Closing Remarks for the 2025 MIT Europe Conference in Vienna
Enhancing Security via AI and Machine Learning Dennis Ross Group Leader of AI Technology and Systems, MIT Lincoln Laboratory
AI-Powered Integrated Development Environments
Adam Chlipala Arthur J. Conner (1888) Professor, MIT Department of Electrical Engineering and Computer Science (EECS)
Welcome and Introduction
Gayathri Srinivasan Executive Director, MIT Corporate Relations
Keynote: The Age of AI Daniel Huttenlocher Dean, MIT Schwarzman College of Computing Professor, MIT Department of Electrical Engineering and Computer Science (EECS)
Future of Knowledge, Systems, Skills, and Intelligence
Moderator:
Panelists:
Overview of the MIT Generative AI Impact Consortium
Vivek Farias Patrick J. McGovern (1959) Professor, MIT Sloan School of Management Faculty Co-Director, MIT Generative AI Impact Consortium
The Future of AI Hardware
Jesús A. del Alamo Donner Professor, MIT Department of Electrical Engineering and Computer Science (EECS)
How AI is Transforming Software Engineering
Armando Solar-Lezama Professor, MIT Department of Electrical Engineering and Computer Science Associate Director and COO, MIT Computer Science & Artificial Intelligence Laboratory
AI is not just transforming industries—it’s revolutionizing software development. From AI-assisted coding to automated testing and lifecycle management, new tools are enhancing productivity, quality, and security. The speaker will explore the impact of AI-driven programming, the evolving role of software engineers, and the challenges of ensuring control, reliability, and trust in AI-generated code.
Marzyeh Ghassemi Associate Professor, MIT Department of Electrical Engineering and Computer Science (EECS) and Institute for Medical Engineering & Science (IMES)
Machine learning in health has made impressive progress in recent years, powered by an increasing availability of health-related data and high-capacity models. While many models in health now perform at, or above, humans in a range of tasks across the human lifespan, models also learn societal biases and may replicate or expand them. In this talk, Dr. Marzyeh Ghassemi will focus on the need for machine learning researchers and model developers to create robust models that can be ethically deployed in health settings, and beyond. Dr. Ghassemi's talk will span issues in data collection, outcome definition, algorithm development, and deployment considerations.