| Page 324 | Kisaco Research
Edge
Generative AI
Infrastructure
Moderator

Author:

Jeff White

CTO, Edge
Dell Technologies

Jeff is the Industry CTO of Dell Technologies of the Automotive sector, specifically in the area of Connected and Autonomous Vehicles and overall Edge Technology strategy lead. Jeff is responsible for leading the team that is developing the overall Dell Technologies technology strategy development, architectural direction and product requirements for the Intelligent Connected Vehicle platform.

He also is the Chairman of the Dell Automotive Design Authority Council responsible for the technical solution design. In his role as Edge Technology Lead he is driving the development of a Dell Technology wide Edge platform including the physical edge systems, heterogenous compute, memory/storage, environment, security, data management, control plane stack and automation/orchestration.

Previously, Jeff has held senior roles at an early stage artificial intelligence/machine reasoning based process automation technology provider and Elefante Group a stratospheric wireless communications platform as CTO. He also held senior positions at Hewlett Packard Enterprise, Ericsson and Alcatel-Lucent where he led technology initiatives, solutions development, business development and services delivery.
Earlier in his career White served in leadership roles at BellSouth and Cingular Wireless (now AT&T). At Cingular, he led the National Transport Infrastructure Engineering with responsibility for national transport, VoIP & IMS engineering. At BellSouth (now AT&T) he led the Broadband Internet Operations & Support organization which included broadband access tier two technical support, customer networking equipment business, broadband OSS & end-to-end process.

White holds a Bachelor of Science degree in Electrical Engineering from Southern Polytechnic University. He also served as Chairman of the Tech Titans Technology Association of North Texas representing over 300 Technology companies in the greater North Texas community. He also served on the North Texas Regional committee of the Texas Emerging Technology fund under Governor Rick Perry.

... read more

Jeff White

CTO, Edge
Dell Technologies

Jeff is the Industry CTO of Dell Technologies of the Automotive sector, specifically in the area of Connected and Autonomous Vehicles and overall Edge Technology strategy lead. Jeff is responsible for leading the team that is developing the overall Dell Technologies technology strategy development, architectural direction and product requirements for the Intelligent Connected Vehicle platform.

He also is the Chairman of the Dell Automotive Design Authority Council responsible for the technical solution design. In his role as Edge Technology Lead he is driving the development of a Dell Technology wide Edge platform including the physical edge systems, heterogenous compute, memory/storage, environment, security, data management, control plane stack and automation/orchestration.

Previously, Jeff has held senior roles at an early stage artificial intelligence/machine reasoning based process automation technology provider and Elefante Group a stratospheric wireless communications platform as CTO. He also held senior positions at Hewlett Packard Enterprise, Ericsson and Alcatel-Lucent where he led technology initiatives, solutions development, business development and services delivery.
Earlier in his career White served in leadership roles at BellSouth and Cingular Wireless (now AT&T). At Cingular, he led the National Transport Infrastructure Engineering with responsibility for national transport, VoIP & IMS engineering. At BellSouth (now AT&T) he led the Broadband Internet Operations & Support organization which included broadband access tier two technical support, customer networking equipment business, broadband OSS & end-to-end process.

White holds a Bachelor of Science degree in Electrical Engineering from Southern Polytechnic University. He also served as Chairman of the Tech Titans Technology Association of North Texas representing over 300 Technology companies in the greater North Texas community. He also served on the North Texas Regional committee of the Texas Emerging Technology fund under Governor Rick Perry.

Author:

Yvonne Lutsch

Investment Principal
Bosch Ventures

Yvonne is an accomplished Investment Principal at Bosch Ventures affiliate office located in Sunnyvale, and sources, evaluates, and executes venture capital deals in North America. Her specialty are investments in deep tech fields such as AI, edge and next gen. computing incl. quantum, robotics, industrial IoT, mobility, climate tech, semiconductors, or sensors. She is an investor and non-executive board member of Bosch Ventures’ portfolio companies Syntiant, Zapata AI, UltraSense Systems, Aclima, and Recogni.
Prior to this position Yvonne was Director of Technology Scouting and Business Development, building up an Innovation Hub in Silicon Valley including startup scouting, business development while advising executives of the Bosch business units on their strategy. She has more than two decades of solid experience in manufacturing operations and engineering in the automotive and consumer electronics space – gained through different executive roles at Bosch in Germany.
Yvonne received a diploma in Experimental Physics from University of Siegen, Germany, and holds a PhD in Applied Physics from University of Tuebingen, Germany.

... read more

Yvonne Lutsch

Investment Principal
Bosch Ventures

Yvonne is an accomplished Investment Principal at Bosch Ventures affiliate office located in Sunnyvale, and sources, evaluates, and executes venture capital deals in North America. Her specialty are investments in deep tech fields such as AI, edge and next gen. computing incl. quantum, robotics, industrial IoT, mobility, climate tech, semiconductors, or sensors. She is an investor and non-executive board member of Bosch Ventures’ portfolio companies Syntiant, Zapata AI, UltraSense Systems, Aclima, and Recogni.
Prior to this position Yvonne was Director of Technology Scouting and Business Development, building up an Innovation Hub in Silicon Valley including startup scouting, business development while advising executives of the Bosch business units on their strategy. She has more than two decades of solid experience in manufacturing operations and engineering in the automotive and consumer electronics space – gained through different executive roles at Bosch in Germany.
Yvonne received a diploma in Experimental Physics from University of Siegen, Germany, and holds a PhD in Applied Physics from University of Tuebingen, Germany.

Author:

Roberto Mijat

Senior Director
Blaize

Roberto leads product marketing and strategy at Blaize. He is an AI technology and product leader with an engineering background and over 20 years of experience in developing and taking to market advanced semiconductor hardware and software solutions.

Roberto spent over 15 years at Arm, holding several senior product and business leadership positions and leading multiple global product teams. He was a member of the company’s Product Line Board and Steering board for AI on CPU. He created and architected the Compute Libraries framework, a key component of Arm’s AI software stack, deployed in billions of devices today. Roberto established the Arm GPU Compute ecosystem from scratch and led collaborations with dozens of industry leaders, including Facebook, Google, Huawei, MediaTek, and Samsung.

At Graphcore, Roberto led the launch of the Bow IPU AI accelerator, promoted the standardization of FP8, and led collaborations with storage partners.

Roberto is an advisor at Silicon Catalyst and a Mentor at London Business School.  He holds a first degree in Artificial Intelligence and Quantum Computing and an Executive MBA from London Business School.

 

... read more

Roberto Mijat

Senior Director
Blaize

Roberto leads product marketing and strategy at Blaize. He is an AI technology and product leader with an engineering background and over 20 years of experience in developing and taking to market advanced semiconductor hardware and software solutions.

Roberto spent over 15 years at Arm, holding several senior product and business leadership positions and leading multiple global product teams. He was a member of the company’s Product Line Board and Steering board for AI on CPU. He created and architected the Compute Libraries framework, a key component of Arm’s AI software stack, deployed in billions of devices today. Roberto established the Arm GPU Compute ecosystem from scratch and led collaborations with dozens of industry leaders, including Facebook, Google, Huawei, MediaTek, and Samsung.

At Graphcore, Roberto led the launch of the Bow IPU AI accelerator, promoted the standardization of FP8, and led collaborations with storage partners.

Roberto is an advisor at Silicon Catalyst and a Mentor at London Business School.  He holds a first degree in Artificial Intelligence and Quantum Computing and an Executive MBA from London Business School.

 

Author:

Adam Benzion

Chief Experience Officer
Edge Impulse
... read more

Adam Benzion

Chief Experience Officer
Edge Impulse
Infrastructure
Data Centres

Author:

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.

... read more

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.

With the ubiquitous and increasing use of computing, the talk will quantitatively demonstrate unsustainable energy and complexity trends in computing and AI, from hardware, algorithms, and software. Our discussion of the unsustainability of these trends will motivate a few exciting directions for computing, especially for applications to AI/ML. Specifically, we will touch upon the evolution of hardware in terms of energy used following Dennard scaling and the challenges posed by continuing these current trends.  We will illustrate opportunities suggested by a few of these unsustainable trends of computing, specifically on applications to Machine Learning and Artificial Intelligence including at the edge. Given the goals of achieving AGI promised by current technologies, we will propose a modified form of Turing’s test that points to a new conceptualization of computing for application beyond the current paradigms.

Inferencing
Systems
Infrastructure

Author:

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).

... read more

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).

Edge
Hardware
Systems

Author:

Jinwook Oh

Co Founder and CTO
Rebellions

Jinwook Oh is the Co-Founder and Chief Technology Officer of Rebellions, an AI chip company based in South Korea. After earning his Ph.D. from KAIST (Korea Advanced Institute of Science and Technology), he joined the IBM TJ Watson Research Center, where he contributed to several AI chip R&D projects as a Chip Architect, Logic Designer, and Logic Power Lead. At Rebellions, he has overseen the development and launch of two AI chips, with a third, REBEL, in progress. Jinwook's technical leadership has been crucial in establishing Rebellions as a notable player in AI technology within just three and a half years.

... read more

Jinwook Oh

Co Founder and CTO
Rebellions

Jinwook Oh is the Co-Founder and Chief Technology Officer of Rebellions, an AI chip company based in South Korea. After earning his Ph.D. from KAIST (Korea Advanced Institute of Science and Technology), he joined the IBM TJ Watson Research Center, where he contributed to several AI chip R&D projects as a Chip Architect, Logic Designer, and Logic Power Lead. At Rebellions, he has overseen the development and launch of two AI chips, with a third, REBEL, in progress. Jinwook's technical leadership has been crucial in establishing Rebellions as a notable player in AI technology within just three and a half years.

In this presentation, we will explore the advanced integration of Digital In-Memory Computing (D-IMC) and RISC-V technology by Axelera AI to accelerate AI inference workloads. Our approach uniquely combines the high energy efficiency and throughput of D-IMC with the versatility of RISC-V technology, creating a powerful and scalable platform. This platform is designed to handle a wide range of AI tasks, from advanced computer vision at the edge to emerging AI challenges.

We will demonstrate how our scalable architecture not only meets but exceeds the demands of modern AI applications. Our platform enhances performance while significantly reducing energy use and operational costs. By pushing the boundaries of Edge AI and venturing into new AI domains, Axelera AI is setting new benchmarks in AI processing efficiency and deployment capabilities.

Edge
Infrastructure
MLOps

Author:

Evangelos Eleftheriou

Co-Founder and CTO
Axelera AI

Evangelos Eleftheriou, an IEEE and IBM Fellow, is the Chief Technology Officer and co-founder of Axelera AI, a best-in-class performance company that develops a game-changing hardware and software platform for AI.

As a CTO, Evangelos oversees the development and dissemination of technology for external customers, vendors, and other clients to help improve and increase Axelera AI’s business.

Before his current role, Evangelos worked for IBM Research – Zurich, where he held various management positions for over 35 years. His outstanding achievements led him to become an IBM Fellow, which is IBM’s highest technical honour.

In 2002, Evangelos became a Fellow of the IEEE, and later in 2003, he was co-recipient of the IEEE ComS Leonard G. Abraham Prize Paper Award. He was also co-recipient of the 2005 Technology Award of the Eduard Rhein Foundation. In 2005, he was appointed an IBM Fellow and inducted into the IBM Academy of Technology. In 2009, he was co-recipient of the IEEE Control Systems Technology Award and the IEEE Transactions on Control Systems Technology Outstanding Paper Award. In 2016, Evangelos received an honoris causa professorship from the University of Patras, Greece. In 2018, he was inducted into the US National Academy of Engineering as Foreign Member. Evangelos has authored or coauthored over 250 publications and holds over 160 patents (granted and pending applications).

His primary interests lie in AI and machine learning, including emerging computing paradigms such as neuromorphic and in-memory computing.

Evangelos holds a PhD and a Master of Eng. degrees in Electrical Engineering from Carleton University, Canada, and a BSc in Electrical & Computer Engineering from the University of Patras, Greece.

... read more

Evangelos Eleftheriou

Co-Founder and CTO
Axelera AI

Evangelos Eleftheriou, an IEEE and IBM Fellow, is the Chief Technology Officer and co-founder of Axelera AI, a best-in-class performance company that develops a game-changing hardware and software platform for AI.

As a CTO, Evangelos oversees the development and dissemination of technology for external customers, vendors, and other clients to help improve and increase Axelera AI’s business.

Before his current role, Evangelos worked for IBM Research – Zurich, where he held various management positions for over 35 years. His outstanding achievements led him to become an IBM Fellow, which is IBM’s highest technical honour.

In 2002, Evangelos became a Fellow of the IEEE, and later in 2003, he was co-recipient of the IEEE ComS Leonard G. Abraham Prize Paper Award. He was also co-recipient of the 2005 Technology Award of the Eduard Rhein Foundation. In 2005, he was appointed an IBM Fellow and inducted into the IBM Academy of Technology. In 2009, he was co-recipient of the IEEE Control Systems Technology Award and the IEEE Transactions on Control Systems Technology Outstanding Paper Award. In 2016, Evangelos received an honoris causa professorship from the University of Patras, Greece. In 2018, he was inducted into the US National Academy of Engineering as Foreign Member. Evangelos has authored or coauthored over 250 publications and holds over 160 patents (granted and pending applications).

His primary interests lie in AI and machine learning, including emerging computing paradigms such as neuromorphic and in-memory computing.

Evangelos holds a PhD and a Master of Eng. degrees in Electrical Engineering from Carleton University, Canada, and a BSc in Electrical & Computer Engineering from the University of Patras, Greece.

Data Center
Infrastructure
Inferencing
Moderator

Author:

Bijan Nowroozi

Chief Technical Officer
The Open Compute Project Foundation

Bijan Nowroozi is Chief Technical Officer of The Open Compute Project Foundation and has more than 30 years of experience with hardware and software development, signal processing, networking, and research with technology companies. Prior to The OCP Bijan developed mission critical infrastructure and was on the leading-edge standards and technology development in multiple technology waves including edge computing, AI/ML, optical/photonics, quantum, RF, wireless, small cells, UAV’s, GIS, HPC, network security, energy and more. 

... read more

Bijan Nowroozi

Chief Technical Officer
The Open Compute Project Foundation

Bijan Nowroozi is Chief Technical Officer of The Open Compute Project Foundation and has more than 30 years of experience with hardware and software development, signal processing, networking, and research with technology companies. Prior to The OCP Bijan developed mission critical infrastructure and was on the leading-edge standards and technology development in multiple technology waves including edge computing, AI/ML, optical/photonics, quantum, RF, wireless, small cells, UAV’s, GIS, HPC, network security, energy and more. 

Author:

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).

... read more

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).

Author:

Ankita Singh

Investment Director
Bosch Ventures
... read more

Ankita Singh

Investment Director
Bosch Ventures

Author:

Dil Radhakrishnan

Engineer
Minlo

Dileeshvar Radhakrishnan is an engineer at MinIO where he focuses on AI/ML and Kubernetes. Prior to joining MinIO, Dil served as Chief Architect at ML pioneer Espressive. He previously worked in engineering roles at ServiceNow and Rewyndr. He began his career at Tata Consulting Services. 

Dil has Bachelor of Engineering in Computer Science and Engineering from Anna University and a Masters in Computer Science from Carnegie Mellon.

 

... read more

Dil Radhakrishnan

Engineer
Minlo

Dileeshvar Radhakrishnan is an engineer at MinIO where he focuses on AI/ML and Kubernetes. Prior to joining MinIO, Dil served as Chief Architect at ML pioneer Espressive. He previously worked in engineering roles at ServiceNow and Rewyndr. He began his career at Tata Consulting Services. 

Dil has Bachelor of Engineering in Computer Science and Engineering from Anna University and a Masters in Computer Science from Carnegie Mellon.

 

Infrastructure
MLOps
Generative AI
Systems
Moderator

Author:

Hira Dangol

Vice President, AI/ML & Automation
Bank Of America

Industry experience in AI/ML, engineering, architecture and executive roles in leading technology companies, service providers and Silicon Valley leading organizations. Currently focusing on innovation, disruption, and cutting-edge technologies through startups and technology-driven corporation in solving the pressing problems of industry and world.

... read more

Hira Dangol

Vice President, AI/ML & Automation
Bank Of America

Industry experience in AI/ML, engineering, architecture and executive roles in leading technology companies, service providers and Silicon Valley leading organizations. Currently focusing on innovation, disruption, and cutting-edge technologies through startups and technology-driven corporation in solving the pressing problems of industry and world.

Speakers

Author:

Puja Das

Senior Director, Personalization
Warner Bros. Entertainment

Dr. Puja Das, leads the Personalization team at Warner Brothers Discovery (WBD) which includes offerings on Max, HBO, Discovery+ and many more.

Prior to WBD, she led a team of Applied ML researchers at Apple, who focused on building large scale recommendation systems to serve personalized content on the App Store, Arcade and Apple Books. Her areas of expertise include user modeling, content modeling, recommendation systems, multi-task learning, sequential learning and online convex optimization. She also led the Ads prediction team at Twitter (now X), where she focused on relevance modeling to improve App Ads personalization and monetization across all of Twitter surfaces.

She obtained her Ph.D from University of Minnesota in Machine Learning, where the focus of her dissertation was online learning algorithms, which work on streaming data. Her dissertation was the recipient of the prestigious IBM Ph D. Fellowship Award.

She is active in the research community and part of the program committee at ML and recommendation system conferences. Shas mentored several undergrad and grad students and participated in various round table discussions through Grace Hopper Conference, Women in Machine Learning Program colocated with NeurIPS, AAAI and Computing Research Association- Women’s chapter.

... read more

Puja Das

Senior Director, Personalization
Warner Bros. Entertainment

Dr. Puja Das, leads the Personalization team at Warner Brothers Discovery (WBD) which includes offerings on Max, HBO, Discovery+ and many more.

Prior to WBD, she led a team of Applied ML researchers at Apple, who focused on building large scale recommendation systems to serve personalized content on the App Store, Arcade and Apple Books. Her areas of expertise include user modeling, content modeling, recommendation systems, multi-task learning, sequential learning and online convex optimization. She also led the Ads prediction team at Twitter (now X), where she focused on relevance modeling to improve App Ads personalization and monetization across all of Twitter surfaces.

She obtained her Ph.D from University of Minnesota in Machine Learning, where the focus of her dissertation was online learning algorithms, which work on streaming data. Her dissertation was the recipient of the prestigious IBM Ph D. Fellowship Award.

She is active in the research community and part of the program committee at ML and recommendation system conferences. Shas mentored several undergrad and grad students and participated in various round table discussions through Grace Hopper Conference, Women in Machine Learning Program colocated with NeurIPS, AAAI and Computing Research Association- Women’s chapter.

Author:

Logan Grasby

Senior Machine Learning Engineer
Cloudflare

Logan Grasby is a Senior Machine Learning Engineer at Cloudflare, based in Calgary, Alberta. As part of Cloudflare's Workers AI team he works on developing, deploying and scaling AI inference servers across Cloudflare's edge network. In recent work he has designed services for multi-tenant LLM LoRA inference and dynamic diffusion model pipeline servers. Prior to Cloudflare, Logan founded Azule, an LLM driven customer service and product recommendation platform for ecommerce. He also co-founded Conversion Pages and served as Director of Product at Appstle, a Shopify app development firm.

... read more

Logan Grasby

Senior Machine Learning Engineer
Cloudflare

Logan Grasby is a Senior Machine Learning Engineer at Cloudflare, based in Calgary, Alberta. As part of Cloudflare's Workers AI team he works on developing, deploying and scaling AI inference servers across Cloudflare's edge network. In recent work he has designed services for multi-tenant LLM LoRA inference and dynamic diffusion model pipeline servers. Prior to Cloudflare, Logan founded Azule, an LLM driven customer service and product recommendation platform for ecommerce. He also co-founded Conversion Pages and served as Director of Product at Appstle, a Shopify app development firm.

Author:

Daniel Valdivia

Engineer
MinIo

Daniel Valdivia is an engineer with MinIO where he focuses on Kubernetes, ML/AI and VMware. Prior to joining MinIO, Daniel was the Head of Machine Learning for Espressive. Daniel has held senior application development roles with ServiceNow, Oracle and Freescale. Daniel holds a Bachelor of Engineering from Tecnológico de Monterrey, Campus Guadalajara and Bachelor of Science in Computer Engineering from Instituto Tecnológico y de Estudios Superiores de Monterrey.

 

... read more

Daniel Valdivia

Engineer
MinIo

Daniel Valdivia is an engineer with MinIO where he focuses on Kubernetes, ML/AI and VMware. Prior to joining MinIO, Daniel was the Head of Machine Learning for Espressive. Daniel has held senior application development roles with ServiceNow, Oracle and Freescale. Daniel holds a Bachelor of Engineering from Tecnológico de Monterrey, Campus Guadalajara and Bachelor of Science in Computer Engineering from Instituto Tecnológico y de Estudios Superiores de Monterrey.