Research

My work focuses on multi-agent reasoning systems, interpretable machine learning, and large-scale experimental evaluation across real-world datasets.

ML Research Extern (Team Lead) — DIMACS

Interpretable ML & Model Multiplicity

  • Spearheading empirical research on whether real-world tabular datasets admit small, optimally accurate, and fully interpretable decision tree models.
  • Implementing and benchmarking SPLIT, GOSDT (with ThresholdGuessBinarizer), RESPLIT, TREEFARMS, and LicketyRESPLIT against XGBoost using scikit-learn's classification report, confusion matrices, and runtime metrics.
  • Preprocessing continuous features via gradient-boosted threshold guessing (30 to 13 binary predictors) to enable efficient optimal tree search.
  • Quantifying Rashomon set size (33 near-optimal trees) and analyzing structural vs. predictive diversity under varying regularization strengths (λ).
  • Designing reproducible experimental pipelines for scalable execution on Rutgers’ Amarel HPC cluster.

AI Researcher — Algoverse

Project 1: DynaStride: Dynamic Stride Windowing with MMCoT

  • Contributed to a hierarchical scene-captioning pipeline integrating dynamic stride window selection and multimodal chain-of-thought reasoning (MMCoT).
  • Worked with Qwen2.5, Qwen3, MiniLM, and subcaption aggregation for temporally coherent caption generation.
  • Designed evaluation frameworks for BLEU-4, METEOR, CIDEr, BERTScore, SBERT similarity, and temporal metrics.

Project 2: Multi-Agent LLM Deliberation & Consensus Dynamics

  • Designing a multi-agent LLM system to analyze how consensus emerges on subjective global opinion questions.
  • Leveraging GlobalOpinionsQA and OpinionsQA datasets for structured multi-round deliberation.
  • Building reproducible experimental pipelines using GPT-4.1, DeepSeek R1, Grok 3, and Llama-3.3-70B-Instruct.
  • Creating visualizations of results with pie charts, bar charts, and line plots via Matplotlib.

Algorithms Researcher — The College of New Jersey

  • Investigated classical and modern RNA secondary structure prediction algorithms including Nussinov, Zuker, and LinearFold.
  • Implemented sparse dynamic programming strategies to reduce computational complexity in large-sequence folding tasks.
  • Deployed large-scale experiments on a SLURM-managed HPC cluster using the ViennaRNA package.
  • Automated batch processing pipelines to benchmark folding accuracy, energy scores, and runtime across thousands of RNA sequences.