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.

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.

Rogier van der Sluijs, MD, PhD

Educational Background
MD,
PhD,
MSc Epidemiology,

Research Focus
My research focuses on the efficient use of unlabeled data for representation learning through self-supervised learning. My main clinical interests are mammography and breast tomosynthesis.

Non-Academic Interest
Everything outdoors: snowboarding, running, biking, hiking, and more!

Kuan-Chieh (Jackson) Wang, PhD

Educational Background
PhD, University of Toronto
MS, University of Toronto
BA, University of Toronto

Research Focus
My current research focus is on biomechanics inspired human mesh recovery.  The goal is to recreate athletes in the digital world.

Non-Academic Interest
I enjoy sports like volleyball and tennis, and also physical training.

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.

Nandita Bhaskhar

Educational Background
B.Tech, Electronics Engineering, Indian Institute of Information Technology – Design & Manufacturing, Kancheepuram
MS, Electrical Engineering, Stanford University
PhD Candidate, Electrical Engineering, Stanford University

Research Focus
Broadly, my research focuses on developing alternate sources of supervision for medical data such as observational supervision using passively-collected event logs and self-supervision strategies for data-efficient medical representation learning. The second pillar of my research is developing strategies for uncertainty estimation and mitigating distribution shifts for reliable clinical deployment.

Non-Academic Interest
Outside of research, I like social dance, gardening, music, art, creative writing, getting lost, hiking, biking and exploring new things.

Louis Blankemeier

Educational Background
BS, Electrical Engineering, University of Southern California
BA, Physics, University of Southern California
MS, Electrical Engineering, Stanford University

Research Focus
My current research focus is in developing algorithms for chronic disease risk stratification.

Non-Academic Interest
I enjoy skiing, biking, and weight lifting, and in a previous life, I wanted to be a professional musician.  

Juan Manuel Zambrano Chaves

Educational Background
BS Biomedical Engineering
MD from Universidad de los Andes in Bogotá, Colombia.

Research Focus
I focus on multimodal data fusion, building predictive models that leverage medical images in addition to other relevant data sources such as structured and free-text health record data. I am also interested in developing knowledge representations that can be used in predictive models.

Non-Academic Interest
I’m an avid cyclist and in general love the outdoors!

Arjun Desai

Educational Background
MS Electrical Engineering, Stanford University
BSE Biomedical Engineering & Computer Science, Duke University

Research Focus
I am broadly interested in how we can safely scale ML methods to interpret, augment, and optimize signals, particularly in medical imaging. My work ranges from developing robust, data-efficient methods for signal reconstruction to designing scalable clinical deployment and validation systems for medical image acquisition and analysis.

Non-Academic Interest
Outside of work, I enjoy golfing, climbing, and playing basketball

Beliz Gunel

Educational Background
MS, Electrical Engineering, Stanford University
BS, Electrical Engineering & Computer Science, University of California, Berkeley

Research Focus
I am broadly in developing machine learning methods for medical imaging & natural language processing that leverage the structure of the underlying data and domain knowledge; and building data-efficient machine learning systems that are robust to distribution drifts.

Non-Academic Interests
Outside of work, I enjoy playing the guitar and singing, comedy shows, playing tennis, swimming, and running.

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.

Dave van Veen

Educational Background
BS, Electrical Engineering, University of Wisconsin
MS, Electrical Engineering, University of Texas

Research Focus
I’m eager to develop computational imaging tools for accelerated MR signal reconstruction and to improve signal representation for more accurate downstream quantitative measurements.

Non-Academic Interests
Outside of research, I get most enthused by bikepacking across foreign countries on mountain roads.

Ben Viggiano

Educational Background
MS Student, Biomedical Informatics, Stanford University 
BS, Biomedical Engineering and Computer Science, University of Wisconsin, Madison 

Research Focus 
Contributing to the development of deep learning detection methods in medical images.

Non-Academic Interest
I like to mess around with photography, UAVs, 3D printing, and open source coding projects!

Isabel Gallegos

Educational Background
B.S. Candidate, Computer Science, Stanford University

Research Focus
I am focused on building multimodal, multi-task machine learning models to predict disease diagnoses, in addition to developing uncertainty metrics for these models.

Non-Academic Interests
Outside of school, I love baking, photography, hiking, and spending time outdoors.

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.

Michael Ko

Educational Background
BA, Psychology, Stanford University, 2020
MS, Statistics, Stanford University, 2020

Research Focus
I am broadly interested in human pose estimation for biomechanical analysis using semi-supervised and generative deep learning methods.

Non-Academic Interest 
Running, Cooking, Board Games

Andrew Schmidt

Educational Background
ME, Biomedical Engineering, University of British Columbia (2019)
BS, Bioengineering, Santa Clara University (2017)

Research Focus
Combining MR imaging and biomechanics to better characterize mobility related diseases and disorders and develop metrics to inform clinical rehabilitation. Also, evaluating generalizability of deep learning segmentation algorithms to help develop clinically relevant AI tools and allow academic researchers to efficiently analyze large datasets.

Non-Academic Interests
Outdoor activities such as skiing, surfing, and hiking. I also enjoy training in Brazilian Jiu Jitsu and traveling.