🧑 Career Summary

I am a recent graduate in Industrial and Production Engineering from Shahjalal University of Science and Technology, with only my thesis defense remaining.

My core research focuses on exploring AI4Science, Biomedical AI, Generative AI, AI agents, AI Agents and Reasoning, with an emphasis on structural biology, therapeutic, clinical, and molecular ML domains. I am also deeply interested in interdisciplinary research, connecting these domains with Graph Neural Networks (GNNs), Digital Twins and Human-Centered AI (Multilinguality, Fairness, and Reliability) and AI applications in industrial engineering. I intend to pursue a PhD in Spring/Fall 2026 to continue my research.

I collaborate with Prof. Alshehri (KSU) on Generative AI, health informatics, GNNs, and AI agents; and Prof. Chae (HYU) on GenAI, LLM-HCI, 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 research has been published in prestigious venues such as ICLR, WWW, COLING, DASFAA, CSCW, and related workshops. I regularly review for major AI-ML conferences and am a Kaggle Grandmaster. I also have 3 years of experience in AI-driven product/content automation and product and project management. I’m a quick learner and highly adaptable, able to lead teams and projects. I enjoy exploring new topics, sharing knowledge, honing skills, experimenting and pushing my limits.

View my Curriculum Vitae →
Feel free to email me (azminetoushik dot wasi at gmail dot com) if you're interested in collaboration or discussing research. I'm open to new ideas and collaborations.

🔍 Research Interests

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• 🧬 Exploring Computational Biology and Biomedical AI Applications
  • Working on Computational Molecular Biology, Bioinformatics, and Computational Drug Discovery (CADGL). Exploring problems like molecular properties prediction, protein discovery, binder design and affinity, molecular interactions, structural biology, and healthcare optimization (ICLR'24).
  • Applying Graph Neural Networks (GNNs) and Geometric Machine Learning in biomedical domains, including protein modeling, molecular graphs, and structure-function alignment problems. Experienced in Flow Matching, GFlowNets, Diffusion models, and energy-based/agentic modeling pipelines. Worked with de novo protein generation models and experienced with RL-inspired/energy-guided geometric/sequential structural biology modeling tools. Currently exploring Digital Twins, clinical reasoning, and Agentic LLMs for Clinical and Biomedical AI Applications.
• 🧠 Understanding and Applying Generative AI Models
  • Focusing on Large Language Models (LLMs), Large Reasoning Models (LRMs), multi-modal alignment, reinforcement learning (RL), reward modeling, and uncertainty-aware reasoning through self-verification and self-correction.
  • Developing agentic AI agents via Retrieval-Augmented Generation (RAG), Prompt Reward Models (PRMs), Long Chain-of-Thought (Long CoT), and planning-aware strategies for multilingual, and human-in-the-loop applications (medical, clinical and legal).
  • Exploring how AI reasoning works with a strong emphasis on AI safety, interpretability, alignment, and governance in real-world deployments.
• 🧑‍💻 Interdisciplinary Research on Humans, AI, and Language
  • My recent work develops trustworthy AI reasoning by integrating RAG, reward-based RL fine-tuning, Digital Twins, and structured reasoning to enhance model reliability. I'm working on multilinguality and reasoning in LLMs, focusing on AI4Good applications focusing on Computational Social Science, Cultural Analytics and Cognitive Modeling. Additionally, I've worked on accessibility, evaluation and improvement of human factors and ergonomics (ICML'24W and EMNLP'24W), religious/cultural values (CSCW'24, CHI'24W, COLING'25 - 1, 2), into AI systems, mainly Generative AI and LLMs (ICLR'25).
  • Additionally, from technical side, I'm applying AI models to industrial engineering, including supply chain optimization (GNNs in SCM) and manufacturing. Interested in extending GNNs through Knowledge Graphs for applications across healthcare, HCI, and NLP (COLING'24, ACL'24W, EMNLP'24W).

📰 News and Updates

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📄 Selected Publications

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• INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
  Angelika Romanou, ... , Azmine Toushik Wasi, ... , Sara Hooker, Antoine Bosselut
  ICLR'25 (Spotlight, Top 5.1%) ▪ [OpenReview] ▪ Dataset ▪ NLP ▪ LLM

• Gaussian Regularization in Neural Graph Learning
  Azmine Toushik Wasi, Taki Hasan Rafi, Dong-Kyu Chae
  DASFAA'25 (Full Paper, Short Oral) ▪ GNN

• GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution
  Azmine Toushik Wasi*, Taki Hasan Rafi*, Raima Islam, Karlo Šerbetar, Dong-Kyu Chae (*Equal Contribution)
  ACCV'24 ▪ [CVF] ▪ [LNCS] ▪ [arXiv] ▪ CV

• Neural Control System for Continuous Glucose Monitoring and Maintenance
  Azmine Toushik Wasi
  ICLR'24 Tiny Papers ▪ [OpenReview] ▪ [arXiv] ▪ [GitHub] ▪ Optimization ▪ Medical AI

• When SMILES have Language: Drug Classification using Text Classification Methods on Drug SMILES Strings
  Azmine Toushik Wasi, Karlo Serbetar, Raima Islam, Taki Hasan Rafi, Dong-Kyu Chae
  ICLR'24 Tiny Papers ▪ [OpenReview] ▪ [arXiv] ▪ [GitHub] ▪ Medical AI

• CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions
  Azmine Toushik Wasi, Taki Hasan Rafi, Raima Islam, Serbetar Karlo, Dong-Kyu Chae
  TCBB In Review ▪ [arXiv] ▪ Medical AI ▪ GNN

• Dialectal Bias in Bengali: An Evaluation of Multilingual Large Language Models Across Cultural Variations
  Azmine Toushik Wasi, Raima Islam, Mst Rafia Islam, Farig Sadeque, Taki Hasan Rafi, Dong-Kyu Chae
  WWW'25 (TheWebConf) (Short Paper) ▪ NLP ▪ LLM ▪ HAI

• Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks
  Azmine Toushik Wasi, MD Shafikul Islam Sohan, Adipto Raihan Akib, Mahathik Muhammad Bappy
  In Review ▪ [arXiv] ▪ [GitHub] ▪ Dataset and Benchmarks ▪ Supply Chains ▪ GNN

View All Publications and Ongoing Works →

🧑‍🔬 Research Experiences

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• Visiting Researcher, AI4CHEMIA Research Group, King Saud University | Apr, 2025 - Present

• Global Committed Listener, How to Grow Almost Anything (HTGAA) 2025, Community Biotechnology Initiative, MIT Media Lab | Feb, 2025 - May, 2025

• Participant, Brown University Dept. of Physics presents the AI Winter School 2025 | Jan, 2025

• Student Research Assistant, Mila Quebec AI Institute | May, 2024 - March, 2025

• Community Researcher, Cohere Labs (formerly, Cohere for AI) | Aug, 2024 - Present

• Researcher, Computational Intelligence and Operations Laboratory, Shahjalal University of Science and Technology | March, 2021 - March, 2025

• Research Intern, Xu Lab, Carnegie Mellon University | Jul, 2024 - December, 2024

• Visiting Researcher, Data Intelligence Lab, Hanyang University | Nov, 2023 - July, 2024

• Computational Neuroscience Summer School (Research Student), Neuromatch Academy | Jul, 2024

• CCAI Summer School (Research Student), Climate Change AI | Jun - Aug, 2024

📜 Selected Projects

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• CIOL Presents Winter ML Bootcamp
  I am the lead organizer and instructor of the ML bootcamp, with more than 50 participants. I prepared materials and took live sessions on EDA, Tabular Data Modeling, Hyperparameter Tuning, ANNs - Deep Learning, NLP, Computer Vision, LLM Agents and mentored 6 research teams in the bootcamp.
  [Website] ▪ [GitHub] (Check Session Notebboks) ▪ [Youtube] (Check Recordings)
• AyaFestPe: A multi-lingual and multi-cultural festival exploration guide
  The pipeline starts by collecting festival data from HF and translating it with AyaExpanse for multilingual access. We then gather user queries, use M-RAG to embed data and queries, rerank results, and combine relevant context with the query to generate output.
  [GitHub] ▪ [Colab] (using Cohere API) ▪ [Kaggle] (Using released model weights, with quantization)
• 🏫 Online ML University: Free AI resource collection for everyone! Currently ~150⭐ in GitHub!
• 📑 Paper Organization and Collection
  
  • ICML 2024 Graph Paper Collection : List of all graph and/or GNN papers accepted at ICML'24. Currently ~160⭐ in GitHub!
  • ICLR 2024 Graph Paper Collection : List of all graph papers accepted at ICLR'24. Currently ~70⭐ in GitHub!
  • ICLR 2024 LLM Paper Collection : List of all LLM papers accepted at ICLR'24. Currently ~35⭐ in GitHub!

👩‍💻 Technical Skills

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• Programming Languages: Python (Advanced), C, C++, MATLAB, R, SQL
• DS & ML Tools (Python): NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, PyTorch, PyCaret, LangChain, VLLM, Pydantic
• Data Science Techniques: EDA, Experimental Design, Hypothesis Testing, Sampling, Statistical Inference, Data-driven Decision Making
• Machine Learning Techniques: Classical ML, Deep Learning, NLP, Graph Neural Networks (GNNs), GFlowNets, Flow Matching, Diffusion Models, Reinforcement Learning, Reasoning in LLMs, RAG, Self-Verification, Uncertainty Estimation, Agentic Decision-Making, Reward-Based Fine-Tuning, Computer Vision
• Biomedical AI & Clinical Applications: Molecular Property Prediction, Molecular Interaction Analysis, Binder and De Novo Protein Design, GNNs for Molecules, Energy-Guided Modeling, Flow-based and Diffusion-based Generative Models, Agentic LLMs, Knowledge Graphs, Drug Discovery, Genomics
• Data Analysis & Visualization: MS Excel, Power BI, Tableau, SAS
• Automation & Productivity: MS Word, PowerPoint, Excel Automation; Google Sheets Scripting; Python-based Automation; Adobe Photoshop and Illustrator
• Human-Computer Interaction (HCI): LLM Customization, Survey Design, Data Collection & Analysis, UI/UX Frameworks
• Other Tools & Skills: GitHub, VS Code, Azure, AnyScale, Replit, Colab, Kaggle, Parallel & Distributed Computing, Product & Project Management, Strategic Planning

💼 Academic Services

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• Conference or Journal Reviewer
  • AI/ML: ICLR'25, ICLR'24 Tiny Papers.
  • NLP: ACL ARR Feb (ACL), Apr, Jun (EMNLP), Aug 2024; May 2025 (EMNLP) [View in official reviewer list]; COLING'25.
  • HCI/HAI: CHI'25, CSCW'24, '25, UbiComp/ISWC'24, TEI'25, ICMI'24, IDC'24.
  • Journals: Complex & Intelligent Systems (Springer Nature).
  • Total conference or journal paper reviewed: 73
• Workshop Program Committee and Reviewer
  • ICLR'25: AI4MaterialsDiscovery Re-Align, LMRL
  • NAACL'25: DravidianLanTech, CLPsych, In2Writing
  • AAAI'25: Social Impact of AI,
  • CHI'25: STAIG, HEAL
  • ICML'25: World Models, MusIML
  • NeurIPS'24: Behavioral ML, AI4MaterialsDiscovery, EvalEval, Military AI, MusIML
  • EMNLP'24: NLP for Positive Impact, Multulingual Represent Learning
  • ICML'24: AI4Science, LLMs & Cognition
  • ACL'24: TextGraphs-17, ClimateNLP, SMM4H, Language + Molecules, GenderBiasNLP, WASSA.
  • MICCAI'24: Fairness in Medical Imaging, GRaphs in biomedicAl Image anaLysis.
  • Total top conference workshop paper reviewed: 74
• Organizer
  • NeurIPS'24, ICML'24, NeurIPS'25 Muslims in ML Affinity Workshop