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About This Session

The increasing industrial usage of machine learning models raises the question of the reliability of machine learning models, which also depends on the quality of the data used for training. The current industry practice of testing with limited data is often insufficient.

We provide techniques for understanding the data, inferring data constraints and using these constraints to improve the data quality by removing anomalies, imputing missing values and also generating synthetic data required for model training and testing.

We address multiple important challenges like realistic and user-controllable data generation, essentially to increase trust in machine learning models. 

About IBM

International Business Machines Corporation is an American multinational technology company headquartered in Armonk, New York.

It was founded in 1911 in Endicott, New York as the Computing-Tabulating-Recording Company and was renamed “International Business Machines” in 1924.

Important Dates

Date: 7th September 2020
Time: 4 PM – 5 PM

How To Register

Interested Candidate can apply for this session by clicking on the given link. below.

Register Here: Click Here

Official Notification: Click Here

About Host & Speaker

Sandeep Hans is a Research Scientist at IBM Research, India.  His current focus is on building dependable AI systems using bias and adversarial AI testing, and data quality for AI.

He received his PhD from Technion(Israel), MS from IIIT-H and B.Tech. from DA-IICT.  Prior to IBM, he was a Post-doc researcher at Virginia Tech(US) and has also worked in Research and Development department at Mindtree Consulting. 

He has published papers in top conferences and journals like Journal of ACM, PODC, DISC, PPoPP and ICDE, and has been PC member of top conferences.

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