Background

Incoming software engineering role in IBM's AI organization. I am keeping the public description concise until the internship begins.

Research role focused on deep learning fundamentals, bilevel optimization, and reproducible large scale experimentation. The work connects theory, implementation, and empirical evaluation.

• Bilevel optimization: Implemented gradient based principal agent contract design with implicit differentiation, Hessian vector products, conjugate gradient solves, and stabilization.
• Experiment systems: Built reproducible training and evaluation pipelines with standardized configs, seed control, sweep automation, logging, aggregation, and figure generation.
• Deep learning: Worked on self supervised learning pipelines, including SimCLR and MoCo style experiments in PyTorch.
• Publication work: Contributed to experiment design, result analysis, manuscript writing, and code/figure preparation for academic submission.
• Result: Coauthored paper accepted at a NeurIPS 2025 workshop; extended version submitted to ICLR 2026.

Independent ML product work around maritime routing, operational risk, and explainable decision support.

• Routing engine: Designed a NetworkX routing system with dynamic weights for time, fuel, weather, cost, and risk.
• NLP risk signals: Integrated Transformer maritime news analysis to create route level risk signals.
• Decision support: Built route comparisons with visible tradeoffs and clear explanations.
• Product interface: Developed a Streamlit app for interactive route exploration, constraint changes, and scenario comparison.
• Operations: Added Prometheus and Grafana monitoring for runtime behavior.

Software development role supporting civil and environmental engineering research through simulation tooling and reusable model components.

• Simulation systems: Built Java and AnyLogic models for infrastructure and environmental process analysis.
• Modeling approach: Implemented agent based and discrete event simulations to test performance under realistic constraints.
• Research support: Translated researcher requirements into modular simulation logic that could be reused and adjusted across experiments.
• Collaboration: Worked with graduate researchers to debug model assumptions, validate outputs, and refine scenario parameters.

Data and probabilistic modeling role focused on uncertainty, resilience, and supply chain risk.

• Probabilistic modeling: Applied Bayesian Networks to represent supply chain dependencies and reason about resilience.
• Inference: Implemented MCMC and inverse modeling workflows to calibrate probabilistic models from data.
• Analysis pipeline: Built Python and R workflows for cleaning, exploratory analysis, visualization, and reporting.
• Communication: Converted model outputs into interpretable research deliverables for technical audiences.

Early research experience in quantum communication and secure protocol modeling, which shaped my interest in the intersection of physics, computation, and uncertainty.

• Security modeling: Studied parity qubit communication and analyzed leakage, errors, and eavesdropping resistance.
• Protocol design: Modeled secure transmission protocols and explored quantum coin flip problems.
• Tooling: Used Qiskit and computational experiments to reason about secure communication behavior.