Anima Anandkumar has spent much of her career working at the intersection of artificial intelligence and physical science. Now, the Caltech professor has been named to TIME’s list of the 100 most influential people in AI, recognising research that aims to make AI a powerful tool for scientific discovery.

Anandkumar’s work is different from the AI applications that dominate everyday headlines. Rather than focusing primarily on chatbots or consumer software, she has developed methods that allow machine-learning systems to model complex physical processes, from weather and nuclear fusion to medical devices and quantum computing.

The recognition comes shortly after Anandkumar publicly launched Accelerated Understanding, a company built around her long-running effort to combine AI with scientific research.

Her career has taken her from IIT Madras and Cornell University to MIT, Amazon Web Services, and the California Institute of Technology. She also serves as a senior AI research leader at NVIDIA and was appointed to the United Nations Secretary-General’s Scientific Advisory Board earlier this year.

Who is Anima Anandkumar?

Anima Anandkumar is an Indian-born computer scientist and professor at the California Institute of Technology, where her research focuses on machine learning, AI, and scientific computing.

Her central idea is straightforward but ambitious: AI should not merely process existing information. It can also help scientists simulate physical systems and explore problems that would otherwise require enormous amounts of computing power.

That approach has made her an influential div in what is increasingly known as AI for science.

Her work has been applied to problems, including:

• Weather forecasting
• Climate and atmospheric modeling
• Nuclear fusion
• Quantum chemistry
• Quantum computing
• Medical device design
• Semiconductor manufacturing

The TIME recognition puts her alongside researchers, entrepreneurs and technology leaders who are helping determine how artificial intelligence develops and where it is ultimately applied.

How did Anima Anandkumar begin her career?

Anandkumar was born in Mysore, Karnataka, and grew up in a family with strong academic and engineering backgrounds.

Both of her parents were engineers, while her grandfather was a mathematician. Her great-grandfather was a Sanskrit scholar.

Her interests were not limited to science. Anandkumar also trained in Bharatanatyam, the classical Indian dance form, and performed during her younger years.

She studied electrical engineering at IIT Madras and completed her BTech in 2004.

She subsequently moved to the United States to pursue doctoral studies at Cornell University. She earned her PhD in 2009 and was supported by an IBM Fellowship during her research.

What did she do after her PhD?

After completing her doctorate, Anima Anandkumar worked as a postdoctoral researcher at MIT.

Her research focused on machine learning and mathematical methods for understanding complex systems. These interests eventually became the foundation for her work on AI models capable of representing physical processes.

She later moved into the technology industry, joining Amazon Web Services as a Principal Scientist.

At Amazon, she was involved in the development and launch of Amazon SageMaker, a machine-learning platform designed to help developers and organizations build, train, and deploy AI models.

The experience gave her a perspective that bridged academic research and large-scale commercial AI infrastructure.

When did Anima Anandkumar join Caltech?

Anima Anandkumar joined Caltech as a professor in 2017.

The move allowed her to pursue fundamental research while continuing to work with industry.

In 2018, she also became senior director of AI Research at NVIDIA.

That dual role has been important to her research because many of the scientific problems she studies require enormous computational resources.

Her work at the intersection of academia and industry has helped turn theoretical advances in machine learning into systems capable of tackling real-world scientific problems.

What are neural operators and why are they important?

One of Anandkumar’s most significant contributions is her work on neural operators.

Traditional machine-learning systems are generally trained to recognise patterns within particular datasets. Neural operators take a broader approach, learning relationships between functions and physical systems.

That matters for scientific computing because many problems involve equations describing how physical systems change over space and time.

Instead of repeatedly solving those equations using conventional numerical methods, AI models can learn how those systems behave and generate predictions much faster once trained.

The potential payoff is enormous.

Scientific simulations can consume vast amounts of computing power. If an AI model can produce accurate approximations much faster, researchers can run many more experiments virtually.

That can help scientists explore possibilities that would otherwise be too expensive or time-consuming to calculate.

How has her AI research been used?

Anandkumar’s research has moved beyond theoretical computer science and into several scientific fields.

One major example is weather forecasting.

Her work contributed to FourCastNet, an AI-based weather forecasting model developed with NVIDIA researchers. The system demonstrated how machine learning could dramatically accelerate certain atmospheric simulations.

The broader goal is not simply to make forecasts faster. Faster simulations allow scientists to run more scenarios and examine extreme weather and climate patterns at a much greater scale.

Her methods have also been explored for nuclear fusion.

Fusion researchers need to simulate extraordinarily complicated physical processes involving plasma, magnetic fields and enormous amounts of energy. Faster AI-assisted modeling could help researchers test designs and understand these systems more efficiently.

What has her research achieved in medicine and manufacturing?

Her work has also found applications outside traditional physics.

Anandkumar has been involved in research using AI to assist with medical-device design. One reported result was a catheter design aimed at reducing bacterial contamination by 100 times.

Her methods have also been applied to semiconductor manufacturing.

Chip production involves extremely precise processes in which tiny design changes can have significant consequences. AI-based optimization can help researchers explore configurations more efficiently.

Another area is quantum computing.

Her research has been used to help improve the design and arrangement of quantum dots, structures that can play an important role in advanced quantum technologies.

The common thread is optimization: use AI to explore enormous numbers of possible solutions faster than conventional computational approaches.

What is Accelerated Understanding?

The TIME recognition arrived just after Anandkumar publicly launched Accelerated Understanding.

The company is built around the same idea that has shaped much of her academic career: combining AI with scientific knowledge to accelerate discovery.

Anandkumar has described the AI-and-science connection as the focus of her work for the past decade.

The company’s name reflects the central ambition. Instead of using AI primarily to generate text, images or code, the goal is to use it as a tool for understanding complex physical systems.

That could eventually allow scientists to conduct computational experiments at speeds that would have been difficult to imagine using conventional simulation methods.

Why does Anima Anandkumar believe AI and physics belong together?

Modern science increasingly depends on computer simulations.

Scientists studying weather, climate, materials, medicine, energy and quantum systems often work with mathematical models that can take enormous amounts of computing power to solve.

AI offers another route.

A machine-learning model can be trained on simulations or scientific data and then used to approximate how a physical system behaves.

The crucial question, however, is accuracy.

A model that produces an answer in a fraction of the time is useful only if scientists can trust the result.

That is why Anandkumar has emphasized physics-informed approaches, where AI systems incorporate knowledge about the underlying physical laws rather than relying exclusively on patterns in historical data.

What is Anandkumar’s role at the United Nations?

Anandkumar was appointed to the United Nations Secretary-General’s Scientific Advisory Board in March 2026.

The board provides scientific expertise to help inform the UN’s work on major global challenges.

Her role also reflects the broader policy questions surrounding artificial intelligence.

AI is developing rapidly, but access to computing infrastructure, data and advanced research is uneven across the world.

Anandkumar has argued that scientific applications of AI should not be viewed solely through the lens of the technology industry’s biggest markets.

She has emphasized the importance of keeping discussions about AI connected to science and ensuring that countries in the Global South are part of those conversations.

What does Anima Anandkumar say about the future of AI?

Anandkumar has increasingly focused on what she calls self-improvement in scientific AI.

The idea is that AI models could potentially evaluate their own predictions against physical principles and improve their performance without depending entirely on new laboratory measurements.

That could change how scientists use AI.

Instead of treating AI as a sophisticated calculator, researchers could eventually use it as an experimental partner that proposes possibilities, tests them computationally and identifies promising directions for further investigation.

The concept remains an active area of research, but it illustrates why scientific AI is attracting growing attention.

What awards has Anima Anandkumar received?

Anandkumar’s TIME recognition adds to several other honors during her career.

She has been recognized with:

• IEEE Fellowship
• Alfred P. Sloan Fellowship
• National Science Foundation CAREER Award
• A position on the UN Secretary-General’s Scientific Advisory Board
• Inclusion in TIME’s 100 most influential people in AI

These honors reflect both her technical contributions and her growing role in discussions about how AI should be used in science and society.

Why is Anima Anandkumar’s TIME recognition significant?

The significance of Anandkumar’s inclusion on TIME’s AI list lies partly in what her work says about the next phase of artificial intelligence.

The AI boom has largely been associated with generative models, search, coding assistants and consumer applications.

But another transformation is taking place in laboratories and research institutions.

Scientists are increasingly exploring AI as a way to model systems that are too complex, expensive or time-consuming to simulate using conventional methods.

Anandkumar sits directly in that movement.

Her research suggests that one of AI’s biggest contributions may not be producing another chatbot. It could be helping scientists run more simulations, test more hypotheses and move from a scientific question to a workable answer faster.

The bottom line

Anima Anandkumar’s journey from IIT Madras to Caltech, NVIDIA and the United Nations reflects the increasingly global and interdisciplinary nature of artificial intelligence.

Her work on Neural Operators has helped push AI beyond conventional data analysis toward the simulation of physical systems.

Weather forecasting, nuclear fusion, quantum computing, medical devices and semiconductor manufacturing may appear to have little in common. For Anandkumar, they share a fundamental problem: scientists need faster and more efficient ways to understand complex systems.

Her inclusion on TIME’s list of the 100 most influential people in AI recognizes that contribution.

As she builds Accelerated Understanding, Anandkumar is betting on a future in which AI does more than generate information. It helps scientists understand the physical world itself.