Mike Davies is Director of Intel’s Neuromorphic Computing Lab. Since 2014 he has been researching neuromorphic architectures, algorithms, software, and systems, and has fabricated several neuromorphic chip prototypes to date, including the Loihi series.  In the 2000s, as a founding employee of Fulcrum Microsystems and director of its silicon engineering, Mike pioneered high-performance asynchronous design methods and led the development of several generations of industry leading Ethernet switches. Before that, he received B.S. and M.S. degrees from Caltech.

Linkedin: https://www.linkedin.com/in/mike-davies-71b4542

Hai “Helen” Li is the Clare Boothe Luce Professor and Department Chair of the Electrical and Computer Engineering Department at Duke University. She received her B.S and M.S. from Tsinghua University and Ph.D. from Purdue University. Her research interests include neuromorphic circuit and system for brain-inspired computing, machine learning acceleration and trustworthy AI, conventional and emerging memory design and architecture, and software and hardware co-design. Dr. Li served/serves as the Associate Editor for multiple IEEE and ACM journals. She was the General Chair or Technical Program Chair of multiple IEEE/ACM conferences and the Technical Program Committee members of over 30 international conference series. Dr. Li is a Distinguished Lecturer of the IEEE CAS society (2018-2019) and a distinguished speaker of ACM (2017-2020). Dr. Li is a recipient of the NSF Career Award, DARPA Young Faculty Award, TUM-IAS Hans Fischer Fellowship from Germany, ELATE Fellowship, nine best paper awards and another nine best paper nominations. Dr. Li is a fellow of ACM and IEEE.

Linkedin: https://www.linkedin.com/in/haili/

Jessie Rosenberg is a Principal Photonics Architect at Lightmatter, developing photonic technologies for supercomputing and machine learning workloads. She received her PhD from Caltech in Applied Physics in 2010, and thereafter joined IBM Research developing a monolithic CMOS-integrated silicon photonics foundry platform for optical interconnects and large-scale photonics applications. After that technology was commercialized in partnership with GlobalFoundries, she moved to the MIT-IBM Watson AI Lab in 2019 as a researcher and Science Strategy Lead, developing AI architectures for intuitive physics as well as aligning the Lab's fundamental research focus with the technology needs of IBM and corporate partners. In 2023, she joined Lightmatter to leverage the scalability of silicon photonics for AI applications.

Linkedin: https://www.linkedin.com/in/jessiecrosenberg

Ben Scellier is a research scientist at Rain, an AI hardware startup whose mission is to radically reduce the cost of AI. Ben's research at Rain focuses on efficient hardware implementations of deep neural network architectures and learning algorithms using compute-in-memory and analog computation.

Linkedin: https://www.linkedin.com/in/benjamin-scellier-42b301100/

Dr. Stefan Leichenauer is the VP of Engineering and lead scientist at SandboxAQ. Stefan joined the Sandbox team at Alphabet as its first employee to bring AI and Quantum technologies to real-world applications, initiating all of the projects that have become the SandboxAQ product portfolio. He recruited teams of engineers and scientists to further develop the SandboxAQ products and became Research and Engineering to oversee all of the projects. Today he drives all product development at SandboxAQ. Stefan is also the bridge between the engineering and business/strategy divisions of SandboxAQ, and works closely with the partnerships team to craft external engagements. He received his Ph.D. in Physics from UC Berkeley, and has held positions in Physics at UC Berkeley and Caltech as a leading researcher. He maintains a close connection with academia through the SandboxAQ PhD Residency and Postdoc programs.

Linkedin: https://www.linkedin.com/in/stefanleichenauer/

Jonathan is a Postdoc Neuromorphic Research Scientist in the Intel Neuromorphic Computing Lab where he studies neuromorphic algorithms and deep learning. He received his Physics PhD from Stanford University as an NSF Graduate Research Fellow studying theoretical neuroscience, and his previous experience includes analog in-memory computing for efficient neural network inference at IBM, transformers for source code generation at Microsoft, and high energy particle physics data analysis at the Large Hadron Collider. Jonathan obtained a MASt degree in Applied Mathematics at the University of Cambridge as a Churchill Scholar and a BS in Engineering Physics with Computer & Information Science concentration from the Ohio State University. His interests include AI, the physical basis of computation, and emerging computing paradigms. He loves to connect with fellow researchers to learn more - please feel welcome to reach out!

Linkedin: https://www.linkedin.com/in/timcheck