Hi, I am an engineer at Two Sigma Investments, where I work on research and engineering problems related
to graphs and automated code generation. I obtained my BS in Computer Science in 2022 from
Georgia Tech.
I have been at:
Two Sigma - LLM Systems and Finetuning/Synthetic Graph Generation
Fundamental AI Research (FAIR) @ Meta - Open World Detection
I am interested in scientifically understanding Machine Learning and its applications to
reasoning tasks. I am also interested in efficient training of large models.
TLDR;
The proliferation of vast data providers and inherent dirtiness of data have increased the value proposition
of Semantic Types. We introduce the concept of Functional Semantic Types (FST)s which are Python classes
that encapsulate the informational and functional context of columnar data. FSTs will normalize and validate
data in a structured/readable manner, allowing automated cross-table joins or fast lookups. In order to
scale the generation of FSTs, we leverage Foundational Models to transform serialized data-tables to FSTs.
Across Kaggle, Harvard, and FactSet Data-Verses we show our method FSTO-Gen, can generate functionally and
informationally correct FSTs.
TLDR; Decision Transformer (DT) is a return-conditioned system
that generates the action that will achieve a desired return in a given state. Achieving high
return should theoretically require drastically different behavior than low return. In the same way that
search is optimized through indexing, ConDT organizes state-action embeddings by return, allowing the transformer
to more easily "recall" the necessary action for that return. ConDT improves performance in
Gym, Atari, and hand-grip tasks.
TLDR;
Collaborative teaming in multi-agent RL is challenging because agents need to consider the conditionality
of their actions, which exponentially grows in complexity with the size of the action space and the number
of agents. Humans excel at learning this conditionality by recursively reasoning
about the actions of others, like in chess where a player A recursively considers how their player B
considers how player A, and so on. We formulated a multi-agent policy gradient (InfoPG) that fosters this
type of reasoning by maximizing inter-agent mutual information. InfoPG improves team-performance in Gym
and Starcraft cooperative games and is robust to adversarial team-mates (Byzantine Generals Problem).
TLDR;
Directed Graphs contain important dependencies and whose structure may be sensitive. Previous work has shown
that node deanonymization isn't enough
so we protect against subgraph isomorphism and
Sybil attacks through the use of random perturbations (Merge-Split). For usability, the perturbed graph has to
maintain similarity to the original, which is achieved by minimizing the change in the graph's eigenspace. Our
experiments showed that Merge-Split locally disrupts random walks while maintaining overall structural
properties, like the graph's steady-state distribution.
TLDR;
Object Detection consists of the localization and classification of objects, and two-stage networks made
this process conditional. However, in practice, one might want to localize any object, regardless of
whether it can be classified (this is called
Open World Detection (OWD)).
Previously, two-stage networks have been studied, but we investigate a one-stage network called
FCOS for its simplicity and decoupling of classification
from localization. We investigate various architectural and sampling improvements that allow FCOS to
retain is classification ability, while improving localization recall.
Also, I recently wrote a blog post
about the benefits of migrating datatable schemas from primitive data-types to entity-driven data-types,
called Semantic Types. We are investigating the usage of LLMs to automatically generate Semantic Type definitions and
data processing code.