BRAIN Lab

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Research Laboratory of Andreas Rauschecker, MD, PhD

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, co-Executive Director of UCSF's Center for Intelligent Imaging (ci²), and co-chair of UCSF's Research Data Science Council.  In his spare time, Dr. Rauschecker loves exploring the outdoors, eating great food, and traveling to new places, especially with his family.

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.

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.

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.

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.

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. 

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. 

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.

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.

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. 

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.

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.