SigOpt AI & HPC Summit 2021

Subutai Ahmad, PhD

VP Research, Numenta, Inc.

Subutai Ahmad is the VP of Research at Numenta, a research company that is applying neuroscience principles to machine intelligence research. Subutai brings experience in deep learning, neuroscience, and real time systems.  His research interests lie in understanding and applying neuroscience insights from areas such as sparsity, dendrites, unsupervised learning, and sensorimotor prediction.   Subutai holds a B.S. in Computer Science from Cornell University, and a Ph.D in Computer Science and Computational Neuroscience from the University of Illinois at Urbana-Champaign.

David Austin

Sr Principal Engineer, Intel

David Austin is a Senior Principal Engineer at Intel Corporation working on AI based solutions for the industrial Internet-of-Things and edge segment. He is currently focusing on developing AI workflows for industrial anomaly detection and federated learning. In the past, he has worked on advanced semiconductor manufacturing process integration among other use cases. David is a Kaggle Grandmaster and has been ranked as high as #11 in the world Kaggle competition rankings.

Sasikanth (Sasi) Avancha

Senior Research Scientist, Intel Parallel Compute Labs

Sasikanth Avancha (Member, IEEE) received the B.E. degree in Computer Science and Engineering from the University Visvesvaraya College of Engineering (UVCE), Bengaluru, India, in 1994. He received M.S. and Ph.D. degrees in Computer Science from the University of Maryland at Baltimore County (UMBC), Baltimore, MD, USA, in 2002 and 2005, respectively. He is currently a Senior Research Scientist with the Parallel Computing Lab in Intel Labs and is based out of Intel India in Bengaluru. He has over 20 years of industry and research experience. He has four patents and over 25 articles spanning security, wireless networks, systems software, computer architecture and deep learning. His current research focuses on high-performance algorithm development, analysis and optimization of large-scale, distributed deep learning and machine learning training and inference, on different data-parallel architectures, including x86 and accelerator architectures, across application domains.

Basem Barakat

Large Scale Machine Learning Engineer, Habana Labs

As a large scale machine learning engineer at Habana, Basem leads efforts to accelerate AI workloads on Habana Gaudi and other Habana AI accelerator hardware, including collaboration with the team on MLPerf submissions. Previously, Basem was a senior performance architect at Samsung. Before Samsung, Basem was a senior systems engineer at Freescale. Basem holds a Ph.D. in Physics from the University of Houston.

Scott Clark

CoFounder & GM, SigOpt, an Intel Company

Scott Clark is the co-founder, former CEO, and current general manager of SigOpt, acquired by Intel in November 2020. Scott leads SigOpt’s ongoing efforts to build a product and vision of an Intelligent Experimentation platform that accelerates and amplifies the impact of modelers everywhere.

Scott has been applying optimal learning techniques in industry and academia for years in areas that include bioinformatics and production advertising systems. Before co-founding SigOpt, he worked on the Ad Targeting team at Yelp, where he led the charge on academic research and outreach with projects like the Yelp Dataset Challenge and open-source Metric Optimization Engine (MOE).

Scott holds a PhD in Applied Mathematics and an MS in Computer Science from Cornell University. He also holds BS degrees in Mathematics, Physics, and Computational Physics from Oregon State University. He was recognized as one of Forbes’ 30 under 30 in 2016.

Evelyn Ding

Senior Machine Learning Engineer, Habana Labs

Evelyn is a a senior machine learning engineer at Habana Labs and previously occupied the same role for Intel. She focuses on data analytics, machine learning, application development, optimization and deployment for at scale machine learning and deep learning projects. Previously, Evelyn held advanced software engineering roles at Heristar, ExxonMobil, Dow and ENGlobal, among other companies. Evelyn graduated with a Ph.D. in Electrical Engineering from the University of Sheffield in the United Kingdom and received her bachelor’s degree in Electrical Engineering from the University of Science and Technology in China.

Ke Ding

Principal AI Engineer Intel, SATG MLP – Applied ML team

Ke has 16 years’ working experience in machine learning and platform SW development at Intel. Currently he is Principal AI Engineer and Engineering Director at Machine Learning Performance group under Intel Software and Advanced Technology Group, responsible for applied machine learning end-2-end workload development, framework optimization and new AI technology exploration for the latest Intel Xeon CPU and upcoming discrete GPU platforms. He has 20+ granted patents in the domain of machine learning, multimedia and context awareness.

Rafael Gomez-Bombarelli

Assistant Professor, MIT

Rafael Gomez-Bombarelli (Rafa) is the Jeffrey Cheah Assistant Professor at MIT’s Department of Materials Science and Engineering since 2018. His works aims to fuse machine learning and atomistic simulations for designing materials and their transformations. The Gomez-Bombarelli group works across molecular, crystalline and polymer matter, combining novel computational tools in optimization, inverse design, surrogate modeling and active learning with simulation approaches like quantum chemistry and molecular dynamics. Through collaborations at MIT and beyond, they develop new practical materials such as therapeutic peptides, organic electronics for displays, electrolytes for batteries, or inorganic oxides for sustainably catalysis. Rafa received BS, MS, and PhD (2011) degrees in chemistry from Universidad de Salamanca (Spain), followed by postdoctoral work at Heriot-Watt (UK) and Harvard Universities, and a stint in industry at Kyulux North America.

Vishwanath Hegadekatte

Global R&D Manager, Novelis

Vishwanath Hegadekatte works for Novelis Inc. as R&D Manager and Principal Scientist at its Kennesaw R&D Center located in Georgia, USA. Apart from people Vishwanath Hegadekatte works for Novelis Inc. as Global R&D Manager at the its R&D Center located in Kennesaw, GA. At Novelis, Vishwanath leads the global AI and Advanced Modeling group. Apart from people and project portfolio management responsibilities, his current scientific areas of interests include machine learning, advanced modeling for sheet metal products and processes. Before joining Novelis, Vishwanath worked as a post-doctoral research associate at Brown University, USA. He holds a doctorate in engineering sciences from Karlsruhe Institute of Technology, Germany. Outside of work, Vishwanath enjoys experimenting with espresso, hiking and landscape photography.

Alexander Rosenberg Johansen

PhD Candidate, Stanford

Alexander is a PhD student in Computer Science at Stanford, supervised by Michael P Snyder. He leads a research lab in the exploration of machine learning in Bioinformatics and MedTech at stanford-health.github.io. Alexander has been featured in venues such as ICLR and Nature Biotechnology relying on SigOpt technologies.

Marat Latypov

Assistant Professor of Materials Science and Engineering, University of Arizona

Marat Latypov is an assistant professor in the Department of Materials Science and Engineering and a faculty member of the Applied Mathematics Graduate Interdisciplinary Program at the University of Arizona. Marat received his Ph.D. from Pohang University of Science and Technology (POSTECH) in South Korea followed by a postdoc at Georgia Tech/CNRS lab in France and the University of California in Santa Barbara. He joined the University of Arizona in 2021 after two years in the aluminum industry. Marat has a wide range of interests in the field of materials science including materials informatics, modeling and simulation, artificial intelligence, materials design and process optimization.

Paul Leu

Associate Professor, University of Pittsburgh

Prof. Paul Leu is an Associate Professor and the BP America Faculty Fellow in the Industrial Engineering Department at the University of Pittsburgh. His research group the Laboratory for Advanced Materials at Pittsburgh (LAMP) focuses on functional materials which have included functionalities such as antireflection, light trapping, and haze control in plasmonics, transparent electrodes, and solar cells.  Recently, he has been integrating simulation and experimental methodologies with machine learning for materials discovery.  He has been recipient of the Oak Ridge Associated University Powe Junior Faculty Enhancement Award, UPS Minority Advancement Award, and the NSF CAREER Award. His research has been showcased in Scientific American Frontiers, Pittsburgh NPR, and Pittsburgh Magazine.

Alex Lowden

Data Science Manager & Team Lead, Accenture

Alex Lowden is a Data Science Manager & Team Lead within the AI practice of Accenture where he is predominantly focused on applying machine learning to unlock value within the Energy & Chemicals industries. Alex has a unique blend of analytical and operational experience, working as a Petroleum Engineer in the UK North Sea on fast-paced operational projects, as well as multiple years’ experience building analytical solutions across a variety of industries ranging from Aerospace to Agriculture. He also holds a BSc in Mathematics, and an MSc in Petroleum Engineering from Imperial College London where he received the Colin Wall Award for his work on modelling transient temperature profiles in Ultra-Deep-Water wells.

Eddie Mattia

Product Manager, SigOpt

For the last few years I have been working closely with SigOpt customers to help them design better Experiments and better optimize their models. Since we joined Intel one year ago my goals have pivoted to extending insights from these often heavily involved, custom projects and using them to build product features that empower data scientists to spend more of their time working on the tasks that are most important to them, like attending SigOpt conferences to learn about fancy new types of neural networks.

Michael McCourt

Head of Engineering, SigOpt

Mike leads research and engineering at SigOpt, an Intel Company.  We are responsible for developing, deploying, and maintaining the SigOpt platform both by creating tools to power intelligent experimentation and making sure these tools robustly meet the usage demands of our users.  He has been with SigOpt for the past 6 years, almost since its founding.  Prior to joining SigOpt, he spent time in the math and computer science division at Argonne and was a visiting assistant professor at CU-Denver where he co-wrote a text on kernel-based approximation. Mike holds degrees in Applied Mathematics from Cornell and IIT.

Shayan Mortazavi

Data Science Manager, Accenture

Shayan Mortazavi is a Data Science Manager at Accenture Industrial Analytics Group, where he is currently working within resources industries with interests in predictive maintenance, digital plant engineering, and optimization for upstream industries and the water industry. Shayan has over 15 years of experience in material science, engineering consulting, data science, and digital change. In his recent work, Shayan is developing scalable machine learning based solutions for the industry by applying Bayesian inference, dynamic control and graph network decision making systems. Shayan received his MSc from the University of Newcastle in Marine Science and his BSc in Fluid Dynamic and Structural Design. He is a chartered member of IMECHE and a winner of TD Williamson award for building a novel machine learning solution to detect and locate leaks in transmission pipelines.

Sulata Patra

Data Scientist, Mindtree

Sulata is a results-driven leader and IT expert with over ten years of experience in manufacturing, finance, data science, and engineering. Since 2016, she has worked as a data scientist for Mindtree to address a wide variety of AI problems, using Python, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Power BI, and Databricks. Most recently,s he led efforts to build conversational AI to enhance customer support at Mindtree. Sulata is passionate about using various AI models to solve different problems., and has expertise in data modeling, data mining, analytics, and artificial intelligence.

Bhanu Prakash

Program Architect, Mindtree

Bhanu is an accomplished machine learning and software architect with over 20 years of experience in the consumer electronics, semiconductor, and automotive industries. He has extensive experience with short-range wireless networks, Linux, firmware, machine learning and deep learning. Currently, Bhanu draws on this expertise to address a variety of problems in conversational AI at Mindtree. Within Mindtree, he is the go-to person for the most difficult and pressing technical challenges that require data science and optimization solutions. He is passionate about fostering the next generation of technical and architects, serving as a mentor and advisor to emerging engineers across these domains.

Venkatesh Ramanathan

Director Data Science, PayPal

Venkatesh is a Director, Data Science at PayPal where he is leading several applied research initiatives including ML on Graphs . He has over 25+ years experience in designing, developing and leading teams to build scalable server side software and AI/ML research. In addition to being an expert in AI/ML and big data technologies, Venkatesh holds a Ph.D. degree in Computer Science with specialization in Machine Learning and Natural Language Processing (NLP) and had worked on various problems in the areas of Anti-Spam, Phishing Detection, and Face Recognition.

Meghana Ravikumar

Product Manager, SigOpt

Meghana is passionate about creating intuitive, joyful, and equitable products that apply machine learning to inspire and create valuable experiences for everyone. Over the past 6 years, she’s worked in identifying user problems, designing AI-powered solutions, driving developer evangelism, authoring influential ML papers, speaking at global industry events and launching new breakthrough Machine Learning products. 

She found her calling as a Product Manager, leading engineering, marketing and client teams in defining new modeling innovation, vetting market fit, developing requirements, managing product releases and launching breakthrough technologies to algorithmic trading firms, US intelligence agencies and leading streaming service providers globally.  She finds joy in delving into the unknown, charting the new, working in intersections, and empowering everyone she works with. 

Pablo Zegers, Ph.D.

Co-Founder & Vice President of Product, Anastasia AI

Pablo Zegers was born in Santiago, Chile, in 1968. He received his Bachelor in Industrial Engineering degree and his Professional Industrial Engineer certification from the Pontificia Universidad Católica, Chile, in 1992, his Master of Science degree from The University of Arizona, USA, in 1998, and his Doctor of Philosophy degree, also from The University of Arizona, in 2002. He started to work as a professor at the College of Engineering and Applied Sciences of the Universidad de los Andes, Chile, in 2002 and left the university as an Associate Professor in 2017. He was the Academic Director of that College from 2006 to 2010, and its Interim Dean for a brief period at the end of 2010. During his stay at the university he led, or was part of, the teams that designed the strategic plan of the college, created Electrical Engineering and Computer Science, renovated the academic curricula of all the departments of the college, hired tens of professors, and reorganized the administrative structure of the college. As a teacher, his main achievement was advising more than 90 engineering students through their final engineering thesis. He has many publications whose topics range from the use of the mathematical theory of information theory in artificial intelligence to the design of algorithms that found thousands of binary stars in the Magellanic Clouds that orbit our galaxy. His interests are information theory, artificial intelligence, machine learning, and neural networks. He is a co-founder of Anastasia, focused on developing artificial intelligence tools for commercial businesses, and Sortbox, dedicated to building machines based on artificial intelligence for the agrotech sector. He was recognized as the 2020 Outstanding Electrical Engineer by the Chilean electrical companies and the IEEE. He was a Member of the Board of the Chilean IEEE Section, and he is a Senior Member of the IEEE.

Jian Zhang

Software Engineering Manager, Intel

Jian Zhang is a senior software engineering manager at Intel, recently he and his team primarily focused on implementing and optimizing end to end AI solutions on distributed CPU cluster, democratizing AI models to improve scalability & usability on commodity hardware. He has 10+ years of experience in software development and optimization, ranging from open source virtualization systems like Xen & KVM, distributed storage systems Swift and Ceph, to big data and AI systems. Jian holds a master degree in Computer Science and Engineering.

Da Zheng

Senior Applied Scientist, AWS AI

Da Zheng is a senior applied scientist at AWS AI, where he develops deep learning frameworks including MXNet, DGL (Deep Graph Library) and DGL-KE. His research interest includes high-performance computing, scalable machine learning systems and data mining. He got a PhD in computer science at the Johns Hopkins University, Baltimore, USA in 2016. He received his master of science from École polytechnique fédérale de Lausanne, Lausanne, Switzerland in 2009 and bachelor of science from Zhejiang University, Hangzhou, China in 2006.