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, exploring Generative AI, Agents and Reasoning, AI4Science, Biomedical AI, as well as Human Compuer/AI Interaction and Computational Social Science. Along with GNN, my other research interests include interdisciplinary research in AI agents and Human-Centered AI (HCI, HAI) with NLP (Multilinguality, Bias and Fairness).
- I am looking forward to pursue a PhD in Spring/Fall 2026 to continue research and looking for potential options.
- I collaborate with Prof. Alshehri (KSU) on Generative AI, health informatics, GNNs, and AI agents; and Prof. Chae (HYU) on GenAI, LLM-HCI, and Biomolecular ML with GNNs.
- I also work with Riashat Islam (Microsoft Research) on molecular ML, GenAI, agents, and reasoning; and with Prof. Min Xu (CMU) on biomolecules. I actively collaborate with researchers from Cohere Labs (formerly Cohere for AI), Harvard, and more. Completed HTGAA 2025 (MIT), focusing on protein engineering.
- At CIOL, I collaborate with Prof. AMM Mukaddes (SUST), Prof. Ahsan (OU), and Prof. Bappy (LSU) on GNNs, agents, and digital twins for industrial and medical applications.
- 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
