The Future of Generative AI and LLM Agents in Enhancing AI Hardware Efficiency | Kisaco Research
Speaker(s): 

Author:

Neeraj Kumar

Chief Data Scientist
Pacific Northwest National Laboratory

As the Chief Data Scientist at Pacific Northwest National Laboratory (PNNL), Neeraj leads a talented team of scientists and professionals in addressing critical challenges in energy, artificial intelligence, health, and biothreat sectors. With over 15 years of experience in quantitative research and data science, he specializes in developing innovative solutions and managing multidisciplinary teams focused on multimillion-dollar programs at the intersection of fundamental discovery and transformative AI-driven product development.


His expertise spans Applied Math, High-Performance Computing, Computational Chemistry and Biology, Health Science, and Medical Therapeutics, enabling his to guide his team in exploring new frontiers. He has a deep understanding and application of Generative AI, AI Safety and Trustworthiness, Natural Language Processing, Applied Mathematics, Software Engineering, Modeling and Simulations, Quantum Mechanics, Data Integration, Causal Inference/Reasoning, and Reinforcement Learning. These competencies are crucial in developing scalable AI/ML models and computing infrastructures that accelerate scientific discoveries, enhance computer-aided design, and refine autonomous decision-making.

Neeraj Kumar

Chief Data Scientist
Pacific Northwest National Laboratory

As the Chief Data Scientist at Pacific Northwest National Laboratory (PNNL), Neeraj leads a talented team of scientists and professionals in addressing critical challenges in energy, artificial intelligence, health, and biothreat sectors. With over 15 years of experience in quantitative research and data science, he specializes in developing innovative solutions and managing multidisciplinary teams focused on multimillion-dollar programs at the intersection of fundamental discovery and transformative AI-driven product development.


His expertise spans Applied Math, High-Performance Computing, Computational Chemistry and Biology, Health Science, and Medical Therapeutics, enabling his to guide his team in exploring new frontiers. He has a deep understanding and application of Generative AI, AI Safety and Trustworthiness, Natural Language Processing, Applied Mathematics, Software Engineering, Modeling and Simulations, Quantum Mechanics, Data Integration, Causal Inference/Reasoning, and Reinforcement Learning. These competencies are crucial in developing scalable AI/ML models and computing infrastructures that accelerate scientific discoveries, enhance computer-aided design, and refine autonomous decision-making.