Machine Learning Researcher
(Agents, Reasoning, Alignment & Safety; Health and AI4Science; Human-AI Interaction)
Kaggle Grandmaster | Explorer | Looking for research opportunities
👨 About :
- My research is dedicated to developing human-centric, safe, reliable, robust, efficient, and capable machine learning systems. Over the past few years, my work has moved toward a single goal: building human-centric AI systems that strengthen healthcare access (bio/medical, clinical, and mental/psychological) and quality. To reach this goal, I am developing myself along three interconnected directions:
- Generative Multimodal AI, Reasoning, Alignment, and Agents: Designing agentic LLM and VLM systems that perform structured reasoning, adaptive planning and agency, and context-aware decision support, for both decision support and patient-facing communication.
- Computational Biology and Health Informatics: Developing data-driven methods that integrate biological, chemical, and clinical evidence, detect mechanistic patterns, and generate clinically actionable insights grounded in scientific foundations.
- Human-Computer/AI Interaction (HCI/HAI): Ensuring measurable safety, alignment, reliability, interpretability, and fairness in ML models, and enabling effective human-AI interaction across multilingual, multicultural, and low-resource settings, especially in high-stakes domains.
- I am actively seeking a PhD or MScR position beginning in Fall 2026 or Spring 2027 to continue research in one or more of these directions.
- My work has been published in venues such as ICLR (A*), ICML (A*), ACL (A*), WWW (A*), FAccT, CSCW (A), IEEE BHI (A*), ACCV, DASFAA, IISE, and COLING, and related workshops at NeurIPS, ICLR, AAAI, ICML, ACL, EMNLP, and CHI.
- I regularly review for major AI/ML conferences and journals, including *ICML (Gold Reviewer 2026), ICLR, NeurIPS, UAI, AAAI, T-PAMI, ACL, EMNLP, ACL ARR, CHI, and CSCW.
- Visiting Researcher, MBZUAI | April, 2026 - Present
- Research Collab., Qatar Computing Research Institute | August, 2025 - Present
- Visiting Researcher, DILab, Hanyang University | October, 2023 - Present
- Founding Researcher, CIOL | March, 2021 - Present
- Research Collab., Microsoft Research | January, 2026 - March, 2026
- Community Researcher, Cohere Labs | August, 2024 - January, 2026
- Fellow (School of AI), Pi School | June, 2025 - August, 2025
- I collaborate with Prof. Razzak (MBZUAI) and Prof. Chae (HYU) on GenAI, LLM-HCI, and biomolecular ML; Riashat Islam, PhD (Microsoft Research) and Md Rizwan Parvez, PhD (QCRI) on biomedical AI, GenAI, agents, and reasoning; Prof. Alshehri (KSU) on generative AI and health informatics; researchers from Cohere Labs on LLM evaluation, alignment, agents-reasoning, and applications; and Prof. Min Xu (CMU) on biomolecules.
- Founded CIOL to help young AI researchers, working with Prof. Ahsan (OU) on human-centric AI, LLM reasoning and agents, and digital twins for industrial and bio/medical applications. Completed HTGAA 2025 (MIT), focusing on protein engineering, and joined as a Global TA.
- Outside research, I have 3 years of experience in AI-driven product/content automation and product and project management. 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 an 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