F A Rezaur Rahman Chowdhury

I am a PhD Candidate at Washington State University, where I work on using Machine Learning to Scale Up Combinatorial Algorithms with applications to Graph Analytics, Genomics, and Life Sciences.

My advisor is Dr. Jana Doppa. I worked with him on Structured Prediction and Learning to Optimize Graph Query Projects.

I've also worked with Dr. Liang Huang in Learning to Fold RNAs in Linear time project during my internship in Baidu Research in Spring and Summer 2019.

I've spent time at Summer 2017 in Google NYC with Speaker Identification team.

I have also worked at TigerIT Bangladesh Ltd and Eyeball Networks Inc as SWE before starting PhD.

I did my bachelors at Bangladesh University of Engineering and Technology.

Email  /  CV  /  Google Scholar  /  LinkedIn

Research

I'm interested in applying Machine Learning to Scale Up Combinatorial Algorithms. Some of the Application domains are Graph Analytics, Genomics, and Life Sciences. My research requires usage of Structured Prediction, Imitation Learning, Deep Learning, Reinforcecment Learning. I have also worked on using Attention based models in Deep Neural Network to improve Text-Dependent Speaker Verification.

Publications

  • Select-and-Evaluate: Learning for Large-Scale Knowledge Graph Search
    F A Rezaur Rahman Chowdhury*, Chao Ma*, Md Rakibul Islam, Mohammad Hossein Namaki, Mohammad Omar Faruk , Janardhan Rao Doppa (* denotes equal contribution)
    Journal of Machine Learning Research , Proceedings Track, Vol 77, PP 129-144, 2017.

    We developed a learning framework to answer graph pattern queries in large scale knowledge graph called Select-and-Evaluate (SCALE) using Imitation Learning approach.

  • Learning to Speed Up Query Planning in Graph Databases
    Mohammad Hossein Namaki*, F A Rezaur Rahman Chowdhury*, Md Rakibul Islam, Janardhan Rao Doppa, Yinghui Wu (* denotes equal contribution)
    Proceedings of 27th International Conference on Automated Planning and Scheduling (ICAPS), 2017

    .We present a Learning to Plan (L2P) framework that is applicable to a large class of query reasoners that follow the Threshold Algorithm (TA) approach.

  • Attention-based models for Text-dependent speaker verification
    F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno, Li Wan
    To appear in Proceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018

    We use Attention based Deep Neural Network models to improve Text-Dependent Speaker Verification.

  • Randomized Greedy Search for Structured Prediction: Amortized Inference and Learning
    Chao Ma*, F A Rezaur Rahman Chowdhury*, Aryan Deshwal, Md Rakibul Islam, Janardhan Rao Doppa, Dan Roth
    Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2019

  • Learning to Fold RNAs in Linear Time
    F A Rezaur Rahman Chowdhury*, He Zhang*, Liang Huang (* denotes equal contribution)
    Submission Under Review in International Conference on Research in Computational Molecular Biology (RECOMB), 2020

    We present a linear-time machine learning-based folding system, using recently proposed approximate folding tool LinearFold as inference engine, and structured SVM (sSVM) as training algorithm. Our tool is faster and more accurate than existing tools.

  • Talks and Abstracts
    • At ACML 2017, I gave a talk on our work on Select-and-Evaluate Framework for Large-Scale Knowledge Graph Search
    • At ISMB/ISCB 2019 RNA COSI track, Our work was presented as a poster of an accepted Abstract.
    Teaching

    Cpt S 350 Design and Analysis of Algorithms - Fall 2017
    Teaching Assistant (TA)


    This guy makes a killer webpage.