H.M. Shadman Tabib

Senior Year Undergraduate

Computer Science & Engineering

Bangladesh University of Engineering and Technology (BUET)

I’m a senior-year undergraduate student in Computer Science and Engineering at Bangladesh University of Engineering and Technology (BUET). My current work lives at the intersection of computational biology, computer vision, and AI for health. I care about two big questions: how we can extract structure and meaning from extremely difficult biological imaging data, and how we can make machine learning systems more contextual, fair, and clinically useful.

Research Focus

My research spans multiple interconnected domains:

Structural Biology & Computational Biology: Cryo-ET segmentation, PFIB tomography, RNA segmentation, and connectomics
Computer Vision for Health: Breast cancer imaging, EEG-based epilepsy prognosis, screening pipelines, and real-time systems
Contextualized & Interpretable ML: Systems that adapt to different settings and explain their decisions
Public Health Modeling: Epidemiological simulation and socioeconomic impact analysis

International Collaborations

I actively collaborate with leading research groups worldwide:

Xu Lab, Carnegie Mellon University (Dr. Min Xu): Cryo-electron tomography and macromolecule localization
University of Wisconsin–Madison (Dr. Ben Lengerich): Contextualized machine learning and interpretability
Stanford University (Dr. Muyuan Chen): PFIB tomography of biological samples

Recognition & Impact

My work bridges theoretical advances with practical applications, focusing on building machine learning systems that are both scientifically rigorous and clinically viable. I’m particularly interested in addressing bias, uncertainty, and interpretability in AI systems for healthcare.

Future Vision

Long term, I want to build machine learning systems that can both interpret complex biological data and support real decision making in health and science, while being aware of bias, context, and uncertainty.

selected publications

Unsupervised Multi-scale Segmentation of Cellular Cryo-electron Tomograms with Stable Diffusion Foundation Model

Mostofa Rafid Uddin, Thanh-Huy Nguyen^*, H.M. Shadman Tabib^*, and 2 more authors

Submitted to CVPR, 2025

Abs Bib PDF

Unsupervised segmentation of cellular cryo-electron tomograms using stable diffusion foundation models for multi-scale biological structure analysis.
@article{tabib2025cryoet_diffusion, title = {Unsupervised Multi-scale Segmentation of Cellular Cryo-electron Tomograms with Stable Diffusion Foundation Model}, author = {Uddin, Mostofa Rafid and Nguyen, Thanh-Huy and Tabib, H.M. Shadman and Gandhi, Kashish and Xu, Min}, journal = {Submitted to CVPR}, year = {2025}, }

Localization of Macromolecules in Crowded Cellular Cryo-electron Tomograms from Extremely Sparse Labels

Mostofa Rafid Uddin, Ajmain Yasar Ahmed^*, H.M. Shadman Tabib^*, and 4 more authors

Briefings in Bioinformatics, 2025

Accepted

Abs Bib PDF

@article{tabib2025macromolecule_sparse,
  title = {Localization of Macromolecules in Crowded Cellular Cryo-electron Tomograms from Extremely Sparse Labels},
  author = {Uddin, Mostofa Rafid and Ahmed, Ajmain Yasar and Tabib, H.M. Shadman and Tahmid, Md Toki and Alam, Md Zarif Ul and Freyberg, Zachary and Xu, Min},
  journal = {Briefings in Bioinformatics},
  year = {2025},
  note = {Accepted},
}

NEUROSKY-EPI: The First Open Single-Electrode Epilepsy EEG Dataset with Context-Aware Modeling

H.M. Shadman Tabib, Md. Hasnaen Adil, Ayesha Rahman, and 3 more authors

In NeurIPS 2025 Workshop on Time Series for Health (TS4H), 2025

Accepted

Abs Bib HTML

@inproceedings{tabib2025neurosky,
  title = {NEUROSKY-EPI: The First Open Single-Electrode Epilepsy EEG Dataset with Context-Aware Modeling},
  author = {Tabib, H.M. Shadman and Adil, Md. Hasnaen and Rahman, Ayesha and Swapnil, Ahmmad Nur and Hasana, Maoyejatun and Islam, A.B.M. Alim Al},
  booktitle = {NeurIPS 2025 Workshop on Time Series for Health (TS4H)},
  year = {2025},
  note = {Accepted},
}

Breast Cancer Detection Using Semi-Supervised Learning: Multimodal Data Integration and Comparative Demographic Analysis

H.M. Shadman Tabib, Mst. Fahmida Sultana Naznin, Sadatul Islam Sadi, and 3 more authors

In 11th International Conference on Networking, Systems, and Security (NSyS), 2024

Accepted as Poster

Abs Bib PDF

@inproceedings{tabib2024breast_cancer,
  title = {Breast Cancer Detection Using Semi-Supervised Learning: Multimodal Data Integration and Comparative Demographic Analysis},
  author = {Tabib, H.M. Shadman and Naznin, Mst. Fahmida Sultana and Sadi, Sadatul Islam and Adil, Md. Hasnaen and Oishi, Zarin Tasnim and Rahman, M. Sohel},
  booktitle = {11th International Conference on Networking, Systems, and Security (NSyS)},
  year = {2024},
  note = {Accepted as Poster},
}