Nikit Begwani

Senior Data and Applied Scientist

Microsoft India Pvt. Ltd.

Biography

I work as a Senior Data and Applied Scientist at Microsoft Bing Ads team. Deep learning models that I have developed have been deployed on millions of devices around the world and are also generating millions of dollars on the Bing search engine. My research interests are in both large scale and cross-lingual techniques for language.

I graduated from IIT Guwahati in 2017 with a major in Computer Science and a department rank of 6/88 students. My bachelor thesis was on understanding multilingual sentiment analysis on twitter data.

I am a continuous and passionate learner with an obsession to optimally solve a problem.

Interests

Computational Linguistics
Natural Language Processing

Deep Learning
Machine Learning


Senior Data and Applied Scientist Microsoft 2017 -	B.Tech, Computer Science IIT Guwahati 2013 - 2017	Winter School Gifu University Dec 2015

Recent Publications

Siddharth Gollapudi, Neel Karia, Varun Sivashankar, Ravishankar Krishnaswamy, Nikit Begwani, Swapnil, Raz, Yiyong Lin, Yin Zhang, Neelam Mahapatro, Premkumar Srinivasan, Amit Singh, Harsha Simhadri.
Filtered-DiskANN: Graph Algorithms for Approximate Nearest Neighbor Search with Filters.
In Proceedings of the ACM Web Conference 2023 (WWW ’23), April 30-May 4, 2023, Austin, TX, USA

Akash Kumar Mohankumar, Nikit Begwani, Amit Singh.
CLOVER: Diversity Driven Query Rewriting in Search Advertising.
In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’21), August 14–18, 2021, Virtual Event, Singapore

Nikit Begwani, Shrutendra Harsola, RahulAgrawal.
Learning From Weights: A Cost-Sensitive Approach For Ad Retrieval.
In Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, Jan 2020.

Nikit Begwani, Shrutendra Harsola, RahulAgrawal.
Cost-sensitive Learning of Deep Semantic Models for Sponsored AdRetrieval.
In Proceedings Microsoft Journal of Applied Research, Vol 10.

Talks and Presentations

Siamese Networks, Extensions and Applications to Retrieval. AMPere 2019, Microsoft Seattle

Cost Sensitive Learning on Deep Semantic Matches. MLADS Fall 2018, Microsoft Redmond

Transcriber - The only college project showcased at Microsoft Future Decoded 2017, Mumbai Video

Methods for Automatic Schema Matching - A Survey. Talk was given as part of coursework Report Presentation

Projects

[Bachelor Thesis Project] Understanding Multilingual Sentiment Analysis on Twitter Data - Under the guidance of Prof. S.R. Singh Report Poster

Generating Responses to Contextual Queries of Reading Comprehensions - Under the guidance of Prof. Ashish Anand Report Presentation

Detection of Driver Distraction using Deep Residual Networks - Under the guidance of Prof. Arijit Sur Report

Labyrinth - Interactive hand gestured based maze game - Under the guidance of Prof. S.B. Nair Report Video

Document Clustering Based On Non-negative Matrix Factorization - Under the guidance of Prof. S.R. Singh Report

Courses

Information Retrieval
Machine Learning
Intelligent systems & Interfaces (NLP)
Probability Theory and Random Processes
Computer Vision using Machine Learning
Data Structures & Algorithms
Algorithmic Game Theory

Optimizations
Compilers
Discrete Mathematics
Databases
Operating Systems
Linear Algebra
Theory of Computation

Contact

<full_name>@gmail.com
Bengaluru, Karnataka, India