Machine Learning Researcher
(Graph Neural Nets, Medical AI, Human-centric AI & NLP)
Kaggle Grandmaster | Explorer | Looking for research opportunities
About :
- An aspiring AI researcher and engineering student, exploring Graph Neural Networks (GNNs) in Bio-Medical AI, mainly focusing on neuro, therapeutic, and molecular ML domains (AI4Science). Along with GNN, my other research interests include AI for Science, Human-Centered AI (HCI, HAI) with NLP for interdisciplinary works.
- I am looking forward to pursue a PhD in Fall 2025 to continue research and looking for potential options.
- Currently, I'm working with Riashat Islam (PhD, McGill U. and Mila; Senior Scientist at SDAIA/NCAI) at Mila Quebec on computational structural biology, molecular ML, and generative AI. Previously, I worked with Prof. Dong-Kyu Chae at Hanyang University for 2 years on GNNs, Medical AI, and HCI-HAI.
- Additionally, I founded CIOL to mentor young researchers and bridge the gap between Industrial Engineering and AI. Here, I collaborate with Prof. Mahathir M Bappy (Louisiana State Uni.) and Prof. Manjurul Ahsan (Uni. of Oklahoma) on GNNs, Digital Twins, AI4Science, PINNs, and Medical AI applications; and guide young researchers. I'm also the 3rd Kaggle Grandmaster of BD.
- My works has been published in prestigious venues such as LREC-COLING'24, CSCW'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, 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).
Research :
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💠 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 Discovery, Interactions, Proteins Design & Binding and Micro/Macro-Molecules) DDI, Knowledge Graphs BanglaAutoKG (COLING'24), and Supply Chains SupplyGraph (AAAI'24W).
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🧬 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, molecular properties prediction, protein discovery, binder design and binding affinity, molecular interactions and affinity, structural biology, and healthcare optimization Glucose level control (ICLR'24). I've worked with de novo protein generation (RFDiffusion, FoldFlow, Croma, etc.) models and experienced with RL-inspired/energy-guided geometric/sequential structural biology modeling tools with GNNs, Flow Matching, GFLowNets and Diffusion models. I'm also experienced in computational neuroscience.
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🧑💻 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)], Religious/Cultural Bias and prevention (CSCW'24, CHI'24W; EMNLP'25-InReview), Computational Social Science (CSS) Social Biases (CHI'24W), Fairness and Reliability ARBEx into AI systems, focusing on HAI perspectives of IPE.
Skills :
- 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.