Abdullah Mamun

Abdullah Mamun

Graduate Research Associate

Arizona State University

About Me

I am a Ph.D. student in Computer Science at Arizona State University. I work as a Graduate Research Associate at Embedded Machine Intelligence Lab (EMIL) under the supervision of Dr. Hassan Ghasemzadeh.

Currently, my work includes building and optimizing deep learning models for time-series data. My works have been published in EMBC'22, BSN'22, and BSN'23 conferences and I have multiple journal and confernce submissions under review. I also actively peer-review conference and journal submissions.

I am conducting a user study at ASU where we collect activity and stress data for machine learning algorithm design. If you are willing to participate, visit here.

Interests
  • Deep Learning
  • Mobile Health
  • Data Privacy
  • Memory Management
Education

  • Ph.D. in Computer Science, Jan 2022 - present

    Arizona State University

  • Ph.D. in Computer Science, Jan 2021 - Dec 2021

    Washington State University

  • B.Sc. in Computer Science & Engineering, 2014 - 2018

    Bangladesh University of Engineering and Technology

Experience

 
 
 
 
 
Grad Research Associate
Arizona State University
Dec 2021 – Present Phoenix, Arizona
I am currently working on a few projects concurrently. My work mostly includes time-series data analysis with machine learning. My accepted publications in 2022 include a conference paper in EMBC 2022, another conference paper in BSN 2022, and a conference abstract in ICAMPAM 2022.
 
 
 
 
 
Teaching Assistant
Washington State University
Aug 2021 – Dec 2021 Pullman, Washington
I was a TA for a semester for the course Advanced Data Structures in C++ (Fall 2021 Cpt_S 223). I helped the students with their queries and difficulties related to programming and theory, graded homeworks and programming assignments, and held office hours. Dr. Yan Yan was the instructor of the course.
 
 
 
 
 
Research Assistant
Washington State University
Jan 2021 – Aug 2021 Pullman, Washington
 
 
 
 
 
Lecturer
United International University
Sep 2019 – Jan 2021 Dhaka, Bangladesh
 
 
 
 
 
Software Developer
HLC Technologies Ltd.
Nov 2018 – Sep 2019 Dhaka, Bangladesh

Recent & Upcoming Talks

Binary imbalanced data classification based on diversity oversampling by generative models
This paper proposes two binary imbalanced data classification methods with extreme learning machine autoencoders and generative models. Their methods outperform SMOTE and similar data balancing tools on most benchmark datasets.
Jan 9, 2024 3:45 PM — 5:00 PM Health Futures Center, ASU
Abdullah Mamun
PDF Slides
Binary imbalanced data classification based on diversity oversampling by generative models
A Survey of Methods for Time Series Change Point Detection
This paper discusses the available methods to detect change points in time-series data.
Nov 8, 2023 1:30 PM — 2:00 PM Health Futures Center, ASU
Abdullah Mamun
PDF Slides
A Survey of Methods for Time Series Change Point Detection
Open-World Semi-Supervised learning
A fundamental limitation of applying semi-supervised learning in real-world settings is the assumption that unlabeled test data contains only classes previously encountered in the labeled training data. However, this assumption rarely holds for data in-the-wild, where instances belonging to novel classes may appear at testing time. Here, we introduce a novel open-world semi-supervised learning setting that formalizes the notion that novel classes may appear in the unlabeled test data.
Sep 27, 2023 1:30 PM — 2:00 PM Health Futures Center, ASU
Abdullah Mamun
PDF Slides
Open-World Semi-Supervised learning
Towards A Rigorous Science of Interpretable Machine Learning
In this position paper, the authors define interpretability and describe when interpretability is needed (and when it is not). Next, they suggest a taxonomy for rigorous evaluation and expose open questions towards a more rigorous science of interpretable machine learning.
Aug 23, 2023 1:30 PM — 3:00 PM Health Futures Center, ASU
Abdullah Mamun
PDF
Towards A Rigorous Science of Interpretable Machine Learning
Self-Supervised Adversarial Distribution Regularization for Medication Recommendation
This paper reduces drug-drug interaction in automated medication recommendation with help of self-supervised adversarial distribution regularization
Jun 21, 2023 12:30 PM — 1:00 PM Online (Zoom)
Abdullah Mamun
PDF video
Self-Supervised Adversarial Distribution Regularization for Medication Recommendation
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