Mo Haghighi

Distinguished Engineer

Discover Financial Services

Dr Mo Haghighi is a director of engineering/distinguished engineer at Discover Financial Services. His current focus is hybrid and multi-cloud strategy, application modernisation and automating application/workload migration across public and private clouds. Previously, he held various leadership positions as a program director at IBM, where he led Developer Ecosystem and Cloud Engineering teams in 27 countries across Europe, Middle East and Africa. Prior to IBM, he was a research scientist at Intel and an open source advocate at Sun Microsystems/Oracle. 

Mo obtained a PhD in computer science, and his primary areas of expertise are distributed and edge computing, cloud native, IoT and AI, with several publications and patents in those areas.

Mo is a regular keynote/speaker at major developer conferences including Devoxx, DevOpsCon, Java/Code One, Codemotion, DevRelCon, O’Reilly, The Next Web, DevNexus, IEEE/ACM, ODSC, AiWorld, CloudConf and Pycon. 

Paul Karazuba

VP of Marketing

Expedera

Paul is Vice President of Marketing at Expedera, a leading provider of AI Inference NPU semiconductor IP. He brings a talent for transforming new technology into products that excite customers. Previously, Paul was VP Marketing at PLDA, specializing in high-speed interconnect IP, until its acquisition by Rambus. Before PLDA, he was Senior Director of Marketing at Rambus. Paul brings more than 25 years of marketing experience including Quicklogic, Aptina (Micron), and others. He holds a BS in Management and Marketing from Manhattan College.

 

Hooman Sedghamiz

Senior Director of AI & ML

Bayer

Hooman Sedghamiz is Director of AI & ML at Bayer. He has lead algorithm development and generated valuable insights to improve medical products ranging from implantable, wearable medical and imaging devices to bioinformatics and pharmaceutical products for a variety of multinational medical companies.

He has lead projects, data science teams and developed algorithms for closed loop active medical implants (e.g. Pacemakers, cochlear and retinal implants) as well as advanced computational biology to study the time evolution of cellular networks associated with cancer , depression and other illnesses.

His experience in healthcare also extends to image processing for Computer Tomography (CT), iX-Ray (Interventional X-Ray) as well as signal processing of physiological signals such as ECG, EMG, EEG and ACC.

Recently, his team has been working on cutting edge natural language processing and developed cutting edge models to address the healthcare challenges dealing with textual data.

Prasad Jogalekar

Head of Global Artificial Intelligence and Accelerator Hub

Ericsson

Stuart Clubb

Technical Product Management Director

Siemens

Stuart is responsible for Catapult HLS Synthesis and Verification Solutions since July 2017. Prior to this new role, Stuart had been successfully managing the North American FAE team for Mentor/Siemens and Calypto Design Systems and was key to the growth achieved for the CSD products after the Calypto acquisition. Moving from the UK in 2001 to work at Mentor Graphics, Stuart held the position of Technical Marketing Engineer, initially on the Precision RTL synthesis product for 6 years and later on Catapult for 5 years. He has held various engineering and application engineering roles ASIC and FPGA RTL hardware design and verification. Stuart graduated from Brunel University, London, with a Bachelors of Science.

Tom Sheffler

Solution Architect, Next Generation Sequencing

Former Roche

Tom earned his PhD from Carnegie Mellon in Computer Engineering with a focus on parallel computing architectures and prrogramming models.  His interest in high-performance computing took him to NASA Ames, and then to Rambus where he worked on accelerated memory interfaces for providing high bandwidth.  Following that, he co-founded the cloud video analytics company, Sensr.net, that applied scalable cloud computing to analyzing large streams of video data.  He later joined Roche to work on next-generation sequencing and scalable genomics analysis platforms.  Throughout his career, Tom has focused on the application of high performance computer systems to real world problems.

Gerald Friedland

Principal Scientist Auto ML

AWS

Dr. Gerald Friedland is a Principal Scientist at AWS working on Low-Code, No-Code Machine Learning. Before that he was CTO and founder of Brainome, a no-code machine learning service for miniature models. Other posts include UC Berkeley, Lawrence Livermore National Lab, and the International Computer Science Institute. He was the lead figure behind the Multimedia Commons initiative, a collection of 100M images and 1M videos for research and has published more than 200 peer-reviewed articles in conferences, journals, and books. His latest book "Information-Driven Machine Learning" was released by Springer-Nature in Dec. 2023. He also co-authored a textbook on Multimedia Computing with Cambridge University Press. Dr. Friedland received his doctorate (summa cum laude) and master's degree in computer science from Freie Universitaet Berlin, Germany, in 2002 and 2006, respectively.

Sadasivan Shankar

Research Technology Manager

SLAC National Laboratory and Stanford University

Sadasivan (Sadas) Shankar is Research Technology Manager at SLAC National Laboratory, adjunct Professor in Stanford Materials Science and Engineering, and Lecturer in the Stanford Graduate School of Business. He was an Associate in the Department of Physics at Harvard University, and was the first Margaret and Will Hearst Visiting Lecturer in Harvard and the first Distinguished Scientist in Residence at the Harvard Institute of Applied Computational Sciences. He has co-instructed classes related to design of materials, computing, sustainability in materials, and has received Excellence in Teaching award from Harvard University. He is co-instructing a class at Stanford University on Translation for
Innovations. He is a co-founder of and the Chief Scientist at Material Alchemy, a “last mile” translational and independent venture that has been recently founded to accelerate the path from materials discovery to adoption, with environmental sustainability as a key goal. In addition to research on fundamentals of Materials Design, his current research is on new architectures for specialized AI methods is exploring ways of bringing machine intelligence to system-level challenges in inorganic/biochemistry, materials, and physics and new frameworks for computing as information processing inspired by lessons from 

nature.
Dr. Shankar’s current research and analysis on Sustainable Computing is helping provide directions for the US Department of Energy’s EES2 scaling initiatives (energy reduction in computing every generation for 1000X reduction in 2 decades) as part of the White House Plan to Revitalize American Manufacturing and Secure Critical Supply Chains in 2022 for investment in research, development, demonstration, and commercial application (RDD&CA) in conventional semiconductors.

In addition, his analysis is helping identify pathways for energy efficient computing. While in the industry, Dr. Shankar and his team have enabled several critical technology decisions in the semiconductor industrial applications of chemistry, materials, processing, packaging, manufacturing, and design rules for over nine generations of Moore’s law including first advanced
process control application in 300 mm wafer technology; introduction of flip chip packaging using electrodeposition, 100% Pb-elimination in microprocessors, design of new materials, devices including nano warp-around devices for the advanced semiconductor technology manufacturing, processing
methods, reactors, etc. Dr. Shankar managed his team members distributed across multiple sites in the US, with collaborations in Europe. The teams won several awards from the Executive Management and technology organizations.

He is a co-inventor in over twenty patent filings covering areas in new
chemical reactor designs, semiconductor processes, bulk and nano materials for the sub 10 nanometer generation of transistors, device structures, and algorithms. He is also a co-author in over hundred publications and presentations in measurements, multi-scale and multi-physics methods spanning from quantum scale to macroscopic scales, in the areas of chemical synthesis, plasma chemistry and processing, non-equilibrium electronic, ionic, and atomic transport, energy efficiency of information processing, and machine learning methods for bridging across scales, and estimating complex materials
properties and in advanced process control.

Dr. Shankar was an invited speaker at the Clean-IT Conference in Germany on Revolutionize Digital Systems and AI (2023), Telluride Quantum Inspired Neuromorphic Computing Workshop (2023) on Limiting Energy Estimates for Classical and Quantum Information Processing, Argonne National
Laboratory Director’s Special Colloquium on the Future of Computing (2022), panelist on Carnegie Science series on Brain and Computing (2020), lecturer in the Winter Course on Computational Brain Research in IIT-M-India (2020), invited participant in the Kavli Institute of Theoretical Physics program
on Cellular Energetics in UCSB (2019), invited speaker to the Camille and Henry Dreyfus Foundation meeting on Machine Learning for problems in Chemistry and Materials Science (2019), a Senior Fellow in UCLA Institute of Pure and Applied Mathematics during the program on Machine Learning and Manybody
Physics (2016), invited to the White House event for starting of the Materials Genome Initiative (2012), Invited speaker in Erwin Schrödinger International Institute for Mathematical Physics-Vienna (2007), Intel’s first Distinguished Lecturer in Caltech (1998) and MIT (1999). He has also given several
colloquia and lectures in universities all over the world and his research was also featured in the publications Science (2012), TED (2013), Nature Machine Intelligence (2022), Nature Physics (2022).