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Date 28th February, 2025
Time 10:00 AM (EST) | 8:30 PM (IST) | 12:00 PM (Brazil) | 7:00 PM (UAE) | 4:00 PM (CET) | 7:00 AM (PST)

Virtual Open House on AI and ML: Leading Business Growth Starts in:

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Description We are pleased to invite you on behalf of MIT Professional Education to participate in the “Virtual Open House on AI and ML: Leading Business Growth”. Join us for an exciting session featuring Devavrat Shah and Jehangir Amjad from MIT, who will share their expertise and research-driven insights while exploring the critical aspects of AI and ML in the context of driving business growth.

They will share more information about the program, its content, and its unique 'no-code' approach. This session will help you understand the immersive learning experience that the program offers and how to leverage AI and ML to benefit the future of your business or organization.

Speakers

Devarat-Shah

Devavrat Shah
Professor of Electrical Engineering and Computer Science, MIT
Director, Statistics and Data Science Center, MIT

Devavrat Shah is a professor with the Department of Electrical Engineering and Computer Science at MIT. He is a member of the Laboratory for Information and Decision Systems (LIDS) and Operations Research Center (ORC), and the Director of the Statistics and Data Science Center (SDSC) in IDSS. His research focus is on theory of large complex networks, which includes network algorithms, stochastic networks, network information theory and large-scale statistical inference.

Jehangir Amjad

Jehangir Amjad
Head of AI Platform at Ikigai Labs

Jehangir Amjad is the Head of AI Platform at Ikigai Labs and has been a Computer Science Lecturer both at Stanford (2023-now) and CSAIL at MIT (2018-2022), teaching courses in Machine Learning (CS 229, 6.036/6.390) and Artificial Intelligence (CS 221). He was awarded the Jerome H. Saltzer Award for Excellence in Teaching at MIT (2022). Jehangir joined Ikigai from Google where he was a Software Engineer (2018-2024) where at first he helped build and deploy distributed pipelines for statistical inference to help make Google’s global networking infrastructure increasingly more robust and reliable. He then joined the open source Data Commons project at Google which represents the largest knowledge graph of public data. Jehangir received his PhD in Machine Learning from MIT (2018) and BSE in Electrical Engineering from Princeton University (2010).

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