Geoffrey Sonn, M.D.
Principal Investigator, Urologic Cancer Innovation Lab
Assistant Professor of Urology and Radiology (by courtesy)
Dr. Sonn is a urologic oncologist who specializes in treating patients with prostate and kidney cancer. He has a particular interest in cancer imaging, MRI-Ultrasound fusion targeted prostate biopsy, prostate cancer focal therapy, and robotic surgery for prostate and kidney cancer. His research is focused on developing novel, imaging-based technologies to improve diagnosis and treatment of urologic cancers. He is a member of Stanford Bio-X, the Stanford Cancer Institute, and Associate Member of the Canary Center at Stanford for Cancer Early Detection.
Richard Fan, Ph.D.
Engineering Director, Urologic Cancer Innovation Lab
Clinical Assistant Professor, Urology
Dr. Fan supports several clinical programs within urologic oncology including MR-US fusion targeted biopsy and prostate cancer focal therapy. He is also a member of the Stanford Byer's Center for Biodesign faculty, where he co-leads several courses including BioE 141A/B - Bioengineering Senior Capstone and MED 275B - Biodesign Fundamentals. Dr. Fan's research interests are on image guided detection and treatment of prostate cancer, including MR-US fusion, focal therapies, embedded systems and robotics.
Indrani Bhattacharya, Ph.D.
Postdoctoral Research Fellow
Dr. Bhattacharyya completed her Ph. D. from the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI), Troy, NY, under the advisement of Dr. Richard J. Radke. Her training and research interests are in in the domain of computer vision, machine learning, and multimodal data analytics for developing systems that assists human beings. Her research focus is on the development of multimodal machine learning models that leverage radiology-pathology fusion for early detection of cancer.
R&D Scientist and Engineer
Sulaiman Vesal is an R&D scientist and engineer at UCIL. His research interests include medical image segmentation, classification and multimodal image analysis using machine learning/deep learning for computer-aided diagnosis and interventions. Before joining Stanford, he was a research data scientist at UCSF. He completed his PhD in medical engineering at Friedrich–Alexander University Erlangen–Nürnberg (FAU) in Germany.
Ahmed Alsinian, Ph.D.
Ahmed Alsinan received his B.Sc. and M.Sc. degrees in Electrical Engineering from Michigan State University (East Lansing, MI) in 2010 and 2013. respectively. He completed his Ph.D. degree in Electrical and Computer Engineering from Rutgers University (New Brunswick, NJ) in 2020. His research interests include signal processing, computer vision, and machine learning. His specific research focus is on the development of deep learning-based solutions to medical image synthesis, segmentation, classification, and reconstruction.
Simon John Christoph Soerenson
PhD Student (Epidemiology 2021-)
Visiting Medical Student (Aarhus University 2019-2020)
Simon John Christoph Soerensen is a last year medical student joining us from Aarhus University, Denmark. His clinical areas of interest include cancer imaging, urologic oncologic outcomes research, prostate cancer, robotic urologic surgeries, and applied deep learning in healthcare. He is currently working on developing a novel deep learning method for diagnosing prostate cancer on MRI based on zonal prostate anatomy.
Yash Khandwala M.D.
Urology Resident, Research Year (2021-2022)
Lukas Hockman, M.D.
Urologic Oncology Fellow (2021-2022)
Stanford Medicine MD/PhD Candidate
Visiting Medical Student (Wayne State University 2021)
Pejman Ghanouni, M.D., Ph.D.
Assistant Professor of Radiology, and by courtesy, of Neurosurgery and Urology
Christian Kunder, M.D., Ph.D.
Assistant Professor of Pathology
Andrei Iagaru, M.D.
Professor of Radiology (Nuclear Medicine)
Nancy Wang, M.D.
Urology Resident, Research Year (2017-2018)
Topic: Improving Magentic Resonance Imaging Interpretation and Education by Urologists
Yong-Hun Kim, B.S.
Computer Scientist and Research Data Analyst (2019-2020)
Yong-hun Kim is a recent alum of Stanford having received his B.S. in computer science. He currently supports various projects in urologic oncology through data processing and management. His research interests lie in the applications of machine learning in medicine.
Canary Crest Scholar (2019)
University of Texas, Austin
Leo Chen, M.D.
Urology Resident, Research Year (2018-2019)
Topic: Machine Learning and Medical Image Processing of Urologic Cancers
Dr. Chen is a urology resident who is interested in applying deep learning to solve problems in healthcare.
Visiting Researcher (2020)
Drexel University College of Medicine
Maxime Rappaport is a second year medical student at the Drexel University College of Medicine. His research is focused on utilizing radiomics and machine learning to enhance prostate mpMRI interpretations. He previously worked as a clinical research coordinator in the Department of Urology at UCLA and as an R&D scientist with Alessa Therapeutics.
Canary Crest Scholar (2020)
Georgia Institute of Technology
Amir is a CS major at Georgia Tech who is passionate about bridging the gap between research and industry. His research interests lie at the intersection of deep learning and healthcare in order to tackle challenges like learning better representations of data and model generalizability.
Arun Seetharaman, B.S.
Graduate Student Research Assistant (2018-2020)
Arun is a current Electrical Engineering M.S. student who is primarily interested in applying machine learning techniques to medical imaging problems. For the past few months I have been working with Drs. Mirabela Rusu, Geoff Sonn and Richard Fan to use of machine learning to segment cancer in MRI scans of the prostate using a fusion of radiology and pathology information.
Bogdana Schmidt, M.D.
Urologic Oncology Fellow (2020-2021)
Nikola Teslovich, M.D.
Med Scholars Research Program (2019)
Nick Teslovich is a fourth year Stanford medical student who will be starting next year as a Stanford Urology resident. His projects as a medical student explore the use of prostate MRI and targeted biopsy data to guide candidacy and implementation of focal therapies for prostate cancer.