[Service] Mar 2023. Organizing a workshop at ICML 2023 on Deployment challenges of Generative AI.
[Service] Feb 2022. I'm serving as General Chair for the 2023 Machine Learning for Health Symposium, which will be co-located with NeurIPS 2023.
[Paper] Feb 2022. New preprint on searching for discriminative windows in irregularly-sampled time series.
[Paper] Feb 2022. New paper on Algorithmic Fairness in Chest X-Rays Models published as a SERC Case Study.
[Talk] Feb 2022. 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 the Worcester Polytechnic Institute 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.