Establish Colby as a nationally recognized center of excellence in interdisciplinary human-centered AI teaching and research.

Learn about our progress in our 2022-2023 Annual Report

Internal Advisory Committee

The Davis Institute for AI Internal Advisory Committee is composed of faculty, staff and students from across the College.

2023-2024 Members: Whitney King (Chemistry), Michael Donahue (Economics), Aaron Hanlon (Science and Technology Studies & Interdisciplinary Studies), Kara Kugelmeyer (Libraries),  Lindsey Madison (Chemistry), Oliver Layton (Computer Science), Jose Martinez (Music), Aleja Ortiz (ES), Dean Allbitton (CAH/Spanish), Mark Wardecker (ITS), Anya Morris ’27 (student member), Bibatshu Thapa Chhetri ’25 (student member), Dawit Boku ’26 (student member), Miz Insigne ’26 (student member)

External Advisory Committee

The Davis Institute for AI External Advisory Committee is composed of Colby alumni and corporate / government / NGO partners.

Profiled External Advisory Board Member: Kristofer Hamel, World Data Lab.

Key Performance Indicators

Key Performance Indicators In 2021-22:

  • 19% of faculty, spanning all four divisions, taught courses that involve AI
  • 15% of students took courses that involve AI
  • 6 Davis AI distinguished speaker series talks, including one co-hosted with the Medical Humanities initiative and one co-hosted with the Critical and Indigenous Studies initiative

Made possible by the Davis Family.


Economics & Finance

Exploring how artificial intelligence and machine learning can process large volumes of information and identify patterns to analyze market behavior and interactions among consumers, firms, and governments.

Computational Social Sciences

Examining the human condition through artificial intelligence by gathering and analyzing data to understand human thoughts, beliefs, attitudes, and behaviors.


Computational Biology, Bioinformatics, & Genomics

Strengthening our research and preparing students to use big data to benefit fields ranging from ecology to medicine.


The Environment & the Oceans

Providing enhanced computational resources, training, and collaborators to support Colby’s ability to keep pace with the rapidly changing tools and techniques for studying the environment. 


Ethics & Society

Filling the need for future leaders of this field to have an appreciation of history, culture, and societies, and the ability to think critically about a range of issues.

Faculty across disciplines—philosophers, ethicists, sociologists, and more—will explore the ethical implications of artificial intelligence and machine learning.

Recent Publications

Chowdhury, Tahiya, Verónica Romero, and Amanda Stent. “Interactional coordination between conversation part­ners with autism using non-verbal cues in dialogues.” Proceedings of the First Workshop on Connecting Multiple Disciplines to AI Techniques in Interaction-centric Autism Research and Diagnosis (ICARD 2023). 2023.

Chowdhury, Tahiya, Veronica Romero, and Amanda Stent. “Parameter Selection for Analyzing Conversations with Autism Spectrum Disorder.” Proceedings of INTERSPEECH. 2023.

Coane, Jennifer H., et al. “Lay Definitions of Intelligence, Knowledge, and Memory: Inter-and Independence of Constructs.” Journal of Intelligence 5 (2023): 84.

Al Madi, Naser. “How readable is model-generated code? examining readability and visual inspection of github co­pilot.” Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering. 2022.

Paxton, Alexandra, Tahiya Chowdhury, and Veronica Romero. “Language in the Time of COVID: Sensitivity of Lin­guistic Alignment to Conversation Type and Communication Modality.” Proceedings of the Annual Meeting of the Cognitive Science Society. Vol. 45. No. 45. 2023.

Raczaszek-Leonardi, Joanna, et al. “Putting interaction center-stage for the study of knowledge structures and processes.” Proceedings of the Annual Meeting of the Cognitive Science Society. Vol. 45. No. 45. 2023.

Good, Aidan, et al. “Recall distortion in neural network pruning and the undecayed pruning algorithm.” Advances in Neural Information Processing Systems 35 (2022): 32762-32776.

Maus, Natalie, and Oliver W. Layton. “Estimating heading from optic flow: Comparing deep learning network and human performance.” Neural Networks 154 (2022): 383-396.

Stent, A., Mann, G., & Kambadur, P. (2023). NLP in Finance. In Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices (pp. 563–592). chapter, Cambridge University Press.

Good, A., Lin, J., Sieg, H., Fergurson, M., Yu, X., Zhe, S., Wieczorek, J., & Serra, T. (2022). Recall distortion in neural network pruning and the undecayed pruning algorithm. In Proc. NeurIPS. arXiv preprint: https://arxiv.org/abs/2206.02976.

Al Madi, N. (2022) How readable is model-generated code? Examining readability and visual inspection of GitHub Copilot. To appear in Proceedings of ASE 2022. 

Li, Y., Li, C., Chen, J., & Roinou, C. (2022) Energy-aware multi-agent reinforcement learning for collaborative execution in mission-oriented drone networks. In Proceedings of IEEE ICCCN.

Layton, O. W., & Fajen, B. R. (2022). Distributed encoding of curvilinear self-motion across spiral optic flow patterns. Scientific Reports, 12(1), 1-15. [HTML] nature.com

Maus, N., & Layton, O. W. (2022). Estimating heading from optic flow: Comparing deep learning network and human performance. Neural Networks, 154, 383-396. [HTML] sciencedirect.com

Layton, O. W., Powell, N., Steinmetz, S. T., & Fajen, B. R. (2022). Estimating curvilinear self-motion from optic flow with a biologically inspired neural system. Bioinspiration & Biomimetics, 17(4), 046013. [PDF] iop.org

Steinmetz, S. T., Layton, O. W., Powell, N. V., & Fajen, B. R. (2022). A Dynamic Efficient Sensory Encoding Approach to Adaptive Tuning in Neural Models of Optic Flow Processing. Frontiers in Computational Neuroscience, 16. [HTML] nih.gov*

Made possible by the Davis Family

Made possible by the tremendous generosity of the Davis family and trustee of its charitable foundation Andrew Davis ’85, LL.D. ’15, the Davis Institute for Artificial Intelligence will provide new pathways for talented students and faculty to research, create, and apply AI and machine learning (ML) across disciplines while setting a precedent for how liberal arts colleges can shape the future of AI.

For decades the Davis family, through The Shelby Cullom Davis Charitable Fund, has contributed generously to the evolution of the academic and cocurricular experience at Colby, and the Davis Institute builds on this tradition of leadership. With Andrew Davis’s support, his family provided a $25-million gift in 2017 to create DavisConnects, Colby’s innovative model for post-graduate success, as well as a $10-million lead gift in 2013 to construct the Davis Science Center on Colby’s campus. In 2000 the Davis family selected Colby as one of five partner institutions to pilot the Davis United World College Scholars Program (UWC), which funded UWC graduates’ attendance at Colby and continues to make a profound impact at the College.