Research
Great are the works of the Lord;
they are pondered by all who delight in them...
He has caused his wonders to be remembered;
the Lord is gracious and compassionate.
(Psalm 111:2,4, NIV)
Research Interests
I’m broadly interested in machine learning interpretability, computational linguistics, and the theoretical foundations of artificial intelligence. I’m especially drawn to questions like: How do we make neural networks more transparent? and How do linguistic structure and semantics interact in multilingual NLP models?
You can view a summary of my research projects and scholarly output on my
ORCID profile.
Current Projects
Interpreting Regression Neural Networks with Linear Surrogates
April 2025–Present
In this project, I evaluate the reliability of linear surrogates for interpreting neural networks. Using a metric I call the lambda score, I measure how well linear models can approximate the predictions and representations of trained networks. While surrogates often achieve high correlation, I show that this does not imply faithful approximation — and in fact, the remaining unexplained variance may correspond to the network’s actual decision logic. This suggests that simple linear proxies can be misleading, especially when key non-linear structure is concentrated in low-volume regions of the input space.
Detecting French Idioms Using Neural Machine Translation Techniques
February 2025–Present
Idiomatic expressions remain a major challenge in neural machine translation (NMT), often leading to errors in both statistical and modern NMT systems. In this project, I’m adapting techniques that have been successful in identifying idioms in English corpora and applying them to French data. This work, currently in its early stages, will become my combined honors thesis in French and Computer Science.
Improving Energy Security with Neural Networks and Decision Trees
January 2025—Present
Energy poverty is a real concern in the United States. In fact, the federal government disburses money to states to help combat this issue. However, there are multiple ways this money can be mobilized, and as a result money is allocated inefficiently. Our work aims to make this allocation simple, integrating NNs and DTs with urban building energy models ( UBEMs). The results from this combination of models will allow policymakers to visualize energy consumption patterns, compare resource allocation strategies, and simulate the impact of various weathering approaches. This project is in a very early stage.
In collaboration with Professor Jorge Silveyra (Lafayette College)
Upcoming Projects
FuncLearn: A Functional Programming Language for Machine Learning
Status: Early Design
Machine learning is used across many disciplines — but for those outside computer science, working with Python and TensorFlow can feel unnecessarily complex. FuncLearn aims to provide a simple, intuitive, English-like functional language that lowers the barrier to entry. The language will compile to TensorFlow-based Python code, allowing users to import datasets, chain models, and train networks using expressive, composable syntax — no ML expertise required.
Long-Term Interests / Ideas
I’m also interested in accessibility within programming languages — particularly the potential for localized keywords (
e.g., using native-language syntax like si instead of if) and rethinking syntax structures for right-to-left
languages. While this work is still conceptual, it reflects a broader interest in language-inclusive design.