Merve Unuvar  Merve Unuvar photo         

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Director, AI Platforms and Automation
Thomas J. Watson Research Center, Yorktown Heights, NY USA
  +1dash914dash945dash2981

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Professional Associations

Professional Associations:  AAAI  |  ACM  |  DIMACS  |  IEEE   |  IEEE Member  |  Mathematical Optimization Society  |  Women in Technology (WIT)

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Merve Unuvar is Director of AI Platforms and Automation team in IBM Research AI. She is also leading the global research strategy for Business and IT Automation, partnered with the IBM Hybrid Cloud Automation business with a focus on bringing AI infusion across IBM's Cloud Paks for automation, and integration. Her team consists of research and data scientists, engineers and designers building platforms, tools and programming models that enable data scientists and developers to create and operate AI models and applications faster and better. Merve's team is developing cutting edge technology in the intersection of core AI, distributed systems, cloud computing, human computer interaction and visualization

Prior to this, Merve worked as Strategy Lead for the AI Platforms and Runtimes org. Merve helped create the strategic collaboration program that reimagined how IBM Research does business with both clients and business units. Together with the team, she made foundational contributions to Cloud Pak for Automation & Integration business units.  Before this, Merve was the Lead Offering Manager of the Blockchain Solutions team where she helped build IBM Food Trust from concept to product, expanding the team from 4 to 100+.  Merve was Technical Assistant (TA) to the VP of Cloud Architecture and Technology and RSM in the Cloud Research team where she worked in machine learning for business process management and invented new techniques on multi-cloud scheduling featured in Fortune.

Prior to IBM, Merve worked in Dun and Bradstreet’s, Global Analytics division as a Data Scientist. In this position, she was responsible for building financial specific models constructed from multiple analytics and big data. Here she utilized, existing and novel self-discovered modeling methods to generate predictive scores for business ratings used to influence billions of dollars in global commerce. In this role she contributed to process improvements in coding, in data audits and in feasibility analytics.

Merve is Adjunct Professor at Boston College, Carroll School of Management, teaching ‘Machine Learning for Business Intelligence’ serves on the board of Stevens Institute of Technology, School of Systems and Enterprises and holds a PhD in Operations Research from Rutgers University.

Merve’s research interests include: Machine Learning, Business Process Management, Stochastic Modeling & Programming, Probabilistic Network Design such as bounding the reliability of a power system, Optimization with Discrete Random Variables, Data Mining/ Management and Predictive Analytics.