PhD in Computer Science, University of Missouri-Kansas City, 2024
B.Sc. in Computer Science and Engineering, Bangladesh University of Engineering and Technology, 2017 (CGPA: 3.81/4, Rank: 17/131)
HSC in Science, Viqarunnisa Noon College, 2011 (GPA: 5/5)
SSC in Science, Viqarunnisa Noon School, 2009 (GPA: 5/5)
Biography
Dr. Sumaiya Tabassum Nimi is a computer science researcher and educator with deep expertise in machine learning, deep learning for resource-constrained environments, and few-shot/generalization tasks. She earned her PhD in Computer Science from the University of Missouri–Kansas City, where her research led to several high-impact publications on neural network optimization, out-of-distribution (OOD) detection, and edge-cloud collaborative AI.
With over seven years of university teaching experience—including current work as an Assistant Professor at North South University—she combines theoretical rigor with real-world application. Her interests now extend toward quantum machine learning and the intersections of computation and physics, reflecting a growing passion for interdisciplinary exploration.
Dr. Nimi is also the founder of the QuICK (Quantum mechanics guided Intelligent Computation for Knowledge-based systems) Research Group and an advocate for research-driven learning, having taught advanced topics like statistical learning methods and big data. She brings a unique blend of academic leadership, applied ML expertise, and curiosity-driven innovation to every endeavor.
Research Group: https://sumtamnimi.wixsite.com/questlab
LinkedIn Research Group: https://www.linkedin.com/groups/14619021/
Research Areas
Research Interests
Quantum Machine Learning
Molecular Machine Learning
Edge Computing
Computer Vision
Recommender Systems
Teaching
Research Projects & Grants
Organization | Title of consultancy/ research project | Amount Received, if any (BDT) | Year |
UMKC Women’s Council | Towards Intelligent deep learning model synthesis for dynamic domain-constrained deployment scenario | 250,000 | 2023 |
School of Graduate Studies, UMKC | Towards making Edge Devices Intelligent through Deployable Model Synthesis | 800,000 | 2022 |
UMKC Women’s Council | Towards developing deployable multi-tasking deep learning model generation for device-edge collaborative inference | 300,000 | 2022 |
UMKC Women’s Council | Towards deep learning model synthesis for Activity recognition using Smartwatches | 150,000 | 2021 |
ICT Division | Biologically Inspired Learning rules for Artificial Neural Networks (ANN) | 15,00,000 | 2017 |