About Me
AI Researcher with 6+ years of experience developing scalable machine learning frameworks for modeling complex scientific data in biomedical and clinical domains. I am a Postdoctoral Fellow at Indiana University and the School of Medicine (IUSM), Department of Biostatistics & Health Data Science, and Co-Founder of H2Alpha Inc. I received my Ph.D. in Computer Science from the University of Kansas.
My research focuses on representation learning from high-dimensional biological sequences and biomedical literature through algorithmic approaches including large language models, transformer-based architectures, KV-cache-optimized inference systems, and multi-agent reasoning frameworks. I have led the design of extensible alignment frameworks and domain-specific information extraction models for large-scale scientific discovery tasks. I am experienced in building generalizable AI systems that bridge machine learning, natural language processing, and computational biology to enable data-driven hypothesis generation and predictive modeling. Proficient in Python, PyTorch, CUDA, and C/C++ for developing research-grade AI infrastructure.
Detailed Research Interests
I am dedicated to exploring how to (1) enable AI systems with knowledge and reasoning skills to solve biomedical tasks; and (2) develop scalable machine learning frameworks for biological sequence analysis.
1) AI for Biomedical Discovery I explore how AI can assist and automate the scientific workflow in biomedical domains.
- Fine-tuned LLMs: Adapt large language models for biomedical applications using parameter-efficient fine-tuning methods such as LoRA.
- Biomedical NLP: Build systems for named entity recognition and information extraction from scientific literature.
- Multi-agent reasoning: Develop multi-agent systems for evidence-based drug discovery and scientific inquiry.
- KV-cache optimization: Accelerate clinical LLM inference for long-context EHR processing and real-time clinical decision support.
- Knowledge integration: Combine parametric and retrieved knowledge for biomedical question answering and hypothesis generation.
2) Biological Sequence Analysis I aim to build extensible frameworks for biological sequence alignment and analysis across scales.
- Multi-scale alignment: Develop general algorithmic frameworks for sequence alignment.
- K-mer methods: Explore k-mer signature-based approaches for early cancer screening, microbiome analysis and biomarker discovery.
- Omics pipelines: Create industry-level, scalable pipelines for high-throughput omics data analysis and integrative modeling.
Research Trajectory
My research develops AI-driven computational systems that translate biological sequencing data into clinically actionable therapeutic insights through an end-to-end precision medicine pipeline: Sequencing → Representation Learning → Clinical Inference → Therapeutic Reasoning → In-Silico Screening. I build scalable frameworks to extract disease-associated molecular patterns from large-scale microbiome and genomic sequencing data, learn patient-specific biological representations, integrate molecular evidence with biomedical knowledge for clinical outcome prediction, and support treatment hypothesis generation through AI-driven reasoning. My long-term goal is to enable integrative AI systems for early cancer detection and personalized treatment in precision oncology.