Dr George D. Montañez, PhD
George D. Montanez is an associate professor in the Department of Computer Science at Harvey Mudd College, in Claremont, California.
He is a former data scientist with Microsoft AI + Research. He obtained a PhD in machine learning from Carnegie Mellon University, with additional degrees in computer science (BS, University of California Riverside; MS, Baylor University) and machine learning (MS, Carnegie Mellon University). His work sits at the intersection of machine learning and information theory. He is a former NSF Graduate Research Fellow and Ford Foundation Predoctoral Fellow. His work has been presented at conferences such as AAAI, IEEE CEC, and ICAART, including a best paper award at CIKM 2014, best student paper award at IEEE SMC 2017, best student paper award at IJCNN 2017, and best paper award at ICAART 2020. He has interned at Microsoft Research, Yahoo!, and Bing, and given invited talks at Meta, Microsoft, General Electric, and research institutions across the US. He has written on topics including applied machine learning, informational complexity, and the ethics of AI.
Select publications
- Bashir D, Montañez G, Sehra S, Sandoval Segura P, Lauw J, “An Information-Theoretic Perspective on Overfitting and Underfitting” Australasian Joint Conference on Artificial Intelligence (AJCAI 2020), November 29-30, 2020.
- Montañez G, Hayase J, Lauw J, Macias D, Trikha A, Vendemiatti J, “The Futility of Bias-Free Learning and Search.” Australasian Joint Conference on Artificial Intelligence (AJCAI 2019), 2019. 277–288
- Montañez G, Bashir D, Lauw J, “Trading Bias for Expressivity in Artificial Learning.” In: Rocha A.P., Steels L., van den Herik J. (eds) Agents and Artificial Intelligence. ICAART 2020. Lecture Notes in Computer Science, vol 12613. Springer, Cham.
- Hom C, Yik W, Montañez G, “Finite-Sample Bounds for Two-Distribution Hypothesis Tests.” IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA 2023) , 2023.
- Yik W, Serafini L, Lindsey T, Montañez G, “Identifying Bias in Data Using Two-Distribution Hypothesis Tests.” 2022 AAAI/ACM Conference on AI, Ethics, and Society (AAAI/ACM AIES 2022), Oxford, United Kingdom, August 1-3, 2022.
- Neubert M, Montañez G, “Virtue as a Framework for the Design and Use of Artificial Intelligence.” Business Horizons, 2019. ISSN 0007-6813.
- Montañez G, “The Famine of Forte: Few Search Problems Greatly Favor Your Algorithm.” IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2017), 2017.
Select awards
- HMC Outstanding Faculty Member Award, Harvey Mudd College, 2021
- Diversity Mentor Award, Claremont Colleges Consortium, 2020
- Best Paper Award, ICAART 2020
- Best Paper Award, CIKM 2014
