IBM webinar registration: The Webinar on Data Quality and Data Synthesis Using Constraints organized by IBM on Sep 7, 2020, at 4:00 PM. The detailed requirement and process of applying are discussed further in the article.
About the company: International Business Machines Corporation 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
IBM webinar registration:
About The webinar:
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.
- firstly, 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.
- Secondly, He received his Ph.D. from Technion(Israel), MS from IIIT-H, and B.Tech. from DA-IICT.
- Thirdly, Prior to IBM, he was a Post-doc researcher at Virginia Tech(US); and will also has also worked in the Research and Development department at Mindtree Consulting.
- Fourthly, He has to published papers in top conferences and also journals like so; the Journal of ACM, PODC, DISC, PPoPP, and ICDE, and has been a PC member of top conferences.
How to Register?
Interested participants can register for the webinar through this link.
For more details, click the link below.
Webinar on Data Quality and Data Synthesis Using Constraints by IBM
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