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Hi! I’m Vineet J Nair, and I develop computational methods to tackle climate change and enable decarbonization, with a focus on power and energy systems problems. I have previously also worked on problems in other areas, like transportation and heavy industry. I recently graduated with a PhD in Computational Science & Engineering from the Massachusetts Institute of Technology (MIT). I am broadly interested in all areas related to clean energy, climate tech, and sustainability, and I’m passionate about conducting novel research as well as exploring its practical applications. I leverage tools from various domains, including optimization, machine learning & artificial intelligence, numerical methods, game theory, statistics, large-scale simulation, and high-performance computing. You can learn more about my PhD research, current research interests & some projects here. I also have extensive industry experience through research internships at Google X, Shell, Siemens, Tata Motors, National Renewable Energy Lab (NREL), and Avangrid.

Education

  • Massachusetts Institute of Technology:
    • MS in Computational Science & Engineering (2021)
    • PhD in Computational Science & Engineering (2025), advisor: Dr. Anuradha Annaswamy
  • University of Cambridge: MPhil in Energy Technologies - Gates Cambridge Scholar (2019)
  • University of California, Berkeley:
    • BS in Mechanical Engineering, BA in Economics (2018)
    • Minors in Electrical Engineering & Computer Sciences, Human-Centered Design, Entrepreneurship & Technology

Research Internship Experience

  • Tata Motors: Development of machine learning-based advanced motor controllers for electric vehicle power converters. Focussed on current control, speed/position observers & pulse pattern generation.
  • Shell: Data-driven optimization and machine learning algorithms for shipping decarbonization, leveraging large amounts of geospatial and onboard sensor data. Successfully improved operational efficiency of LNG carrier vessels to reduce fuel usage, emissions, and methane slip.
  • Google X, the Moonshot Factory: Applied scientific machine learning (physics-informed neural networks and neural ordinary differential equations) to accelerate and improve the accuracy of numerical simulations of fast timescale dynamics of synchronous generators and inverters, to assess the transient stability of power grids.
  • Scale AI: Evaluating the capabilities and limitations of frontier AI models for applications in my domains of expertise (e.g., control, optimization, power systems), as a Human Frontier Collective Program Fellow.
  • Siemens: Developed a novel, hierarchical market structure to coordinate distributed energy resources and flexible demand response in the future grid, based on distributed and decentralized optimization.
  • NREL: Power grid modeling & digital real-time simulation for hardware-in-the-loop validation of optimization/control algorithms
  • Avangrid: Developed a hybrid, federated software architecture & decision-making method to enhance cybersecurity & interoperability of a new distributed energy resources management system (DERMS) pilot.
  • Catalyst Investment Management: Techno-economic analysis and optimization of sustainable AI data centers in the Middle East and North Africa (MENA) region.

Other Fun Projects

Over the past several years, I’ve had the pleasure of collaborating with many wonderful people at MIT and beyond. These include collaborators at other institutions in the US like Princeton University, West Virginia University, the National Renewable Energy Lab, Pacific Northwest National Lab, and General Electric. In addition, I was involved in global collaborations with researchers at ETH Zürich in Switzerland, Universidad Politécnica de Madrid in Spain, and Instituto Superior Técnico & INESC TEC in Portugal. I’m always open to new collaboration opportunities - please feel free to reach out to me via email (listed below)!

Contact

For more information, see my publications and my resume/CV.