Our work drawing lessons from aviation safety for health AI was covered by MIT News and Innovate Healthcare
GRACE was featured in the Microsoft Research blog
ToxiGen was covered by TechCrunch and Microsoft Research
Our work on Fair Explainability was covered by MIT News
[Paper] June 2024. Paper accepted to MICCAI'24 on federated learning for medical imaging
[Paper] May 2024. Paper accepted to ACL'24 on categorical knowledge editing for LLMs
[Service] Organized ICLR'24 Workshop on Time Series for Health
[Paper] Apr 2024. Paper published in Nature Medicine on bias in computational pathology
[Paper] Feb 2024. Paper accepted to Knowledge and Information Systems on explaining multi-class time series classifiers
[Talks] Spring 2024. Invited talks at Dartmouth, UAB, and UCSF/Berkeley, UMass Amherst, IBM Research, and Arizona State University.
[News] Feb 2024. MIT News covered our perspective drawing lessons from aviation safety for AI in health
[Paper] Feb 2024. Paper accepted to Knowledge and Information Systems on explaining multi-class time series classifiers.
[Paper] Feb 2024. Paper accepted to Nature Medicine on identifying demographic biases in computational pathology models.
[Grant] Dec 2023. Steven L. Johnson and I were awarded a grant from the University of Virginia’s Darden-Data Science Collaboratory for Applied Data Science to work on Detecting Implicit Bias in Natural Language with Large Language Models. This award will fund a 2-year postdoc position.
[Grant] Dec 2023. Antonios Mamalakis (UVA Data Science & Environmental Sciences) and I were awarded a grant from UVA's Environmental Institute to work on Artificial Intelligence for Local Climate Policy Decision Making. This award will fund a 2-year postdoc position.
[Invited talk] Nov 2023. Talk on Updatable and Responsible LLMs for Healthcare at UVA's Department of Anesthesiology Grand Rounds Seminar.
[Paper] Nov 2023. Paper accepted to npj Digital Medicine on understanding patterns of stress using wearable device time series.
[Papers] Oct 2023. Two papers accepted to IEEE BigData'23 on Brain Structury Discovery and Stabilizing GAN Training.
[Papers] Sept 2023. Two papers accepted to NeurIPS'23 on Model Editing and Explainability for Time Series Models.
[Award] Sept 2023. Tim Althoff and I were awarded the Microsoft Accelerating Foundation Models Research grant.
[New group member] Sept 2023. Arinbjorn Kolbeinsson has joined my group as a visiting researcher.
[New Position] Aug 2023. I have officially started as a tenure-track assistant professor at the University of Virginia in the School of Data Science. Before moving to Virginia in Summer 2024, I'm remaining at MIT CSAIL in Cambridge as visiting faculty.
[Best Paper Award] July 2023. Our paper on interpretable deep metric learning for clinical subtyping was awarded Best Paper at the Interpretable Machine Learning for Healthcare Workshop at ICML'23.
[Paper] July 2023. Paper accepted to EAMMO'23 on steering Health AI development using lessons from aviation safety.
[Paper] June 2023. New preprint on explaining big time series models.
[Talk] May 2023. Talk in Stanford's MedAI Seminar Series on Model Editing with GRACE.
[Talk] Apr 2023. Talk in Tanmoy Chakraborty's group at IIT Delhi on Model Editing with GRACE.
[Talk] Mar 2023. Talk in Marinka Zitnik's group at Harvard Medical School on responsibly deploying machine learning for health.
[Service] Mar 2023. Organizing a workshop at ICML 2023 on Deployment challenges of Generative AI.
[Service] Feb 2023. I'm serving as General Chair for the 2023 Machine Learning for Health Symposium, which will be co-located with NeurIPS 2023.
[Paper] Feb 2023. New preprint on searching for discriminative windows in irregularly-sampled time series.
[Paper] Feb 2023. New paper on Algorithmic Fairness in Chest X-Rays Models published as a SERC Case Study.
[Talk] Feb 2023. Talk in Yoon Kim's group at MIT CSAIL on responsibly deploying machine learning for health.
[Talk] Dec 2022. Talk in Jim Glass's group at MIT CSAIL on GRACE.
[Talk] Dec 2022. Guest lecture in MIT Course 6.S898 (Graduate Deep Learning).
[Talk] Dec 2022. Invited speaker at the AJCAI 2022 Workshop on Toxic Language Detection.
[Paper] Nov 2022. Paper accepted to AAAI 2023 on multi-label knowledge amalgamation.
[Talk] Nov 2022. Invited talk at Worcester Polytechnic Institute's CS Colloquium.
[Talk] Oct 2022. Invited talk at Northeastern University on making machine learning broadly deployable and socially-responsible.
[Papers] Oct 2022. Four papers accepted to workshops at NeurIPS 2022 on detecting stress from wearable devices (spotlight), learning multi-modal clinical checklists (spotlight), lifelong model editing (spotlight), and generative counterfactual explanations.
[Paper] Oct 2022. Paper accepted to ML4H 2022 on detecting stress from wearable devices.
[Paper] Sept 2022. Paper accepted to ICDM 2022. This is my first last-author paper and I'm thrilled for the first-author Ramesh!
[Talk] August 2022. Gave a contributed talk at the epiDAMIK workshop at KDD 2022.
[Papers] August 2022. Two papers accepted to CIKM 2022.
[Talk] July 2022. Gave an invited talk at Tufts University.
[Talk] July 2022. Gave an invited talk at the University of Rochester.
[Service] July 2022. Organizing a workshop at NeurIPS 2022 this year on Learning from Time Series for Health.
[Talk] July 2022. Giving a talk at MIT's Large Language Model working group on constructing datasets with LLMs, hosted by Yoon Kim.
[Service] July 2022. I am serving on the program committee for AAAI 2023.
[Talk] June 2022. I gave an invited talk at Google Research's Responsible AI seminar series.
[Paper] June 2022. I'll be presenting a workshop paper at KDD 2022. Let's chat in DC!
[Workshop] May 2022. I've been invited to attend the MIT IDSS Workshop on Sustainability, Just Transitions, and Systemic Racism.
[Paper] April 2022. Our paper on Fair Explainability was accepted to FAccT 2022.
[Paper] April 2022. Our paper TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks was accepted to LREC 2022.
[Talk] April 2022. I've been invited to give a talk at MIT Horizons on Building Safe AI Systems for Health.
[Workshop] April 2022. I've been invited to attend the NSF Rules of Life Workshop.
[Paper] Feb 2022. Our paper ToxiGen: Controlling Language Models to Generate Implied and Adversarial Toxicity was accepted to ACL 2022.
[New job] Jan 2022. Started at MIT as a postdoc.
[Paper] Dec 2021. Our paper Positive Unlabeled Learning with a Sequential Selection Bias was accepted to SDM 2022.
[Paper] Dec 2021. Our paper Recovering the Propensity Score from Biased Positive Unlabeled Data was accepted to AAAI 2022.
[Event] Successfully defended my dissertation Prediction and Observation Timing in Time Series Classification. Thanks to my committee, Elke Rundensteiner, Xiangnan Kong, Randy Paffenroth, and Jenna Wiens!
[Papers] Oct 2021. Two papers accepted to IEEE BigData 2021.
[PC] Oct 2021. PC for ACL 2022 and NAACL 2022.
[Paper] Sep 2021. Our paper Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification was accepted at NeurIPS 2021.
[PC] Aug 2021. PC for AAAI 2022.
[Paper] Aug 2021. Our paper Learning Saliency Maps for Deep Time Series Classifiers was accepted at CIKM 2021.
[Paper] May 2021. Our paper Energy-Efficient Models for High-Dimensional Spike Train Classification using Sparse Spiking Neural Networks was accepted at KDD 2021.
[PC] Apr 2021. PC for EMNLP 2021.
[PC] Jan 2021. PC for ICCV 2021.
[PC] Jan 2021. PC for ACL 2021.
[Paper] December 2020. Our paper Semi-Supervised Knowledge Amalgamation for Sequence Classification was accepted at AAAI 2021.
[News] Dec 2020. I am joining Microsoft next summer as a PhD intern.
[News] Dec 2020. I volunteered at NeurIPS 2020.
[PC] Nov 2020. PC for CVPR 2021.
[Paper] Oct 2020. Our paper Learning Similarity-Preserving Meta-Embedding for Text Mining was accepted at IEEE BigData 2020.
[Talk] Oct 2020. I gave a talk at the Computational Sustainability Doctoral Consortium in October.
[Talk] Sep 2020. I gave a talk on my research at Harvard University.
[Award] Sep 2020. I was awarded the CIKM Travel Grant from ACM.
[PC] Aug 2020. PC for AAAI 2021.
[Award] Aug 2020. I was been awarded the KDD Travel Grant this year from ACM and the NSF.
[Paper] Jul 2020. Our paper Learning to Selectively Update State Neurons in Recurrent Networks was accepted at CIKM 2020.
[Talk] Jun 2020. I gave a talk on my research at Florida State University.
[Paper] May 2020. Our paper Recurrent Halting Chain for Early Multi-label Classification was accepted at KDD 2020.
[Paper] Apr 2020. Our paper Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words? was accepted at ACL 2020.
[Award] Feb 2020. Our paper at HEALTHINF this year was awarded best poster.
[Paper] Dec 2019. Our paper Clinical Performance Evaluation of a Machine Learning System for Predicting Hospital-Acquired Clostridium Difficile Infection was accepted at HEALTHINF 2020.
[Paper] Oct 2019. Our paper Patient-Level Classification of Clinical Note Sequences Guided by Attributed Hierarchical Attention was accepted to IEEE BigData 2019.
[Paper] Oct 2019. Our paper Learning Temporal Relevance in Longitudinal Medical Notes was accepted to IEEE BigData 2019.
[Talk] Sep 2019. I presented some of my work at the University of Minnesota Workshop on Recent Progress in Foundational Data Science.
[Event] Aug 2019. I attended Big Data 2019 at Harvard University.
[Talk] Aug 2019. I attended KDD 2019 and presented our paper.
[Talk] May 2019. I presented some of my work at the New England Machine Learning Day.
[Paper] Apr 2019. Our paper Adaptive-Halting Policy Network for Early Classification was accepted at KDD 2019.
[Award] Apr 2019. I won first place in WPI’s annual graduate research showcase.
[Paper] Mar 2019. Our paper Comparing General and Locally-Learned Word Embeddings for Clinical Text Mining was accepted to the IEEE BHI.
[Event] Jan 2019. I attended the Geometric Analysis Approach to AI Workshop at Harvard University.
[News] Nov 2018. I defended my Master’s Thesis: Adaptively-Halting RNN for Tunable Early Classification of Time Series.
[News] Sep 2018. I began a year-long collaboration with UMass Medical School.
[News] Aug 2018. Two of my advised undegrads presented their summer project at the REU symposium in Washington, D.C.
[Award] Apr 2018. I was awarded the WPI Data Science Citizen Award for driving community growth in our department.
[Talk] Jan 2018. I presented our paper “Early Prediction of MRSA Infections Using Electronic Health Records” at HEALTHINF 2018 in Madeira, Funchal.
[Award] Dec 2017. Our paper Early Prediction of MRSA Infections Using Electronic Health Records was nominated for the HEALTHINF 2018 best student paper award.
[Paper] Oct 2017. Our paper Early Prediction of MRSA Infections Using Electronic Health Records was accepted to HEALTHINF 2018.
[Paper] Jun 2017. Our paper CREST - Risk Prediction for Clostridium Difficile Infection Using Multimodal Data Mining was accepted to ECML-PKDD 2017.
[Award] May 2017. I was awarded the WPI Data Science Citizen Award for driving community growth in our department.
[Award] Feb 2017. I presented joint work with Cansu Sen at the WPI Graduate Research and Innovation Exchange and received the People’s Choice award.
