Machine Learning Researcher
(Graph Neural Nets, Biomedical AI, AI Agents, Human-centric AI & NLP)
Kaggle Grandmaster | Explorer | Looking for research opportunities
About :
- An aspiring AI researcher and engineering student, developing human-centric, safe, reliable, and robust machine learning systems. Over the past few years, my work has converged on a single goal: building AI systems that strengthen healthcare access (bio/medical, clinical, and mental) and overall quality of life. To achieve this, I am focusing on three interconnected directions:
- Generative AI, Agents, and Reasoning: Designing Agentic LLM and VLM systems capable of structured reasoning, adaptive planning, and context-aware decision support with measurable accuracy and clear human-facing communication.
- Computational Biology and Health Informatics: Developing data-driven methods that integrate biological, chemical, and clinical evidence. This includes using Graph Neural Networks (GNNs) and computational biology to detect mechanistic patterns, quantify uncertainty, and generate clinically actionable insights grounded in scientific principles.
- *Human-Computer/AI Interaction (HCI/HAI): Ensuring measurable safety, reliability, interpretability, and fairness in ML models. I focus on enabling effective human-AI interaction across multilingual, multicultural, and low-resource settings, with a specific interest in Computational Social Science to understand bias and fairness in high-stakes domains.
- I am looking forward to pursue a PhD in Spring/Fall 2026 to continue research and looking for potential options.
- I work/worked with Riashat Islam (Microsoft Research), Md Rizwan Parvez (QCRI), Prof. Min Xu (CMU), Prof. Chae (DILab, HYU), Prof. Alshehri (KSU), Prof. AMM Mukaddes (SUST) and Prof. Ahsan (OU) on these areas. I actively collaborate with researchers from Cohere Labs (formerly Cohere for AI), Harvard, MIT, and more.
- Co-founded CIOL. Here, I collaborate with on GNNs, agents, and digital twins for industrial and medical applications. Completed HTGAA 2025 (MIT), focusing on protein engineering and joined as Global TA.
- My works has been published in prestigious venues such as ICLR, WWW, COLING, DASFAA, ACCV, CSCW, Workshops of NeurIPS, AAAI, ICML, ACL and CHI, with ongoing reviews in some others.
- Outside research, I have work experience in AI-integrated IT Automation, Project - Product Management and Analytics roles. I'm also the 3rd Kaggle Grandmaster of BD.
- Passionate about learning new things, sharing my knowledge, improving myself regularly, experimenting with acquired skills and challenging my capabilities. Building all-in-one free AI/ML resources collection here.
- Languages: Python (Advanced), C, C++, MATLAB, R, SQL
- DS & ML Tools (Python): NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, PyTorch, LangChain, VLLM, Pydantic
- Data Science Techniques: EDA, Experiment Design, Hypothesis Testing, Sampling, and Data-Driven Decision Making
- Machine Learning Techniques: Statistical ML Methods, Deep Learning, NLP, Computer Vision, Graph Neural Networks (GNNs), GFlowNets, Flow Matching, Diffusion Models, RL and Reasoning in LLMs, Self-Verification, Uncertainty, Agentic Decision-Making, AI Reasoning, RAG, and Reward-Based RL Fine-Tuning
- Biomedical AI and Clinical Applications: Molecular Properties, Binder Design, Molecular Interaction, De Novo Protein Design, GNNs, RL/Energy-Guided Modeling, Generative Modeling with Flow Matching and Graph Diffusion, Reward-Based Generative AI, Agentic LLMs, Knowledge Graphs, AI-based Drug Discovery and Genomics
- Interdisciplinary AI Research: AI for Good, Multilinguality, Accessibility, Fairness, Human Factors, Local and Cultural Values
- Others: GitHub, Collaborative Tools (AMs, VS Code, Azure, AnyScale, Replit, Colab, Kaggle), Parallel & Distributed Computing