I am a Machine Learning Scientist at Amazon (Seattle) leading the information extraction efforts to build the Amazon Product Knowledge Graph. I work on developing machine learning and deep learning models for information extraction and natural language understanding. My doctoral thesis at Max Planck Institute for Informatics (Germany) on misinformation and fact-checking obtained the prestigious SIGKDD 2018 Doctoral Dissertation Award Runner-up (one of the top-3 best doctoral dissertations world-wide in data mining). My research interests involve representation learning and graphical models to capture the joint interaction between structure, content, and dynamics of information --- with a particular focus on interpretability and user-centric information needs.
In my PhD dissertation, I worked on probabilistic graphical models to extract "credible", "trustworthy" and "expert" information from large-scale, non-expert, user-generated online content. I developed machine learning models that exploit the joint interaction between users, language, and their evolution in online communities for tasks like: credibility analysis, personalized content recommendation, (latent) experience-aware item recommendation, finding (latent) topic-specific experts in online communities, spam and anomaly detection etc.[PhD Thesis on Credibility Analysis] [SIGKDD 2018 Dissertation Talk Slides] [CV]