Ph.D. Students
Jerrin Bright
Jerrin Bright is a doctoral student in Systems Design Engineering. Jerrin’s research focuses on understanding and interacting with the dynamic, 3D world around us, with a particular emphasis on human behavior. By combining computer vision, computer graphics, and machine learning, Jerrin is exploring how to capture, perceive, and understand the 4D world from what we can see in 2D images and videos.
Ken M. Nsiempba
Ken is a PhD student, he teaches computers how to interpret ice hockey games events by improving player tracking, action recognition and play detection.
Marjan Shahi
A Ph.D. student interested in Sports Analytics
Jinman (Eddie) Park
Jinman (Eddie) Park is a doctoral student in Systems Design Engineering under the supervision of Prof. Paul Fieguth and Prof. David Clausi. His thesis work is on superpixel salient object detection supported by Microsoft Office Media Group to perceive images in an abstract way. He is also involved with various other research areas such as egocentric pose estimation, unsupervised domain adaptation, and sea ice classification.
Nicholas Pellegrino
Nicholas Pellegrino is a doctoral student in Systems Design Engineering, supervised by Prof. Paul Fieguth and associated with the Vision and Image Processing (VIP) Lab and the Statistical Image Processing (SIP) Lab. His main research focus is on machine vision, specifically object recognition. In support of the BIOSCAN program, associated with the International Barcode of Life project, Nicholas has undertaken the task of taxonomic order-level insect image classification. Broadly, this research will enable a far more extensive and detailed understanding of global biodiversity and the interactions between species and ecosystems.
Henry A. Leopold
Henry is conducting a joint PhD in Systems Design Engineering and Vision Sciences, under the supervision of John Zelek and Vasudevan Lakshminarayanan. His research is on self-supervised reinforcement learning for robotic control.
M.A.Sc. Students
Vasyl Chomko
Master of Applied Science student in the Systems Design Engineering program, supervised by Prof. David Clausi and Prof. Alex Wong. He is part of the Sports Analytics group. Currently, his research focuses on object segmentation and key point detection in low-resolution videos.
Saeejith Nair
Saeejith is an MASc student in Systems Design Engineering, supervised by Prof. Alexander Wong and Prof. Javad Shafiee. His research primarily involves improving machine learning architecture efficiency, with a focus on applications in embedded systems and robotics.
Chun-Cheng (Kris) Feng
Kris is a MASc student in the Systems Design Engineering department with a research focus on photometric calibration and camera calibration, working under the supervision of Prof. John Zelek.
Yan Song Hu
Yan Song Hu is a MASc student at this lab working on combining 3D Gaussian Splatting and Simultaneous Localization and Mapping (SLAM).
Junfeng Lei
M.A.Sc student in remote sensing research group.
Howard Nguyen-Huu
Howard is a Master’s student in Systems Design Engineering, co-supervised by Prof. David Clausi and Prof. Yuhao Chen as part of the Sports Analytics Research Group
Jayden Hsiao
Jayden is interested in the semantic segmentation of sea ice using synthetic aperture radar (SAR) and passive microwave (PM) data to improve Indigenous community safety, climate modelling, and ship navigation.
Lily de Loë
Lily is a master’s student in Systems Design Engineering, supervised by Dr. David Clausi and Dr. Andrea Scott. She is a member of the Remote Sensing Group, where her research investigates multimodal data fusion and multi-task learning of sea ice parameters, specifically incorporating visible and infrared data.
Chang Liu
Chang is a Master’s student in Systems Design Engineering at the University of Waterloo, co-supervised by Prof. Sirisha Rambhatla and Prof. Alex Wong. Chang’s research focus is on unsupervised domain adaptation in Computer Vision, with application to manufacturing and medical imaging.
Matthew Bradley
Matthew Bradley is a Master’s student at the Department of Systems Design Engineering supervised by Dr. John Zelek. He works on Visual place recognition which is the ability of a camera system to detect when a previously seen location has been revisited. VPR is core to Simultaious Localization and Mapping and its use in mobile robots like self-driving cars. Matthew’s research focuses on improving the robustness of Visual Place recognition through integration of recovered 3D structural information.
Kimathi Kaai
Kimathi is completing his MASc under the Systems Design Engineering department with a research focus on domain generalization for vision-related tasks.
Kseniia (Ksusha) Buzko
Kseniia (Ksusha) Buzko is a Master of Applied Science student in the Systems Design Engineering program. She is supervised by Prof. David Clausi and Prof. Yuhao Chen. Her research interests include action recognition and action detection.
Bavesh Balaji
Bavesh Balaji is a MASc student in the Systems Design Engineering department, working under the supervision of Prof. David Clausi and Prof. Sirisha Rambhatla
Dheeraj Khanna
Dheeraj Khanna is a Master of Applied Science (MASc) student in the Systems Design Engineering Department, supervised by Prof. John Zelek. His current research focuses on tracking and detection of various assembly parts in manufacturing machines for determining part irregularities using visual surveillance systems.
Chi-en (Amy) Tai
Chi-en (Amy) Tai is a Master of Applied Science student in Systems Design Engineering, supervised by Professor Alexander Wong. She is part of the Cancer-Net initiative focused on cancer diagnosis and prognosis using medical imaging and computer vision. She is also part of the NutritionVerse initiative centered around personalized nutrition intervention and intake estimation.
Hayden Gunraj
Hayden Gunraj is a master’s student in Systems Design Engineering, supervised by Prof. Alexander Wong and associated with the Vision and Image Processing (VIP) Lab. His research focuses on medical image analysis, computational imaging, and explainable artificial intelligence.
