About
With computer science degrees from Cornell and Princeton, I am an AI engineer focused on turning complex models into practical, real-world solutions. My work centers on designing scalable Generative AI applications and low-latency Explainable AI (xAI) systems that build user trust and drive business impact. I genuinely enjoy solving tough technical puzzles to make advanced AI both highly performant and completely transparent.
Experience
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July 2025 - Current
AI Scientist
Verizon- Generative AI Commerce Engine: Supported the development of an LLM-powered search interface that translates natural language customer prompts into automated cart builds; successfully scales to support 1M+ transactions annually.
- Explainable AI (xAI) Framework: Engineered a low-latency implementation of LLM-generated explanations for Verizon Business customers, providing real-time, profile-based justifications for product recommendations to increase buyer confidence.
- Publications: APLE: A Framework for Adaptable and Personalized Live Explainability for Recommender Systems. To appear in the Proceedings of the 18th ACM Conference on Web Science (WebSci’26).
- Generative AI
- LLMs
- xAI (Explainable AI)
- Recommender Systems
- Python
- Google Cloud Platform
- Gemini
- Natural Language Processing
- API Development
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Jun - Aug 2023
Software Engineering Intern
VerizonGenerative AI Commerce Engine: Spearheaded the development of an LLM-powered search interface that translates natural language customer prompts into automated cart builds; successfully scales to support 1M+ transactions annually.rnExplainable AI (xAI) Framework: Engineered a low-latency implementation of LLM-generated explanations for Verizon Business customers, providing real-time, profile-based justifications for product recommendations to increase buyer confidence.rnPublications: Banerjee et al. (2026). APLE: A Framework for Adaptable and Personalized Live Explainability for Recommender Systems. To appear in the Proceedings of the 18th ACM Conference on Web Science (WebSci’26).rn- Generative AI
- LLMs
- xAI (Explainable AI)
- Recommender Systems
- Python
- Google Cloud Platform
- Gemini
- Natural Language Processing
- API Development
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Jun - Aug 2022
Software Engineering Intern
VerizonIn the 5G Reality Lab, I created a system for the coordinated driving of robots based on computer vision. I was responsible for designing and creating a computer vision pipeline to track multiple objects and their velocities to allow for seamless movement of robots, with computing done on an AWS instance.- Robot Operating System (ROS & ROS2)
- Machine Learning
- Computer Vision
- Python
- Multi-Agent Systems
- AWS
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Jul - Aug 2019
Data Analytics Intern
NJITNJIT Provost High School Summer Research Program, as a member of the Interactive Cross-Reality Lab (iXR) research team, I used machine learning and data analytics tools (Python & R) to analyze data from mixed reality (XR) applications to aid in the design of gaze-contingent displays for gaming and social interaction applications- Virtual Reality
- R - Statistical Computing Language
- 3D analytics
- Data Analytics
- Unity
Projects
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Detecting AI-Generated Images Created by Diffusion models
This project involves the development of a tool for detecting AI-generated images, specifically from diffusion models, to help counter misinformation and qualitatively understand identifying key image attributes for this class of classification task.
- Computer Vision
- Generative AI
- Tensorflow
- GANs
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Robotic Path Planning
A proof of concept project completed as part of my Verizon internship in the Summer of 2022. The system tracks multiple robots and changes their velocities autonomously. The project was used to demo the low-latency capabilities of 5G edge computing via AWS and Verizon's 5G network in the automotive space.
- Robotics
- ROS 1 & 2
- Python
- Computer Vision
- Spatial Mapping
- AWS
- Cloud Computing
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Unsupervised Discovery of Textual Implicit Gender Bias: A New Analysis of Reddit and Fitocracy
A group project completed alongside Pierce Maloney and Christian Ronda for Princeton's NLP Course. We implement a causal framework established by Field et al. to identify implicit gender bias at the comment level in two corpora: Reddit and Fitocracy. Our work offers insight into how implicit gender bias detection can differ across different social platforms.
- NLP
- Unsupervised Learning
- Sentiment Analysis
- GANs
- Adversarial Training
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Courses