About Our Team
The Ko Lab brings together postdoctoral fellows and graduate researchers working across hardware design, efficient AI, security and privacy, medical imaging and computer vision, under the supervision of Prof. Seok-Bum Ko.
Lab Members
Supervisor
Prof. Seok-Bum Ko
Professor and Department Head, Electrical and Computer Engineering
Professor in the Department of Electrical and Computer Engineering and in the Division of Biomedical Engineering. His research covers computer architecture and arithmetic, efficient hardware implementation of compute-intensive applications, deep learning processor architecture and biomedical engineering. Senior Member of the IEEE and a former IEEE Circuits and Systems Society Distinguished Lecturer (2024-2025).
Postdoctoral Fellows
Riel Castro-Zunti
Postdoctoral Fellow
Applies deep learning to medical image analysis, including automated rib-fracture diagnosis and the early detection of disease from radiological images.
Hamish Haider
Postdoctoral Fellow
Develops efficient transformer and large language model compression techniques, including quantization and pruning, to enable deep learning on resource-constrained hardware.
Doctoral Researchers
Ebrahim Fard
PhD Candidate, Electrical and Computer Engineering (2023-present)
Designs, implements and evaluates a power-efficient microarchitecture for sparse deep neural networks. Recent work includes FlexPWL, a flexible, scalable and multiplier-free approach for activation functions on FPGA.
Francis Ferri
PhD Student
Develops efficient deep learning for computer vision, including pose estimation for animal monitoring and livestock welfare.
Gabriel Guerra
PhD Student, Electrical and Computer Engineering (2024-present)
Applies deep learning to natural language processing, particularly named-entity recognition over scientific text, and to AI for industry including predictive maintenance, alongside continuing work in robotics.
Maria Kousar
PhD Student, Biomedical Engineering (2026-present)
Applies deep learning to biomedical engineering, including signal processing and sequential modeling for clinical data analysis.
Mohammadreza Mostafavi
PhD Student, Electrical Engineering (2024-present)
Works on biomedical signal and image processing, and on vision-based livestock monitoring systems using artificial intelligence and computer vision.
Sayed Muhsin
PhD Student
Focuses on the compression and acceleration of AI models, including efficient transformers and channel decoders.
Stephany Valarezo-Plaza
PhD Student
Develops efficient deep learning for edge deployment, spanning crop yield prediction from drone hyperspectral imagery, transformer compression and combustion instability prediction.
YoungJoo Hyun
PhD Student
Works on computer vision and signal processing for manufacturing, with a focus on AI-based anomaly detection, visual inspection and condition monitoring.
Master’s Students
Syed Mohsin Shah
MSc Student, Electrical Engineering (2024-2026)
Designs hardware accelerators for deep neural networks, with an emphasis on improving resource utilization, performance and computational efficiency.
Saghar Emami
MSc Student
Applies computer vision to inspection tasks, including HAZMAT placard detection with YOLO-family detectors.
Dorsa Robatjazy
MSc Student
Applies deep learning to medical imaging, building models that read diagnostic images.
Muhammad Fahad
MSc Student, Electrical Engineering (2024-present)
Designs energy-efficient hardware for AI, including a runtime-reconfigurable multi-precision posit multiplier and FPGA neural network accelerators.
Interns
Noor Fatima
Mitacs Globalink Research Intern, Computer Engineering (UET Lahore)
Applies transformer models to EEG signal decoding, and is bringing the same efficiency-minded approach to the automated design and pruning of variational quantum circuits.
Rafed Aftab
Mitacs Globalink Research Intern, Electrical Engineering (NUST Islamabad)
Works on efficient AI for constrained hardware, including structured pruning of object detectors for cross-domain robustness and embedded deployment.