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sdooley1 [at] cs [dot] umd [dot] edu
I am actively looking for full-time jobs and/or opportunities for Summer 2023.
I am a fifth year graduate student at the University of Maryland, advised by John P. Dickerson, working at the intersection of machine learning and society. I am a human-centered machine learning researcher where I develop large-scale, production systems and research how they impact individuals. I work on technologies like Human-Computer Interaction (HCI), Neural Architecture Search (NAS), Hyperparameter Optimization (HPO), and computer vision. I particularly take a lens in my research which centers the experience of minoritized populations.
I have a Master's in Statistics from George Washington University, and a Bachelor's in Mathematics from University of Chicago.
Starting in late 2022, I am an intern researcher with Colin White at Abacus.AI. For summer 2022, I was an applied scientist intern with Amazon. For summer 2021, I was an intern researcher with Elissa M. Redmiles at Max Planck Institue for Software Systems.
I have published works in NeurIPS, IJCAI, CHI (🎉award winning🎉), FAccT, and PoPETs; and my work has been covered in popular press at Scientific American, WIRED, and VentureBeat.
Samuel Dooley, Geroge Z Wei, Tom Goldstein, and John P Dickerson. Robustness Disparities in Face Detection. Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), 2022. [Link] [Press]
Angelica Goetzen, Samuel Dooley, and Elissa M Redmiles. Ctrl-shift: How privacy sentiment changed from 2019 to 2021. The 22nd Privacy Enhancing Technologies Symposium (PoPETs), 2022. [Link]
Marina Knittel, Samuel Dooley, and John P Dickerson. The dichotomous affiliate stable matching problem: Approval-based matching with applicant-employer relations. The 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI), 2022. [Link]
Samuel Dooley, Dana Turjeman, John P Dickerson, and Elissa M Redmiles. Field Evidence of the Effects of Pro-sociality and Transparency on COVID-19 App Attractiveness. The 2022 ACM Conference on Human Factors in Computing Systems (CHI), 2022. 🎉Best Paper Award Honorable Mention🎉 [Link]
Neehar Peri, Michael J Curry, Samuel Dooley, and John P Dickerson. PreferenceNet: Encoding human preferences in auction design with deep learning. Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), 2021. [Link.]
Vedant Nanda*, Samuel Dooley*, Sahil Singla, Soheil Feizi, and John P Dickerson. Fairness through robustness: Investigating robustness disparity in deep learning. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), pages 466 - 477,2021. [Link.]
Samuel Dooley, Candice Schumann, Han-Chin Shing, John P Dickerson, and Philip Resnik. Sequential decision making in resource constrained global health settings. ML For Global Health at ICML, 2020. [Link]
Duncan C McElfresh, Samuel Dooley, Yuan Cui, Kendra Griesman, Weiqin Wang, Tyler Will, Neil Sehgal, and John P Dickerson. Can an algorithm be my healthcare proxy? Workshop on Health Intelligence at AAAI, 2020. [Link.]
Samuel Dooley, Michael Rosenberg, Elliott Sloate, Sungbok Shin, and Michelle Mazurek. Libraries' approaches to the security of public computers. 5th Workshop on Inclusive Privacy and Security at SOUPS-20, 2020. [Link.]
Darius Lam, Richard Kuzma, Kevin McGee, Samuel Dooley, Michael Laielli, Matthew Klaric, Yaroslav Bulatov, and Brendan McCord. xView: Objects in context in overhead imagery. ML for the Developing World at NeurIPS, 2018. [Link] [Press]
Eliza Mace, Keith Manville, Monica Barbu-McInnis, Michael Laielli, Matthew Klaric, and Samuel Dooley. Overhead detection: Beyond 8-bits and RGB. Naval Applications of Machine Learning, NAML 2018. [Link.]
Samuel Dooley*, Rhea Sukthanker*, John P Dickerson, Colin White, Frank Hutter, and Micah Goldblum. On the importance of architectures and hyperparameters for fairness in face recognition. Under Submission. [Link]
Alan F. Luo, Noel Warford, Samuel Dooley, Rachel Greenstadt, Michelle Mazurek, and Nora McDonald. How library IT staff navigate privacy and security challenges and responsibilities. Under Submission.
Samuel Dooley, Ryan Downing, George Wei, Nathan Shankar, Bradon Thymes, Gudrun Thorkelsdottir, Tiye Kurtz-Miott, Rachel Mattson, Olufemi Obiwumi, Valeriia Cherepanova, Micah Goldblum, John P Dickerson, Tom Goldstein. Comparing Human and Machine Bias in Face Recognition. Under Submission. [Link]
Kevin Kuo, Anthony Ostuni, Elizabeth Horishny, Michael J Curry, Samuel Dooley, Ping-yeh Chiang, Tom Goldstein, and John P Dickerson. Proportionnet: Balancing fairness and revenue for auction design with deep learning. Under Submission. [Link.]
Samuel Dooley and John P Dickerson. Global best arm identification in contextual bandits. Working Paper.