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Research Scientist at Oracle Labs | PhD at University of Oxford, MS and BS at Brown University, pre-doc at Microsoft Research
Currently, I am a Research Scientist at Oracle Labs working on coding agents, following a DPhil (PhD) at the University of Oxford. During my DPhil in the WhiRL lab, I was supervised by Shimon Whiteson, funded by the Oxford-Google DeepMind Doctoral Scholarship, and studying deep reinforcement learning (RL). My main research area was meta-RL. I've worked on hypernetworks, initialization methods, and sequence models for in-context learning – in addition to authoring a survey of meta-RL. Previously, I did my MS and BS at Brown University, completed a pre-doc at Microsoft Research on sequence models in RL, and researched autonomous vehicles in both academia and industry. At Brown University, I was advised by Michael Littman. My research focused on human feedback, imitation learning, and multi-agent game theory. Some of our work in the self-driving car lab gained publicity in New Scientist. Other projects included: an RL agent in Minecraft using emotion detection as feedback and a GAN to reconstruct corrupted images. As a TA for the first iteration of Brown's graduate-level deep learning course, I designed a lab and gave a guest lecture on sequence-to-sequence machine translation. In industry, I worked at Microsoft, Lyft, Adobe, and several smaller companies. I completed a pre-doc at Microsoft Research with Katja Hofmann on long-term memory in RL. At DeepScale, acquired by Tesla, I worked on perception for autonomous vehicles, developing novel methods for instance segmentation. At Lyft I designed a framework for sequential decision-making problems, including a special-case solver specific to autonomous vehicles at stop intersections. At Adobe I built neural networks to forecast marketing data. I also worked at a robotics startup on software and hardware, co-created Food with Friends (an iOS app), and researched in-context learning with large language models (LLMs) f
University of Oxford
Doctor of Philosophy - PhD, Computer Science
January 1, 2020 – January 1, 2025
Brown University
Master of Science, Computer Science
January 1, 2019 – January 1, 2020
Brown University
Bachelor of Science, Computer Science
January 1, 2014 – January 1, 2018
Oracle
Research Scientist
January 1, 2025 – Present
InstaDeep
PhD Research Intern
April 1, 2024 – September 1, 2024
Microsoft
Research Predoc
February 1, 2019 – August 1, 2019
Cambridge, United Kingdom
DeepScale
R&D Intern
September 1, 2018 – December 1, 2018
Lyft
Intern
January 1, 2018 – January 1, 2018
Palo Alto, California
Michael Littman’s self-driving car lab at Brown
Student Researcher
September 1, 2017 – May 1, 2020
Brown University
Deep Learning Teaching Assistant
September 1, 2017 – December 1, 2017
Adobe
Data Science Intern at Adobe
June 1, 2017 – August 1, 2017
San Jose
Food With Friends
Co-founder and full stack developer
July 1, 2016 – July 1, 2017
Pied Piper Robotics, LLC
Engineering Intern
July 1, 2015 – August 1, 2015
BBK Worldwide
Intern
June 1, 2015 – July 1, 2015
Museum of Science, Boston
Volunteer
January 1, 2011 – January 1, 2013
Robert L. Ford School
Tutor
January 1, 2011 – January 1, 2014
Food with Friends
June 1, 2016 – Present
Co-founded, designed, and developed Food with Friends, currently on the app store, written in Swift. Responsible for: multi-threading, algorithms, maps and texting api, user data storage, user management, product ideas, Xcode certificates, UI, logo, design, dividing work Link: http://itunes.apple.com/us/app/FoodwithFriends/id1135640855 (screenshots available on App Store depending on device)
Cultural Fit Analysis
The candidate's background in both academic research and industry roles, including startups and large tech companies, suggests adaptability to various work environments. Their involvement in a personal project (Food with Friends) and teaching roles indicates a collaborative and proactive mindset. The breadth of experience in ML/AI sub-fields aligns well with a dynamic research and development culture. However, the primary focus on research and academic publications might require assessment of their ability to transition to product-focused engineering roles if the ML Engineer role is not purely research-oriented.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and leadership through co-founding a startup and leading research projects. Their teaching assistant role suggests good communication and mentorship potential. The diverse project portfolio indicates adaptability and a proactive approach to learning and problem-solving. Experience in both academic research and industry internships suggests a balanced perspective on theoretical and practical applications.