Research

My research focuses on multi-agent reasoning systems, multimodal ML, interpretable ML, and large-scale experimental evaluation across real-world datasets. I have not only contributed to research projects in these areas, but also designed and executed large-scale experiments, built reproducible pipelines, and led research teams.

Lead ML Research Extern — DIMACS / Rutgers MBS Exchange

Interpretable ML & Model Multiplicity

  • Leading an empirical study under Dr. Linda Ness on interpretable decision trees (SPLIT, GOSDT, LicketySPLIT, LicketyRESPLIT) against 3 boosting models (XGBoost, LightGBM, CatBoost) across 6 real-world datasets examining conditions where simple models can achieve near-identical accuracy to complex models, and how dataset properties influence model structure and multiplicity.
  • This work explores the Rashomon effect in decision trees, demonstrating that many structurally distinct models can achieve near-identical accuracy while differing significantly in interpretability.
  • Quantifying performance–interpretability tradeoffs using accuracy, class-specific recall, macro F1, tree depth, leaf count (log-scale), and Rashomon set size.
  • Analyzing how preprocessing (SMOTE, TGB) reshapes Rashomon set size and model interpretability.
  • Empirically demonstrating that large Rashomon sets correspond to multiple equally performant but structurally diverse decision trees, highlighting the non-uniqueness of interpretable models.
  • Designing reproducible experimental pipelines and executing large-scale experiments on Rutgers’ Amarel HPC cluster.

AI Researcher — Algoverse

Multi-Agent Reasoning & Multimodal Machine Learning

Multi-Agent Deliberation & Consensus Dynamics (First Author, Solo)

  • Developed a multi-agent LLM framework to investigate how consensus emerges across subjective and objective tasks, identifying model-to-model deference as a primary driver of convergence rather than independent reasoning.
  • Designed and executed large-scale 20-round deliberation experiments across GlobalOpinionsQA, Anthropic Persona-Written Evals, and Humanity’s Last Exam using named and anonymized agents from the GPT-4.1 family (GPT-4.1, GPT-4.1-nano, GPT-4.1-mini).
  • Introduced a rotation-based experimental paradigm to disentangle the effects of model identity vs. answer quality, demonstrating that response quality plays a stronger role in deference dynamics.
  • Formalized quantitative metrics for multi-agent interaction, including inter-round disagreement, pairwise disagreement, and directional model deference.
  • Showed that system-level interventions (e.g., prompting strategies) can significantly alter or destabilize consensus formation.
  • Paper in preparation for submission to ICML workshops (Pluralistic Alignment, AI4Good, Epistemic Intelligence in ML).

DynaStride: Dynamic Stride Windowing with MMCoT (Second Author)

  • Contributed to a hierarchical video captioning pipeline combining dynamic stride window selection with multimodal chain-of-thought reasoning (MMCoT) for temporally coherent scene understanding.
  • Implemented and integrated Qwen2.5, Qwen3, and MiniLM models with subcaption aggregation to improve long-range temporal consistency.
  • Designed a comprehensive evaluation framework spanning BLEU-4, METEOR, CIDEr, BERTScore, SBERT similarity, and temporal alignment metrics.
  • Achieved +17% CIDEr over GPT-4o and +14% over VideoLLaMA-3 on the YouCook2 dataset.
  • Accepted to NeurIPS 2025 (7HVU Workshop, Oral) and AAAI 2026 (AI4EDU Workshop) .

Algorithms Research Shadow — The College of New Jersey

Sparse Dynamic Programming for RNA Folding

  • 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.
  • RNA Folding Research Summary