Machine Learning Researcher
(Graph Neural Nets, Biomedical 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 Spring/Fall 2026 to continue research and looking for potential options.
- Currently, I'm working with Riashat Islam 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, ACCV'24, 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.
Research :
- 🧬 Exploring Computational Biology and Biomedical AI Applications: Focused on molecular biology, bioinformatics, and drug discovery, including problems like protein discovery, molecular interactions, structural biology, and healthcare optimization. Experienced with de novo protein generation, RL-inspired modeling, and advanced tools like GNNs, Flow Matching, GFLowNets, and Diffusion models.
- 💠 Understanding and Applying Graph Neural Networks (GNN): Specializing in geometric machine learning and GNNs, with applications in biomedical AI (discussed above), supply chain optimization, and manufacturing. Exploring the extension of GNNs to knowledge graphs for diverse systems and domains.
- 🧑💻 Interdisciplinary Research on Humans, AI, and Language: Researching human factors, ergonomics, and cultural values in AI systems, particularly in Generative AI and Large Language Models, with a focus on fairness, reliability, and AI for good.
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.