My research lies at the intersection of deep reinforcement learning and data mining, with a focus on time series. I aim to advance time series data mining and machine learning to increase the quality and accessibility of healthcare to ultimately empower disadvantaged communities.
My work has appeared at several top data mining and machine learning conferences and I have been fortunate enough to collaborate with some wonderful folks to push the envelope on machine learning for time series and text. I also spent a year collaborating with the data science department in the UMass Medical School to assist doctors in expediting the clinical trial writing process using machine learning.
My CV can be found here.
Please feel free to contact me with questions regarding my research or our program at WPI. I am always looking to chat about research!
- Time series modeling and classification
- Sequential Learning in Time-Sensitive Domains (see [KDD'19][KDD'20])
- Recurrent Neural Networks (see [AAAI'21][CIKM'20])
- Explainable Deep Learning (see [ACL'20])
- Healthcare/sustainability applications
- Dec 2: New AAAI paper on combining knowledge from multiple RNNs.
- Dec 1: I will be joining Microsoft next year as a PhD intern.
- Oct 20: New IEEE BigData paper on Meta Word-Embeddings.
- Oct 18: Talk at Computational Sustainability Doctoral Consortium.
- Sept 17: Talk at Harvard.
- July 17: New CIKM paper on conditional computation in RNNs.
- June 4: Talk at FSU.
- May 15: New KDD paper on Early Multi-label Classification.
- April 4: New ACL paper on the interpretability of attention mechanisms for text classification.