Jigyasa Nigam

Jigyasa Nigam is a materials physicist who left an MIT-IBM Distinguished Postdoctoral Fellowship in Electrical Engineering and Computer Science at MIT, alongside a visiting postdoctoral position at Harvard, to join the University of Vienna's Faculty of Physics as an APART-USA fellow, a programme of the Austrian Academy of Sciences created specifically for scientists leaving American institutions.

Nigam earned her BTech from the Indian Institute of Space Science and Technology before completing her PhD in physics at EPFL (École Polytechnique Fédérale de Lausanne) in Switzerland. Her earlier research experience includes roles as an AI4Science intern at Microsoft Research, a Future Research Talent Scholar at the Australian National University, and positions at NASA's Jet Propulsion Laboratory and Caltech.

Her research focuses on machine learning for atomistic systems, investigating how the mathematical structure of machine-learning models interacts with the inherent constraints of physical problems such as symmetry, smoothness, and conservation laws. At MIT, she worked on incorporating higher-body-order effects and equivariance into machine-learning representations, with applications spanning quantum chemistry, sustainable energy, healthcare, and quantum computing. As a postdoctoral fellow, she pursued modeling symmetry-breaking phenomena and integrating dissipative and open quantum systems into her broader modeling framework.