Panels

Tuesday, July 8, 8:00AM-9:00AM in Westminster Ballroom III, First Floor

Organized by the Computational Heat Transfer Technical Committee (K20)

This 60-minute panel discussion will explore recent advances in solving inverse heat transfer problems, with a focus on key insights from the International Conference on Inverse Problems in Engineering (ICIPE 2024), held in Brazil in June 2024. As the flagship event in the field, ICIPE brings together leading researchers and practitioners every three years to discuss the latest developments in inverse methods for engineering applications.

The panel session will highlight emerging methodologies, computational strategies, and real-world applications across industries such as thermal management of electronics, energy systems, aerospace, and non-destructive testing. The panelists, who participated in ICIPE 2024 with support from the National Science Foundation (NSF), will share their perspectives on the state of the field and future directions.

Topics of discussion will include:

• Introduction to Inverse Problems in Heat Transfer –concepts, challenges, and applications
• Key Takeaways from ICIPE 2024: Notable findings and emerging research directions
• AI/ML Techniques for Solving Inverse Heat Transfer Problems
• Physics-Informed Neural Networks (PINNs) for Heat Transfer Applications – Bridging physics-based and machine learning models
• Applications and Case Studies

This session will provide valuable insights for researchers, engineers, and practitioners working in heat transfer, computational modeling, and related fields.

Presenters/Panelists

Keith Woodbury, Ph.D.
Professor Emeritus of Mechanical Engineering
University of Alabama

Hamidreza Najafi, Ph.D.
Associate Professor of Mechanical Engineering
Florida Institute of Technology

Benjamin Kubwimana, M.Sc.
Senior Software Engineer
NVIDIA

Forooza Samadi, Ph.D.
Assistant Professor of Mechanical Engineering
University of Alabama

K8- Panel 2
Tuesday, July 8 | 1:30 PM – 3:10 PM | Grays Peak, First Floor

Description: The Panel on Fundamentals of Machine Learning for Heat Transfer showcases emerging research and opportunity that leverage machine learning to help us understand thermal transport phenomena and design better heat transfer materials, devices, and systems. Topics include but are not limited to: machine learning-accelerated design and optimization, machine learning-accelerated solution of transport phenomena, new machine learning methods driven by heat transfer needs, etc.

Moderators:
Prof. Van Carey, University of California, Berkeley
Prof. Xiulin Ruan, Purdue University

Panelists:
Prof. Jay Gore, Mechanical Engineering, Purdue University
Prof. Ming Hu, Mechanical Engineering, University of South Carolina
Prof. Justin Weibel, Mechanical Engineering, Purdue University
Prof. Hyeongyun Cha, Mechanical and Aerospace Engineering, University at Buffalo

K8–Panel 2:
Wednesday, July 9 | 1:30 PM – 3:10 PM | Gray's Peak, First Floor

Organizing Committee: K8
Chair: Professor Xiulin Ruan

Panel Moderators:
Professor Vaibhav Bahadur, University of Texas, Austin, and Professor Amitabh Narain, Michigan Technological University

Panelists:
Dr. Moises Levy, Managing Director, Research and Market Intelligence, Data Center Dynamics
Dr. Kaushik Mysore, Principal Member of Technical Staff (Thermal Packaging and Advanced Technology Integration), Advanced Micro Devices, Inc. (AMD)
Professor David Cahill, Materials Science and Engineering, University of Illinois, Urbana-Champaign
Professor Satish Kandlikar, Mechanical Engineering, Rochester Institute of Technology
Professor Amitabh Narain, Mechanical Engineering, Michigan Technological University

Description: This interactive panel aims to create a vibrant platform for exchanging ideas and insights on cutting-edge advancements and pressing challenges in the field of semiconductor thermal management. This topic is of critical relevance to the semiconductor industry as continued advancements in the AI ecosystem are strongly contingent on effective thermal management at various length scales. The panelists (mix of industry and academia) will explore a diverse array of topics in the context of AI-driven computing advancements that are pushing the development of next-generation xPUs (GPU, CPU, etc.) with large thermal power dissipation (700 – 2000 W per xPU), and data centers approaching heat dissipation requirements of 1 MW per rack. Accordingly, advancements like thermal interface materials (TIM)-free interfaces and high heat flux liquid cooling (Direct-to-Chip, Immersion, and Hybrid) etc. are becoming cornerstone technologies for thermal management.

The panelists will explore a diverse array of topics relevant to cooling and thermal management, from chip to data center levels. At the chip and package levels, challenges related to 3D metrology, low interfacial resistances, high thermal conductivity heat spreaders, bonded interfaces between semiconductors, phase change-based cooling, and composite thermal interface materials will be discussed. At the data center level, the state of the market, emerging technologies, evolving business models, and the opportunities and challenges shaping the future of data center cooling will be discussed.

This panel will be advertised across ASME to encourage attendance from attendees of other SHTC sessions. To promote dynamic and engaging interactions, the presentations will be interspersed with lively discussions involving both the panelists and the audience.

Organized by: ASME Journal of Engineering for Sustainable Buildings and Cities

As the urgency to create high-performance buildings and resilient and healthy urban environments grows, artificial intelligence (AI) and machine learning (ML) are emerging as powerful tools for optimizing building performance, enhancing energy efficiency, and informing data-driven urban planning. This panel brings together leading experts from academia and industry to explore the latest advancements in AI/ML applications for high-performance and resilient buildings and cities.

Discussions will cover a range of critical topics, including predictive energy modeling, AI-driven optimization of HVAC and building controls, smart grid integration, climate-adaptive energy infrastructure planning, and the role of digital twins in improving building operation as well as energy performance.

Panelists will also address key challenges such as data availability, scalability, and the practical implementation of AI-driven solutions in the built environment.

Attendees will gain insights into how AI and ML are shaping the future of energy efficient and resilient urban development and the opportunities for interdisciplinary collaboration in driving innovation at the intersection of technology, engineering, and environmental science.

The Panel will be proceeded by a Special Issue on the ASME Journal of Sustainable Buildings and Cities.

Panelists:

Troy Harvey, CEO and Co-Founder, PassiveLogic
Bian Freeman, Lead Data Scientist, Trane Technologies
Hohyun Lee, Professor and Department Chair, San José State University
Juan Pablo Montoya-Rincon, Postdoctoral Associate, University at Albany

Moderator: Hamidreza Najafi, Associate Professor, Florida Institute of Technology