Jacqueline Maasch is a PhD Advisor with InGenius Prep and an AI research scientist based in New York City. She completed her PhD in Computer Science at Cornell University with a minor in applied probability and statistics, and holds a Master of Computer and Information Technology from the University of Pennsylvania. Her doctoral research, which focuses on probabilistic machine learning for reasoning and decision-making under uncertainty, was supported by three prestigious fellowships: the NSF Graduate Research Fellowship, Cornell’s Presidential Life Science Fellowship, and the Digital Life Initiative Doctoral Fellowship.
What makes Jacqueline’s academic path particularly distinctive is where it began. She completed her undergraduate studies at Smith College summa cum laude, ranking in the top 1% of her class with a BA in Anthropology and Environmental Science, and earning Phi Beta Kappa and Sigma Xi honors before transitioning to computer science at the graduate level. Her master’s research at the University of Pennsylvania introduced the new scientific field of molecular de-extinction, applying machine learning to drug discovery in a project published in Cell Host & Microbe and covered by NPR, Nature News, and CNN.
Her graduate research career spans some of the most respected institutions in her field. During her PhD, she conducted research internships at Microsoft Research in Cambridge, UK, where her work produced publications at ICML 2025 and an ICLR 2025 oral presentation accepted in the top 1.8% of submissions, and at YRIKKA, resulting in a spotlight presentation at NeurIPS 2025. She was also invited for a long-residency program at the Isaac Newton Institute for Mathematical Sciences at the University of Cambridge and completed a clinical data science internship at Boehringer Ingelheim. Her work has resulted in publications at ICML, ICLR, AAAI, NeurIPS, and Cell Host & Microbe, as well as a pending patent.
Jacqueline brings direct, firsthand experience in PhD admissions to her advising. She served two years as a PhD Application Reviewer for Cornell Computer Science graduate admissions, reviewing over 100 applications for the machine learning research area, and spent three years as a student leader for the department’s PhD recruitment events and one year as a mentor in the PhD student mentor program. She also co-developed Cornell CS 6006: Succeeding in the Graduate Environment, a course designed to help incoming PhD students navigate the demands of doctoral training. As an advisor, she draws on this deep institutional knowledge to help students craft applications that are rigorous, authentic, and positioned to succeed at the most competitive research programs in the world.
