Join the Machine Learning in Medical Imaging Consortium (MaLMIC) for an opportunity to network on machine learning
Computer- and Robotics- Assisted Cancer Surgery
September 12, 2025
3:00 to 4:30 p.m. Eastern
Join us for talks by Drs. Amoon Jamzad and Laura Connolly, who will share two cutting-edge perspectives on advancing cancer surgery through robotics and intelligent tools. Laura will speak about her work on integrating AI-enabled robotics and haptic feedback into breast-conserving surgery to improve tumor removal in highly mobile, deformable tissue. She will also share how robotics combined with photoacoustic imaging can inspect resection cavities post-surgery, aiming for a future where no cancer is left behind. Amoon will present his research on applying deep learning to mass spectrometry data—capturing molecular tissue signatures in real time—to help surgeons accurately distinguish healthy from cancerous tissue. Amoon will highlight strategies for making these models trustworthy, explainable, and generalizable, and for translating mass spectrometry into intraoperative and post-resection workflows. Together, these innovations promise to enhance surgical precision, reduce positive margins, and improve patient outcomes.
Interested in joining? Please contact us.
Computer- and Robotics- Assisted Cancer Surgery
September 12, 2025
3:00 to 4:30 p.m. Eastern
Join us for talks by Drs. Amoon Jamzad and Laura Connolly, who will share two cutting-edge perspectives on advancing cancer surgery through robotics and intelligent tools. Laura will speak about her work on integrating AI-enabled robotics and haptic feedback into breast-conserving surgery to improve tumor removal in highly mobile, deformable tissue. She will also share how robotics combined with photoacoustic imaging can inspect resection cavities post-surgery, aiming for a future where no cancer is left behind. Amoon will present his research on applying deep learning to mass spectrometry data—capturing molecular tissue signatures in real time—to help surgeons accurately distinguish healthy from cancerous tissue. Amoon will highlight strategies for making these models trustworthy, explainable, and generalizable, and for translating mass spectrometry into intraoperative and post-resection workflows. Together, these innovations promise to enhance surgical precision, reduce positive margins, and improve patient outcomes.
Interested in joining? Please contact us.
Laura Connolly, PhD
Postdoctoral Fellow, Johns Hopkins University
Dr. Laura Connolly completed her Ph.D. in Electrical Engineering at Queen’s University in July 2025 under the mentorship of Dr. Gabor Fichtinger, Dr. Parvin Mousavi and Dr. Russell H. Taylor. She is currently beginning her postdoctoral fellowship at Johns Hopkins University in Baltimore, MD, USA with Dr. Axel Krieger and Dr. Russell H. Taylor. Her research is primarily focused on the application of robotics and image guidance for margin detection in cancer surgery.
Amoon Jamzad, PhD
Adjunct Assistant Professor, Queen’s University
Dr. Amoon Jamzad is an Adjunct Assistant Professor at the School of Computing and a Postdoctoral Fellow at the Med-i Lab, Queen’s University. His research lies at the intersection of artificial intelligence and surgical oncology, with a focus on computer-assisted interventions and cancer diagnosis. His areas of expertise include self-supervised learning, uncertainty estimation, graph-based modeling, and explainable AI. Amoon holds a BSc in Electrical Engineering and MSc and PhD in Biomedical Engineering. He works closely with an interdisciplinary team of clinicians, engineers, and biologists to develop AI-driven tools that enhance multimodal clinical decision-making. His work has been featured in top-tier venues such as NeurIPS, MICCAI, IPCAI, and IJCARS. Amoon was recognized with the School of Computing Research Award in 2021 for his impactful contributions.