Richard J. Chen

I am a 5th 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).

Research Highlights

Recent News

Aug, 2023 Excited to share our latest preprint on UNI, a general-purpose self-supervised model for computational pathology. In addition, my Master’s student, Tong Ding, is joining the Computer Science Ph.D. program at Harvard University (SEAS). Congratulations Tong!
Jul, 2023 Our perspective on algorithm fairness in AI and medicine/healthcare was published in Nature BME. In addition, excited to share our latest preprint on CONCH (CONtrastive learning from Captions for Histopathology), a visual-language foundation model for computational pathology. Stay tuned!
Jun, 2023 Our work on zero-shot slide classification with visual-language pretraining was published in CVPR. Code + pretrained model weights are made available.
Aug, 2022 Our work on PORPOISE (Pathology-Omic Research Platform for Integrated Survival Estimation), and our review on multimodal learning for oncology were both 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. Lastly, my visiting student, Yicong Li, is joining the Computer Science Ph.D. program at Harvard University (SEAS). Congratulations Yicong!
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. Lastly, our work on federated learning for CPATH (HistoFL) was published in Medical Image Analysis.
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 BME. Lastly, two papers, Patch-GCN and Multimodal Co-Attention Transformers (MCAT), were accepted into MICCAI and ICCV respectively.

Select Publications

  1. A General-Purpose Self-Supervised Model for Computational Pathology
    Richard J. Chen, Tong Ding, Ming Y. Lu, Drew F. K. Williamson, Guillaume Jaume, Bowen Chen, Andrew Zhang, Daniel Shao, Andrew H. Song, Muhammad Shaban, Mane Williams, Anurag Vaidya, Sharifa Sahai, Lukas Oldenburg, Luca L. Weishaupt, Judy J. Wang, Walt Williams, Long Phi Le, Georg Gerber, and Faisal Mahmood
    arXiv preprint arXiv:TBD 2023
  2. Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
    Richard J Chen, Chengkuan Chen, Yicong Li, Tiffany Y Chen, Andrew D Trister, Rahul G Krishnan, and Faisal Mahmood
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022
    Oral Presentation
  3. Developing Measures of Cognitive Impairment in the Real World from Consumer-Grade Multimodal Sensor Streams
    Richard J. Chen*, Filip Jankovic*, Nikki Marinsek*, Luca Foschini, Lampros Kourtis, Alessio Signorini, Melissa Pugh, Jie Shen, Roy Yaari, Vera Maljkovic, Marc Sunga, Han Hee Song, Hyun Joon Jung, Belle Tseng, and Andrew Trister
    In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2019
    Oral Presentation & Best Paper Runner-Up
  4. Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning
    Richard J Chen, Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Jana Lipkova, Muhammad Shaban, Maha Shady, Mane Williams, Bumjin Joo, Zahra Noor, and Faisal Mahmood
    Cancer Cell 2022
    Best Paper, Case Western Artificial Intelligence in Oncology Symposium, 2020. Cover Art of Cancer Cell (Volume 40 Issue 8).
  5. Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images
    Richard J. Chen, Ming Y. Lu, Wei H. Weng, Tiffany Y Chen, Drew FK Williamson, Trevor Manz, Maha Shady, and Faisal Mahmood
    In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021
  6. Algorithmic fairness in artificial intelligence for medicine and healthcare
    Richard J. Chen, Judy J. Wang, Drew FK. Williamson, Tiffany Y. Chen, Jana Lipkova, Ming Y. Lu, Sharifa Sahai, and Faisal Mahmood
    Nature Biomedical Engineering 2023
  7. Synthetic Data in Machine Learning for Medicine and Healthcare
    Richard J. Chen, Ming Y. Lu, Tiffany Y. Chen, Drew F. K. Williamson, and Faisal Mahmood
    Nature Biomedical Engineering 2021
  8. Federated Learning for Computational Pathology on Gigapixel Whole Slide Images
    Ming Y. Lu*, Richard J. Chen*, Dehan Kong, Jana Lipkova, Rajendra Singh, Drew FK. Williamson, Tiffany Y. Chen, and Faisal Mahmood
    Medical Image Analysis 2022
  9. Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis
    Richard J. Chen, Ming Y. Lu, Jingwen Wang, Drew F. K. Williamson, Scott J. Rodig, Neal I. Lindeman, and Faisal Mahmood
    IEEE Transactions on Medical Imaging 2020
    Top 5 Posters, NVIDIA GTC 2020