WEIWEI PAN

AI Researcher, Educator, Technologist Advocating for Humanism

About

Weiwei Pan

I am: a researcher who is deeply interested in using interdisciplinary lenses to understand the potential harms and benefits of technology; an educator dedicated to bringing underrepresented voices to decision making in technology; an advocate seeking to help shape the way society approaches technology so as to preserve the meaning of human endeavors.

For the past decade at Harvard, I served as the Assistant Director for Graduate Studies in Data Science, co-led research in the Data to Actionable Knowledge lab (DtAK), taught machine learning and data science at the undergraduate and graduate level, and led initiatives in Equity, Diversity, Inclusion and Belonging.

In the decade prior, I did research in pure math (specifically algebraic topology) and was an Assistant Professor of Mathematics at Saint Mary’s College of California (2009-2015), where I focused on STEM community building, outreach and mentorship for first-generation college students and students from historically minoritized backgrounds.

My education is the peculiar product of an early-college-entrance program at an historically women’s college (1997-2001), a PhD from a liberal-arts focused research university (2005-2009), a mid-career-change inspired master’s (2015-2017), and post-doctoral tours at young research centers born of old institutions in Germany (2011-2013) and the US (2017-2020).

Research

I am generally interested in unfolding consequences of design choices in machine learning pipelines (e.g. model assumptions, inductive biases, inference, optimization and model explanations) for social-technical decision making systems. I am also broadly interested in issues of AI safety, ethics and regulation. My interests include uncertainty quantification, deep generative models, deep Bayesian models, approximate inference, user modeling in RL, explanable AI and HCI.

Since 2024, I’ve been co-leading an interdisciplinary research initiative to understand the impact of AI on frontline humanitarian negotiation. This work focuses both on machine learning and HCI.

Recent Papers

More publications can be found at my Google Scholar.

  1. Ian M. Moore, Eura Nofshin, Siddharth Swaroop, Susan Murphy, Finale Doshi-Velez, Weiwei Pan, When and Why Hyperbolic Discounting Matters for Reinforcement Learning Interventions, RLC, 2025
  2. Hiwot Belay Tadesse, Alihan Hüyük, Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez, Directly Optimizing Explanations for Desired Properties, UAI, 2025.
  3. Kirsten N. Morehouse, Siddharth Swaroop, Weiwei Pan, Rethinking LLM Bias Probing Using Lessons from the Social Sciences, ICML (Position Track), 2025.
  4. Philipp Arens, D. Adam Quirk, Weiwei Pan, Yaniv Yacoby, Finale Doshi-Velez, Connor J. Walsh, Preference-based assistance optimization for lifting and lowering with a soft back exosuit, Science Advances, 2025.
  5. Salma Abdel Magid, Weiwei Pan, Simon Warchol, Grace Guo, Junsik Kim, Mahia Rahman, Hanspeter Pfister, Is What You Ask For What You Get? Investigating Concept Associations in Text-to-Image Models, TMLR, 2025.
  6. Zilin Ma, Yiyang Mei, Claude Bruderlein, Krzysztof Z. Gajos, Weiwei Pan, “ChatGPT, Don’t Tell Me What to Do”: Designing AI for Context Analysis in Humanitarian Frontline Negotiations, CHI Work, 2025.
  7. Jonas B Raedler, Siddharth Swaroop, Weiwei Pan, AI Companions Are Not The Solution To Loneliness: Design Choices And Their Drawbacks, ICLR Workshop on Building Trust in Language Models, 2025.
  8. Sudhan Chitgopkar, Noah Dohrmann, Stephanie Monson, Jimmy Mendez, Finale Doshi-Velez, Weiwei Pan, Accuracy Isn’t Everything: Understanding the Desiderata of AI Tools in Legal-Financial Settings, Neurips Workshop on Behavioral Machine Learning, 2024.
  9. Soline Boussard, Susannah Cheng Su, Helen Zhao, Siddharth Swaroop, Weiwei Pan, Understanding Model Bias Requires Systematic Probing Across Tasks, Neurips Workshop on Socially Responsible Language Modelling Research, 2024.
  10. Julia Smakman, Lisa Soder, Connor Dunlop, Siddharth Swaroop, Weiwei Pan, AI Agents & Liability – Mapping Insights from ML and HCI Research to Policy, Neurips Workshop on Socially Responsible Language Modelling Research, 2024.
  11. Julia Smakman, Lisa Soder, Connor Dunlop, Siddharth Swaroop, Weiwei Pan, An Autonomy-Based Classification: Liability in the Age of AI Agents, Neurips Workshop on Regulatable ML, 2024.
  12. S.B. Wang, R.D.I. Van Genugten, Y Yacoby, W Pan, K.H. Bentley, S.A. Bird, R.J. Buonopane, A. Christie, M. Daniel, A. Haim, L. Follet, R.G. Fortgang, F. Kelly-Brunyak, E.M. Kleiman, A.J. Millner, O. Obi-Obasi, J.P. Onnela, N. Ramlal, J.R. Ricard, J.W. Smoller, T. Tambedou, K.L. Zuromski, M.K. Nock, Idiographic prediction of suicidal thoughts: Building personalized machine learning models with real-time monitoring data, Nature Mental Health, 2024.
  13. Esther Brown, Shivam Raval, Alex Rojas, Jiayu Yao, Sonali Parbhoo, Leo A Celi, Siddharth Swaroop, Weiwei Pan, Finale Doshi-Velez, Where do doctors disagree? Characterizing Decision Points for Safe Reinforcement Learning in Choosing Vasopressor Treatment, American Medical Informatics Association (AMIA), 2024.
  14. Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez, Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders, Advances in Approximate Bayesian Inference (non-Archival), 2024.
  15. Zilin Ma, Susannah Cheng Su, Nathan Zhao, Linn Bieske, Blake Bullwinkel, Jinglun Gao, Gekai Liao, Siyao Li, Ziqing Luo, Boxiang Wang, Zihan Wen, Yanrui Yang, Yanyi Zhang, Claude Bruderlein, Weiwei Pan, Using Large Language Models for Humanitarian Frontline Negotiation: Opportunities and Considerations, ICML Workshop on NextGenAISafety, 2024.
  16. Eura Nofshin, Esther Brown, Brian Lim, Weiwei Pan, Finale Doshi-Velez, A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning, ICML Workshop on NextGenAISafety, 2024.
  17. Hiwot Belay Tadesse, Weiwei Pan, Finale Doshi-Velez, Optimizing Machine Learning Explanations for Properties, ICML Workshop on Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact, 2024.
  18. Kirsten Morehouse, Weiwei Pan, Juan Manuel Contreras, Mahzarin R. Banaji, Bias Transmission in Large Language Models: Evidence from Gender-Occupation Bias in GPT-4, ICML Workshop on NextGenAISafety, 2024.
  19. David Berthiaume, Yuan Tang, Chau Nguyen, Siyu Gai, Emilia Mazzolenis, Weiwei Pan, Synthetic Data-driven Prediction of Height for Childhood Malnutrition, ICML Workshop on AI4Science, 2024.
  20. Paul Nitschke, Lars Lien Ankile, Eura Shin, Siddharth Swaroop, Finale Doshi-Velez, Weiwei Pan, AMBER: An Entropy Maximizing Environment Design Algorithm for Inverse Reinforcement Learning, ICML Workshop on Models of Human Feedback for AI Alignment, 2024.
  21. Eura Nofshin, Siddharth Swaroop, Weiwei Pan, Susan Murphy, Finale Doshi-Velez, Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks, International Conference on Autonomous Agents and Multiagent Systems, 2024.
  22. Jiayu Yao, Weiwei Pan, Finale Doshi-Velez, Barbara E Engelhardt, Inverse Reinforcement Learning with Multiple Planning Horizons, Reinforcement Learning Conference, 2024.

Teaching

I teach undergraduate and graduate courses in data science and machine learning. Students interested in directed research should contact me.

  • AC 297r: Computational Science and Engineering Capstone Project (Fall, Spring)
  • AC 298r: Diversity, Inclusion, Ethics and Leadership in Tech (Fall)
  • CS 181: Machine Learning (Spring 2023)
  • AM 207: Advanced Scientific Computing: Stochastic Optimization Methods (Fall 2019-2021)
  • DSC 6232: Machine Learning and Computational Statistics at the University of Rwanda (Summer 2019-2021)
  • Workshop: Data Science Workshop at the University of Rwanda (Summer 2019)
  • AC 299r: Directed Graduate Research (Fall/Spring)
  • AM/CS 91r: Directed Undergraduate Research (Fall/Spring)

Advocacy

Women in Data Science

From 2018-2022, I organized the data science workshop for the annual Women in Data Science Cambridge (WiDS) conference: WiDS Datathon Workshop 2022. From 2021-2022, I served as the co-director of the Worldwide WiDS Datathon.

Diversity, Inclusion and Belonging at Harvard

I am the organizer of OMPP’s Data Science Pedagogy Winter Workshop for educators of underrepresented college students in data science. I am also the faculty advisor of the OMPP Graduate Advisory Committee and the facilitator of the OMPP Diversity, Inclusion, Leadership Reading Group.

Community Building at Harvard

I am a member of the Senior Common Room at Leverett House. I am also a co-organizer of the OMPP/CS Communitas – please reach out if you have ideas for food, activities or programming for Communitas!

PhD and Early Career Mentoring

I’m the faculty mentor of the SEAS PhD Working Group, a working group for masters and undergraduate students who are interested in receiving support and mentorship through their PhD application process. Sign up for the PhD Working Group. Recently, I have also begun serving as a mentor in the Career Development program at DataPoint Armenia.