Hariri Institute 2025 Junior Faculty Fellows and Graduate Student Fellows
The Hariri Institute for Computing is pleased to announce our 2025 Fellows!
The Institute has awarded four Junior Faculty Fellows and five Graduate Student Fellows, representing early-career faculty and exemplary first-year and second-year graduate students who are pursuing innovative computationally-driven research with the potential for high impact. Our 2025 cohort of fellows represents a variety of colleges and schools across Boston University. They are applying computational methods and tools to address complex challenges in diverse fields spanning anatomy & neurobiology, biomedical engineering, biostatistics, computer science, computing & data science, global health, neuroscience, physics, and systems engineering.
2025 Junior Faculty Fellows
Huitong Ding, PhD, Research Assistant Professor, Anatomy & Neurobiology, Chobanian & Avedisian School of Medicine, Boston University
Dr. Ding’s research explores brain aging and cognitive heterogeneity, using data science, AI, and digital tools to advance inclusive Alzheimer’s research. Ding’s work focuses on early detection, sex differences, and novel assessment methods to collect cognitive data without formal testing, bridging cognitive science and translational impact.
Hongwan Liu, PhD, Assistant Professor, Physics, College of Arts & Sciences, Boston University
Dr. Liu’s research spans particle and astro-particle physics to cosmology, aiming to uncover the nature of dark matter, dark energy, and the Universe’s evolution. Liu develops novel computational methods using machine learning, simulation-based inference, and variational techniques to tackle key challenges in cosmology and dark matter detection.
Allison Portnoy, PhD, Assistant Professor, Global Health, School of Public Health, Boston University
Dr. Portnoy’s work extends computational research to address global public health challenges. She uses data science to optimize vaccination strategies, assess cost-effectiveness, and inform equitable health policy. Portnoy’s research relies on large-scale modeling and multilateral datasets to guide infectious disease prevention in high-burden settings.
Pawel Przytycki, PhD, Assistant Professor of Computing & Data Science, Faculty of Computing & Data Sciences, Boston University
Dr. Przytycki is a computational scientist whose research combines machine learning, network science, and statistical modeling to study noncoding variants in development and disease. Przytycki’s work looks to integrate large-scale genomics data to decipher the role of the noncoding genome in dictating how cells change state.
View all our Junior Faculty Fellows here.
2025 Graduate Student Fellows
Tushar Arora, PhD student, Computational Neuroscience, Chobanian & Avedisian School of Medicine, Boston University. (Advised by Professors Brian DePasquale and Chand Chandrasekaran.)
Arora’s research centers on studying brain function underlying the control of movement through advanced statistical analysis and modeling of neural circuits, with applications to brain–machine interfaces. Arora examines the neural circuitry that allows primates to perform precision grips, a line of research poised to transform the core algorithms and technologies behind brain–machine interfaces.
Zijian Guo, PhD student, Systems Engineering, College of Engineering, Boston University (Advised by Professor Wenchao Li.)
Guo’s research focuses on neuro-symbolic AI, combining neural networks with symbolic systems to enhance reasoning, reliability, and interpretability in AI. Guo develops methods that integrate formal logic into data-driven decision-making, with applications in robotics and safety-critical systems. Work includes safe offline reinforcement learning and temporal logic-guided transformers.
Zachary Loschinskey, PhD Student, Biomedical Engineering, College of Engineering, Boston University (Advised by Professors Michael Economo and Brian DePasquale.)
Loschinskey conducts interdisciplinary computational research, developing and applying advanced dynamical systems models to understand neural circuits controlling movement. Loschinskey has pursued rigorous coursework in state-space modeling, AI for biomedicine, and computational biology to prepare for these projects, driving innovation in computational neuroscience.
Sarah Milligan, PhD candidate, Biostatistics, School of Public Health, Boston University. (Advised by Professors Fatema Shafie Khorassani and Janice Weinberg.)
Milligan develops machine learning methods for surrogate outcome validation, improving the use of existing research on surrogate–true outcome pairs. Milligan’s work enhances clinical trial accuracy and causal inference, with potential applications beyond surrogate validation. Milligan is also building software packages with detailed documentation to broaden the impact of these methods across fields.
Yuwen Tan, PhD student, Computer Science, College of Arts & Sciences, Boston University (Advised by Professor Boqing Gong.)
Tan’s research advances machine unlearning by focusing on removing specific learned concepts from AI models to enhance privacy, legal compliance, and ethical use. Tan developed a novel concept-based unlearning framework and a hinge loss to prevent over-forgetting, achieving high efficiency with minimal data. Tan’s work helps modern AI systems built on vast datasets remain flexible, ethically aligned, and trustworthy.
View all our Graduate Student Fellows here.