| Upcoming |
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| 2024 Jul 27 |
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Oral @ ICML FoRLaC Workshop |
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Better Memory Learning by Reducing Value Discrepancies |
| 2024 |
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| 2024 Jul 10 |
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Lab Talk @ CHAI All-Hands |
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Better Memory Learning by Reducing Value Discrepancies |
| 2024 May 01 |
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Lab Talk @ CHAI All-Hands |
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Breaking Down the Alignment Problem |
| 2023 |
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| 2023 Jun 30 |
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Thesis Defense @ Brown |
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Structured Abstractions for General-Purpose Decision Making |
| 2023 Apr 18 |
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Invited Talk @ Berkeley |
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Structured Abstractions for General-Purpose Decision Making |
| 2023 Apr 14 |
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Invited Talk @ Stanford |
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Structured Abstractions for General-Purpose Decision Making |
| 2023 Mar 09 |
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Invited Talk @ Harvard |
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Structured State and Action Abstractions for Learning and Planning |
| 2023 Feb 01 |
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Invited Talk @ Northeastern |
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Structured State and Action Abstractions for Learning and Planning |
| 2023 Jan 27 |
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Invited Talk @ IBM Neuro-Symbolic AI Workshop |
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Efficient Black-Box Planning Using Macro-Actions with Focused Effects |
| 2022 |
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| 2022 Nov 16 |
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Invited Talk @ UMass Amherst |
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Structured State and Action Abstractions for Learning and Planning |
| 2022 Nov 10 |
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Invited Talk @ Duke |
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Structured State and Action Abstractions for Learning and Planning |
| 2022 Oct 07 |
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Lab Talk @ Brown Robotics Lab |
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Learning and Memory in General Decision Processes |
| 2021 |
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| 2021 Dec 21 |
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Thesis Proposal @ Brown |
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Discovering and Exploiting Structure for Abstract Decision Making |
| 2021 Dec 09 |
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Paper Presentation @ NeurIPS 2021 |
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Learning Markov State Abstractions for Deep Reinforcement Learning |
| 2021 Aug 23 |
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Paper Presentation @ IJCAI 2021 |
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Efficient Black-Box Planning Using Macro-Actions with Focused Effects |
| 2021 Aug 04 |
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Paper Presentation @ ICAPS 2021 HSDIP Workshop |
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Efficient Black-Box Planning Using Macro-Actions with Focused Effects |
| 2021 Jul 09 |
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Lab Talk @ Brown Robotics Lab |
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Focused Macro-Actions and Markov State Representations |
| 2021 Mar 15 |
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Invited Talk @ Oxford |
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Learning Markov State Abstractions for Deep Reinforcement Learning |
| 2021 Feb 19 |
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Invited Talk @ ASU |
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Learning Markov State Abstractions for Deep Reinforcement Learning |
| 2020 |
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| 2020 Dec 11 |
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Paper Presentation @ NeurIPS 2020 Deep RL Workshop |
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Learning Markov State Abstractions for Deep Reinforcement Learning |
| 2020 Nov 13 |
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Poster @ Northeast RL and Decision Making Symposium (NERDS) |
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Learning Markov State Abstractions for Deep Reinforcement Learning |
| 2020 Nov 05 |
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Lab Talk @ Brown Robotics Lab |
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Navigating the Research Meta-Problem |
| 2020 Aug 14 |
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Lab Talk @ Brown Robotics Lab |
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The Geometry of Value Functions |
| 2020 Jun 12 |
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Lab Talk @ Brown Robotics Lab |
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Onager: A Lightweight Python Library for Launching Experiments and Tuning Hyperparameters |
| 2020 Feb 12 |
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Lab Talk @ Brown Intelligent Robot Lab |
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Markov State Abstractions |
| 2019 |
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| 2019 Dec 06 |
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Lab Talk @ Brown Robotics Lab |
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Learning Skills for Efficient Factored-State Planning |
| 2019 Oct 31 |
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Guest Lecture @ Brown CS1410 (Artificial Intelligence) |
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Supervised Learning, Part 2 |
| 2019 Oct 29 |
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Guest Lecture @ Brown CS1410 (Artificial Intelligence) |
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Supervised Learning, Part 1 |
| 2019 Oct 21 |
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Co-instructor @ Woods Hole Oceanographic Institution |
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Machine Learning Workshop |
| 2019 Oct 02 |
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Lab Talk @ Brown Intelligent Robot Lab |
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Learning Factored State and Action Abstractions for MDPs |
| 2019 May 10 |
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Lab Talk @ Brown Robotics Lab |
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Learning Factored State Representations for Reinforcement Learning |
| 2018 |
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| 2018 May 29 |
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Research Talk @ Cornell University |
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Deep Symbolic Representations for Planning |
| 2018 Mar 22 |
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Guest Lecture @ Brown CS2951X (Reintegrating AI) |
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Discussion: Building Machines That Learn and Think Like People |
| 2018 Feb 16 |
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Lab Talk @ Brown Robotics Lab |
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Mean Actor-Critic - Project Overview |
| 2018 Feb 01 |
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Guest Lecture @ Brown CS2951X (Reintegrating AI) |
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Classical AI - Symbols and Search |
| Earlier |
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| 2017 Sep 12 |
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Research Talk @ Cornell University |
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High-Level Symbolic Representations for Planning |
| 2016 Mar 30 |
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Lab Talk @ Duke DRIV Research Group |
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Viterbi Decoders: Introduction and Parallelization for High-Throughput Communications |
| 2016 Jan 25 |
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Guest Lecture @ Duke CS270 (Artificial Intelligence) |
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Introduction to Python |
| 2015 Nov 11 |
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Lab Talk @ Duke DRIV Research Group |
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What are Priors? From Coin Flips to Dirichlet Distributions |
| 2011 Sep 07 |
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Invited Talk @ American Science & Engineering |
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The Memristor Project: Simulation Software for a New Analog Circuit Device |
| 2011 Jun 28 |
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Invited Talk, Gordon Research Conference on Detecting Illicit Substances |
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Anomaly Detection in X-ray Backscatter Leg Images Using Machine Learning |