Mohit Shrivastava pioneers physics-informed AI to build intelligent, energy-efficient data centers that redefine sustainable engineering.
Mohit Shrivastava
In the global effort to build sustainable digital infrastructure, data centers have become the new frontier. These vast systems power everything from financial markets to artificial intelligence, consuming enormous amounts of energy in the process. For Mohit Shrivastava, a mechanical engineer specializing in energy analytics and data center systems, this challenge represents an opportunity to redefine how engineering and data science converge.
As Director of Engineering Analytics at Switch, Mr. Shrivastava leads initiatives that apply artificial intelligence and predictive modeling to optimize thermal management and mechanical performance. His work focuses on creating intelligent frameworks that allow data centers to anticipate inefficiencies and adapt to changing load conditions without human intervention. By combining classical engineering principles with computational intelligence, he is helping transform facilities that were once reactive into self-optimizing systems.
Mohit Shrivastava’s career reflects a shift occurring across the entire engineering landscape. The traditional boundaries between physical systems and digital tools are dissolving, giving rise to what he calls “physics-informed AI.” In this model, machine learning algorithms do not replace engineering knowledge but amplify it. The data collected from sensors, airflow simulations, and control systems becomes more meaningful when interpreted through the laws of thermodynamics and fluid mechanics. This approach allows engineers to predict thermal loads, optimize cooling configurations, and improve energy efficiency with a level of precision unattainable by empirical data alone.
During his years at Amazon Web Services, Mr. Shrivastava applied this concept to large-scale operations. He was part of the team responsible for developing predictive energy models for hundreds of hyperscale data centers worldwide. These models supported strategic decisions on power usage effectiveness and water conservation, helping align the company’s sustainability goals with operational reliability. His work contributed to frameworks now used by engineers across regions to balance performance, energy consumption, and environmental impact.
At Switch, his vision extends beyond efficiency. Mohit Shrivastava and his team are working on building digital twins of critical infrastructure—virtual replicas that mirror real-world behavior in real time. These models make it possible to simulate months of operation in minutes, revealing subtle interactions between airflow, humidity, and equipment layout. The insights gained enable proactive maintenance and system design improvements that can reduce downtime and extend asset life.
Mr. Shrivastava’s approach combines precision engineering with an understanding of human and environmental systems. He believes that the next evolution in mechanical design will not be measured only by how much energy is saved but by how systems learn from their own behavior. “Every mechanical process generates data that tells a story,” he often explains. “The challenge is teaching machines to listen, interpret, and respond in a way that reflects sound engineering judgment.”
His leadership reflects a broader transformation across the energy and technology sectors. As organizations confront the dual pressures of digital growth and sustainability mandates, the ability to design systems that learn, predict, and adapt is becoming essential. Engineers like Mohit Shrivastava are at the center of this transition, showing that technical depth and data-driven innovation can coexist within a framework of environmental responsibility.
Through his work, Mr. Shrivastava represents the modern engineer: fluent in both physics and code, driven by the pursuit of sustainability, and committed to bridging the gap between computation and craftsmanship. In a world increasingly defined by digital demand, his vision points toward a future where machines not only operate efficiently but also think intelligently about the energy they consume.
Author: Susmita Gupta
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