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Elisabeth Hennes

The conference provided a great overview of the new developments in the field of TPD. Excellent discussions and inspiring talks.

 

Justin Lee

Digital Innovator
Sumitovant Biopharma

Justin Lee

Digital Innovator
Sumitovant Biopharma

Justin Lee

Digital Innovator
Sumitovant Biopharma
2022 Innovation Showcase Finalists

Macrotrends in innovation are leveraging both software and chips to create the next round of world-changing products. Unlocking the vast potential offered by this innovation model is daunting however. Systemic complexity across all disciplines from silicon to software must be addressed in a holistic way to achieve success. AI applications change over months while chip design can take years, adding to the challenges. Talent shortages also create headwinds. And as more system companies engage in chip design, these headwinds can have a profound impact on the pace of innovation.

Complex chip and system design must be easier to achieve in less time. Sassine Ghazi will discuss several developing strategies that use AI and machine learning techniques to dramatically reduce design time and design risk, opening the opportunity for substantial increases in the pace of innovation.

Chip Design
Edge AI
Novel AI Hardware
Hardware Engineering
Systems Engineering

Author:

Sassine Ghazi

CEO
Synopsys

Sassine Ghazi leads and drives strategy for all business units, sales and customer success, strategic alliances, marketing and communications at Synopsys. He joined the company in 1998 as an applications engineer. He then held a series of sales positions with increasing responsibility, culminating in leadership of worldwide strategic accounts. He was then appointed general manager for all digital and custom products, the largest business group in Synopsys. Under his leadership, several innovative solutions were launched in areas such as multi-die systems, AI-assisted design and silicon lifecycle management. He assumed the role of chief operating officer in August, 2020 and was appointed to the role of president in November 2021. Prior to Synopsys he was a design engineer at Intel.

 

Sassine holds a bachelor’s degree in Business Administration from Lebanese American University; a B.S.E.E from the Georgia Institute of Technology and an M.S.E.E. from the University of Tennessee.

 

Sassine Ghazi

CEO
Synopsys

Sassine Ghazi leads and drives strategy for all business units, sales and customer success, strategic alliances, marketing and communications at Synopsys. He joined the company in 1998 as an applications engineer. He then held a series of sales positions with increasing responsibility, culminating in leadership of worldwide strategic accounts. He was then appointed general manager for all digital and custom products, the largest business group in Synopsys. Under his leadership, several innovative solutions were launched in areas such as multi-die systems, AI-assisted design and silicon lifecycle management. He assumed the role of chief operating officer in August, 2020 and was appointed to the role of president in November 2021. Prior to Synopsys he was a design engineer at Intel.

 

Sassine holds a bachelor’s degree in Business Administration from Lebanese American University; a B.S.E.E from the Georgia Institute of Technology and an M.S.E.E. from the University of Tennessee.

 

Many system companies are discovering that optimizing AI/ML SoC devices is a very powerful way to achieve differentiation for specific end-applications. In 2021 the semiconductor industry experienced more rounds of venture capital funding and dollars invested than ever before. What’s more, the investments in new AI companies alonewere higher than all prior yearly totals for all design types combined. Most of these new semiconductor companies targeted specific use cases of AI/ML to achieve aggressive performance, power/heat and other system objectives. Now, system companies are designing their own custom AI/ML SoCs—whether it is hyperscalers, automotive OEMs, edge or telecommunication companies–to address their own unique system-level needs.

Joe Sawicki, executive vice president, IC Siemens EDA, will explain how SoC design solutions are enabling both semiconductor and system companies to efficiently arrive at the global optimization point between power, performance, cost, yield and other factors in their AI/ML hardware designs.  All focused on achieving a holistic, optimized system-level differentiation.

Chip Design
Edge AI
Novel AI Hardware
Hardware Engineering
Systems Engineering

Author:

Joseph Sawicki

EVP, IC EDA
Siemens

Joseph Sawicki is a leading expert in IC nanometer design and manufacturing challenges. Formerly responsible for Mentor's industry-leading design-to-silicon products, including the Calibre physical verification and DFM platform and Mentor's Tessent design-for-test product line, Sawicki now oversees all business units in the Siemens EDA IC segment.

Sawicki joined Mentor Graphics in 1990 and has held previous positions in applications engineering, sales, marketing, and management. He holds a BSEE from the University of Rochester, an MBA from Northeastern University's High Technology Program, and has completed the Harvard Business School Advanced Management Program.

 

Joseph Sawicki

EVP, IC EDA
Siemens

Joseph Sawicki is a leading expert in IC nanometer design and manufacturing challenges. Formerly responsible for Mentor's industry-leading design-to-silicon products, including the Calibre physical verification and DFM platform and Mentor's Tessent design-for-test product line, Sawicki now oversees all business units in the Siemens EDA IC segment.

Sawicki joined Mentor Graphics in 1990 and has held previous positions in applications engineering, sales, marketing, and management. He holds a BSEE from the University of Rochester, an MBA from Northeastern University's High Technology Program, and has completed the Harvard Business School Advanced Management Program.

 

Theoretical metrics such as TOPS frequently fail to predict real-world AI chip performance accurately and to varying degrees, typically overpromise and underdeliver. There is a lot of angst and discussion about this root cause, but an often-overlooked culprit is the clock network, one of the largest networks on an SoC. 

The clock network can be the ultimate gating factor or enabler in data flow on a chip. Data can only move as far as one clock cycle allows. As chips grow larger and approach reticle limits, clock paths also significantly lengthen, further complicating existing clocking problems such as skew and silicon variation (at finer process geometries). An optimized clock network can streamline data flow and raise on-chip interconnect bandwidth.

Standard clock topologies that work well on small chips cannot scale to today’s very large chips. A new approach called intelligent clock networks, delivers an “ideal” clock close to the point of use, simplifying SoC designs and virtually eliminating overhead typically expended for clock distribution. Mo Faisal, the CEO and Founder of Movellus, will examine how intelligent clock networks can usher in a new era of big chip design for AI and HPC applications. Throughout his presentation, Mo will showcase how these new clock network types can help architects reach their architectural goals while generating differentiation in silicon cost and power efficiency in an already crowded market segment.

Chip Design
Novel AI Hardware
Hardware Engineering

Author:

Mo Faisal

Founder & CEO
Movellus

Prior to founding Movellus, Mo held positions at semiconductor companies including Intel and PMC Sierra. He received his B.S. from the University of Waterloo, and his M.S. and Ph.D. from the University of Michigan, and holds several patents. Mo was named a “Top 20 Entrepreneur” by the University of Michigan Zell Lurie Institute.

Mo Faisal

Founder & CEO
Movellus

Prior to founding Movellus, Mo held positions at semiconductor companies including Intel and PMC Sierra. He received his B.S. from the University of Waterloo, and his M.S. and Ph.D. from the University of Michigan, and holds several patents. Mo was named a “Top 20 Entrepreneur” by the University of Michigan Zell Lurie Institute.

Developer Efficiency
Enterprise AI
Data Science
Software Engineering
Systems Engineering
Moderator

Author:

Carlos Guestrin

Professor, Computer Science
Stanford

Carlos Guestrin is a Professor in the Computer Science Department at Stanford University. His previous positions include the Amazon Professor of Machine Learning at the Computer Science & Engineering Department of the University of Washington, the Finmeccanica Associate Professor at Carnegie Mellon University, and the Senior Director of Machine Learning and AI at Apple, after the acquisition of Turi, Inc. (formerly GraphLab and Dato) — Carlos co-founded Turi, which developed a platform for developers and data scientist to build and deploy intelligent applications. He is a technical advisor for OctoML.ai. His team also released a number of popular open-source projects, including XGBoost, LIME, Apache TVM, MXNet, Turi Create, GraphLab/PowerGraph, SFrame, and GraphChi. Carlos received the IJCAI Computers and Thought Award and the Presidential Early Career Award for Scientists and Engineers (PECASE). He is also a recipient of the ONR Young Investigator Award, NSF Career Award, Alfred P. Sloan Fellowship, and IBM Faculty Fellowship, and was named one of the 2008 ‘Brilliant 10’ by Popular Science Magazine. Carlos’ work received awards at a number of conferences and journals, including ACL, AISTATS, ICML, IPSN, JAIR, JWRPM, KDD, NeurIPS, UAI, and VLDB. He is a former member of the Information Sciences and Technology (ISAT) advisory group for DARPA.

Carlos Guestrin

Professor, Computer Science
Stanford

Carlos Guestrin is a Professor in the Computer Science Department at Stanford University. His previous positions include the Amazon Professor of Machine Learning at the Computer Science & Engineering Department of the University of Washington, the Finmeccanica Associate Professor at Carnegie Mellon University, and the Senior Director of Machine Learning and AI at Apple, after the acquisition of Turi, Inc. (formerly GraphLab and Dato) — Carlos co-founded Turi, which developed a platform for developers and data scientist to build and deploy intelligent applications. He is a technical advisor for OctoML.ai. His team also released a number of popular open-source projects, including XGBoost, LIME, Apache TVM, MXNet, Turi Create, GraphLab/PowerGraph, SFrame, and GraphChi. Carlos received the IJCAI Computers and Thought Award and the Presidential Early Career Award for Scientists and Engineers (PECASE). He is also a recipient of the ONR Young Investigator Award, NSF Career Award, Alfred P. Sloan Fellowship, and IBM Faculty Fellowship, and was named one of the 2008 ‘Brilliant 10’ by Popular Science Magazine. Carlos’ work received awards at a number of conferences and journals, including ACL, AISTATS, ICML, IPSN, JAIR, JWRPM, KDD, NeurIPS, UAI, and VLDB. He is a former member of the Information Sciences and Technology (ISAT) advisory group for DARPA.

Author:

Sakyasingha Dasgupta

Founder & CEO
EdgeCortix

Sakya is the founder and Chief Executive officer of EdgeCortix. He is an artificial intelligence (AI) and machine learning technologist, entrepreneur, and engineer with over a decade of experience in taking cutting edge AI research from ideation stage to scalable products, across different industry verticals.  He has lead teams at global companies like Microsoft and IBM Research / IBM Japan, along with national research labs like RIKEN Japan and the Max Planck Institute Germany. Previously, he helped establish and lead the technology division at lean startups in Japan and Singapore, in semiconductor technology, robotics and Fintech sectors. Sakya is the inventor of over 20 patents and has published widely on machine learning and AI with over 1,000 citations. 

Sakya holds a PhD. in Physics of Complex Systems from the Max Planck Institute in Germany, along with Masters in Artificial Intelligence from The University of Edinburgh and a Bachelors of Computer Engineering. Prior to founding EdgeCortix he completed his entrepreneurship studies from the MIT Sloan School of Management.

Sakyasingha Dasgupta

Founder & CEO
EdgeCortix

Sakya is the founder and Chief Executive officer of EdgeCortix. He is an artificial intelligence (AI) and machine learning technologist, entrepreneur, and engineer with over a decade of experience in taking cutting edge AI research from ideation stage to scalable products, across different industry verticals.  He has lead teams at global companies like Microsoft and IBM Research / IBM Japan, along with national research labs like RIKEN Japan and the Max Planck Institute Germany. Previously, he helped establish and lead the technology division at lean startups in Japan and Singapore, in semiconductor technology, robotics and Fintech sectors. Sakya is the inventor of over 20 patents and has published widely on machine learning and AI with over 1,000 citations. 

Sakya holds a PhD. in Physics of Complex Systems from the Max Planck Institute in Germany, along with Masters in Artificial Intelligence from The University of Edinburgh and a Bachelors of Computer Engineering. Prior to founding EdgeCortix he completed his entrepreneurship studies from the MIT Sloan School of Management.

Author:

Luis Ceze

Co-founder and CEO
OctoML

Luis Ceze is Co-founder and CEO at OctoML, Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, and Venture Partner at Madrona Venture Group. His research focuses on the intersection between computer architecture, programming languages, machine learning and biology. His current focus is on approximate computing for efficient machine learning andDNA-based data storage. He co-directs the Molecular Information Systems Lab (MISL), the Systems and Architectures for Machine Learning lab (SAMPL) and the Sampa Lab for HW/SW co-design. He is a recipient of an NSF CAREER Award, a Sloan Research Fellowship, a Microsoft Research Faculty Fellowship, the IEEE TCCA young Computer Architect Award and UIUC Distinguished Alumni Award.

Luis Ceze

Co-founder and CEO
OctoML

Luis Ceze is Co-founder and CEO at OctoML, Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, and Venture Partner at Madrona Venture Group. His research focuses on the intersection between computer architecture, programming languages, machine learning and biology. His current focus is on approximate computing for efficient machine learning andDNA-based data storage. He co-directs the Molecular Information Systems Lab (MISL), the Systems and Architectures for Machine Learning lab (SAMPL) and the Sampa Lab for HW/SW co-design. He is a recipient of an NSF CAREER Award, a Sloan Research Fellowship, a Microsoft Research Faculty Fellowship, the IEEE TCCA young Computer Architect Award and UIUC Distinguished Alumni Award.

Author:

Jian Zhang

Director, Machine Learning
SambaNova Systems

Jian Zhang

Director, Machine Learning
SambaNova Systems

Transformers are in high demand, particularly in industries like BFSI and healthcare, for language processing, understanding, classification, generation and translation. The parameter counts for models like GPT, that are fast becoming the norm in the world of NLP, are mind-boggling, and the cost involved in training and deploying even more so. If the vast potential for LLMs is to extend beyond the wealthiest companies and research institutions on the planet, then there is a need to evaluate how to lower the barriers of entry for experimentation and research on models like GPT. There's also a need to discuss the extent to which bigger is better, in the field of practical and commercial NLP.

This panel will look at the state of play of how enterprises are using large language models today, what their plans are for future research in NLP, and how hardware & systems builders and organizations like HuggingFace can help bring state-of-the-art performance into production in smaller, more resource-constrained enterprises and labs.

Developer Efficiency
Enterprise AI
ML at Scale
NLP
Novel AI Hardware
Systems Design
Data Science
Hardware Engineering
Software Engineering
Strategy
Systems Engineering

Author:

Phil Brown

VP, Scaled Systems Product
Graphcore

Phil leads Graphcore’s efforts to build large scale AI/ML processing capability using Graphcore unique Intelligence Processing Units (IPUs) and IPU-Fabric and Streaming Memory technology. Previously he has held a number of different roles at Graphcore including Director of Applications, leading development of Graphcore’s flagship AL/ML models, and Director of Field Engineering, which acts as the focal point for technical engagements with our customers. Prior to joining Graphcore, Phil worked for Cray Inc. in a number of roles, including leading their engagement with the weather forecasting and climate research customers worldwide and as a technical architect. Phil holds a PhD in Computational Chemistry from the University of Bristol.

Phil Brown

VP, Scaled Systems Product
Graphcore

Phil leads Graphcore’s efforts to build large scale AI/ML processing capability using Graphcore unique Intelligence Processing Units (IPUs) and IPU-Fabric and Streaming Memory technology. Previously he has held a number of different roles at Graphcore including Director of Applications, leading development of Graphcore’s flagship AL/ML models, and Director of Field Engineering, which acts as the focal point for technical engagements with our customers. Prior to joining Graphcore, Phil worked for Cray Inc. in a number of roles, including leading their engagement with the weather forecasting and climate research customers worldwide and as a technical architect. Phil holds a PhD in Computational Chemistry from the University of Bristol.

Author:

Selcuk Kopru

Director, Engineering & Research, Search
eBay

Selcuk Kopru is Head of ML & NLP at eBay and is an experienced AI leader with proven expertise in creating and deploying cutting edge NLP and AI technologies and systems. He is experienced in developing scalable Machine Learning solutions to solve big data problems that involve text and multimodal data. He is also skilled in Python, Java, C++, Machine Translation and Pattern Recognition. Selcuk is also a strong research professional with a Doctor of Philosophy (PhD) in NLP in Computer Science from Middle East Technical University.

Selcuk Kopru

Director, Engineering & Research, Search
eBay

Selcuk Kopru is Head of ML & NLP at eBay and is an experienced AI leader with proven expertise in creating and deploying cutting edge NLP and AI technologies and systems. He is experienced in developing scalable Machine Learning solutions to solve big data problems that involve text and multimodal data. He is also skilled in Python, Java, C++, Machine Translation and Pattern Recognition. Selcuk is also a strong research professional with a Doctor of Philosophy (PhD) in NLP in Computer Science from Middle East Technical University.

Author:

Jeff Boudier

Product Director
Hugging Face

Jeff Boudier is a product director at Hugging Face, creator of Transformers, the leading open-source NLP library. Previously Jeff was a co-founder of Stupeflix, acquired by GoPro, where he served as director of Product Management, Product Marketing, Business Development and Corporate Development.

Jeff Boudier

Product Director
Hugging Face

Jeff Boudier is a product director at Hugging Face, creator of Transformers, the leading open-source NLP library. Previously Jeff was a co-founder of Stupeflix, acquired by GoPro, where he served as director of Product Management, Product Marketing, Business Development and Corporate Development.

Author:

Morteza Noshad

Senior ML/NLP Scientist
Vida Health

Morteza Noshad is a senior ML/NLP scientist at Vida health. He is skilled at designing large scale NLP models for different healthcare applications such as automated clinical documentation, symptom detection and question answering. Morteza was a research scientist at Stanford University focusing on graph neural networks for clinical decision support systems where he received the SAGE Scientist Award for his research. Morteza received his Ph.D. in Computer Science from University of Michigan where he contributed to the theory of information bottleneck in deep learning. 

Morteza Noshad

Senior ML/NLP Scientist
Vida Health

Morteza Noshad is a senior ML/NLP scientist at Vida health. He is skilled at designing large scale NLP models for different healthcare applications such as automated clinical documentation, symptom detection and question answering. Morteza was a research scientist at Stanford University focusing on graph neural networks for clinical decision support systems where he received the SAGE Scientist Award for his research. Morteza received his Ph.D. in Computer Science from University of Michigan where he contributed to the theory of information bottleneck in deep learning. 

AI acceleration is a full stack effort and involves a multidisciplinary and holistic approach to design and optimization.

The field of deep learning has gained substantially from co-design concepts across the AI technology stack. The simultaneous design and optimization of hardware and software has led to new algorithms, numerical optimizations, and AI hardware. 

Looking at the AI stack for workloads like computer vision, NLP and Ads, in both a vertical and horizontal sense, there are significant opportunities and challenges for optimization through co-design. This panel will focus on software-defined chips and systems for AI (specs & evaluation, datacenter & edge) and look at the systems-level approach to co-design, including compilers and runtime etc.

Chip Design
Novel AI Hardware
Systems Design
Hardware Engineering
Software Engineering
Systems Engineering

Author:

Nick Ni

Senior Director, Datacenter AI & Compute Markets
AMD

Nick Ni is Senior Director, Data Center AI and Compute Markets at Adaptive Embedded Computing Group (AECG) at AMD, responsible for the P&L of the fast-growing Data Center AI and compute segment. His team is responsible for product marketing and product management including AI product planning, go-to-market, business development and solution architecture.

Nick Ni

Senior Director, Datacenter AI & Compute Markets
AMD

Nick Ni is Senior Director, Data Center AI and Compute Markets at Adaptive Embedded Computing Group (AECG) at AMD, responsible for the P&L of the fast-growing Data Center AI and compute segment. His team is responsible for product marketing and product management including AI product planning, go-to-market, business development and solution architecture.

Author:

Xiaoyong Liu

Director, AI Platform
Alibaba

Xiaoyong Liu

Director, AI Platform
Alibaba

Author:

Shubho Sengupta

Software Engineer
Meta

Shubho Sengupta is a Software Engineer at Meta, where he designs Meta’s Research Infra for AI training. He started working on AI in 2014, on speech related AI models like DeepSpeech and DeepVoice. Before that he pioneered many of the foundational algorithms in general purpose programming in GPUs, which has won Test of Time award. These days, he also works at the intersection of cryptography and computation, specifically in bi-partite and multi-partite matching algorithms.

Shubho Sengupta

Software Engineer
Meta

Shubho Sengupta is a Software Engineer at Meta, where he designs Meta’s Research Infra for AI training. He started working on AI in 2014, on speech related AI models like DeepSpeech and DeepVoice. Before that he pioneered many of the foundational algorithms in general purpose programming in GPUs, which has won Test of Time award. These days, he also works at the intersection of cryptography and computation, specifically in bi-partite and multi-partite matching algorithms.

Author:

Dr. Charles Fan

CEO and Co-Founder
MemVerge

Charles Fan is CEO and co-founder of MemVerge. Prior to MemVerge, Charles was the CTO of Cheetah Mobile leading its global technology teams, and an SVP/GM at VMware, founding the storage business unit that developed the Virtual SAN product. Charles also worked at EMC and was the founder of the EMC China R&D Center. Charles joined EMC via the acquisition of Rainfinity, where he was a co-founder and CTO. Charles received his Ph.D. and M.S. in Electrical Engineering from the California Institute of Technology, and his B.E. in Electrical Engineering from the Cooper Union.

Dr. Charles Fan

CEO and Co-Founder
MemVerge

Charles Fan is CEO and co-founder of MemVerge. Prior to MemVerge, Charles was the CTO of Cheetah Mobile leading its global technology teams, and an SVP/GM at VMware, founding the storage business unit that developed the Virtual SAN product. Charles also worked at EMC and was the founder of the EMC China R&D Center. Charles joined EMC via the acquisition of Rainfinity, where he was a co-founder and CTO. Charles received his Ph.D. and M.S. in Electrical Engineering from the California Institute of Technology, and his B.E. in Electrical Engineering from the Cooper Union.

Author:

Zaid Kahn

VP, Cloud AI & Advanced Systems Engineering
Microsoft

Zaid is currently a VP in Microsoft’s Silicon, Cloud Hardware, and Infrastructure Engineering organization where he leads systems engineering and hardware development for Azure including AI systems and infrastructure. Zaid is part of the technical leadership team across Microsoft that sets AI hardware strategy for training and inference. Zaid's teams are also responsible for software and hardware engineering efforts developing specialized compute systems, FPGA network products and ASIC hardware accelerators.

 

Prior to Microsoft Zaid was head of infrastructure at LinkedIn where he was responsible for all aspects of architecture and engineering for Datacenters, Networking, Compute, Storage and Hardware. Zaid also led several software development teams focusing on building and managing infrastructure as code. This included zero touch provisioning, software-defined networking, network operating systems (SONiC, OpenSwitch), self-healing networks, backbone controller, software defined storage and distributed host-based firewalls. The network teams Zaid led built the global network for LinkedIn, including POP's, peering for edge services, IPv6 implementation, DWDM infrastructure and datacenter network fabric. The hardware and datacenter engineering teams Zaid led were responsible for water cooling to the racks, optical fiber infrastructure and open hardware development which was contributed to the Open Compute Project Foundation (OCP).

 

Zaid holds several patents in networking and is a sought-after keynote speaker at top tier conferences and events. Zaid is currently the chairperson for the OCP Foundation Board. He is also currently on the EECS External Advisory Board (EAB) at UC Berkeley and a board member of Internet Ecosystem Innovation Committee (IEIC), a global internet think tank promoting internet diversity. Zaid has a Bachelor of Science in Computer Science and Physics from the University of the South Pacific.

Zaid Kahn

VP, Cloud AI & Advanced Systems Engineering
Microsoft

Zaid is currently a VP in Microsoft’s Silicon, Cloud Hardware, and Infrastructure Engineering organization where he leads systems engineering and hardware development for Azure including AI systems and infrastructure. Zaid is part of the technical leadership team across Microsoft that sets AI hardware strategy for training and inference. Zaid's teams are also responsible for software and hardware engineering efforts developing specialized compute systems, FPGA network products and ASIC hardware accelerators.

 

Prior to Microsoft Zaid was head of infrastructure at LinkedIn where he was responsible for all aspects of architecture and engineering for Datacenters, Networking, Compute, Storage and Hardware. Zaid also led several software development teams focusing on building and managing infrastructure as code. This included zero touch provisioning, software-defined networking, network operating systems (SONiC, OpenSwitch), self-healing networks, backbone controller, software defined storage and distributed host-based firewalls. The network teams Zaid led built the global network for LinkedIn, including POP's, peering for edge services, IPv6 implementation, DWDM infrastructure and datacenter network fabric. The hardware and datacenter engineering teams Zaid led were responsible for water cooling to the racks, optical fiber infrastructure and open hardware development which was contributed to the Open Compute Project Foundation (OCP).

 

Zaid holds several patents in networking and is a sought-after keynote speaker at top tier conferences and events. Zaid is currently the chairperson for the OCP Foundation Board. He is also currently on the EECS External Advisory Board (EAB) at UC Berkeley and a board member of Internet Ecosystem Innovation Committee (IEIC), a global internet think tank promoting internet diversity. Zaid has a Bachelor of Science in Computer Science and Physics from the University of the South Pacific.