Aaron David Tucker

aarondtucker@cs.cornell.edu

https://www.cs.cornell.edu/∼aarondtucker/

Education
Cornell University
Computer Science PhD Candidate, advised by Thorsten Joachims
M.S. Computer Science
Harvard College
A.B. Computer Science, cum laude, Departmental Honors

Ithaca, NY
Aug. 2019 - present
awarded Feb. 2023
Cambridge, MA
Aug. 2012 – June 2016

Peer-Reviewed Publications
Aaron D. Tucker. “Applying Torts to Juridical Persons: Corporate and AI Governance” ICML Workshop on
Generative AI and Law (GenLaw), 2023.
pdf
Aaron D. Tucker, Caleb Biddulph∗ , Claire Wang∗ , and Thorsten Joachims. “Bandits with costly rewardobservations” ACM Conference on Uncertainty in Artificial Intelligence, 2023.

pdf

Aaron D. Tucker and Thorsten Joachims. “Minimum variance augmentation logging for counterfactual
evaluation in contextual Bandits” ACM International Conference on Web Search and Data Mining, 2023.

pdf

Aaron D. Tucker, Markus Anderljung, and Allan Dafoe. “Social and governance implications of improved data
efficiency” AAAI/ACM Conference on AI Ethics and Society, 2020.
pdf
Aaron D. Tucker, Adam Gleave, and Stuart Russell. “Inverse reinforcement learning for video games.” NeuralInformation Processing Systems (NeurIPS) Deep Reinforcement Learning Workshop, 2018.
pdf
Scott W. Linderman, Aaron D. Tucker, and Matthew J. Johnson. “Bayesian latent state space models of neuralactivity.” Computational and Systems Neuroscience (COSYNE) Abstracts, 2016.
pdf

Invited and Contributed Talks“Bandits with Costly Reward Observations”, TU Dortmund LS8 Lab

December 2022

“Implications of Increased Data Efficiency”, Future of Humanity Institute

August 2019

“Scaling Inverse RL”, Future of Humanity Institute/Deepmind Joint AI Safety Seminar“Scaling Inverse RL”, Center for Human Compatible AI Seminar

July 2019
October 2018

Research and Academic Experience
PhD Candidate, advised by Thorsten Joachims
Cornell University

Research Intern, Microsoft Research

Ithaca, NYAug. 2019 - present

Remote; Redmond, WA

supervised by Jennifer NevilleJune 2022 - Sept. 2022
• Off-policy estimation of network objectives: Devised conditions and algorithm for evaluating the impact ofrecommendation policies on network connectivity objectives. Presented within Microsoft, paper forthcoming.

Summer Fellow, Center for the Governance of AI

Oxford, UK

Future of Humanity InstituteJune 2019 - Aug. 2019• Governance implications of data efficiency: Investigated the impact of improved data efficiency on the AI
governance landscape. This focused in particular on analyzing the market power of large data-rich actors, and on
finding potentially consequential new application areas of ML in data-limited domains. Presented work in FHI
seminar, and AI Ethics and Society Conference 2020.

Visiting Scholar, UC Berkeley

Berkeley, CA

Center for Human-Compatible Artificial Intelligence
June 2018 - Sept. 2018
• Inverse reinforcement learning for video games: Extended algorithms for inverse reinforcement learning toscore in simple Atari games, which have a much higher dimensionality (order 10k rather than order 100) and more
non-linear dynamics than the traditional settings in which they succeed. Presented work at NeurIPS 2018 DeepReinforcement Learning Workshop and joint DeepMind/FHI Safety Seminar in July 2019.

Professional Experience
Head Antifraud Engineer, Sendwave

RemoteNew York, Boston

June 2016 – May 2019

Head Antifraud Engineer at Sendwave, a Y Combinator backed money transfer start-up sending fromthe US, UK, and Canada to Kenya, Tanzania, Uganda, Ghana, and Nigeria, and the largest remitter toKenya. Helped bring fraud from the company’s biggest problem to under control, with a roughly 90%
drop in loss rate, while maintaining a low review rate. Helped build Risk Team from its start (oneteammate + me) to roughly 30 people over roughly 3 years.
• Interim Risk Lead: Set engineering roadmap, established priorities, and realized need for professionalizing
antifraud support team for 5 months until longer-term hire with over 10 years experience was found. Company
launched Nigeria corridor during this time.
• Nigeria Antifraud: No resulting major fraud loss incidents for the > 1 year remainder of my time at Wave.
• Antifraud ML: Built and deployed ML system for fraud classification from ground up.
• Featurization Pipeline: Built featurization pipeline to run at training and runtime from scratch.
• Account Security and User Authentication: Investigated hackings and implemented fixes bringing hacking
incidents from a company-threatening level to rare and relatively minor.
• Anomaly Detection: Built production anomaly detection system from ground up. System automaticallyretunes expected rate parameters.
• Onboarding Analyst: Improved onboarding flow for UK users by improving data service integrations andfinding weaknesses. Refactored onboarding logic for all users to be more streamlined and explicable.
• Data Integrations: Integrated with data providers to get risk information for users during the signup flow.
• Customer Support Tooling: Built MVP for antifraud representatives to look at user information, makedecisions, and take necessary actions in a web app integrated into a broader customer service tool.

Leadership Experience
Co-President, Harvard College Effective Altruism

Cambridge, MA

previously Organizer, Philanthropy Fellow

2012 – 2016• Organized semester-long fellowships and speaker series• Helped grow organization from 5 members to over 30 active, with over 100 alumni as of Dec. 2015
• Group still active 6+ years later

President, FIRST Team 449

Silver Spring, MDpreviously Vice President, Team Lead, Team Member

2007 – 2011

• Organized a team that builds a 120 pound robot in 6 weeks• Helped grow team from 70 members to 100 members• Redesigned pre-season training program to improve recruitment, retention, and team skills

Teaching Experience
Graduate Teaching Assistant, CS4700 Foundations of AI

Ithaca, NYCornell University

Fall 2019, Fall 2020

Graduate Teaching Assistant, CS1110 Introduction to Programming using PythonIthaca, NYCornell University

Summer 2020

Teaching Fellow, CS181 Machine Learning

Cambridge, MAHarvard CollegeSpring 2015

ServiceSecretary, Cornell Computer Science Graduate Organization

Fall 2021, Spring 2022, Fall 2022

Reader, Cornell CS Pre-application Support Program for underrepresented minority students
PhD Admissions Reader, Cornell Computer Science Department
Reviewer, NeurIPS 2022, ICML 2022, SIGKDD 2021, SIGKDD 2022
Reviewer, NeurIPS Workshop on Human and Machine Decisions

Fall 2021

2020 Admissions Cycle2021, 2022
WHMD 2021