Rate My Professor Molly Feldman

Molly Q. Feldman is an Assistant Professor of Computer Science at Oberlin College, where she joined the faculty in 2021. She teaches courses such as Introduction to Computer Science (CSCI 150), Programming Abstractions (CSCI 275), People-Powered Procedures, and Software from Start to Start Over. Feldman's academic journey includes a Ph.D. in Computer Science from Cornell University in 2020, with a thesis titled "Realizing the Human Potential of Program Synthesis & Crowdsourcing," an M.S. in Computer Science from Cornell in 2018, and a B.A. in Mathematics and Computer Science from Swarthmore College in 2015. Prior appointments include Visiting Assistant Professor of Computer Science at Williams College from 2020 to 2021, where she taught Introduction to Computer Science and Human Work in Computational Systems, and Lecturer at Cornell University in Fall 2019, teaching Crowdsourcing and Human Computation.

Feldman's research bridges powerful computational methods with human-centered problems, focusing on areas such as how beginning programmers interact with large language models for code generation, program synthesis, crowdsourcing, and automatic diagnosis of student misconceptions. Her publications appear in leading venues including NAACL, CHI, ACM Transactions on Programming Languages and Systems (OOPSLA), IEEE Transactions on Software Engineering, and CSCW. Notable works include "Substance Beats Style: Why Beginning Students Fail to Code with LLMs" (NAACL 2025, co-authored with Lucchetti, Wu, Guha, and Anderson), "How Beginning Programmers and Code LLMs (Mis)read Each Other" (CHI 2024, co-authored with Nguyen, Babe, Zi, Guha, and Anderson), "MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation" (IEEE TSE 2023, co-authored with Cassano et al.), "How We Write with Crowds" (CSCW 2020, co-authored with McInnis), and "Automatic Diagnosis of Students' Misconceptions in K-8 Mathematics" (CHI 2018, co-authored with Feldman, Cho, Ong, Gulwani, Popovic, and Andersen). Her research receives support from the National Science Foundation (Award ID: 2326175). Awards include the W. M. Keck Foundation Faculty Fellowship at Oberlin College (2024), four Cornell University Outstanding Teaching Assistant Awards (2016), and Best Presentation Award for "StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code" at ICSE 2024 Workshop (co-authored). Feldman delivers invited talks, such as "Automatic Code Generation Through a Human Lens" at Tufts University (2024) and Harvey Mudd College (2024), and previously at Harvard University (2019). She contributes to the field through extensive service, including PC Co-Chair for CHIWORK 2025, SPLASH-E 2023, and PLMW@SPLASH (2021-2024), Steering Committees for Programming Languages Mentoring Workshop (2022) and SPLASH-E (2021), and at Oberlin as Chair of the Cognitive Science Program Committee (2023-2024).