Akshay Chaudhari, PhD

Dr. Chaudhari is an Assistant Professor of research in the Integrative Biomedical Imaging Informatics at Stanford (IBIIS) section in the Department of Radiology and (by courtesy) in the Department of Biomedical Data Science. He leads the Machine Intelligence in Medical Imaging research group at Stanford and has a primary research interest that lies at the intersection of artificial intelligence and medical imaging. Dr. Chaudhari graduated with honors from UCSD with a B.S. in Bioengineering in 2012. He completed his Ph.D. from Stanford University’s Department of Bioengineering in 2017, focusing on novel MRI methods for musculoskeletal imaging; supported through the National Science Foundation Graduate Research Fellowship, the Whitaker Fellowship, and the Siebel Fellowship. Dr. Chaudhari trained as a postdoctoral fellow in Radiology at Stanford University, where he combined machine learning with medical imaging acquisition and analysis. Dr. Chaudhari has won many awards, including the W.S. Moore Young Investigator Award, the Junior Fellow Award, and an Outstanding Teacher Award from the International Society for Magnetic Resonance in Medicine. He has 6 additional young investigator awards for his work on advanced musculoskeletal medical imaging. Dr. Chaudhari is the Associate Director of Research and Education at the Stanford AIMI Center and is an internal advisory board member of the Precision Health and Integrated Diagnostics Center.

Anthony A. Gatti, PhD

Educational Background
PhD, McMaster University, 2021
MSc, McMaster University, 2015
BSc, McMaster University, 2013

Research Focus
I am a musculoskeletal health researcher with a passion for using machine learning to augment and integrate biomechanical and medical imaging data to understand joint health.

Non-Academic Interest
When not doing something tech or research related, I love being physically active, running, biking, surfing, snowboarding, if it’s outside and gets your heart rate up count me in.

Camila González, PhD

Educational Background

PhD, Technical University of Darmstadt, 2023
MSc, Technical University of Darmstadt, 2020
BSc, Technical University of Darmstadt, 2017

Research Focus
My research lies at the intersection of representation learning and continuous model monitoring in clinical settings. I develop methods and evaluation frameworks that assess the suitability of commercially available AI tools for a given institution without requiring labeled on-site data. This relies on vision-language models trained in a self-supervised fashion, which allow us to identify distribution shifts and image quality limitations in the latent space.

Non-Academic Interest
I love running and dancing, mostly to reggaeton and EDM music, and I recently started learning tango. I also enjoy reading (both fiction and non-fiction) and playing board games; two of my favorite games are Zombicide and Gloomhaven.

Robbie Holland, PhD

Educational Background
PhD, Imperial College London, 2024
MRes, King’s College London, 2020
MEng, Imperial College London, 2019
BEng, Imperial College London, 2018

Research Focus
My research focuses on developing self-supervised methods for aiding image-based clinical decision making and accelerating the discovery of new, prognostic biomarkers for disease. I am now advancing these applications by developing foundation models that integrate longitudinal, multimodal medical data from population-scale cohorts.

Non-Academic Interest
I love reading (especially biographies), long walks, staying active, and writing and recording the occasional song. I’m currently trying out different string instruments, surfing, and new dance styles.

Jiaming Liu, PhD

Educational Background
PhD, Washinton University in St. Louis, 2024
MSc, Washinton University in St. Louis, 2018
BSc, University of Electronic Science and Technology of China, 2017

Research Focus
My research focuses on developing explainable, robust, and efficient frameworks for biomedical and scientific imaging, image analysis, and computer vision. This includes advancing the theoretical foundations of optimization algorithms and creating novel deep learning methods for specific applications.

Non-Academic Interest
I enjoy staying active and learning new skills. Recently, I've been focused on scuba diving, aiming to get licensed.

Sophie Ostmeier, MD

Educational Background
MD, Technical University of Munich, 2021
Dr. med. , Technical University of Munich, 2022
CS Master Student, Stanford University

Research Focus
My research focuses on machine learning models to learn representations of medical knowledge and their evaluation. Currently, I am exploring how vision/language models can learn temporal information.

Non-Academic Interest
I love endurance sports, including track running, long-distance cycling tours, and pool swimming.

Magdalini Paschali, PhD

Educational Background
PhD, Technical University of Munich, 2021. MSc, Technical University of Munich, 2017. BSc, Aristotle University of Thessaloniki, 2015

Research Focus
I am a machine learning researcher focusing on evaluating the robustness of large-scale AI models for healthcare and identifying early disease biomarkers.

Non-Academic Interests
I enjoy hanging out with friends and family, cooking, going to the movies, hiking and have recently gotten into lifting weights.

Alan Tian Tan, PhD

Educational Background
Ph.D., Mechanical Engineering, Shanghai Jiao Tong University

Research Focus
I leverage machine learning principles to extract information from wearable and portable sensing for widespread biomechanical assessment outside of gait laboratories.

Non-Academic Interest
I love watching TV shows and educational Youtube channels. Also, I enjoy outdoors.

Joyce Yan-Ran Wang, PhD

Educational Background
PhD, Computer Science, Northwestern University, 2019

Research Focus
Dr. Joyce Yan-Ran Wang is a researcher at the forefront of artificial intelligence and healthcare. She earned her Ph.D. in Computer Science from Northwestern University. Dr. Wang’s work focuses on developing cutting-edge machine learning methods to address critical challenges in healthcare, including automated diagnosis, precision medicine, and safer medical imaging. Dr. Wang aims to bridge the gap between machine learning advancements and real-world healthcare needs, driving discoveries in complex human diseases and enhancing healthcare quality.

Non-Academic Interest
photography

McKenzie White, PhD

Educational Background
PhD, University of Michigan, 2023
MSc, University at Buffalo, 2018
BSc, University at Buffalo, 2016

Research Focus
I work at the intersection of machine learning, medical imaging, and biomechanics. I'm committed to developing tools that bridge gaps between computational methods, musculoskeletal research, and clinical care - enabling more precise analyses, efficient workflows, and improved surgical decision-making.

Non-Academic Interest
Outside of research, I love working out, hiking, and being active as much as I can. I enjoy learning new skillsets and exploring new places.

Phil Adamson

Educational Background
BA, Physics, Colby College
BE, Engineering, Dartmouth College

Research Focus
I am broadly interested in working with diverse interdisciplinary teams of engineers and clinicians to develop technologies that improve patient lives. My current research projects include developing an MR method for imaging metabolism of tumors in vivo, and leveraging self-supervised feature representations for the evaluation of MR image reconstruction quality.

Non-Academic Interest
Outside of work I enjoy hiking, skiing, and lying around a fire with some combination of dogs, family and friends.

Kamyar Rajabali Fardi

Educational Background
BSc, Electrical Engineering, Sharif University of Technology, 2023

Research Focus
I am highly interested in signal processing and optimization, particularly in their applications to image processing and computer vision.

Non-Academic Interest
In my free time, I enjoy biking, reading novels, and playing the piano.

Ates Fettahoglu

Educational Background
PhD student in Biomedical Physics, Stanford University

Research Focus
I focus on discovering new biomarkers and developing deep learning pipelines for opportunistic imaging and theranostics.

Non-Academic Interest
I enjoy traveling, cooking and hiking

Ashwin Kumar

Educational Background
BS, Computer Science & Neuroscience, Vanderbilt University
MS, Computer Science, Vanderbilt UniversityBS

Research Focus
I am interested in developing deep learning tools that utilize multimodal imaging for early diagnosis and treatment of neurological disorders.

Non-Academic Interest
I enjoy playing basketball, piano, lifting, and engaging in educational STEM outreach activities.

Stefania Moroianu

Educational Background
BA, Natural Sciences (Physics), University of Cambridge, MSci, Physics, University of Cambridge
PhD Candidate in Applied Physics with PhD Minor in Computer Science, Stanford University

Research Focus
My current research focus is on generative AI for medical imaging and text, and exploring how we can leverage synthetic data to build better models.

Non-Academic Interest
Skiing, tennis, the gym, hiking, and dancing (anytime!). I travel whenever and wherever I can, and I try all the good food.

Anoosha Pai S

Educational Background
BA, Mechanical Engineering, BMS College of Engineering, Bangalore, India
MS, Biomedical Engineering, University of British Columbia, Vancouver, Canada

Research Focus
I am interested to apply techniques of medical imaging, machine learning, and biomechanics to quantify and assess various aspects of musculoskeletal disorders with a hope to improve diagnosis, treatment, and rehabilitation.

Non-Academic Interests
While not at work, I really enjoy singing, hiking, yoga, creating and experimenting new food recipes.

Ivan Lopez Rodriguez

Educational Background
BS, Physiological Sciences, UCLA
MS, Physiological Sciences, UCLA

Research Focus
My research centers on developing machine learning algorithms to improve healthcare access and delivery. I'm also interested in AI policy, particularly in ensuring the safe, responsible development and effective implementation of AI in healthcare.

Non-Academic Interest
Gym, basketball, tennis, table tennis, playing music, trying your favorite food restaurants.

Maya Varma

Educational Background
BS, Computer Science, Stanford University

Research Focus
I am broadly interested in developing reliable AI systems that can be deployed in clinics to improve disease diagnosis and medical image interpretation. My research focuses on developing accurate and robust representation learning methods for medical images..

Non-Academic Interest
Outside of research, I enjoy reading, drawing and painting, hiking, yoga, and traveling.

Jessica L. Asay

Educational Background
BS, Mechanical Engineering, University of Portland
MS, Mechanical Engineering, Stanford University. 

Research Focus
Jessica has years of motion laboratory management experiences and research with knee osteoarthritis. She is interested in analyzing lower limb joint movement and loading with respect to pain.

Non-Academic Interests
In my free time, I enjoy taking dance and gymnastics classes and decorating cakes.

Peyman Shokrollahi, PhD

Educational Background
Ph.D., Biomedical Engineering, University of Toronto
M.A.Sc., Electrical and Computer Engineering, Ryerson University
B.Sc., Electrical – Electronic Engineering, Shiraz University

Research Focus
My research focused on automating the translation of clinical imaging orders to radiology imaging protocols using electronic medical records (EMRs) and machine learning (ML) techniques. I am also interested in developing fusion models utilizing medical images and EMRs.

Non-Academic Interest
I am passionate about music and performing hammered dulcimer.