I am a Research Scientist, Bioinformatician, and Data Scientist working at the intersection of computational biology, cancer genomics, artificial intelligence, biostatistics, and precision medicine.

My research combines genomics, transcriptomics, multi-omics integration, machine learning, and large language models to transform complex biological and health data into evidence for:

• Biomarker discovery
• Therapeutic-target prioritization
• Precision oncology
• AI-assisted drug discovery
• Reproducible biomedical research

I currently contribute to cancer-genomics research as a Bioinformatician at the University of Arkansas for Medical Sciences (UAMS), developing scalable NGS workflows and analyzing TCGA and CPTAC datasets. I am also pursuing an MSc in Bioinformatics at Northeastern University, strengthening my expertise in translational bioinformatics, machine learning, and trustworthy biomedical AI.

Beyond biomedical research, I independently reproduced and extended machine-learning workflows for streamflow prediction and National Water Model bias correction. My HYDRO-FLOW-AI project builds on open workflows from the Alabama Water Institute’s NWM-ML project and research involving a University of West Florida researcher, while remaining an independent project with no claim of institutional affiliation.

My long-term goal is to build trustworthy computational systems that connect biological evidence, clinical data, and artificial intelligence to accelerate scientific discovery and improve patient outcomes.

• 🧬 Computational biology and bioinformatics
• 🎗️ Cancer genomics and precision oncology
• 🧪 NGS analysis and variant interpretation
• 🔗 Multi-omics integration
• 💊 Computational drug discovery
• 🤖 Artificial intelligence and deep learning
• 📚 Biomedical large language models
• ⚡ Agentic AI and retrieval-augmented generation
• ☁️ Cloud-based scientific computing
• 🏥 Healthcare data engineering
• 🌊 Scientific machine learning for hydrology

HYDRO-FLOW-AI is an independent, extreme-aware machine-learning framework for streamflow prediction and site-specific National Water Model bias correction at USGS gauges.

• Leakage-safe temporal training, validation, and testing
• Historical USGS streamflow and climate-data integration
• Site-specific model-performance diagnostics
• Evaluation using RMSE, MAE, bias, and NSE
• Q95 and Q99 high-flow evaluation
• Peak-magnitude error analysis
• Extreme-event detection and threshold-based assessment

• XGBoost residual bias correction
• Quantile-regression uncertainty intervals
• LSTM and Transformer-based forecasting
• River-network graph neural networks
• Explainability and model-drift monitoring

Computational framework for rational drug-combination prioritization using complementary biological and pharmacological evidence.

Identification of compensatory kinase alteration patterns and potential drug-target combinations using TCGA cancer-genomics data.

LLM-supported retrieval, evaluation, and synthesis of biomedical evidence for research decision support.

Transformer-based representation-learning approaches for genomic sequences and downstream biological prediction.

Reproducible lakehouse and streaming architectures for clinical-trial, public-health, and biomedical data.

• AI for precision oncology
• Computational drug discovery
• Cancer multi-omics
• Biomedical large language models
• Genomic foundation models
• Agentic AI for scientific discovery
• Trustworthy and explainable AI
• Scalable scientific computing
• Extreme-aware streamflow forecasting

• MSc Bioinformatics — Northeastern University (in progress)
• MSc Molecular Biology (Bioinformatics) — Umeå University
• BSc Biotechnology and Genetic Engineering — Khulna University
• Advanced Diploma in Data Science and Data Engineering
• Graduate Certificate in Project Management

• Health Informatics — Johns Hopkins University
• Business Analytics and Data-Driven Decision-Making — University of Toronto
• Project Management
• Cloud Computing
• Machine Learning and Data Science

• Trustworthy and causal AI
• Agentic and multi-agent systems
• Biomedical large language models
• Genomic foundation models
• Scalable multi-omics analytics

I welcome research and open-source collaboration in:

• Computational biology and bioinformatics
• Cancer genomics and precision medicine
• Biomedical artificial intelligence
• Computational drug discovery
• Healthcare data engineering
• Scientific machine learning
• Reproducible research software