Team

Dr. Andreas Rauschecker is a neuroradiologist and a neuroscientist by training.  He is interested in better understanding brain function and dysfunction through the use of magnetic resonance imaging, and specially through the use of advanced image analytics and artificial intelligence applied to brain imaging data.  Dr. Rauschecker completed his undergraduate education in Biology and Psychology at Georgetown University and a Master's in Neuroscience at Oxford University.  He obtained his MD and PhD (Neuroscience) degrees at Stanford University.  He then completed his residency training in diagnostic radiology at the University of Pennsylvania and his neuroradiology fellowship at UCSF.  In addition to directing his laboratory, Dr. Rauschecker is attending neuroradiologist at UCSF and Benioff Children's Hospital, Associate Program Director of UCSF's Neuroradiology Fellowship Program, and co-Executive Director of UCSF's Center for Intelligent Imaging (ci²).  In his spare time, Dr. Rauschecker loves exploring the outdoors, eating great food, and traveling to new places, especially with his family.

Dr. Leo Sugrue is a neuroscientist and neuroradiologist who researches the brain circuits that control complex thought and behavior and their involvement in mental illness. He has a joint appointment in Psychiatry and works closely with colleagues at the Dolby Center for Mood Disorders and the Weill Institute for Neurosciences to develop novel circuit-based approaches to image and treat neuropsychiatric conditions using closed-loop brain stimulation and brain-directed focused ultrasound. 

Sugrue directs the Laboratory for Precision Neuroimaging at UCSF, which emphasizes integrating brain imaging with genetic, health, and behavioral data to better understand, diagnose, and treat brain disease. He is a co-investigator for the National Adolescent Brain and Cognitive Development Study (ABCD), and his group works to leverage “big data” from studies like ABCD to better understand neuropsychiatric disorders and improve their diagnosis and treatment in individual patients.

Sugrue completed his undergraduate education and PhD in Neuroscience at Stanford University and received his MD from Johns Hopkins University School of Medicine. He completed residency in diagnostic radiology and fellowship in Neuroradiology at UCSF before joining the faculty in 2014. He co-founded the Fucused Ultrasound in Neuroscience program (FUN) at UCSF, a multidisciplinary effort to use brain-directed focused ultrasound to treat movement disorders like Essential Tremor and to develop it as a tool to modulate brain activity with high spatial and temporal precision. Leo grew up in Dublin and visits Ireland regularly, in his spare time he enjoys cooking, gardening, and tinkering with old motorcycles.

Atlas is a Postdoctoral Scholar in the lab, focusing on the applications of deep learning and advanced MRI techniques in Diffuse Midline Glioma, a rare pediatric tumor. She completed her M.D. degree from Mashhad University of Medical Sciences (MUMS) in Iran and previously worked on cancer radiomics and radiogenomics combined with machine learning. In her free time, she enjoys going on a run or hike with friends, dancing, and playing the piano.

Radhika is an Associate Research Specialist in the lab, focusing on developing tools and applying artificial intelligence and deep learning to increase understanding of the brain and disease progression. Radhika completed her undergraduate education in Bioengineering and Data Science at UC Berkeley. In her free time, she sings Hindustani Classical music and tutoring math and science to high school and college students.

Pierre is a Data & Computer Scientist in the lab, focusing on developing new methods and tools to enable the lab's various research projects. He strives to develop and adapt both traditional statistical & imaging methods and state-of-the-art deep learning algorithms, and is passionate about teaching these methods to other lab members. Pierre completed a Bachelor in Mathematics & Physics and a Master of Science in Bioengineering from Ecole polytechnique in Paris, and a Master of Translational Medicine between UC Berkeley and UCSF. He previously co-founded a medical device startup with a UCSF researcher and helped a metal 3D printing for medical devices startup grow from 2 employees to 20. In his free time, he loves backpacking, sailing, biking, or playing the piano and guitar to singalong with friends.

Ryan is a research assistant who graduated from Carleton College. He is interested in the physical brain changes that occur in patients with neurodegenerative disease and psychiatric disorders (i.e. differences in cortical thickness/volume, diffusion, and functional activity). While comparatively less experienced in this aspect of research, Ryan is also interested in investigating the genetic background of neurological diseases. Outside of the lab, Ryan is a gamer and martial artist.

Simon Pan is a graduate of the UCSF MSTP program and an incoming diagnostic radiology resident at UCSF who is interested in neuroradiology. His research interests are centered on using informatics methods to investigate multimodal interactions of environmental, genetic, and socioeconomic factors on brain development, plasticity, and neuropsychiatric disease. Outside of work, Simon enjoys running a cooking & food photography blog, rock climbing, fishing/crabbing, kayaking, playing music, reading/writing about history, and cycling.

Mike is a radiology resident with a background in deep learning, statistical modeling, and image processing. He did his PhD in Computational Neuroscience at Boston University where he used calcium imaging to study the striatum in mice, and then moved from mice to humans in whom he worked to forecast disease progression in Alzheimer's dementia using structural MRI and survival modeling. He is interested in using imaging and machine learning to inform one another. In his free time, he enjoys reading and meditating.

Yannan Yu is a first year diagnostic radiology resident at UCSF. Her research focused on neuroimaging especially stroke imaging, including MR/CT perfusion, diffusion weighted imaging, vessel wall imaging. She has specific interest in artificial intelligence to improve diagnosis on medical imaging as well as improving prognostication and clinical workflow. Outside work, she enjoys painting, board game, forest/coastal foraging, and video gaming.

Minerva received her MD from the Washington University in St Louis and is currently a second year diagnostic radiology resident at UCSF. Her research involves the automated segmentation of fetal brain MRI. Outside of work, her interests include sitting outside and eating delicious food.

Neel is a fourth-year undergraduate at Georgetown University majoring in Neurobiology. His research interests include mapping white matter abnormalities in pediatric brain cancer survivors. In his free time, Neel enjoys playing the guitar and mountain biking. 

Gunvant is a fourth-year MD student at UCSF applying into Diagnostic Radiology residency. His research interests include developing explainable machine learning-based tools to aid radiologists in interpreting imaging and generating radiology reports. Outside of work, Gunvant enjoys rooting for the Warriors and playing piano.

Josh Chen is a 4th year MD student at UCSF interested in diagnostic radiology. Josh completed his undergraduate degree in Public Health at UC Berkeley in 2018. Josh’s current research involves deep learning algorithms in assisting radiologists’ with the interpretation of infant myelination status on MRI. His work focuses on identifying high quality datasets for machine learning models, biases in datasets, and data subanalyses. Additional research interests include 3D printing in medicine. 

Reza Eghbali is a Health Innovation fellow and UCSF/UC Berkeley. Reza Eghbali received his Ph.D. in Electrical Engineering and a master's degree in Mathematics from the University of Washington, Seattle. He has a B.Sc. in Electrical Engineering from Sharif University of Technology, Tehran. Before joining Innovate For Health, Reza was a member of the machine learning and security team at Cisco Tetration Analytics, where he developed and implemented online learning algorithms for detecting network security threats in real-time. Reza was a Simons Institute Research Fellow at UC Berkeley in the 2017-18 academic year. He visited the institute for the programs on “Bridging Continuous and Discrete Optimization” and “Brain and Computation,” where he worked on modeling the corticothalamic feedback in the early visual system. His research interest lies in the areas of machine learning, medical imaging, and computational neuroscience.

Céleste is a Masters student at École polytechnique, where she studies Applied Mathematics. Her research focuses on developing deep learning models on MRI data to detect anomalies in the brain's white matter structure related to ADHD. Outside of work, she enjoys dancing, reading and cooking.

Julien is a third-year French engineering student at Ecole polytechnique, majoring in Applied Mathematics. His research is centered around computational methods for brain tractography.

Erik is a graduate student of Data Science at University of Erlangen–Nuremberg studying machine learning and artificial intelligence (AI). He obtained his BS from the University of Applied Sciences Mittweida. His research is focused on MRI segmentation using transformer architectures. In his spare time he likes to play the guitar or go hiking.

Justin Huynh is an assistant research specialist in the lab, focused on building deep learning models to detect cognitive and neurological disorders from brain MRI.  He received his B.S. and M.S. degrees in computer science from the University of California at San Diego. He is broadly interested in applications of artificial intelligence to radiology, ophthalmology, and other areas of medicine and healthcare.

Samuel is a research fellow supported through the Yearlong Inquiry program through the UCSF School of Medicine Inquiry Office. Samuels’ work is centered around understanding behavior and neuropsychiatric disease through the combination of neuroimaging, big data, and electrophysiology to improve both our fundamental understanding and patient care. Samuel received his BA from Hampshire College in 2012 and started at UCSF School of Medicine in 2019. 

Aditya is a junior at UC Berkeley majoring in Bioengineering. As of now, he's currently working on classifying radiology protocols using free clinical text as input. In the future, he hopes to develops computational tools which integrate seamlessly within the clinical environment and aid in providing accurate and effective diagnoses and treatment options. When he's not on his computer, he enjoys reading science fiction and getting flyered on Sproul.

Maxence Pajot is a Masters student at Ecole Normale Supérieure and Ecole Polytechnique, where he studies Applied Mathematics and Cognitive Science. His research pertains to reading, and the impact it has on white matter. For this, he studies MRI data from the ABCD dataset.

Soo is a second-year MD student at the Geisel School of Medicine at Dartmouth. He's interested in leveraging artificial intelligence and neuroimaging to better understand human cognition and develop prognostic tools for neurological and psychiatric disorders. When he's not looking at brain images, he enjoys watching movies, going on hikes, and jamming with friends.

Rahul is a second-year undergraduate at UC Berkeley majoring in Computer Science. His research is centered around clustering methods for MRI diagnoses. Outside of work, he enjoys playing soccer and poker.

Dr. Jeffrey Rudie completed his MD PhD at UCLA in neuroscience, followed by radiology residency at U Penn and Neuroradiology fellowship at UCSF. He is an emergency/neuroradiologist at Scripps Clinic in San Diego and an adjunct assistant professor of Radiology at UCSD. He is actively involved in both academic research and industry projects focused on developing artificial intelligence tools that can be integrated into the clinical workflow to improve the accuracy and efficiency of neuroradiology. This includes tools for automated segmentation and longitudinal assessment of intracranial metastases and diffuse gliomas.

Stephen received his MD from Duke University in 2019 and is currently a third-year diagnostic radiology resident at UCSF. His research interests include developing deep learning-based tools to streamline the radiology workflow, specifically with regard to interpretation of brain MRI exams for follow-up of multiple sclerosis. Outside of work, he enjoys hiking, water sports, and traveling.