Avishek Anand is an Assistant Professor in the Leibniz University of Hannover. His research aims to develop intelligent and transparent machine learning approaches to help humans find relevant information. His research broadly falls in the intersection of Machine learning on Web and information retrieval. Specifically, he is interested in scalable and interpretable representation learning methods for text and graphs. He holds a PhD in computer science from the Max Planck Insitute for Informatics, Saarbrücken. His research is supported by Amazon research awards and Schufa Gmbh. He has been a visiting scholar in Amazon Search.
PhD in Information Retrieval, 2013
Max Planck Insitute for Computer Science, Saarland University
MSc in Computer Science, 2009
BTech in Computer Science, 2005
Indian Institute for Information Technology
Predictive models are all pervasive with usage in search engines, recommender systems, health, legal and financial domains. But for the most part they are used as black boxes which output a prediction, score or rankings without understanding partially or even completely how different features influence the model prediction.
Machine learning models are progressively becoming complex and training datasets are getting larger by the data. Embeddings models are trained over Web scale collections of text and graphs, language models are learnt over millions or billions of sentences.
My main focus in information retrieval is currently investigating neural models for ranking models for document retrieval and open-domain question answering. I also work extensively on temporal information retrieval and enrichment of textual knowledge sources like Wikipedia.
Question Answering over Curated and Open Web Sources