I'm a third-year grad student at UMass Amherst working in the Resource-Bounded Reasoning Lab under Dr. Shlomo Zilberstein. My research focuses mainly on problems in automated reasoning. I currently work on metareasoning techniques that enable autonomous systems to optimize their own process of deliberation. I also work on computational approaches to safety that allow autonomous systems to reason about and learn from exceptional circumstances that may occur during normal operation. Before coming to grad school, I worked as a software developer on Wall Street. Outside of all of that, I'm interested in many problems in philosophy, especially free will, mind, metaphysics, and epistemology.

Belief Space Metareasoning for Exception Recovery

Autonomous systems use decision-making models that rely on simplifying assumptions to reduce the complexity of the real world. However, as a result of their assumptions, these systems may encounter a wide range of exceptions. Our paper offers a new type of autonomous system, called an introspective autonomous system, that resolves exceptions by interleaving regular decision making with exception handling based on a belief over whether or not any exceptions have been encountered during operation.

Meta-Level Control of Anytime Algorithms with Online Performance Prediction

We develop a meta-level control technique to determine the point at which a robot should stop thinking and start doing. In dynamic environments, a robot rarely has enough time to determine the optimal solution to a decision-making problem. To handle this limitation, a robot can use an anytime algorithm, which is a type of algorithm that slowly improves a solution over time. This means that as the robot thinks more and more, the solution slowly gets better and better. Now here's the question: how long should the robot think for? That's what we answer in this paper.

Distributed Graph Snapshot Placement and Query Performance in a Data Center Environment

We investigate how to best place the vertices of graph snapshots across workers in a distributed dynamic graph database to speed up queries like PageRank and average degree. By extending G*, our own distributed dynamic graph database, we examine how each query performs when the vertices of a set of graph snapshots are placed on a single worker or spread across all of the workers. It turns out that vertex placement has a big impact on query performance.

OpenSR: An Open-Source Stimulus-Response Testing Framework

We present OpenSR, an open source stimulus-response testing framework. In our framework, you can create, manage, and track an implicit association test (IAT). Basically, an IAT can help researchers determine whether or not someone has an implicit bias for or against something. Since most researchers currently have to buy complicated, expensive software to do this, we built a free, easy-to-use, customizable alternative that's better than what's already on the market.

Deep Jammer

We built Deep Jammer, a music generator that learns how to compose classical music by listening to 320 classical piano pieces. By using deep learning, specifically two LSTMs, it can learn the spatial and temporal patterns of classical music. In a survey of over 50 participants, the classical piano pieces composed by Deep Jammer scored a 7.5 rating compared to a piece by Bach that scored an 8.1 rating. Finally, to experiment with transfer learning, we trained Deep Jammer on just twenty jazz piano pieces. While it wasn't perfect, it quicky picked up on the rhythm and sound of jazz.

Logos

To learn more about automated decision-making, I built Logos, a library that can solve MDPs. Logos offers dynamic programming methods like value iteration and policy iteration and reinforcement learning algorithms like Monte Carlo learning and TD learning. I currently use Logos in semi-autonomous system simulations to control a self-driving car and a reinforcement learning agent that learns how to optimally play tic-tac-toe by playing games against itself.

Deep Rainbow

We built an optimal Rubik’s Cube solver, Deep Rainbow, that uses IDA*. We used an admissible heuristic based on three disjoint pattern databases where each database is associated with the set of edge cubees and two different sets of corner cubees. We based our solver on Richard Korf’s approach. In just a few minutes, Deep Rainbow can find optimal solutions to problems that are twelve steps or less away from the goal.

Shallow Blue

We built a chess agent named Shallow Blue that uses minimax with alpha-beta pruning. The evaluation function incorporates many important dimensions of chess, such as piece development, material balance, mobility, and attack range. Luckily, Shallow Blue won a chess tournament that had over fifteen teams that built their own chess agents. To add to the fun, I also built a Connect Four agent and a Tic-Tac-Toe agent using a similar method.

SvegOS

In an awesome class (CMPT 424) taught by Dr. Alan Labouseur, I built SvegOS, a browser-based operating system from scratch. Just like any Unix operating system, you can manage files (create, read, update, delete, and ls), load a program (load), manage processes (run, ps, and kill), and a lot more. To top it all off, the interface shows the CPU registers, the memory, the hard drive, and the state of every process, so you can follow my operating system as it runs.

Svegliator

In another great class (CMPT 432) taught by Dr. Alan Labouseur, I built Svegliator, a compiler that compiles a C-like language into 6502alan assembly language. The compiler shows the resulting machine code, the concrete syntax tree, the abstract syntax tree, and the symbol table. Just so you can trace exactly what the compiler is doing, it records each step of the scanner, the parser, the semantic analyzer, and the code generator during compilation.

Awarded to the top graduating student

Awarded to high-potential grad students early in their career

Included a full scholarship for every year of college

Awarded to the top graduating student in computer science

Awarded to the top graduating student in philosophy

Awarded to a graduating student for excellence in industry

Awarded to students with at least a 3.85 GPA

Awarded to a top incoming grad student in artificial intelligence

Included a grant for the 50 Years of the ACM Turing Award Celebration

Admitted to an honors organization of only the top 3% of students

Awarded to students with at least a 3.6 GPA

Awarded for excellence in cross country and track and field

Selected to give a talk to the top accepted prospective students