How Cars Learn to Drive Without Real Roads
Tuesday, 27 January 2026
8:00 PM IST
Zoom
Dr. Eugene Vinitsky
Asst. Professor NYU Department of Civil, Urban, and Environmental Engineering
NYU Tandon School of Engineering Dr. Eugene Vinitsky is an assistant professor of civil and urban engineering at NYU where he works on scaling up multi-agent reinforcement learning for the design of safe, autonomous systems. At UC Berkeley, where he was advised by Alexandre Bayen, he received his PhD in controls engineering with a specialization in reinforcement learning and received an MS and BS in physics from UC Santa Barbara and Caltech respectively. He has spent time at Tesla, Deepmind, Facebook AI Research, and was a researcher at the Apple Special Project Group before moving to NYU.
Asst. Professor NYU Department of Civil, Urban, and Environmental Engineering
NYU Tandon School of Engineering Dr. Eugene Vinitsky is an assistant professor of civil and urban engineering at NYU where he works on scaling up multi-agent reinforcement learning for the design of safe, autonomous systems. At UC Berkeley, where he was advised by Alexandre Bayen, he received his PhD in controls engineering with a specialization in reinforcement learning and received an MS and BS in physics from UC Santa Barbara and Caltech respectively. He has spent time at Tesla, Deepmind, Facebook AI Research, and was a researcher at the Apple Special Project Group before moving to NYU.This masterclass offers an inside look at how reinforcement learning (RL) is being used to design and train self-driving cars entirely within simulated environments. By relying on high-quality synthetic data instead of costly and time-consuming real-world driving data, this approach presents a scalable and practical pathway for developing autonomous vehicle systems. Participants will be guided through the end-to-end process of building reinforcement learning training pipelines, with a deep dive into the challenges of simulating realistic driving environments and generating diverse training data at scale. The session will highlight how billions of synthetic driving scenarios—including rare and high-risk edge cases—can be efficiently created to improve model robustness and safety. The masterclass will also explore how models trained in simulation can successfully transfer to real-world driving conditions, addressing the critical gap between controlled virtual environments and the complexity of real roads. Attendees will gain practical insights into the tools, techniques, and research strategies shaping the future of autonomous driving.
Explore bridging sim-to-real gap
Learn how to build robust driving models
Learning inside simulations
Exclusive Application Fee Waivers
Access to SEED Scholarships
Learn actionable strategies for adopting advanced technologies
Networking and Access to Expert Insights
Founded in 1854, New York University - Tandon School of Engineering (NYU Tandon) is the second oldest private engineering and technology school in the United States. The schools main campus is in Brooklyn MetroTech Centre, an urban academic-industrial research park. NYU Tandon has been ranked #38 as the Best Global University and its top employers are Nordstrom, Microsoft, PwC, Amazon, Google, Oracle, EY, Deloitte, Apple, Goldman Sachs, and more. NYU graduate engineering programs exist in the fields of mechanical, civil, urban, industrial, electrical, computer, chemical, biomedical and financial engineering alongside programs in computer science, management of technology, cybersecurity, and integrated digital media. Our goal is to produce highly desirable graduates prepared for industry. This has led us to be one of the top ranked schools in the nation with regards to graduate employability, salary potential and return on investment.
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20 January 2026
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