professor
Ph.D.: Carnegie Mellon University 

Biwei Huang’s research interests are mainly in three aspects: (1) automated causal discovery in complex environments with theoretical guarantees, (2) advancing machine learning from the causal perspective, and (3) using or adapting causal discovery approaches to solve scientific problems. She successfully led a NeurIPS’20 workshop on causal discovery and causality-inspired machine learning and co-organized the first Conference on Causal Learning and Reasoning (CLeaR 2022). She was named a Rising Star of the Trustworthy ML Initiative and is a recipient of the Presidential Fellowship at CMU in 2017 and the Apple Scholars in AI/ML Ph.D. fellowship in 2021. 

Haojian Jin, Assistant Professor
Ph.D.: Carnegie Mellon University 

Haojian Jin’s research lies at the intersection of human-computer interaction, specializing in systems for machine learning, the Internet of Things, and privacy systems. His work has been recognized with a UbiComp Gaetano Borriello Outstanding Student Award, Research Highlights at Communications of the ACM and GetMobile, and best paper awards at Ubicomp and ACM Computing Reviews. 
 

Tauhidur Rahman, Assistant Professor
Ph.D.: Cornell University 

Tauhidur Rahman previously served as an Assistant Professor in Computer Science at the University of Massachusetts Amherst where he co-directed the Mobile Sensing and Ubiquitous Computing Laboratory (MOSAIC Lab). His current research focuses on building novel ubiquitous and mobile health sensing technologies that capture observable low-level physical signals in the form of acoustic and electromagnetic waves from our bodies and surrounding environments and map them to relevant biological and behavioral measurements. Some of his notable accomplishments include a Google Ph.D. fellowship in 2016 in mobile computing, a finalist position in Qualcomm innovation fellowship in 2015, Outstanding Teaching Award 2015 from Cornell University, one best paper award in ACM Digital Health 2016, and the best paper award from ACM IMWUT in 2021. Tauhidur received his B.S. in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology, his M.S. in Electrical Engineering from the University of Texas at Dallas, and his Ph.D. in Information Science from Cornell University.

Duncan Watson-Pariss, Assistant Professor Ph.D.: University of Manchester

Duncan Watson-Pariss previously worked as a senior research associate at the University of Oxford. His research addresses the representation of aerosols and clouds within climate models. Aerosols and clouds together represent some of the greatest challenges for climate models, and Dr. Watson-Parris has pioneered strategies to capitalize on the machinery of data science in tackling these research problems. "He was hired under the Chancellor’s Joint FTE initiative to promote interdisciplinary research. The Chancellor's Interdisciplinary Collaboratories are an opportunity to apply for multiyear funding to encourage faculty and student research collaborations across disciplines. The Collaboratories program is intended to provide fellowship support for groups of students (undergraduate, graduate, and/or professional) who will work jointly under the supervision of an interdisciplinary faculty group. The aim is to encourage faculty to form and support such groups as they develop preliminary research results sufficient to support successful applications for extramural funding.

Learn more by visiting HDSI at https://datascience.ucsd.edu