• An aspiring AI researcher and engineering student, exploring Graph Neural Networks (GNNs) in Bio-Medical AI, mainly focusing on neuro, biomedical and bio-/molecular domains (AI4Science). Along with GNN, my other research interests include Natural Language Processing (NLP) and Human-Centered AI for interdisciplinary works.
• I am looking forward to pursue a PhD in Fall 2025 to continue research and looking for potential options.
• I am working on Data Intelligence Lab, HYU, KR. I have founded Computational Intelligence and Operations Lab - CIOL, SUST, BD, to mentor young researchers and bridge the gap between Industrial Engineering and AI. I'm also the 3rd Kaggle Grandmaster of BD.
• My works has been published in prestigious venues such as LREC-COLING'24, ICLR'24 Tiny Papers Track, Workshops of NeurIPS'23, AAAI'24, ICML'24, ACL'24 and CHI'24, with ongoing reviews in ACCV'24, TCBB, EMNLP'24, CIKM'24, UIST'24, CSCW'24, among others.
• Outside research, I have work experience in AI-integrated IT Automation, Project - Product Management and Analytics roles.
• 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.
• Serving as reviewer in top ML conferences, workshops and journals like ACL ARR, ICLR, IDC, ICML, MICCAI regularly; and program chair in multiple ACL'24 workshops.
• Actively looking for research opportunities in theoretical or applied GNNs in medical domains (molecular/biomedical/neuroscience).

• ⚙️ Machine Learning (AI4Science): I am working on theoretical and applied machine learning, specially probabilistic modeling and inference, generative models, GFlowNets and its applications, etc. I've published in ICLR'24 and COLING'24, and workshops of AAAI'24, NeurIPS'23, ACL'24, ICML'24 and CHI'24. I regularly serve as reviewer for top ML conferences (ACL-RR, ICLR) and workshops (ICML, ACL, MICCAI).

• 💠 Graph Neural Networks (GNN): I am exploring Graph Neural Network or Geometric Machine Learning Theories, applying and improving GNN models and resources in Healthcare (Drugs, Proteins and Molecules) DDI, Knowledge Graphs BanglaAutoKG (COLING'24), and Supply Chains SupplyGraph (AAAI'24W).

• 🧬 Medical AI: In Medical AI, I am working on developing AI systems for Healthcare, mainly focusing on Computational Molecular Biology - Neuroscience, Bioinformatics, Computational Drug Discovery CADGL, and Healthcare Optimization Glucose level control (ICLR'24).

• 🧑‍💻 Human-Centered AI (HAI): Despite extensive coursework in ergonomics, Human Factors Engineering (HFE), behavior studies, and psychology within our IPE curriculum, there's a notable gap in inter-disciplinary research between IPE and AI in BD. Motivated by this, I am working on integrating HFE AI Ownership, Individuality (CHI'24W), [Ergonomics in LLMs/UIs (UIST'24 In Review, ICML'24-W)], Computational Social Science (CSS) Social Biases (CHI'24W), Fairness and Reliability ARBEx into AI systems, focusing on HAI perspectives of IPE.

• 📝 Natural Language Processing (NLP): In Natural Language Processing, I am developing Knowledge Graphs (COLING'24) and Bangla Knowledge Systems; motivated by NLP + GNNs. I am also working on inter-disciplinary CSS, Climate, and BioMedNLP Molecules+NLP (ICLR'24).

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• Programming: Python (Advanced), C (For Contests), R, SQL.
• ML Techniques : Deep Learning, NLP, Graph Neural Networks, GANs.
• DS & ML Tools (Python) : NumPy, Pandas, Matplotlib, Seaborn, Stats-models, Scikitlearn, Keras, Tensorflow, PyTorch.
• Data Analysis: MS Excel, SAS, Tableau, Power BI.
• Computational Biology and Bio-molecules: Molecular Networks, Classification, Molecular Interaction Detection and Classification, Generative Modeling with Flow Matching and Graph Diffusion.
• Human-Computer Interaction: LLM Customization, Survey Design, UI/Framework Design and Development, Data Collection and Analysis.
• IT Automation:
  • Automation in MS Word, Powerpoint, Excel, Google Sheets, Adobe Photoshop, Illustrator using Python, built-in toolkits and ML;
  • Photo Manipulations at large scale using OpenCV and Pillow;
  • NLP and CV-based ML models to detect error in textuala and visual contents.
• Product Development, Project Management, Business Development and Strategic Planning and Analysis.