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Machine Learning & Statistics



Rapid generation of data, cheaper storage and greater processing power are pushing businesses to adopt sophisticated machine learning and statistical models to derive insights. At Conduent Labs India, we believe that machine learning will provide the sophisticated tools required to understand and capitalize value from the deluge of data that clients often face, as evidenced in our businesses like Transportation, where cities get data from parking, tolling, buses, etc. Machine learning provides tools to analyze these sophisticated transportation systems to understand patterns, predict trends, and provide actionable insights to improve efficiency and plan effectively for the future.

The machine learning and statistics team consists of researchers with a strong background and expertise in machine learning. Members of the team have diverse interests and proficiency in different areas of machine learning including deep learning, ranking, online learning, probabilistic inference techniques, graphical models, and reinforcement learning, to name a few. Researchers bring together these diverse skills to develop new tools that can be used to analyze complex and unstructured data. Many of the researchers in the team have successful collaborations with top universities across the globe, producing truly innovative research.

Team


Anuj

Arun

Bhupendra

Narayanan

Poorvi

Shailesh

Simarjot

Tuhin

Featured Publications

  • Narayanan U. Edakunni, Koyel Mukherjee, Theja Tulabandhula, Kaushik Baruah, Tuhin Bhattacharya, Geetha Manjunath, Demand Sensitive Scheduling of Public Transport using past ticketing data, Intelligent Transportation Systems World Conference, 2016
  • Sai Praneeth Reddy, Shailesh Vaya:Brief Announcement: Multi-Broadcasting under the SINR Model. PODC 2016: 479-481
  • Bogdan S. Chlebus, Shailesh Vaya: Distributed communication in bare-bones wireless networks. ICDCN 2016: 1:1-1:10
  • L. Elisa Celis, Sai Praneeth Reddy, Ishaan Preet Singh, Shailesh Vaya: Assignment Techniques for Crowdsourcing Sensitive Tasks. CSCW 2016: 834-845
  • Static and dynamic scheduling to minimize passenger waiting times Theja Tulabandhula, Koyel Mukherjee and Narayanan U. Edakunni Transportation Research Board 2016 Annual Meeting, Washington D.C., USA January 10-14, 2016

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