Research
Feature ordering, structured representation learning, foundation model adaptation, and HDLSS learning.
Explore projects →About
Zadid Habib (full name: Al Zadid Sultan Bin Habib) is a PhD Candidate in Computer Science at West Virginia University (WVU), USA, and an NSF NRT Bridges in Digital Health Fellow.
His research focuses on representation learning for structured, high-dimensional, and multimodal data, with emphasis on tabular deep learning, foundation models for tabular data, feature ordering and sequencing, HDLSS learning, multimodal representation learning, and generative modeling.
A recurring theme of his work is understanding how feature organization and ordering can improve learning across tabular, foundation, and multimodal models.
He has first-authored research published or accepted at ICML 2026, ECCV 2026, ECML PKDD 2026, and ICPR 2024/2026, with additional first-authored work appearing in the AAAI 2026 NeuroAI Workshop and ICLR 2026 DeLTa Workshop.
He holds an MSc in Computer Science from Jahangirnagar University and a BSc in Electronics and Communication Engineering (ECE) from Khulna University of Engineering & Technology (KUET).
Highlights
Open Source
Research code, Python packages, model weights, and interactive demos for reproducible machine learning.
Browse GitHub →Collaboration
Open to collaborations in tabular learning, multimodal ML, digital health, and structured-data problems.
Get in touch →Updates
ICLR 2027 Reviewer
AAAI 2027 Reviewer
PhD Candidacy + Proposal Defense
ICML · ECCV · ECML PKDD · ICPR 2026
Invited to serve as a Reviewer for ICLR 2027.
Reviewed 6 papers for AAAI 2027.
Passed my PhD Candidacy Exam and successfully defended my Dissertation Proposal at West Virginia University.
Reviewed 4 papers for NeurIPS 2026.
First-authored paper accepted at the ECCV 2026.
The arXiv version of our ICML paper “GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data” is available now, along with the OpenReview, GitHub repository, project webpage, ICML portal, live demo, CPU backup demo, and PyPI package.
First-authored paper accepted at the ECML PKDD 2026.
Paper “DynaTab: Dynamic Feature Ordering as Neural Rewiring for High-Dimensional Tabular Data” published in the Proceedings of the AAAI 2026 NeuroAI Workshop .
First-authored paper accepted at the ICML 2026.
Two first-authored papers accepted at the ICPR 2026.
Full paper accepted at the ICLR 2026 DeLTa Workshop.
Reviewed 7 papers for the ICPR 2026.
Reviewed 3 short papers for the ICLR 2026 Gen2 Workshop.
Received a paper decision of “Accepted for archival publication” for my submission to AAAI 2026 NeuroAI Workshop (NeuroAI 2026) .
Reviewed 3 papers for AAAI 2026 NeuroAI Workshop .
Reviewed 3 papers for AISTATS 2026.
Paper “Tabular Deep Learning vs Classical Machine Learning for Urban Land Cover Classification” published in the NeurIPS 2025 MusIML Affinity Workshop .
Reviewed 1 paper for ICLR 2026.
Attended the Duke Medical Robotics Symposium 2025 and presented a poster. Organized by Duke TAST; part of my NSF NRT BridgesDH traineeship visit and collaboration.
Reviewed 3 papers for the NeurIPS 2025 Efficient Reasoning (ER) Workshop.
Reviewed 4 papers for AAAI 2026.
WVAR-CRESH: 2025 WVU Summer Workshop on AI, Digital Health & BioML,
and NSF BridgesDH NRT Mini-Workshop Series.
Multimodal Learning for Image and Tabular Data.
Awarded the NSF NRT Bridges in Digital Health (BridgesDH) Fellowship. Details
Published paper, “TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering” at ICPR 2024. See the paper: Springer chapter.
Reviewed 1 paper for ICLR 2025.
Reviewed 4 papers for ICPR 2024.