Hi, I'm Sachin.

I am a Research Scientist at Apple and Affiliate Assistant Professor at the University of Washington, Seattle, where I do research in computer vision, machine learning, artificial intelligence, and natural language processing. More specifically, I am interested in designing efficient machine learning architectures for visual and textual data that can run on resource-constrained device (e.g., smartphones and embedded devices) with good generalization properties. I am also interested in real-world applications of machine learning, especially in medical imaging and accessibility.

I received my Ph.D. from The University of Washington (UW), Seattle under the supervision of Prof. Linda Shapiro and Prof. Hannaneh Hajishirzi.

• Machine learning for diagnosing breast cancer
  • GeekWire
  • MedicalResearch.com
  • UW press release
• Iconary: An AI-powered drawing and guessing game
  • GeekWire
  • MIT Technology Review
  • Seattle Times

• Apr 18, 2022: HATNet accepted to Medical Image Analysis journal.
• Jan 28, 2022: MobileViT accepted to ICLR'22.
• Oct 22, 2021: Released CVNets, a library for training computer vision networks.
• Oct 5, 2021: Became part of UW again, now as Affiliate Assistant Professor.
• Oct 5, 2021: Preprint of our work on designing light-weight visual transformers (MobileViT) is available on arXiv.
• July 16, 2021: Our paper, Iconary, is accepted at EMNLP'21 for oral presentation.
• July 16, 2021: Our paper on collecting and mapping sidewalk data is accepted at ASSETS'21.
• July 8, 2021: Our paper, EVRNet, is accepted for oral presentation at ACM MM'21.
• June 8, 2021: Our paper on analysing breast biopsy distractor regions of interest is accepted at BHI'21.
• Apr. 19, 2021: Joined Apple as Research Scientist.
• March 9, 2021: Defended my thesis (though virtually) at UW.
• Jan 12, 2021: Our paper, DeLighT, is accepted at ICLR'21.
• Dec 7, 2020: Preprint of our paper, EVRNet: Efficient Video Restoration on Edge Devices, is now online.
• Nov 29, 2020: DiCENet accepted in Transactions of Pattern Analysis and Machine Intelligence (TPAMI).
• Oct 18, 2020: MedICat accepted in the Findings of EMNLP'20.
• Oct 18, 2020: Two papers accepted in ICPR'20.
• Aug 2, 2020: Preprint of our paper, DeLighT: Very Deep and Light-weight Transformers, is now online.
• Jun 15, 2020: Interning (remotely) at Facebook Reality Labs.
• July 25, 2020: Preprint of our paper, HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images, is now online.
• Jan 29, 2020: Our paper, MLCD: A Unified Software Package for Cancer Diagnosis, has been accepted for publication at JCO Clinical Cancer Informatics.
• Dec 19, 2019: Our paper, DeFINE, has been accepted for publication at ICLR'20.
• Aug 10, 2019: Released the source code of our iOS app, ESPNetv2-COREML, that demonstrates the real-time performance of ESPNetv2 on edge devices.
• Aug 9, 2019: Our paper on breast cancer diagnosis is published in JAMA Network Open.
• Jun 8, 2019: Released the source code for our work on efficient network designs (ESPNetv2 and DiCENet). Check my Github page.
• Apr 30, 2019: Our paper for ASD classification got accepted at ICIP'19.
• Feb 24, 2019: Our paper, ESPNetv2, got accepted at CVPR'19.
• Aug 10, 2018: Our paper, PRU, got accepted at EMNLP'18.
• Jun 3, 2018: Our paper, ESPNet, got accepted for publication at ECCV'18.
• May 25, 2018: Our paper, Y-Net, got accepted at MICCAI'18.