Md Abrar Jahin
Scholar

Md Abrar Jahin

Google Scholar ID: VKKO-zAAAAAJ
Center on Knowledge Graphs, Information Sciences Institute, University of Southern California
Deep LearningQuantum Machine LearningGeometric Deep LearningTrustworthy AI
Citations & Impact
All-time
Citations
240
 
H-index
8
 
i10-index
8
 
Publications
20
 
Co-authors
38
list available
Resume (English only)
Academic Achievements
  • Awarded the prestigious Viterbi Graduate School Fellowship (2025-2026). First Bangladeshi undergraduate to secure two international research internships in the same year. Led a team to win the “Smart Roads – Winter Road Maintenance Hackathon 2021”.
Research Experience
  • Participated in a 3-week Machine Learning research internship at UiT - The Arctic University of Norway, contributing to the DIT4BEARs Smart Roads project. First Bangladeshi undergraduate to receive two international research internships in the same year. Conducted research at Okinawa Institute of Science and Technology Graduate University (OIST) as a Research Intern (RI) under Prof. Jonathan Miller. Continued as a Visiting Research Student (VRS) in 2023 and then as a Visiting Researcher (VR) until March 2025, working with Prof. Miller and other researchers on evolutionary dynamics.
Education
  • Currently a CS PhD student at the Thomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California (USC). Affiliated with the Center on Knowledge Graphs at the Information Sciences Institute (ISI). Co-advised by Prof. Craig Knoblock and Prof. Jay Pujara. Obtained B.Sc. in Industrial & Production Engineering from Khulna University of Engineering and Technology (KUET) in March 2024.
Background
  • Research interests include efficient deep learning, quantum machine learning, geometric deep learning, and trustworthy AI. Previous research includes reinforcement learning, sentiment analysis, operations research, and comparative genomics. Has a background in Industrial & Production Engineering (IPE), which allows for a uniquely interdisciplinary approach to solving complex problems in machine learning.
Miscellany
  • Hobbies and personal interests not detailed.