Richard J. Chen
I am a 4th year Ph.D. Candidate (and NSF-GRFP Fellow) advised by Faisal Mahmood at Harvard University, and also within Brigham and Women’s Hospital, Dana-Farber Cancer Institute, and the Broad Institute.
Prior to starting my Ph.D., I obtained my B.S/M.S. in Biomedical Engineering and Computer Science at Johns Hopkins University, where I worked with Nicholas Durr and Alan Yuille. In industry, I have also worked at Apple Inc. in the Health Special Project and Applied Machine Learning Groups (with Belle Tseng and Andrew Trister), and at Microsoft Research in the BioML Group (with Rahul Gopalkrishnan).
Multimodal Integration: Multimodal learning has emerged as an interdisciplinary field to solve many core problems in machine perception, human-computer interaction, and recently in biology & medicine, in which there is often an enormous wealth of multimodal data collected in parallel to study the same underlying disease. Since first starting out in research, I have a range of experiences working on multimodal learning for integrating: 1) multimodal sensor streams from the Apple Watch and iPhone Data to predict mild cognitive decline, 2) RGB and depth images for non-polyploidal lesion classification and SLAM in surgical robotics, and 3) pathology images and genomics for cancer prognosis.
Weakly-Supervised & Set-Based Deep Learning: Though deep learning has revolutionized computer vision in many disciplines, gigapixel whole-slide imaging (WSI) in computational pathology is a complex computer vision domain that renders traditional, Convnet-based supervised learning approaches infeasible. To address this issue, I have been working on interpreting large gigapixel images as permutation-invariant sets (or bags in MIL literature), and then developing set-based learning algorithms for weakly-supervised learning on WSIs.
Synthetic Data Generation & Domain Adaption: “What constitutes authenticity, and how would the lack of authenticity shape our perception of reality?” The science fiction American writer Philip K. Dick posited similar questions throughout his literary career and, in particular, in his 1972 essay “How to build a universe that doesn’t fall apart two days later”. I am interested in: using synthetic data for domain adaptation / generalization, developing synthetic environments for simulating challenging scenarios for neural networks, as well as the the policy challenges in training AI-SaMDs with synthetic data,
|Aug, 2022||Our work on PORPOISE (Pathology-Omic Research Platform for Integrated Survival Estimation) was published in Cancer Cell. See the associated demo!|
|Jun, 2022||Our work on Hierarchical Image Pyramid Transformer (HIPT) is highlighted as an Oral Presentation in CVPR, and as a Spotlight Talk in the Transformers 4 Vision (T4V) CVPR Workshop. Code + pretrained model weights are made available.|
|Mar, 2022||Our work on CRANE was published in Nature Medicine. Also, code + pretrained model weights are made available for our recent Self-Supervised ViT work in NeurIPSW LMRL 2021.|
|Feb, 2022||Federated learning for CPATH (HistoFL) was published in Medical Image Analysis. Lastly, my visiting student, Yicong Li, was accepted into the Computer Science Ph.D. program at Harvard University (SEAS). Congratulations Yicong!|
|Jul, 2021||Joined Microsoft Research as an PhD Research Intern, working with Rahul Gopalkrishnan in the BioML Group. In press, our commentary on synthetic data for machine learning and healthcare was also published in Nature Biomedical Engineering. Lastly, two papers, Patch-GCN and Multimodal Co-Attention Transformers (MCAT), were accepted into MICCAI and ICCV respectively.|
|Jun, 2021||Joined Microsoft Research as an PhD Research Intern, working with Rahul Gopalkrishnan in the BioML Group. In press, our commentary on synthetic data for machine learning and healthcare was also published in Nature Biomedical Engineering.|