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Overview

The 5th Joint International Conference on Data Science and Management of Data (CODS-COMAD) 2022 (9th ACM IKDD CODS and 27th COMAD) will be held on January 7-10, 2022, in a hybrid mode at the International Institute of Information Technology (IIIT), Bangalore, India. The event is being held as a common conference for the fifth time, bringing together COMAD and the CODS communities, inviting researchers in the field of databases, data sciences and their application, to submit their work.

CODS-COMAD is a leading international data science conference focusing on scientific work in databases and applications. The two conferences came together for the first time in 2018. COMAD, primarily directed at data management was started in 1989, while CODS in 2014—featuring excellent program in AI, machine learning and data science.

This year, the conference is introducing Applied Data Science (ADS) track, which is distinct from research track, wherein submissions focus on applied work, addressing real-world problems and systems demonstrating tangible impact in their respective domains. Also, proposals must clearly specify an audience or a group of users that have or will benefit from the solution presented in the submission.         

Key topics of focus at the conference:

  • Data management
  • Data sciences
  • AI learning
  • Machine learning

TCS is the platinum partner

and the sponsorship co-chair at the event.

Highlights of Our Participation:
 

Lipika Dey

Lipika Dey

Scientist

TCS Research

Sponsorship Co-Chair

  


 

Hrishav Bakul Barua

Hrishav Bakul Barua

Researcher

TCS Research

Speaker

Green computing for big data and machine learning


 

Vivek Srivastava

Vivek Srivastava

Researcher

TCS Research

Speaker

 Code-mixed NLG: resources, metrics, and challenges

Publications:

Papers:

  1.  Identifying efficient curricula for reinforcement learning in complex environments with a fixed computational budget, by Omkar Shelke
  2. Deep reinforcement learning algorithm for fast solutions to vehicle routing problem with time-windows, by Abhinav Gupta

Tutorials

  1.  Code-Mixed NLG: Resources, Metrics, and Challenges by Vivek Srivastava
  2.  Green Computing for Big data and Machine Learning by Hrishav Bakul Barua
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