Mert R. Sabuncu

Professor

Mert received a Ph.D. in Electrical Engineering from Princeton University. He was then a post-doctoral fellow at MIT Computer Science and Artificial Intelligence Laboratory. After several years as a faculty member at Martinos Center for Biomedical Imaging (Harvard Medical School and Massachusetts General Hospital), he moved to Cornell where he is Professor in Electrical and Computer Engineering. Broadly speaking, his research interests lie at the intersection of artificial intelligence, biomedical data analysis, and healthcare.

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Benjamin C. Lee

Assistant Professor of AI Research, Department of Radiology at Weill Cornell Medicine

Ben’s current research involves developing machine learning algorithms for cardiovascular CT, echo, ECG, histopathology, and longitudinal data for heart failure, heart transplantation, and coronary plaque characterization. Ben’s previous appointments include the Dalio Institute of Cardiovascular Imaging at WCM and in industry at INVIA Solutions in Michigan researching advanced PET/SPECT algorithms. He received his Ph.D. at the University of Michigan, Ann Arbor in Electrical Engineering in image reconstruction and his B.S. from Cornell University.

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Heejong Kim

Instructor, Department of Radiology at Weill Cornell Medicine

As a computer scientist, Heejong’s interest lies in addressing clinical challenges through machine learning and deep learning approaches. Her primary research focus is on prostate cancer investigation and longitudinal image analysis. She earned her Ph.D. in computer science from New York University. Before pursuing her doctoral degree, she studied biomedical engineering at Korea University, where she developed an interest in medical image research.

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Ruining Deng

Instructor of AI, Department of Radiology at Weill Cornell Medicine

Ruining earned a Ph.D. in Computer Science from Vanderbilt University, specializing in biomedical data analysis with a particular focus on digital pathology and gene expression data. His research emphasized integrating domain knowledge from medical fields into the design of deep learning models to support clinical research. This included developing AI-powered computer vision tools for digital pathology and gene expression data, providing explainable AI insights and biomarkers for clinical applications.

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Mina Chookhachizadeh Moghadam

Postdoctoral Researcher, Department of Radiology at Weill Cornell Medicine

Mina earned her PhD in computer science from the University of California, Irvine, after completing her master’s in computer engineering at Tehran Polytechnique University. Her research primarily develops machine learning and deep learning algorithms for healthcare applications. During her PhD, Mina interned at Edwards Lifesciences and collaborated with Cleveland Clinic researchers to enhance her research’s real-world impact. Currently, Mina focuses on advancing AI and image processing in MRI, specifically developing image segmentation and Super Resolution techniques. Her goal is to create motion-free, high-resolution 3D MR volumes, enhancing disease assessment in abdominal imaging.

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Fengbei Liu

Postdoctoral Researcher, Cornell Tech

Fengbei obtained his PhD degree in Computer Science from Australian Institute for Machine Learning (AIML), University of Adelaide. His research interest lies in computer vision and medical image analysis, particularly in weakly-supervised learning and its application in medical imaging.

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Leo Milecki

Postdoctoral Researcher, Department of Radiology at Weill Cornell Medicine

Co-advised by Qingyu Zhao

Leo is working on deep learning-based methodologies applied to neuroimaging data, focusing on better apprehending neurological developments and diseases. Previously, Leo received a PhD in Computer Science from Paris-Saclay University in France in January 2024. His PhD thesis focused on applying novel deep learning algorithms to analyze biomedical data toward graft rejection diagnostic or prognosis after renal transplantation, focusing on representation and un-/weakly-/self-supervised learning methodologies for multimodal and longitudinal data.

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Samah Khawaled

Postdoctoral Researcher, Department of Radiology at Weill Cornell Medicine and Cornell Tech

Samah holds a Ph.D. from the Technion’s interdisciplinary program of applied mathematics, after completing her M.Sc. (Magna Cum Laude), and B.Sc. degree, from the Technion’s ECE Department in 2017 and 2019, respectively. Her research interests lie in machine learning and deep learning for healthcare, focusing on developing algorithms to characterize and predict stroke risk.
She is the recipient of the Eric and Wendy Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences, and previously received the Ariane de Rothschild Women Doctoral Fellowship.

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Seungbin Park

Postdoctoral Researcher, Department of Radiology at Weill Cornell Medicine

Seungbin earned her Ph.D. in Biomedical Engineering from Purdue University and her M.S. in Computer Science from Korea University. Her research interests lie in the application of machine learning and deep learning in the biomedical field, particularly in medical image analysis. She has extensive experience applying these techniques to a wide range of biomedical data, including medical images, neural signals, and clinical data.

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Cagla Deniz Bahadir

BME PhD student, Cornell University and Cornell Tech

Cagla graduated with a BS degree in Electrical and Electronics Engineering from Bilkent University, Turkey. She, then received the Fulbright Scholarship and graduated with an MS degree in Biomedical Engineering from Cornell University. Her research interests are Magnetic Resonance Imaging, Digital Pathology and Machine Learning. She plays the piano and sings with Cornell University Chorale.

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Batuhan Karaman

ECE PhD student, Cornell University and Cornell Tech

Batuhan previously received a BS degree in Electrical and Electronics Engineering from Middle East Technical University. His research interests center around machine learning and its application to biomedical problems. He is currently working on multimodal learning for Alzheimer’s disease.

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Minh Nguyen

ECE PhD student, Cornell University and Cornell Tech

Minh is from Vietnam and has previously been in Singapore prior to coming to the US. His research interests include statistically efficient machine learning, robust machine learning with noisy data, and causal inference. Minh likes to play tennis and go hiking in his free time. He is also interested in robotics and space exploration.

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Xinzi He

BME PhD student, Cornell University and Cornell Tech

Xinzi He is a dedicated Ph.D. student at Cornell Tech, specializing in machine learning for medicine under the guidance of Prof. Sabuncu. Collaborating with Dr. Martin R. Prince, Xinzi focuses on the development of automatic organ volume calculation techniques for patients with ADPKD. His innovative work has been implemented at Weill Cornell, leading to more accurate and efficient diagnoses. Xinzi holds a B.S. degree from Shenzhen University and an M.S. from Columbia University, showcasing his strong academic background.

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Rachit Saluja

ECE PhD student, Cornell University and Cornell Tech

Rachit’s research focuses on building multi-modal AI models for clinical radiology and developing large-scale medical imaging datasets and models. He also interested in building ML systems that are tightly integrated with the radiologist’s workflow. He received his M.S. degree in electrical engineering from the University of Pennsylvania and his bachelors in Electrical and Electronics Engineering at PES Institute of Technology, India.

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Nusrat Binta Nizam

BME PhD student, Cornell University and Cornell Tech

Nusrat completed her M.Sc. and B.Sc. in BME from Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh. She also served as a lecturer in the Department of BME at BUET and is now on study leave for her higher studies. Her research interests are Medical Image Processing, Computer Vision, and AI for Radiology. She loves cooking, traveling, and enjoys capturing moments through photography. Her aspiration is to leverage her engineering skills to contribute meaningfully in healthcare.

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Haomiao Chen

ECE PhD student, Cornell University and Cornell Tech

Co-advised by Amy Kuceyeski

Haomiao received his BS degree in Physics from University of Illinois at Urbana-Champaign. His research interest lies at the intersection of machine learning and biomedical imaging, particularly the application of machine learning to neuroscience.

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Jorge Tapias Gomez

CS PhD student, Cornell University and Cornell Tech

Jorge graduated with a degree in Computer Science from the University of California Santa Cruz. Before entering his PhD program, Jorge completed his Master’s at Cornell, during which he collaborated with MSK Cancer Center. His research focused on reducing noise and artifacts in colonoscopy images to enhance cancer detection. Additionally, he worked on sanitizing a large dermoscopy dataset, removing Personal Health Information from the images. Jorge’s research interests center on the intersection of Computer Vision and Medical Imaging.

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Jacob Rosenthal

MD-PhD student, Tri-Institutional PhD Program in Computational Biology & Medicine, Weill Cornell Medicine

Jacob is a student in the Tri-Institutional MD-PhD program of Weill Cornell/Rockefeller/Sloan Kettering. His research is motivated by the belief that AI integration will enable the future of medical diagnostics, treatments, and systems that are more effective, efficient, and safe. Specific areas of interest include computer vision, multimodal machine learning, clinical deployment and validation of AI tools, and medical ethics of AI.

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