I am a doctoral student in the CPCB Ph.D. Program in Carnegie Mellon University School of Computer Science, working with Dr. Min Xu. I am also a Center for Machine Learning and Health (CMLH) Fellow in Digital Health Innovation.
My research interests include un/self-supervised learning, Representation Learning, Bioinformatics, 3D Computer Vision, Object detection and segmentation, Probabilistic graphical models, and Deep generative models.
My PhD thesis involves developing unsupervised algorithms for generative modelling of subcellular structure morphology from 2D and 3D microscopic images. Before joining my PhD, I graduated with B.Sc.Engg. in Computer Science and Engineering from Bangladesh University of Engineering and Technology(BUET) and later worked as a lecturer. During that time, I worked with Dr. Md. Shamsuzzoha Bayzid on leveraging machine translation for protein structure prediction.
Click here to download my full CV.
Please refer to my Google Scholar page for an up-to-date list with citations.
Mostofa Rafid Uddin, Gregory Howe, Xiangrui Zeng, and Min Xu. Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content From Parameterized Transformations In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 20646-20655. [News link]
Mostofa Rafid Uddin, Sazan Mahbub, Md Saifur Rahman, Md Shamsuzzoha Bayzid. SAINT: Self-Attention Augmented Inception-Inside-Inception Network Improves Protein Secondary Structure Prediction . Bioinformatics , Volume 36, Issue 17, 2020, Pages 4599-4608.
Sayali Onkar, Jian Cui, Carly Cardello, Anthony Cillo, Mostofa Rafid Uddin, April Sagan, Marion Joy, Hatice Osmanbeyoglu, Katherine Pogue- Geile, Priscilla McAuliffe, Peter Lucas, Adrian Lee, Tullia Bruno, Steffi Oesterreich, and Dario Vignali. Immune landscape in invasive ductal and lobular breast cancer reveals a divergent macrophage-driven microenvironment Nature Cancer. 2023. 4(4), 516-534.
Hmrishav Bandyopadhyay, Zihao Deng, Leiting Ding, Sinuo Liu, Mostofa Rafid Uddin, Xiangrui Zeng, Sima Behpour, Min Xu. Cryo-shift: Reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization. Bioinformatics , Volume 38, Issue 4, 2022, Pages 977–984.
Najibul Sarker, Zaber Hakim, Ali Dabouei, Mostofa Rafid Uddin, Zachary Freyberg, Andrew Macwilliams, Joshua Kangas, Min Xu. Detecting Anomalies from Liquid Transfer Videos in Automated Laboratory Setting. Frontiers in Molecular Biosciences . 2023.
Tarun Gupta, Xuehai He, Mostofa Rafid Uddin, Xiangrui Zeng, Andrew Zhou, Jing Zhang, Zachary Freyberg, Min Xu. Self-supervised learning for macromolecular structure classification based on cryo-electron tomograms. Frontiers in Physiology
Xiangrui Zeng, Ziqian Lin, Mostofa Rafid Uddin, Bo Zhou, Chao Cheng, Jing Zhang, Zachary Freyberg, Min Xu. Structure Detection in 3D Cellular Cryo-electron Tomograms by Reconstructing 2D Annotated Tilt-series. Journal of Computational Biology, 2022. .
Tianyang Wang, Bo Li, Jing Zhang, Xiangrui Zeng, Mostofa Rafid Uddin, Wei Wu, Min Xu. Deep Active Learning for Cryo-Electron Tomography Classification. International Conference on Image Processing (ICIP), 2022.
Served as a reviewer in IEEE Computer Vision and Pattern Recognition (CVPR) 2022, 2023, International Conference on Computer Vision (ICCV) 2023, European Conference on Computer Vision (ECCV) 2022, and AAAI Conference 2023, 2024.
Works as a mentor in CMU AI Mentoring Program, where I mentor CMU undergraduate students coming from underrepresented communities interested in AI research.
Worked as a moderator of East West University Electronics, Programming and Robotics Club. (Jan 2020- Dec 2020)
Designed and developed a responsive website for International Conference on Networking, Systems and Security(NSysS) jointly with Ajoy Das, under supervision of Dr. Rifat Shahriyar. Website Link.
Participated in a workshop on ``Reverse Engineering" arranged by ICT Division, Bangladesh Government. A team consisting of 18 members from CSE, BUET was provided with the opportunity to attend this workshop. The workshop was conducted by Dr. Desmond Devendran.
Participated in reviewing National ICT books as a team member of CSE, BUET.
Actively worked as an organizer of BUET CSE FEST 2018.
We address the problem of in silico protein design with a high propensity for liquid-liquid phase separation (LLPS) and droplet formation. Recently, there has been a surge in computational protein design methods that exhibit certain functions or structures. Moreover, no current method explicitly addresses the problem of computationally designing proteins with a high propensity for phase separation. To this end, we, for the first time, developed an adaptive sampling-based approach for in silico phase-separation protein design. Our method consists of multiple components, including a relaxed “energy" based sequence generator, a biochemical condition-aware attention-neural network-based surrogate model, a Bayesian acquisition function, and its optimizer. We demonstrate that our pipeline effectively generates in silico proteins with a high propensity for droplet formation in LLPS experiments, which outperforms other design methods.
This project attempts to assess the performance of various methods for predicting the citation of academic articles. Many researchers have sought to predict the future citation of new articles, and this interest has resulted in researchers using various machine learning methods for prediction. Our work asks a slightly different but related question. Given an article, how likely is it to cite another particular article? For our specific task, we found that sophisticated graph structure-based model does not achieve very promising performance. To this end, we developed an intelligent and novel feature engineering pipeline that could generate highly accurate predictions with relatively simpler models. We achieved around 95% F1 score with random forest classifier with our engineered features, which largely outperformed the graph neural network-based model.
In this project, we implemented the openmm local energy minimizer (that is used to minimize the free energy of protein in protein dynamics) using pytorch. We extended the autograd mechanics of pytorch for a custom backpropagation where in the forward pass the energy is calculated and in the backward pass, each atom's coordinate is updated according to the energy gradients. This work was done under supervision of Prof. David Koes.
In this project, we have analyzed the scRNA-seq data for 28 control patients to predict biological age from them. We tested with different machine learning approaches along with popular feature extraction methods and reported the results.This project was done as a lab rotation work with Prof. Ziv Bar-Joseph.
In this term project, we did an experiment on Neural Machine Translation(NMT) for Bangla to English Translation. We used a moderate size dataset containing 4379 sentence translations from English to Bangla. We used seq2seq encoder-decoder model containing Word2Vec and LSTMs with and without attention for small epochs. With finely tuned hyperparameters, we observed that using Bahdanau's attention with the vanilla encoder-decoder model improves the BLEU score for Bangla to English translation.
In this work, we developed a posture corrector android application that can detect unusual bending of the user. The application is connected with a wearable device containing Arduino and flex sensor. A user wearing a dress containing the device gets a notification in his application if he bends in a way that is harmful to his posture. Later a small physical motor was also introduced with the device that will force the user to correct his posture in case he doesn't has his phone nearby. However, the work was done for term project purpose and not commercially deployable.
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Carnegie Mellon University
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