Hi, I am a Ph.D. student at the Purdue computer science department. Before this, I received my bachelor's degree in Computer Science and Engineering from the Bangladesh University of Engineering and Technology.

My research interest is machine learning,  especially computer vision, equivariant, and machine learning for scientific discovery. I am working with Professor Raymond A. Yeh.

Please feel free to send me an anonymous message by clicking the box I've included below.

Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs.

Md Ashiqur Rahman, Robert Joseph George, Mogab Elleithy, Daniel Leibovici, Zongyi Li, Boris Bonev, Colin White, Julius Berner, Raymond A. Yeh, Jean Kossaifi, Kamyar Azizzadenesheli, Anima Anandkumar (Arxiv 2024)

Truly Scale-Equivariant Deep Nets with Fourier Layers.

Md Ashiqur Rahman, and Raymond A. Yeh. (NeurIPS 2023, Project Page)

U-NO: U-shaped Neural Operators.

Md Ashiqur Rahman, Zachary E. Ross, Kamyar Azizzadenesheli. (TMLR 2023, code)

PaCMO: Partner Dependent Human Motion Generation in Dyadic Human Activity using Neural Operators.

Md Ashiqur Rahman, Jasorsi Ghosh, Hrishikesh Viswanath, Kamyar Azizzadenesheli, Aniket Bera  (Arxiv 2022)

Generative Adversarial Neural Operators.

Md Ashiqur Rahman, Manuel A. Florez, Anima Anandkumar, Zachary E. Ross, Kamyar Azizzadenesheli. (TMLR 2022, code)

BEENE: Deep Learning-based Nonlinear Embedding Improves Batch Effect Estimation.

Md Ashiqur Rahman, Abdullah Aman Tutul, Mahfuza Sharmin, Md Shamsuzzoha Bayzid. (Bioinformatics, 2023)

CHAPAO: Likelihood and hierarchical reference-based representation of biomolecular sequences and applications to compressing multiple sequence alignments.

Md Ashiqur Rahman, Abdullah Aman Tutul, Sifat Muhammad Abdullah, Md. Shamsuzzoha Bayzid. (PLos One, 2022, code)

Solving The Maze of Diagnosing Parkinson’s Disease on Portable EEG Sensing to be Adaptable to Go In-The-Wild

Md Ashiqur Rahman, Abdullah Aman Tutul, A. B. M. Alim Al Islam. (7th NSysS 2020) (Best Paper Award)

An Efficient Recognition Method for Handwritten Arabic Numerals Using CNN with Data Augmentation and Dropout.

Ashiquzzaman, Akm, Abdul Kawsar Tushar, Ashiqur Rahman, and Farzana Mohsin. (ICDMI 2019)