PhD Candidate · Computer Science · NMSU

Tathagata
(Dave)
Debnath.

Building agentic AI systems and fine-tuning foundation models on multi-GPU infrastructure.

Also available: Bioinformatics CV · Resume

About

PhD candidate building AI systems that fix bugs.

I'm a PhD candidate at New Mexico State University, advised by Dr. Mingzhou (Joe) Song, building AI systems that autonomously find and fix software bugs. My current work combines Monte Carlo Tree Search, Direct Preference Optimization, and vision-language model fine-tuning — all running on distributed NVIDIA H100 GPU infrastructure.

My research background is in bioinformatics and algorithm design, with 10 peer-reviewed publications (80+ citations) in venues including IEEE TPAMI and IEEE/ACM TCBB, and two published CRAN R packages for clustering algorithms.

I'm targeting Summer 2026 AI/ML internships and full-time roles in agentic AI, LLM fine-tuning, and ML systems engineering.

Current Work

· CodeQ — MCTS + DPO debugging agent
· VisionTriage — multimodal bug triage
· Multi-agent code generation pipelines

Seeking

Summer 2026 AI/ML internships and full-time roles.

Get in touch →

Education

PhD in Computer Science

New Mexico State University · Expected 2026

Focus: AI/ML, Agentic Systems · Advised by Dr. Mingzhou (Joe) Song

M.Tech in Computer Science

Tripura University · 2017

B.Tech in Computer Science

NIT Agartala · 2015

Featured Projects

AI systems and research software.

View All Projects

CodeQ

Autonomous Code Debugging Agent

Flagship

Self-improving code debugging agent using Monte Carlo Tree Search to explore fix strategies, dual-temperature self-critique to rank them, and Direct Preference Optimization to learn from its own exploration data. Built on Qwen2.5-Coder-7B across two H100 nodes.

81.3%

fix rate on DebugBench

84%

MCTS mode after DPO Round 2

MCTS DPO Qwen2.5-Coder-7B LoRA 4-bit Quantization HuggingFace TRL Docker Sandbox 2× H100

GitHub → Blog Post → Details →

Parallel Multi-Agent Codegen

DAG-Based Agent Orchestration for Code Synthesis

AI / LLM

Multi-agent code generation system using LangGraph with a DAG-based orchestrator that dispatches concurrent coder workers via asyncio. Extended version adds an LLM-as-Judge evaluator and autonomous prompt evolution loop where the system iteratively rewrites its own generation prompts based on evaluation feedback.

LangGraph Anthropic SDK asyncio LLM-as-Judge Prompt Evolution Docker LangSmith

GitHub (parallel) → GitHub (self-evolving) → Details →

VisionTriage

Multimodal Bug Report Triage

In Progress

Fine-tunes Qwen2.5-VL-7B-Instruct with QLoRA to triage software bug reports from screenshots + text descriptions. Text-only baseline benchmarked against SevPredict on the standard Eclipse/Mozilla dataset. Multimodal extension shows that adding screenshot context improves severity prediction accuracy.

Qwen2.5-VL-7B QLoRA Eclipse/Mozilla Dataset Rico Synthetic Data Gradio HuggingFace

Details →

Technical Skills

Tools and systems I work with.

AI/ML & Foundation Models

PyTorch, HuggingFace (Transformers, TRL, PEFT), LoRA/QLoRA, DPO, MCTS, Flash Attention 2, bitsandbytes (4-bit/8-bit), vLLM, Qwen2.5, DeepSeek, LangGraph, LangChain, RAG

Infrastructure & Systems

NVIDIA H100 (multi-GPU), CUDA, NCCL, FSDP, DeepSpeed ZeRO, Docker, Git, Linux, screen/tmux, W&B, SSH

Languages

Python, R, C/C++, SQL, Bash, JavaScript, LaTeX

Bioinformatics (Research)

Samtools, STAR, DESeq2, DEXSeq, GATK, rMATS, RSEM, Gviz, gprofiler2, MUMmer4, BUSCO, Singularity

Tathagata(Dave)Debnath.