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School of Data Science Welcomes 14 New Faculty in 2023
The University of Virginia’s School of Data Science, which added 11 new faculty members in 2022, has expanded further, with 14 distinguished scholars joining the school’s ranks. Their expertise and research priorities include artificial intelligence, machine learning, computing, technology studies, the environment, data governance, physics, genetics, and much more.
Aaron Abrams
Professor of Data Science
Aaron Abrams is a mathematician with broad interests spanning geometry, topology, combinatorics, and probability. Abrams views mathematics as a social discipline for which collaboration is vital. Accordingly, a key goal undergirding his work as a researcher and an educator is to broaden access to the ideas, tools, methods, and beauty of mathematics.
Fun Fact: "I used to live in a tree house on a Caribbean island."
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Prince Afriyie
Associate Professor of Data Science
Prince Afriyie also serves as Program Director of the M.S. in Data Science residential program. Afriyie’s educational background is in mathematics and statistics. He currently serves on the Statistics Review Committee for the Centers for Disease Control and Prevention journal, Preventing Chronic Diseases, where he helps to advance understanding and dissemination of statistical methods and testing in the public health field. Prior to joining the School of Data Science, he was an Assistant Professor of Statistics at UVA where he taught courses in introduction to data science with R, statistical analysis with Python, and mathematical statistics.
Afriyie’s research includes multiple comparisons, survival analysis as well as statistics and data science education.
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Farhana Faruqe
Assistant Professor of Data Science
Farhana Faruqe is a data scientist and a researcher, with a focus on trustworthy AI, human-centered design, and AI acceptance. With a professional journey that spans over 12 years across diverse industries and academia, she has held roles as a data scientist, software developer, lecturer, and researcher in Trinidad and Tobago, Bangladesh, and the United States.
Fun Fact: “During my childhood, I trained in classical Bangla music.”
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Tom Hartvigsen
Assistant Professor of Data Science
Tom Hartvigsen works to make machine learning trustworthy, robust, and socially responsible enough for deployment in high-stakes, dynamic settings. He is active in the machine learning community, serving as the General Chair for the Machine Learning for Health Symposium in 2023, helping organize the 2023 Conference on Health, Informatics, and Learning, and co-chairing workshops on time series and generative AI at NeurIPS'22 and ICML'23.
Fun Fact: “I can juggle up to 5 balls.”
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Mar Hicks
Associate Professor of Data Science
Mar Hicks does research on the history of computing, labor, technology, and queer science and technology studies. Their work studies how collective understandings of progress are defined by competing discourses of social value and economic productivity, and how technologies often hide regressive ideals while espousing "revolutionary" or "disruptive" goals. Hicks’s current work focuses on how gender and sexuality bring hidden technological dynamics to light, and how the experiences of women and LGBTQIA people change the core narratives of the history of computing in unexpected ways.
Fun Fact: “My rabbit was in TIME magazine (no, he didn't do anything special).”
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Neal Magee
Associate Professor of Data Science
Neal Magee is the Director of Faculty Systems Architecture and an Associate Professor at the School of Data Science. His areas of interest include solution architecture, cloud computing, event-driven computing, and cloud-native design patterns.
Before coming to the University in 2016, he worked for over a decade in industry as a developer, systems administrator, cloud architect, and as the manager of internet operations and data teams. In 2012 he led the effort to migrate a Fortune 1000 client out of their datacenter and fully into the AWS Cloud with no downtime.
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Antonios Mamalakis
Assistant Professor of Data Science
Assistant Professor of Environmental Sciences
Antonios Mamalakis is an environmental data scientist interested in exploring data science tools like statistical and Bayesian analysis, machine/deep learning, and explainable AI to solve challenges in environmental applications. Among others, these challenges include improving predictive skill of hydroclimate and extreme events, understanding climate teleconnections and predictability, advancing climate attribution and causal discovery, etc.
Fun Fact: “I love Greek music and to sing Greek songs. If I was not a scientist, I would probably be a mediocre singer somewhere back in Greece.”
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Aaron Martin
Assistant Professor of Data Science
Assistant Professor of Media Studies
A social scientist specializing in technology policy and data governance, Aaron Martin studies how regulation can facilitate just, inclusive, and secure digital societies. In addition to focusing on how transnational policy is established by international bodies and humanitarian organizations, he explores how users in historically marginalized communities, including refugees and other vulnerable people, understand and shape technology and its regulation.
Fun Fact: “I believe in the power of bay leaves to improve the flavor of soups, stews, and sauces.”
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Javier Rasero
Assistant Professor of Data Science
Javier Rasero is a physicist and data scientist interested in obtaining the maximum amount of information from the brain. His research focuses on developing and applying new pipelines and methodologies to efficiently integrate multimodal brain data, aiming to predict and explain individual differences in both healthy and neuropathological populations.
Fun Fact: “I am an electronic music enthusiast (I am even learning to DJ and produce), so don’t get surprised if you see me dancing while I work or even walk.”
Mona Sloane
Assistant Professor of Data Science
Assistant Professor of Media Studies
As a sociologist, Mona Sloane studies the intersection of technology and society, specifically in the context of AI design, use, and policy. She also convenes the Co-Opting AI series, a public speaker series focused on all aspects of AI technology and its application. Her current work includes the development of new methods for AI auditing and AI transparency, innovating AI procurement, AI in hiring and talent acquisition, AI participation and public education, new AI tools for investigative journalism, global AI policy and local governance innovation on AI, and a range of different responsible AI topics.
Fun Fact: “I used to train as a professional dancer from the age of 4 to 19.”
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Jon Tupitza
Assistant Professor of the Practice in Data Science
Jon Tupitza previously served as a Lecturer in the School of Data Science following two decades in the IT services industry, including at Microsoft. He has a master's degree in data analytics from Western Governors University and a bachelor's of fine arts from the Maryland Institute College of Art.
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Jason Williamson
Assistant Professor of the Practice in Data Science
Jason Williamson is an Assistant Professor of the Practice in Data Science, a position he assumed after serving as a Lecturer at the School. He has also worked at Oracle in a variety of capacities, including as Vice President for Oracle for Startups and Oracle for Research.
Prior to joining Oracle in 2017, Williamson created and led several key initiatives within the investment and startup community at Amazon Web Services (AWS). There he launched AWS’s Private Equity Program and alliances with Bain & Company, McKinsey & Company, Boston Consulting Group, PwC, and Deloitte. Williamson has a background in both Fortune 500 firms (Capital One, GE, Amazon, and Oracle) and the startup world. Williamson was also an assistant professor for five years at the University of Virginia’s McIntire School of Commerce and has authored three books.
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Jianxin Xie
Assistant Professor of Data Science
Jianxin Xie's research interests encompass the use of advanced machine learning and data analytics tools to tackle challenging health care and engineering problems. She holds a multidisciplinary background in machine learning, material science, and physics. Her future research aims to extend physics-augmented machine learning for patient-specific detection and characterization of diseases, enhancing the confidence of AI-assisted clinical decision support.
Fun Fact: “I love to watch my cats involving in a playful fight. Another thing is that I am kind of a shopaholic.”
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Aiying Zhang
Assistant Professor of Data Science
Aiying Zhang's expertise lies in statistical modeling, multimodal neuroimaging, and genetics, with a focus on graphical models and network science. She is interested in developing data-driven approaches leveraging multi-level information to understand typical and atypical brain development. She is working on various collaborative projects involving psychiatric disorders and neurological disorders.
Fun Fact: “I am a bit of a foodie; I am pretty proud of myself for trying cliff jumping in Hawaii.”
