Hello and thank you for stopping by!
Welcome to my corner of the web. I’m currently a Graduate Teaching Assistant in the Department of Computer Science at Purdue University, where I have the privilege of working in the Cyber2s Lab under the guidance of the esteemed Dr. Elisa Bertino. Prior to this, I worked as a Lecturer at the Department of CSE, United International University, Bangladesh and an adjunct faculty at Bangladesh University of Engineering and Technology (BUET). I graduated from the Department of Computer Science and Engineering at BUET, an experience that laid the foundation for my passion for technology and innovation.
My research interests span a variety of exciting fields, including Systems and Network Security, Machine Learning, Data Mining, Privacy, and Ubiquitous Computing.
Beyond the world of code and algorithms, I love immersing myself in new experiences—whether it’s traveling to unexplored destinations, savoring exotic cuisines, meeting fascinating people, or embracing diverse cultures. Feel free to browse around, learn more about my work, and reach out if you’d like to connect or collaborate—I’d love to hear from you!
Working in Cyber2s lab under supervision of Dr. Elisa Bertino
Working as a Lecturer at Department of CSE, United International University, Bangladesh.
Courses Conducted: Structured Programming Language, Object Oriented Programming, Artificial Intelligence, Bioinformatics,
Algorithms, Computer Networks lab, Human Computer Interaction and Society, Technology & Engineering Ethics
Worked as a Part-time Lecturer at Department of CSE, Bangladesh University of Engineering and Technology, Bangladesh.
Courses Conducted: Structured Programming Language Lab
Worked as a graduate research assistant under supervision of Dr. Atif Hasan Rahman and Dr. Mohammed Eunus Ali in CSE, BUET. Multiple research projects are funded by the government of Bangladesh.
Collaborators:
Dr. Imtiaz Karim,
Mirza Masfiqur Rahman,
Dr. Elisa Bertino
This work focuses on analyzing and finding security flaws in the 5G control plane protocols. We implemented a testbed using radio drivers by modifying open source suites deployed on mini base stations. With testbed we can asynchronously send protocol messages to smartphones via the 5G network to find out the vendor specific deviations from the 3GPP standards and potentials security implications.
Current state: Ongoing
Collaborators:
Sarkar Snigdha Sarathi Das,
Masum Rahman,
Md. Saiful Islam,
Dr. Atif Hasan Rahman,
Mohammad Mehedy Masud,
Dr. Mohammed Eunus Ali
This work focuses on reliable prediction on Photoplethysmography (PPG) signals that noisy due to motion artifacts. We have used Bayesian Deep Learning Model implemented in Python using PyTorch framework to provide an uncertainty estimate along with the prediction. We have also implemented a Tizen Native and Background Service App in C for continuous and timed raw signal data collection from Smartwatch. Our Model beats the state of the art work both on the largest publicly available dataset and on the MIMIC-III dataset. It was the first application of Bayesian Deep Learning in this domain.
Current state: Accepted in UbiComp 2022, Published in IMWUT
Publication URL arXiv
Supervisor:
Dr. Mohammed Eunus Ali,
Dr. Atif Hasan Rahman
In this work we are experimenting on contrastive learning to learn patient similarity from physiological signals, particularly Photoplethysmography (PPG) signals. Due to limited availability of dataset, we are currently conducting a case study on Atrial Fibrillation Detection from PPG signals. It is the first application of Contrastive Similarity Learning in this domain.
Current state: Ongoing
arXiv Link
Supervisor:
Dr. Shagufta Mehnaz
In this work we are experimenting on vulnerabilities and privacy risks of Trained Classification Models introduced particularly from Model Explainability. We explore potential application of various model explanation techniques like LIME, SHAP, Counterfactual Explanation etc. for implementing model inversion attacks. We are currently working on how we can generate a more robust attack dataset capable of leaking more information about the black box model using minimal knowledge about the training dataset. We are also exploring the direction in which LIME explanations can be exploited for property inference attacks.
Current state: Ongoing
Sarkar Snigdha Sarathi Das, Subangkar Karmaker Shanto, Masum Rahman, Md. Saiful Islam, Atif
Rahman, Mohammad Mehedy Masud, Mohammed Eunus Ali.
BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data.
[UbiComp 2022] Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous
Technologies, 6, 1, Article 8 (March 2022)
[pdf] [code]
[arXiv]
CGPA: 3.88/4.00 (Ranked 8th in a class of 143 graduating students)
Major CGPA: 3.95/4.00
An information system web-app developed in Django for an online restaurant hub on the perspective of Dhaka
City of Bangladesh
GitHub: https://github.com/Subangkar/Foodsquare-Web-App
Docker: https://hub.docker.com/r/subangkar/foodsquare
A Deep Learning Project to generate image caption on Flickr8k Image dataset using Resnet-101 & LSTM with
Attention Mechanism
GitHub: https://github.com/Subangkar/Image-Captioning-Attention-PyTorch
A Tizen Native UI app and Background Service to collect raw sensor data for analysis
GitHub (BG Service): https://github.com/Subangkar/Tizen-Sensor-Raw-Data-Saving-Service
GitHub (Native UI App): https://github.com/Subangkar/Gear-Fit-2-Sensor-Raw-Data-Sync
A project to demonstrate frequency spectrum visualization from real time audio via time domain to frequency domain conversion on Atmega32 microcontroller
GitHub: https://github.com/Subangkar/Real-Time-Audio-to-Frequency-Spectrum-Transformation-on-Atmega32