In my research, I aim to build systems that learn from large amounts of data to support time-sensitive decision making in ongoing environments without perpetuating societal biases. To this end, I build data mining and machine learning models and tools for mainly time series and text.
My work has been published in several top papers (KDD, NeurIPS, AAAI, ACL) and I spent a year collaborating with the UMass Medical School using machine learning to help doctors write better clinical trials faster.
My research is funded by a GAANN Research Fellowship and with robust methods in hand, I aim to improve healthcare.
Please feel free to contact me, I am always happy to chat!
- Time Series (see our KDD 2019, KDD 2020, and KDD 2021 papers)
- Recurrent Neural Networks (see our NeurIPS 2021, AAAI 2021, and CIKM 2020 papers)
- Explainability (see our ACL 2020 and CIKM 2021 papers)
- Healthcare (see our ECML 2017, HEALTHINF 2018, BIGDATA 2019, and BIGDATA 2019 papers)
- NeurIPS'21 paper on recurrent bayesian classifier chains.
- CIKM'21 paper on explainability for time series.
- Microsoft internship.
- KDD'21 paper on spike train classification.
- AAAI'21 paper on combining knowledge from multiple RNNs.