Sujan Kumar Roy

Contact

sroy5@mtu.edu
906-487-1657
Rekhi Hall 305

Assistant Teaching Professor, Data Science

Ph.D. – Machine Learning and Signal Processing, Griffith University
M.A.Sc. – Electrical and Computer Engineering, Concordia University
M.Sc. – Computer Science and Engineering, University of Rajshahi
B.Sc. – Computer Science and Engineering, University of Rajshahi

Biography

Sujan Kumar Roy earned his Ph.D. in Machine Learning and Signal Processing from Griffith University, Australia, in 2021. He received the “Award of Excellence in the Research Thesis” and was considered for the “Chancellor’s Medal for Excellence in Ph.D. Thesis 2021.” He holds an M.A.Sc. in Electrical and Computer Engineering from Concordia University, Canada, and B.Sc. and M.Sc. degrees with Distinctions in Computer Science and Engineering from the University of Rajshahi, Bangladesh.

Dr. Roy is currently an Assistant Teaching Professor in Data Science at Michigan Technological University. His teaching interests include Data Science, Data Science Projects, Machine Learning, Deep Learning, Data Mining, and Computational Intelligence. His research focuses on the applications of Artificial Intelligence, Machine Learning, Deep Learning, and Data Science to Medical Image Analysis, Computer Vision, and Signal Processing. In his free time, he enjoys traveling, exploring cultures, watching movies, and reading.

Links of Interest

Data Science

Google Scholar

Areas of Expertise

Data Science
Artificial Intelligence
Machine Learning and Deep Learning
Data Mining and Big Data Analytics
High-Performance Computing (HPC) with Linux and SLURM
Applications: Medical Image Analysis, Computer Vision, and Signal Processing

Recent Publications

A. Islam, F. Aktar, S. K. Roy, March 17, 2024, "BengaliSent140 - A Bengali Hate Speech Fusion Dataset", IEEE Dataport, doi: https://dx.doi.org/10.21227/0bsx-z948.
S. K. Roy, A. Nicolson, and K. K. Paliwal, "On Supervised LPC Estimation Training Targets for Augmented Kalman Filter-based Speech Enhancement," in Speech Communication, vol. 142, pp. 49-60, doi: https://doi.org/10.1016/j.specom.2022.06.004.
S. K. Roy, A. Nicolson, and K. K. Paliwal, "DeepLPC-MHANet: Multi-Head Self-Attention for Augmented Kalman Filter-based Speech Enhancement," in IEEE Access, vol. 9, pp. 70516-70530, 2021, doi: 10.1109/ACCESS.2021.3077281.
S. K. Roy, A. Nicolson, and K. K. Paliwal, "DeepLPC: A Deep Learning Approach to Augmented Kalman Filter-Based Single-Channel Speech Enhancement," in IEEE Access, vol. 9, pp. 64524-64538, 2021, doi: 10.1109/ACCESS.2021.3075209.
S. K. Roy and K. K. Paliwal, " Robustness and Sensitivity Metrics-Based Tuning of the Augmented Kalman Filter for Single-Channel Speech Enhancement," in Applied Acoustics, Volume 185, 2022, doi: 10.1016/j.apacoust.2021.108355.
S. K. Roy and K. K. Paliwal, " Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement," in signals, vol. 2, pp. 434-455, 2021, doi: 10.3390/signals2030027.
S. K. Roy and K. K. Paliwal, " A Noise PSD Estimation Algorithm using Derivative-based High-pass Filter in Non-stationary Noise Conditions," in EURASIP Journal on Audio, Speech, and Music Processing, 32 (2021), doi: 10.1186/s13636-021-00220-9.
S. K. Roy, A. Nicolson, and K. K. Paliwal. (2020) "A Deep Learning-Based Kalman Filter for Speech Enhancement," Proc. Interspeech 2020, pp. 2692-2696.
S. K. Roy, A. Nicolson, and K. K. Paliwal, "Deep Learning with Augmented Kalman Filter for Single-Channel Speech Enhancement," 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 2020, pp. 1-5.
S. K. Roy and K. K. Paliwal, "Deep Residual Network-Based Augmented Kalman Filter for Speech Enhancement," 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2020, pp. 667-673.
S. K. Roy and K. K. Paliwal, "Causal Convolutional Encoder Decoder-Based Augmented Kalman Filter for Speech Enhancement," 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS) 2020, pp. 1-7.
S. K. Roy and K. K. Paliwal, "Causal Convolutional Neural Network-Based Kalman Filter for Speech Enhancement," 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2020, pp. 1-6.
S. K. Roy and K. K. Paliwal, "Sensitivity Metric-Based Tuning of the Augmented Kalman Filter for Speech Enhancement," 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS), 2020, pp. 1-6.

Research Funding

Capacity Enhancement of the Computer Science & Engineering department, University of Rajshahi for Realizing Digital Bangladesh, funded by World Bank (4,19,100 USD), April 2012-March 2014. I was working with other faculty members on this project.
Prepared some part of the research grant proposal "Single and Multi-Microphone Techniques of Speech Enhancement for Reliable Voice Communications under Adverse Acoustic Conditions" --- Part II (May 2015-Feb 2016) sponsored by Microsemi (Canada).

Research Projects

Current Research Activities

Machine Learning and Deep Learning for Biomedical Data: Exploring the use of Pandas with machine learning and deep learning techniques for analyzing wearable sensor data to enhance clinical decision-support systems.
Vision Transformer and Multimodal Learning: Investigating the application of Vision Transformers and multimodal learning approaches for histological image analysis aimed at cancer detection.
Explainable AI (XAI): Researching algorithms for explainable AI to improve transparency in clinical decision-support systems.
Fake Image Detection in Social Media: Examining multimodal learning and deep learning algorithms to detect fake images on social media platforms.
Large Language Models for Information Retrieval: Investigating the use of large language models for information retrieval and cyberbullying detection in social media, focusing on both English and Bengali languages.
Deep Learning for Speech Intelligibility: Exploring advanced deep learning algorithms to enhance speech intelligibility and improve hearing aids.

Teaching Experience

Fall 2026

DATA2201: Foundations of Data Science
DATA4991: Data Science Project
UN5550: Introduction to Data Science

Fall 2025

DATA2201: Foundations of Data Science
DATA4991: Data Science Project
UN5550: Introduction to Data Science

Spring 2026

CS5841 / EE5841: Machine Learning
CS4821 / CS5831: Data Mining / Advanced Data Mining
UN5550: Introduction to Data Science

Fall 2024

CS5821 / EE5821: Computational Intelligence
DATA2201: Foundations of Data Science

Spring 2025

CS5841 / EE5841: Machine Learning
CS4821 / CS5831: Data Mining / Advanced Data Mining
UN5550: Introduction to Data Science

Skills and Expertise

Programming: Python (Pandas, NumPy, SciPy, Scikit-Learn, TensorFlow, Keras), SQL, NoSQL, MongoDB
Data Handling: Proficient in data wrangling, large-scale processing (Pandas, NumPy, Dask)
Visualization: Skilled with Matplotlib, Seaborn, Tableau, Power BI
Machine Learning: Grid search, Bayesian optimization; experience with Decision Trees, SVM, XGBoost, MLP, and more
Deep Learning: Expertise in CNNs, ResNets, GANs, Transformers, DenseNet, TCN, RL, etc.
Collaboration: Git, GitHub, GitLab for version control and team development

Teaching Statement

My extensive tenure in academia has provided me with invaluable insights into both learning and teaching. Drawing from these experiences, I have formulated a teaching philosophy centered on fostering independent problem-solving skills among students, particularly in the fields of computer science and data science. My aim is to promote literacy, critical thinking, effective communication, and collaboration.

In computer science, I emphasize algorithmic thinking and debugging strategies alongside teaching programming languages like Python and Java. This approach enables students to tackle complex coding challenges independently, enhancing their problem-solving abilities.

In data science courses, I integrate real-world data projects involving data collection, cleaning, analysis, and visualization using tools such as R and SQL. This hands-on experience helps students understand the entire data pipeline, from theoretical concepts to practical applications, while also considering ethical implications and data privacy.

Collaboration and communication are vital components of my teaching philosophy. I incorporate group projects and peer reviews to develop students' teamwork and communication skills, preparing them for professional environments where interdisciplinary collaboration is essential. Additionally, I require students to present their projects and research findings, helping them refine their public speaking and technical writing abilities.

Ultimately, my goal is to empower students to become independent, critical thinkers and lifelong learners. Through a supportive and engaging learning environment, I strive to inspire students to take ownership of their education and succeed in the rapidly evolving fields of computer science and data science.