Taseef Rahman
PhD candidate, Department of Computer Science
George Mason University
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I am a Ph.D. Candidate at George Mason University under the supervision of Professor Amarda Shehu. Before that, I completed my BSc in Electrical and Electronic Engineering from Islamic University of Technology (IUT), Dhaka.
I am a Ph.D. Candidate at George Mason University under the supervision of Professor Amarda Shehu. Before that, I completed my BSc in Electrical and Electronic Engineering from Islamic University of Technology (IUT), Dhaka.
Research
Research
My primary research interests are in the fields of Bioinformatics, Deep learning, data mining, and machine learning. My research focus is
on the exploration of the protein structure space. With this goal in mind, there are 3 principal aspects of my research-
1) Building generative models that sample sequence-agnostic but physically realistic / protein-like conformations.
2) Incorporate more powerful representations that better capture constraints in protein conformations.
3)Build end-to-end models that sample sequence specific conformations and devise a way to score conformations
Publications
Publications
Taseef Rahman, Fardina Fathmiul Alam, and Amarda Shehu. Equivariant Encoding based GVAE (EqEn-GVAE) for Protein Tertiary Structure Generation. Computational Structural Bioinformatics Workshop At IEEE BIBM, 2022. (accepted)
Taseef Rahman, Yuanqi Du, and Amarda Shehu.Graph Representation Learning for Protein Conformation Sampling. In Proceedings of the 11th International Conference on Computational Advances in Bio and medical Sciences (ICCABS), Lecture Notes in Computer Science, vol 13254. Springer, Cham, DOI: 10.1007/978-3-031-17531-2 22022.
Taseef Rahman, Yuanqi Du, Liang Zhao, and Amarda Shehu Generative Adversarial Learning of Protein Tertiary Structures. Molecules 26(5):1209, 2021. Impact Factor: 4.927.
Fardina Fathmiul Alam, Taseef Rahman, and Amarda Shehu Evaluating Autoencoder-Based Featurization and Supervised Learning for Protein Decoy Selection. Molecules 25(5):1146, 2020. Impact Factor: 4.927.
Alam, Fardina Fathmiul, Taseef Rahman, and Amarda Shehu. Learning Reduced Latent Representations of Protein Structure Data. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, 2019, pg. 592-597. acceptance rate: 26.8%)
Recent News
Recent News
11/28/2022- I will be attending CSBW 2022 to be held in Las Vegas, NV as a part of IEEE BIBM.
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