CDS Welcomes Five New Faculty Members
The Faculty of Computing & Data Sciences (CDS) is thrilled to welcome a dynamic cohort of new faculty whose expertise spans climate science, data systems and privacy, optimization, AI for physics, and large language models. Together, they embody the interdisciplinary spirit of CDS—pushing boundaries in research, championing innovation, and inspiring the next generation of leaders at the intersection of data and society.
Read about the five new CDS faculty members and the groundbreaking work they’re bringing to Boston University.
Elizabeth A. Barnes is the incoming* Dalton Family Chair in Environmental Data Science & Sustainability and Professor of Computing & Data Sciences and of Earth & Environment at Boston University. Her group's research focuses on understanding Earth system variability, predictability, and change across time and space, with an emphasis on developing and implementing artificial intelligence tools in a way that mimics scientific human reasoning to improve intrinsic interpretability. Her overarching research goal is to responsibly harness AI to anticipate human-Earth system futures in support of a thriving society in the decades ahead. She teaches graduate courses on statistical analysis, AI for scientific discovery, and data-driven forecasting across timescales from days-to-decades. Read more.
Kinan Dak Albab, an incoming Assistant Professor in CDS, focuses on developing real systems and practical tools that enhance privacy in the real world. In his research, Kinan uses techniques from computer systems, cryptography, and programming languages. He builds systems to assist developers in ensuring that their applications comply with their desired privacy policies, including requirements mandated by privacy and data protection laws such as the GDPR and CCPA. These policies range from access control and user consent to purpose limitation, data deletion, and many others. Kinan also works on building and deploying efficient and easy-to-use systems to bring privacy-enhancing technologies to practice, including secure multiparty computation (MPC) and differential privacy. Read more.
CDS Assistant Professor Siddharth Mishra-Sharma will be joining CDS in 2026. Siddharth works bidirectionally at the intersection of AI and physics, with a focus on applications that contribute to fundamental physics discovery. His research explores how AI can be leveraged to optimize the utilization of complex datasets from current and future experiments in cosmology, astrophysics, and particle physics across diverse modalities, scales, and physical systems. At the same time, he uses data from physics as a sandbox for methodological developments with broad applicability in the natural sciences, particularly focusing on neural approaches to simulation-based inference, scalable generative modeling, probabilistic/differentiable programming, and symmetry-preserving data processing. Read more.
Naomi Saphra is an incoming CDS Assistant Professor to begin in 2026, and is currently completing a Kempner Research Fellowship at Harvard University. Her core agenda focuses on a single goal: to completely and comprehensively understand language model training. This objective combines linguistics, optimization, the science of deep learning, interpretability, and behavioral evaluation. She is also interested in each of these component topics on its own. Recently, she has begun collaborating with natural and social scientists to use interpretability to understand scientific discovery models and the world around us. Previously, she earned a PhD from the University of Edinburgh on Training Dynamics of Neural Language Models; worked at NYU, Google, and Facebook; and attended Johns Hopkins and Carnegie Mellon University. Read more.
Lauren Wheelock, Clinical Assistant Professor in CDS, brings a multidisciplinary background in mathematical optimization, machine learning, and applied data science to her teaching and research. She holds a BS in Mathematics from Yale University and a PhD in Operations Research from MIT. Lauren’s research sits at the intersection of optimization, machine learning, and real-world applications—from biological sequence engineering to the development of AI-powered tools for college admissions. She has worked across industry and academia, including roles in operations research-based consulting, machine learning-guided protein engineering for a gene therapy startup, and building educational AI platforms. As an educator, Lauren is passionate about helping students build strong mathematical foundations and develop intuition for applying data science and machine learning methods. Read more.