About
I am a Principal Researcher at Microsoft Research based in Montreal, Canada. My research focuses on AI-mediated information and knowledge access and questions of fairness and ethics in the context of these sociotechnical systems. I am also interested in evaluation and benchmarking, and co-organized the MS MARCO ranking leaderboards (opens in new tab), the TREC Deep Learning Track (opens in new tab) (2019-2023), and the TREC Tip-of-the-Tongue Track (opens in new tab) (2023). I received my Ph.D. in Computer Science from University College London under the supervision of Dr. Emine Yilmaz (opens in new tab).
Featured Items
Book: An Introduction to Neural Information Retrieval
Neural models have been employed in many Information Retrieval scenarios, including ad-hoc retrieval, recommender systems, multi-media search, and even conversational systems that generate answers in response to natural language questions. An Introduction to Neural Information Retrieval provides a tutorial introduction to neural methods for ranking documents in response to a query, an important IR task. The monograph provides a complete picture of neural information retrieval techniques that culminate in supervised neural learning to rank models including deep neural network architectures that are trained end-to-end for ranking tasks. In reaching this point, the authors cover all the important topics, including the learning to rank framework and an overview of deep neural networks. This monograph provides an accessible, yet comprehensive, overview of the state-of-the-art of Neural Information Retrieval.
PhD Thesis: Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval
Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents---or short passages---in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms---such as a person's name or a product model number---not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections---such as the document index of a commercial Web search engine---containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks.
Blog: Adversarial and reinforcement learning-based approaches to information retrieval
Traditionally, machine learning based approaches to information retrieval have taken the form of supervised learning-to-rank models. Recent advances in other machine learning approaches—such as adversarial learning and reinforcement learning—should find interesting new applications in future retrieval systems. At Microsoft AI…