You can find my CV here.
With Abir De, Aasish Pappu, and Manuel Gomez-Rodriguez, I have uncovered a connection between complexity of online discussions and the notion of sign-rank of matrices. This allows us to determine the complexity of online discussions just by looking at the pattern of upvotes/downvotes cast by users on others' comments; the key insight is using humans as oracles and by-passing the nuances of sarcasm and humor often present in online comments.
- "On Complexity of Opinions and Online Discussions" ~ WSDM (2019); Paper.
With Abir De and Manuel Gomez-Rodriguez, I have described how reinforcement learning can be applied to control processes in which actions are performed and feedback from the environment is received in continuous real time instead of the classical setup where the actions and rewards (feedback) are synchronously given to the agent at discrete points in time.
With Behzad Tabibian, Abir De, Ali Zarezade, Bernhard Schölkopf and Manuel Gomez-Rodriguez, I have determined the optimal reviewing schedule to keep knowledge fresh in your memory for optimal recall while minimizing effort spent on learning it.
With Isabel Valera and Manuel Gomez-Rodriguez, I am developing models to understand how learning happens on Crowdlearning sites, such as Stack Overflow and Wikipedia.
- "On Crowdlearning: How do People Learn in the Wild?", oral presentation at Workshop on Machine Learning for Education at NIPS (2016);
- "Uncovering the dynamics of Crowdlearning and the Value of Knowledge", oral presentation at WSDM (2017); Paper.
With Nan Du, Hanjun Dai, Rakshit Trivedis, Manuel Gomez-Rodriguez, and Le Song, I developed am model which uses recurrent neural networks to model point processes, yielding impressive predictive results.
- "Recurrent marked temporal point processes: Embedding event history to vector", Poster presetned at KDD (2016); Paper.
A Python program for converting pdf slides and annotated text notes into Anki decks.
See how users in different tags ask and answer questions on Stack Overflow.
See how many users and upvotes diffrent tags see over time on Stack Overflow.