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Comprehensive curriculum vitae showcasing research experience, publications, awards, and technical expertise in computational biology and machine learning.

Basics

Name H.M. Shadman Tabib
Label Computational Biology | Computer Vision & AI for Health | Machine Learning
Email shadmantabib2002@gmail.com
Phone +880-1880-198766
Url https://shadmantabib.github.io
Summary Senior year undergraduate student in Computer Science and Engineering at Bangladesh University of Engineering and Technology (BUET). Research interests at the intersection of computational biology, computer vision, and AI for health. Currently collaborating with Carnegie Mellon University, University of Wisconsin–Madison, and Stanford University on cutting-edge projects in cryo-electron tomography, contextualized machine learning, and biological image analysis.

Work

  • 2025.09 - Present
    Stanford University

    Advisor: Dr. Muyuan Chen. Conducting segmentation and analysis of Hydra Plasma Focused Ion Beam (PFIB) tomography data for structural interpretation and morphology reconstruction.

    • Hydra Plasma Focused Ion Beam (PFIB) tomography analysis
    • Structural interpretation and morphology reconstruction
    • Advanced biological imaging techniques
  • 2025.02 - Present
    Research & Innovation Centre (RIC), Bangladesh University of Engineering and Technology (BUET)

    Project: Identification of Dengue Breeding Sites through Object Recognition (HE-01-244). Contributing to dengue vector surveillance using deep learning–based object recognition and image analysis for public health applications.

    • Deep learning-based object recognition for dengue vector surveillance
  • 2024.09 - 2025.09
    University of Wisconsin–Madison (Adaptive Inference Lab)

    Advisor: Dr. Ben Lengerich. Co-authoring a comprehensive review on contextualized machine learning and interpretability in foundation models. Contributing to the AdaptInfer framework and contextual awareness in data-driven modeling.

    • Comprehensive review on contextualized machine learning
    • Interpretability in foundation models
    • AdaptInfer framework development
    • Contextual awareness in data-driven modeling
  • 2024.06 - Present
    Carnegie Mellon University (Xu Lab)

    Advisor: Dr. Min Xu. Working on unsupervised Cryo-ET segmentation and particle picking through tomogram preprocessing and denoising for high-resolution biological structure reconstruction.

    • Unsupervised Cryo-ET segmentation and particle picking
    • Tomogram preprocessing and denoising
    • High-resolution biological structure reconstruction
    • Foundation model-based approaches for biological imaging
  • 2023.01 - Present
    Bangladesh University of Engineering and Technology (BUET) - Multiple Research Projects

    Multiple research projects under different supervisors covering diverse areas of computer science and engineering.

    • Semi-supervised and multimodal breast cancer classification (Prof. Dr. M. Sohel Rahman)
    • RNA segmentation and multimodal model training frameworks (Prof. Dr. Md. Shamsuzzoha Bayzid)
    • EEG-based context-aware deep learning for epilepsy prognosis (Prof. Dr. A.B.M. Alim Al Islam)
    • Real-time drone detection and tracking systems (Prof. Dr. Ch. Md. Rakin Haider)
    • Bias-aware retrieval-augmented generation for clinical predictions (Prof. Dr. M. Saifur Rahman)
    • Stochastic spatio-temporal epidemiology modeling on multiplex networks (Dr. K.M. Ariful Kabir)

Volunteer

  • 2025.11 - 2026.01

    Dhaka, Bangladesh

    BUET CSE Fest Deep Learning Sprint 2025

    Coordinated largest national AI datathon; designed problems and rubric, and supervised judging. Collaborated with academic & industry mentors to ensure high-quality competition standards.

    • Largest national AI datathon coordination
    • Problem design and rubric development
    • Academic and industry mentor collaboration
    • High-quality competition standards
  • 2025.01 - Present

    Dhaka, Bangladesh

    PinkLifeLine

    Health-tech startup from NeoScreenix (JHU 2025 Global Champion); funded by Bangladesh National ICT Division. Partnered with Sustainlaunch Labs (global innovation accelerator) and Herwill (USA based global women's empowerment & digital inclusion).

    • Built and leads a 50+ medical professionals' network
    • Leads ML clinical data pipelines & screening workflows
    • Partnership with Sustainlaunch Labs and Herwill
    • Bangladesh National ICT Division funding

Education

  • 2022.01 - Present

    Dhaka, Bangladesh

    Bangladesh University of Engineering and Technology (BUET)

    Computer Science and Engineering

    • Machine Learning
    • Computer Vision
    • Bioinformatics
    • Database Systems
    • Software Engineering
    • Algorithms and Data Structures
    • Artificial Intelligence

Awards

Publications

Skills

Programming Languages
Python
PyTorch
TensorFlow
Keras
C/C++
Java
SQL
JavaScript
Dart
Machine Learning & AI
Computer Vision
Deep Learning
Natural Language Processing
Unsupervised Learning
Agent-Based Modeling
Semi-supervised Learning
Multimodal Learning
Specialized Domains
Cryo-ET Processing
Medical Imaging
Bioimage Analysis
Bioimage Segmentation
Brain-Computer Interfaces
Real-time Systems
Mesa Framework
Flutter
Development & Tools
Django
HTML/CSS
Node.js
React
Spring
Database Design
Git
LaTeX

Languages

English
Fluent
Bengali
Native speaker

Interests

Computational Biology
Cryo-ET image analysis
Connectomics segmentation
RNA task accuracy estimation
Biological structure reconstruction
Computer Vision & AI for Health
Medical imaging (cancer detection)
Multimodal and semi-supervised learning
Bioimage segmentation
Brain-computer interfaces (epilepsy)
Real-time systems
Machine Learning
Unsupervised detection/segmentation
Contextualized ML
LLM-as-a-judge for synthetic data evaluation
Foundation models

References

Prof. Dr. M. Sohel Rahman
Professor, Department of CSE, BUET. Email: sohel.kcl@gmail.com | Google Scholar: https://scholar.google.com/citations?user=IUwFD9gAAAAJ&hl=en
Prof. Dr. Md. Shamsuzzoha Bayzid
Professor, Department of CSE, BUET. Email: bayzid@cse.buet.ac.bd | Google Scholar: https://scholar.google.com/citations?user=h2vHz3wAAAAJ&hl=en
Dr. Min Xu
Associate Professor, Computational Biology Department, Carnegie Mellon University. Faculty Profile: https://scholars.cmu.edu/6103-min-xu
Dr. Ben Lengerich
Assistant Professor, University of Wisconsin–Madison. Personal Website: https://web.mit.edu/~blengeri/www/

Projects

  • 2024.06 - Present
    Cryo-ET Segmentation with Stable Diffusion Foundation Models
    Unsupervised multi-scale segmentation of cellular cryo-electron tomograms using stable diffusion foundation models for high-resolution biological structure reconstruction.
    • CVPR 2025 submission
    • Carnegie Mellon University collaboration
    • Foundation model adaptation for 3D biological imaging
    • Self-supervised learning techniques
  • 2024.01 - Present
    NeoScreenix - AI-Powered Breast Cancer Detection
    Health-tech solution combining semi-supervised learning with multimodal data integration for early breast cancer detection. Evolved into PinkLifeLine startup.
    • Johns Hopkins Healthcare Design Competition 2025 Global Champion
    • PinkLifeLine startup (Co-Founder & Head of Research)
    • Bangladesh National ICT Division funding
    • 50+ medical professionals network
    • Sustainlaunch Labs and Herwill partnerships
  • 2023.01 - 2025.01
    NEUROSKY-EPI: Context-Aware Epilepsy Detection
    First open single-electrode epilepsy EEG dataset with context-aware deep learning models for real-time brain-computer interface applications.
    • NeurIPS 2025 Workshop on Time Series for Health acceptance
    • First open-source single-electrode epilepsy EEG dataset
    • Context-aware neural networks
    • Real-time processing optimization
  • 2024.01 - 2025.01
    SpectraSentinel: Real-Time Drone Detection System
    Lightweight dual-stream architecture combining spectral and spatial fusion cues for real-time drone detection, tracking, and payload identification.
    • IEEE SPS Video & Image Processing Cup 2025 Global 2nd Runner-up
    • Dual-stream spectral-spatial fusion
    • Real-time performance optimization
    • Lightweight CNN architecture
  • 2024.09 - 2025.09
    Contextualized Machine Learning Research
    Comprehensive review on contextualized machine learning and its role in creating adaptive, interpretable, and generalizable AI systems with focus on foundation models.
    • University of Wisconsin–Madison collaboration
    • AdaptInfer framework contribution
    • Foundation models interpretability
    • Context-aware AI systems development