Saad Hossain, an undergraduate Biomedical Engineering student, was selected as an Honorable Mention for the 2024-2025 Computing Research Association (CRA) Outstanding Undergraduate Researcher Award (URA).
This prestigious recognition highlights undergraduate students across North America who show exceptional promise in computing research. With hundreds of nominations this year, this achievement is truly remarkable.
Saad has been actively involved in research since his first co-op and 2A academic term, completing five Undergraduate Research Assistantships (URAs) and one Undergraduate Research Internship to date. His dedication and extensive research contributions have led to co-authorship in multiple publications: a conference paper in the KDD 2023 Conference, a journal paper accepted for publication in the American Journal of Emergency Medicine, workshop papers in NeurIPS 2024 and NeurIPS 2023, a workshop paper in ICLR 2023, and a workshop paper in CVPR 2022.
Saad’s research spans several key areas. In Domain Adaptation, he works on developing methods to adapt models trained in one setting, such as synthetic or daytime images, to perform effectively in other settings, like real-world or nighttime images. His work in Pose Estimation involves predicting the location of human joints, such as shoulders, head, and hands, from images to facilitate tasks like human activity recognition. In Medical Imaging Diagnostics, he applies AI to automate lung ultrasound diagnostics.
According to Jinman Park, PhD, who worked with Saad in 2022, "Saad was an exceptionally motivated and highly efficient research assistant who consistently delivered outstanding contributions to our team. On the paper we published in KDD, Saad played a pivotal role, particularly in managing and processing complex datasets."
Throughout his research journey, Saad has demonstrated remarkable expertise and initiative. He spearheaded data management components, designed and implemented workflows for image-to-video parsing, developed methods for pose transformations, and applied advanced data augmentation techniques to enhance model training. His proactive role in prediction validation ensured the accuracy and reliability of results through meticulous testing and analysis.
Saad’s recognition as an Honorable Mention showcases his impressive ability to tackle technical challenges, meet tight deadlines, and maintain the highest quality of work. His strong organizational skills, innovative thinking, and relentless pursuit of excellence have significantly contributed to every project he has undertaken.