iMerit ML DataOps Summit 2022
November 8, 2022
Speakers
Radha Basu
Founder and CEO iMerit
Manohar Paluri
Director of AI Meta Platforms
Seth Dobrin
Former Chief AI Officer IBM
Beata Kouchnir
Director, Machine Learning Science Glassdoor
Abhijit Bose
Head of the Center for Machine Learning Capital One
Eric Chu
Senior Manager, Data Science Zoox
Anna Bethke
Ethical AI Data Scientist Salesforce
Vinesh Sukumar
Senior Director of Product Management Qualcomm Technologies, Inc.
Avi Yashar
Co-founder and CPO Dataloop
Shweta Shrivastava
Senior Director of Product Management for Behavior Waymo
Sriram Subramanian
Global Domain Lead for Data & AI, FastTrack for Azure Microsoft
Alok Gupta
Head of Data DoorDash
Alessandra Sala
Senior Director of AI & Data Science Shutterstock
Alfred Chuang
Founder and General Partner Race Capital
Chris Karlin
Head of Sales Superb AI
Raj Aikat
Chief Product & Technology Officer iMerit
Michael Hazard
Product Manager Applied Intuition
Danny Lange
Senior Vice President of Artificial Intelligence and Machine Learning Unity
Josh Hollin
VP of Engineering AMP Robotics
Jeff Mills
President and Chief of Revenue Operations iMerit Technology
Jai Natarajan
Vice President, Strategic Business Development iMerit
Sudeep George
Vice President, Engineering iMerit
Anurag Wadehra
Board of Director iMerit
Anshuman Patnaik
Deep Learning Lead Embark Trucks
Chris Barker
Founder and CEO CBC Transportation Consulting
Andy Pavlo
Associate Professor / Co-Founder Carnegie Mellon University / OtterTune
Shyam Rajagopalan
Co-founder/CTO Infinitus Systems, Inc.
Pavan Tripathi
Partner Bregal Sagemount
Lucas Chatham
Senior Product Manager
Vidyaranya Devigere
Head of Algorithms Overstock
Dr. Itai Orr
VP of Technology Autobrains Technologies
Radha Basu is the founder and CEO of iMerit, a global data annotation company delivering high-quality data that powers machine learning and artificial intelligence applications for Fortune 500 companies. Under Radha’s leadership, iMerit employs an inclusive workforce of more than 5,500 people worldwide, with 80% from underserved communities and 54% being women. Radha led iMerit through two funding rounds, raising $23.5 million to date from investors and led the company to reach new revenue heights. Previously, Radha was the SupportSoft chairwoman and CEO. She spent 20 years at Hewlett Packard, where she grew HP’s electronic software division into a $1.2 billion business and launched HP in India. Radha has received accolades, including the Global Thinkers Forum Award, UN Women-ITU Gender-Equality Mainstreaming Technology Award, Silicon Valley Business Journal Women of Influence Award, Top 25 Women of the Web, and CEO of the Year. She serves on the boards of NetHope, Jhumki Basu Foundation, and the Miller Center for Social Entrepreneurship. Radha founded the SCU’s Frugal Innovation Hub and co-founded the Anudip Foundation.
Radha's Sessions
Join The Journey: ML DataOps for Advancing AI
iMerit Founder and CEO Radha Basu welcome thousands of data scientists, engineers, and ML professionals to join her on the journey to explore the machine learning data operations landscape. Attendees will gain insights on the road ahead for advancing AI with data intelligence, ML DataOps readiness for enterprise AI, solving edge cases in production AI and the latest trends happening in the ML DataOps.
Pushing the Frontiers in AI For Billions Around the World
At Meta, AI is pervasive and critical to the proliferation of our products for billions of users worldwide. Join Meta AI’s Manohar Paluri to learn a simple but effective framework for pushing the frontiers in AI research, while advancing technology that is impactful to the product end game. Attendees will gain insights into the structure for developing AI applications including scaling ML models, adopting a multi-modal understanding, pairing tools and human intelligence to accelerate AI and more. Wrapping the session, iMerit’s Founder and CEO Radha Basu and Manohar will delve into a discussion about the power of human intelligence to improve AI.
The Journey Wraps: ML DataOp Summit Key Takeaways
iMerit Founder and CEO Radha Basu wraps the day by sharing several key takeaways attendees can use as tools to move forward in deploying their enterprise AI application.
•Joined Meta in 2012 as an Intern •Built the foundation models, platforms and infrastructure for Image and Video understanding at Meta over the past decade •Currently leads the Perception & Action organization in FAIR Accel •“Passionate about making machines understand the way humans do!”
Manohar's Sessions
Pushing the Frontiers in AI For Billions Around the World
At Meta, AI is pervasive and critical to the proliferation of our products for billions of users worldwide. Join Meta AI’s Manohar Paluri to learn a simple but effective framework for pushing the frontiers in AI research, while advancing technology that is impactful to the product end game. Attendees will gain insights into the structure for developing AI applications including scaling ML models, adopting a multi-modal understanding, pairing tools and human intelligence to accelerate AI and more. Wrapping the session, iMerit’s Founder and CEO Radha Basu and Manohar will delve into a discussion about the power of human intelligence to improve AI.
Widely recognized as one of the industry’s most preeminent leaders in AI, Dr. Seth Dobrin has ideated and spearheaded some of the most innovative AI strategies for a variety of Fortune 500 companies. Trained as a human-geneticist, he launched his career in tech with an unwavering passion for the rigor of the scientific method applied to business, combined with his interest in human nature described through data. Dr. Dobrin is currently the President of the Responsible AI Institute, CEO of Trustwise and Founder of Qantm AI. As a thought leader, Dr. Dobrin’s unique combination of experience and high level of technical expertise has allowed him to carve out a niche space in the AI field. His human-centered approach to AI in business utilizes humans as a lens to frame data and AI issues, while simultaneously uncovering new business opportunities to design AI solutions around. As advocate for ethical AI and inclusivity in tech, Dr. Dobrin’s thoughtful approach delivers comprehensive returns, helping each client achieve their unique goals. Until recently, Dr. Dobrin designed and led cutting edge technology strategy initiatives within IBM. He was appointed as IBM’s first-ever Global Chief AI Officer and led the entire corporate AI strategy. In this role, Dr. Dobrin was responsible for connecting the AI development and governance across IBM’s divisions with a systemic creation of business outcomes. Before joining IBM, Dr. Dobrin held a variety of leadership roles focused on data and digital strategy with Monsanto Company.
Seth's Sessions
The Stakes Are High: Best Practices For Deploying Responsible AI
AI is sparking significant change across industries worldwide, with projections to add more than $15 trillion to the global economy by 2030. By 2022, more than 60% of enterprises will have implemented machine learning, big data analytics, and related AI tools into their operations. Enterprises navigating the complexities of artificial intelligence– from data operations to full-scale commercialization–must do it with a focused, practical lens. Join this session to hear from IBM’s Former Chief AI Officer and newly appointed President of the Responsible AI Institute to gain insight into the best practices for deploying artificial intelligence responsibly.
Beata fell in love with machine learning when studying for her master’s degree at the University at Edinburgh in the early 2000s, long before it was considered “cool.” After a brief stint in academia, she sought to apply her skills in the retail e-commerce space, leading to roles at Amazon, Zulily, and Facebook, among others. Earlier this year, Beata left the retail space to focus on a new industry as Director of Machine Learning Science at Glassdoor. In her free time, she loves to travel, read fiction, and learn foreign languages with apps like Duolingo.
Beata's Sessions
It’s a Duo: Human-in-the-Loop and High-Quality Data Are Catalysts in ML and AI
With the proliferation of production AI, enterprises realize that when machine learning can’t solve the problem, humans need to intervene. To account for this gap, enterprises are integrating robust human-in–the-loop programs to improve their AI training data and precision of their models. Attendees will get a front-row seat to better understand why human-in-the-loop and high-quality data go hand-in-hand to achieve widespread production AI applications.
Abhijit Bose is the founder and CEO of Ezetap.
Abhijit's Sessions
Convergence of ML Ops and Data Pipelines
Machine learning has grown exponentially in the last decade, drastically transforming machine learning data operation efforts. Data scientists and professionals alike realize the daunting effort of converging healthy data pipelines with machine learning models to produce successful AI applications. Join this session to hear ML experts share how they are transforming their data pipelines across collection, preparation, management and development to better manage their data, improve ML throughput and create meaningful AI applications for their business.
Eric Chu is the head of the Data Science and Engineering team at Zoox, where he oversees the company’s strategic data analysis capability in order to better understand and improve Zoox’s robotaxi performance. Prior to this role, Chu served numerous roles in data science, motion planning, and software at Zoox with increasing responsibility. Chu also held data science and research roles at previous companies, including Winton, Expanse, and Google across domains as varied as finance, cybersecurity, and energy. Chu holds a Bachelor’s degree in Electrical Engineering and Mathematics, and a PhD in Electrical Engineering from Stanford University where he studied mathematical optimization under Stephen Boyd, contributing to various open-source optimization solvers and parser-solvers.
Eric's Sessions
Mastering Anomaly Detection and Test Case Generation in ML
Enterprises building AI applications recognize that mastering anomaly detection and test case generation brings incredible value to improving machine learning model performance. Join this session to hear ML experts unveil the challenges and opportunities of identifying data anomalies and generating test cases to optimize your machine learning.
Anna Bethke is a Principal Data Scientist focused on fair, accountable, transparent, & explainable (FATE) AI in Salesforce’s Ethical AI Practice Team, collaborating with product and research teams to create AI responsibly and empower our customers to use it responsibly. They research and implement innovative techniques for assessing and mitigating bias and harm in AI. Anna received their MS and BS in Aerospace Engineering from the Massachusetts Institute of Technology concentrating on Human Factors Engineering. Anna was formerly the Head of AI for Social Good at Intel and previously worked at Facebook, MIT Lincoln Labs, Argonne National Labs, and Lab41.
Anna's Sessions
It’s a Duo: Human-in-the-Loop and High-Quality Data Are Catalysts in ML and AI
With the proliferation of production AI, enterprises realize that when machine learning can’t solve the problem, humans need to intervene. To account for this gap, enterprises are integrating robust human-in–the-loop programs to improve their AI training data and precision of their models. Attendees will get a front-row seat to better understand why human-in-the-loop and high-quality data go hand-in-hand to achieve widespread production AI applications.
Dr. Vinesh Sukumar currently serves as Senior Director of Product Management at Qualcomm Technologies, Inc. Vinesh acts as head of AI/ML, leading AI product definition, strategy and solution deployment across multiple business units. He has about 20 years of industry experience spread across research, engineering and application deployment. He holds a doctorate degree specializing in imaging and vision systems and also holds an MBA focused on strategy and marketing. He is a regular speaker in many AI industry forums and has authored several journal papers and two technical books.
Vinesh's Sessions
Why Solving Data Edge Cases is Key to Accelerating AI
It’s no secret that AI systems are being used in more and more high-stakes applications – self-driving cars, robotic surgery and more. As AI advances, it’s becoming critical to ensure that AI systems navigate real-world anomalies successfully. Join this session to hear veteran machine learning experts discuss what it takes to identify and solve data edge cases to maximize AI performance and achieve widespread adoption.
Avi is the Co-Founder and Chief Product Officer of Dataloop AI, a company that builds data infrastructure and data operating systems for AI companies. Avi has extensive knowledge of emerging AI technologies and an extensive perspective on the emerging MLOps market. Over the past 5 years, Avi has worked on many varied topics ranging from highly technical and industry trends to building products from scratch to MM revenues and developing roadmaps.
Avi's Sessions
Navigating Data Tooling and Expertise To Achieve High-Quality AI Training Data
As enterprises navigate the data tooling ecosystem, many realize the need to activate two vital components to achieve high-quality AI training data – data tooling and a talented tooling workforce. Join this session to hear from data tooling experts as they unveil what makes enterprise-grade worthy tools, why data tools require super users, realization that one tool isn’t a fit for all and what lies ahead in the data tooling ecosystem.
Shweta Shrivastava is Senior Product Leader at Waymo, leading product management for autonomous driving behavior capabilities. Before joining Waymo, Shweta was Chief Product Officer at Nauto, an AI startup focusing on driver and automotive safety, where she led Product Management, Design, Strategy and Marketing. Prior to Nauto, she was the Head of Product Management at Amazon Web Services for their database and analytics services. Shweta held various strategy and product management leadership roles at McKinsey and Cisco prior to that. She holds M.S. in Electrical Engineering from Pennsylvania State University and M.B.A. from INSEAD.
Shweta's Sessions
Why Solving Data Edge Cases is Key to Accelerating AI
It’s no secret that AI systems are being used in more and more high-stakes applications – self-driving cars, robotic surgery and more. As AI advances, it’s becoming critical to ensure that AI systems navigate real-world anomalies successfully. Join this session to hear veteran machine learning experts discuss what it takes to identify and solve data edge cases to maximize AI performance and achieve widespread adoption.
Sriram Subramanian is currently the global lead for Data & AI domain at the FastTrack for Azure group within Microsoft. Before joining Microsoft, he was a Research Director at IDC covering AI/ ML Lifecycle Management Software. Major themes of his research included MLOps, Trustworthy AI, AI Build, and Data Labeling software. Prior to that, he was the founder and principal analyst at CloudDon, independent market research and advisory services firm, where his research focused on advising vendors and buyers on cloud-native technologies and stacks. Sriram has also previously held various positions of increasing responsibilities at Microsoft and Intel and a few startups.
Sriram's Sessions
Convergence of ML Ops and Data Pipelines
Machine learning has grown exponentially in the last decade, drastically transforming machine learning data operation efforts. Data scientists and professionals alike realize the daunting effort of converging healthy data pipelines with machine learning models to produce successful AI applications. Join this session to hear ML experts share how they are transforming their data pipelines across collection, preparation, management and development to better manage their data, improve ML throughput and create meaningful AI applications for their business.
As Head of Data and Machine Learning at DoorDash, Alok Gupta directs teams managing data and building models to optimize critical metrics necessary to balance a three-sided marketplace. With previous roles as Director of Data Science for both Lyft and Airbnb, Alok has abundant experience in using data science to make predictions at scale. Prior to this industry experience, Alok was a Research Fellow in Mathematics at Oxford University, earning his PhD in derivative pricing, work that he applied on Wall Street as a high frequency trader to predict movements in foreign exchange rates at high frequency.
Alok's Sessions
Convergence of ML Ops and Data Pipelines
Machine learning has grown exponentially in the last decade, drastically transforming machine learning data operation efforts. Data scientists and professionals alike realize the daunting effort of converging healthy data pipelines with machine learning models to produce successful AI applications. Join this session to hear ML experts share how they are transforming their data pipelines across collection, preparation, management and development to better manage their data, improve ML throughput and create meaningful AI applications for their business.
Alessandra Sala is Director of AI and Data Science at Shutterstock; Global President of Women in AI (https://www.womeninai.co); Technology Advisory Board Member at CeADAR (http://www.ceadar.ie); Governance Committee Chair at the Science Foundation Ireland Centre for Research Training in Machine Learning; and Member Of The Board Of Advisors at Continuous Ventures. A research and scientific leader in Artificial Intelligence, Alessandra has over 10 years’ experience in research and innovation gained whilst working in academic and commercial environments. Alessandra is passionate in advanced analytics, machine learning, and computational models with the focus of transferring innovation from research to products. Alessandra is the Ambassador of Women in AI Ireland (a nonprofit do-tank working towards gender-inclusive AI that benefits global society) promoting a strong community of women and minorities in Ireland by providing means for growing knowledge and expertise in AI thanks to the strong support of the government, academia and private enterprises. In her previous appointment, Alessandra was Head of Analytics Research at Nokia Bell Labs where she was leading research teams in several locations and developing a successful strategy for the Data Analytics division while driving changes across different activities, including her contributions to the Nokia AI Ethics Advisory Board.
Alessandra's Sessions
Unforeseen Challenges and Opportunities in Commercializing AI
AI powers a plethora of real-world applications, ranging from facial recognition and image detection to language translators and assistants like Siri and Alexa. As more companies harness the power of AI, many are navigating unforeseen challenges and opportunities in commercializing AI. Hear from AI experts as they dive into overcoming the obstacles – good and bad – that enable the widespread adoption of their AI applications.
Alfred Chuang is general partner at Race Capital, an early-stage venture capital firm.
Alfred's Sessions
Current and Future State of ML DataOps Landscape
As enterprises dive deeper into commercializing AI applications to improve business efficiencies, many realize the massive transformation and increasing complexity of the machine learning data operations landscape. Catch this session to hear seed stage and growth stage venture capitalists share their perspectives on the current and future state of the machine learning data operations landscape.
With several years in the AI services and technology industry, Chris leads sales and supports GTM strategy at Superb AI. At Superb AI, Chris guides organizations and ML teams towards building computer vision applications faster and with better cost efficiency. Superb’s truly differentiated AI-centric technology and innovative approach to ML infrastructure solutions gives them a unique perspective on how to be successful in the exciting arena of computer vision.
Chris's Sessions
Navigating Data Tooling and Expertise To Achieve High-Quality AI Training Data
As enterprises navigate the data tooling ecosystem, many realize the need to activate two vital components to achieve high-quality AI training data – data tooling and a talented tooling workforce. Join this session to hear from data tooling experts as they unveil what makes enterprise-grade worthy tools, why data tools require super users, realization that one tool isn’t a fit for all and what lies ahead in the data tooling ecosystem.
Raj Aikat is iMerit’s newly appointed Chief Product and Technology Officer. Raj joins iMerit from Qualcomm, where he was Senior Director of Product Management. He has more than 18 years of technical and product experience across multiple verticals, including automotive, IOT, robotics and telecom. Before Qualcomm, he was the Director of Product at Brain Corporation, where he was responsible for scaling BrainOSTM, an autonomous mobile robot platform & ecosystem, as well as overseeing the development and commercialization of commercial cleaning and delivery robots globally.
Raj's Sessions
Overcoming the Obstacles of Achieving High Quality Data
Ready to reinvent ML DataOps 2.0? Join iMerit experts as they talk industry trends, insights, and challenges that hundreds of companies face as they work towards production ready AI. Learn about the 6 obstacles holding companies back from achieving high quality data and the innovative solutions needed to overcome them
Role of Automation in ML DataOps
Integrating intelligent automation across machine learning data operations simplifies data preparation, optimizes data workflows and saves significant time and resources. In this session, attendees will hear AI experts reveal the role automation plays in machine learning data operations today, and in the foreseeable future.
Michael received his Bachelor’s degree in Computer Science from Stanford University, where he concentrated in artificial intelligence, specifically natural language processing. He is the Product Manager for Applied Intuition’s sensor simulation and synthetic datasets products.
Michael's Sessions
Navigating Data Tooling and Expertise To Achieve High-Quality AI Training Data
As enterprises navigate the data tooling ecosystem, many realize the need to activate two vital components to achieve high-quality AI training data – data tooling and a talented tooling workforce. Join this session to hear from data tooling experts as they unveil what makes enterprise-grade worthy tools, why data tools require super users, realization that one tool isn’t a fit for all and what lies ahead in the data tooling ecosystem.
As SVP of Artificial Intelligence and Machine Learning at Unity Dr. Danny Lange leads the company’s innovation around AI and ML, focusing on bringing AI to simulation and gaming. Prior to joining Unity, Lange was the head of machine learning at Uber. Lange also served as General Manager of Amazon Machine Learning — an AWS product that offers Machine Learning as a Cloud Service. Before that, he was Principal Development Manager at Microsoft where he led a product team focused on large-scale Machine Learning for Big Data. Lange also spent 8 years on Speech Recognition Systems early in his career.
Danny's Sessions
Role of Automation in ML DataOps
Integrating intelligent automation across machine learning data operations simplifies data preparation, optimizes data workflows and saves significant time and resources. In this session, attendees will hear AI experts reveal the role automation plays in machine learning data operations today, and in the foreseeable future.
Josh Hollin is vice president of engineering for AMP Robotics. With nearly 30 years of operations, engineering and technology experience, he most recently served as vice president of engineering and global product launch teams at Flex, a leader in technology innovation, supply chain, and manufacturing solutions. He previously held operations and program management leadership roles at Flex, in addition to Garmin International and Nortek, Inc. Hollin received a bachelor’s degree in mechanical engineering from Drexel University.
Josh's Sessions
Unforeseen Challenges and Opportunities in Commercializing AI
AI powers a plethora of real-world applications, ranging from facial recognition and image detection to language translators and assistants like Siri and Alexa. As more companies harness the power of AI, many are navigating unforeseen challenges and opportunities in commercializing AI. Hear from AI experts as they dive into overcoming the obstacles – good and bad – that enable the widespread adoption of their AI applications.
Jeff Mills is the President and Chief of Revenue Operations at iMerit. Bringing more than 20 years of experience, Jeff oversees the global customer experience, revenue initiatives and delivery at iMerit. Previously, Jeff held leadership roles at Gengo, Criteo, Sojern and SideStep (acquired by Kayak). Jeff holds a strong passion for working with emerging technology companies, which began during his time at Yahoo! From 1998 to 2006, Jeff immersed himself in multiple phases of company growth across the commerce and media divisions at Yahoo!. In 2021, Jeff completed the Stanford University Graduate School of Business Executive Program.
Jeff's Sessions
Unforeseen Challenges and Opportunities in Commercializing AI
AI powers a plethora of real-world applications, ranging from facial recognition and image detection to language translators and assistants like Siri and Alexa. As more companies harness the power of AI, many are navigating unforeseen challenges and opportunities in commercializing AI. Hear from AI experts as they dive into overcoming the obstacles – good and bad – that enable the widespread adoption of their AI applications.
Jai Natarajan is the Vice President, Strategic Business Development at iMerit, a global AI data solutions company delivering high-quality data that powers machine learning and artificial intelligence applications for Fortune 500 companies. Bringing more than 24 years of experience, Jai works with more than 5500 data experts who label and enrich data at scale to help customers get better results from their machine learning algorithms. Jai works with iMerit’s partner ecosystem to develop iMerit’s solutions for its customers, and provides strategic inputs to the company. Previously, Jai worked at Lucasfilm and Sony, and founded Xentrix, an Emmy-winning animation studio. He is a board member of the Anudip Foundation. JaI has an M.S. in Computer Science from UCLA, and undergraduate degrees from Birla Institute of Technology and Science.
Jai's Sessions
The Stakes Are High: Best Practices For Deploying Responsible AI
AI is sparking significant change across industries worldwide, with projections to add more than $15 trillion to the global economy by 2030. By 2022, more than 60% of enterprises will have implemented machine learning, big data analytics, and related AI tools into their operations. Enterprises navigating the complexities of artificial intelligence– from data operations to full-scale commercialization–must do it with a focused, practical lens. Join this session to hear from IBM’s Former Chief AI Officer and newly appointed President of the Responsible AI Institute to gain insight into the best practices for deploying artificial intelligence responsibly.
Navigating Data Tooling and Expertise To Achieve High-Quality AI Training Data
As enterprises navigate the data tooling ecosystem, many realize the need to activate two vital components to achieve high-quality AI training data – data tooling and a talented tooling workforce. Join this session to hear from data tooling experts as they unveil what makes enterprise-grade worthy tools, why data tools require super users, realization that one tool isn’t a fit for all and what lies ahead in the data tooling ecosystem.
Sudeep George is the vice president of engineering at iMerit, where he develops production-ready frameworks for a data-centric approach to machine learning. He has a strong background in imaging sensors, computer vision and has built and manufactured multi-sensor computational imaging platforms for several market verticals.
Sudeep's Sessions
Why Solving Data Edge Cases is Key to Accelerating AI
It’s no secret that AI systems are being used in more and more high-stakes applications – self-driving cars, robotic surgery and more. As AI advances, it’s becoming critical to ensure that AI systems navigate real-world anomalies successfully. Join this session to hear veteran machine learning experts discuss what it takes to identify and solve data edge cases to maximize AI performance and achieve widespread adoption.
Anurag coaches tech driven companies on high grow strategies. He has operating experience in scaling companies from seed to IPO to $500 Million. His experience includes Fortune 10 companies – Google, P&G – as well as founder or operator roles at six B2B startups in data, cloud & AI technologies. He was also the Chief Marketing Officer at 2 public tech companies. Anurag has an MBA from Chicago Booth, and engineering degree from IIT in India.
Anurag's Sessions
It’s a Duo: Human-in-the-Loop and High-Quality Data Are Catalysts in ML and AI
With the proliferation of production AI, enterprises realize that when machine learning can’t solve the problem, humans need to intervene. To account for this gap, enterprises are integrating robust human-in–the-loop programs to improve their AI training data and precision of their models. Attendees will get a front-row seat to better understand why human-in-the-loop and high-quality data go hand-in-hand to achieve widespread production AI applications.
Anshuman Patnaik is the Deep Learning Lead at autonomous trucking company Embark. In his role, he oversees machine learning and deep learning development for the Embark Driver, the company’s autonomous driving software. He has been with Embark for over four years, and previously served as a software engineer focusing on perception. Anshuman is a seasoned engineer with deep tech industry experience. He previously worked at Amazon (A9.com) and Wish in various roles. He holds a degree in Systems Engineering from the University of Waterloo.
Anshuman's Sessions
Mastering Anomaly Detection and Test Case Generation in ML
Enterprises building AI applications recognize that mastering anomaly detection and test case generation brings incredible value to improving machine learning model performance. Join this session to hear ML experts unveil the challenges and opportunities of identifying data anomalies and generating test cases to optimize your machine learning.
Chris is the founder and CEO for CBC, a global transportation and technology consulting firm focused on helping cities, companies, universities, and governments around the world to advance autonomous transportation and other new mobility services. Chris has more than 25 years of experience in deploying aerospace, autonomous transportation, cybersecurity, and smart city innovations for companies such as Boeing, Cisco, Honeywell, Lucent, Microsoft, Raytheon, Real Networks, and the Volvo Group. Chris is currently a transportation/smart cities advisor for the Cities Today Institute helping cities around the world with transportation infrastructure and autonomous surface transportation planning. His CBC team has also developed and deployed self-driving car simulators for State DOTs/DMVs, universities, museums and other public facilities – to teach the public about self-driving vehicles. Previously, Chris was the Vice President of New Mobility at Keolis Group and helped launch the first open road #autonomous #shuttle service in North America. Chris is a national Board Member for the Association for Commuter Transportation. He’s also a global keynote speaker on topics ranging from smart cities and new mobility services to cybersecurity, alternative energy options and advancements in artificial intelligence.
Chris's Sessions
Mastering Anomaly Detection and Test Case Generation in ML
Enterprises building AI applications recognize that mastering anomaly detection and test case generation brings incredible value to improving machine learning model performance. Join this session to hear ML experts unveil the challenges and opportunities of identifying data anomalies and generating test cases to optimize your machine learning.
Andy Pavlo is an Associate Professor of Databaseology in the Computer Science Department at Carnegie Mellon University. His (unnatural) infatuation with database systems has inadvertently caused him to incur several distinctions, such as VLDB Early Career Award (2021), NSF CAREER (2019), Sloan Fellowship (2018), and the ACM SIGMOD Jim Gray Best Dissertation Award (2014). He is also the CEO & co-founder of the OtterTune database tuning start-up (2020). He earned his Ph.D. in 2013 at Brown University under Stan Zdonik and Mike Stonebraker.
Andy's Sessions
Current and Future State of ML DataOps Landscape
As enterprises dive deeper into commercializing AI applications to improve business efficiencies, many realize the massive transformation and increasing complexity of the machine learning data operations landscape. Catch this session to hear seed stage and growth stage venture capitalists share their perspectives on the current and future state of the machine learning data operations landscape.
Shyam Rajagopalan is a Co-founder & CTO of healthcare-focused conversational AI automation company, Infinitus Systems, Inc. Shyam is a seasoned technology leader having led impactful projects across several technical areas. Prior to Infinitus, as a software architect, Shyam designed, built and launched highly secure, high throughput systems for Snap Inc and Google’s login and security platforms. Shyam previously led the engineering team as Director of Engineering at the mobile intelligence startup Quettra (acquired by Similar Web). Shyam started his career at MIPS and Nvidia, designing and building high performance CPUs.
Shyam's Sessions
Unforeseen Challenges and Opportunities in Commercializing AI
AI powers a plethora of real-world applications, ranging from facial recognition and image detection to language translators and assistants like Siri and Alexa. As more companies harness the power of AI, many are navigating unforeseen challenges and opportunities in commercializing AI. Hear from AI experts as they dive into overcoming the obstacles – good and bad – that enable the widespread adoption of their AI applications.
Pavan Tripathi is a Partner and Co-Founder at Bregal Sagemount. Prior to Bregal Sagemount, Pavan was an investment banker and private equity investor at Goldman Sachs & Co. Most recently, he was a member of the growth equity team in Goldman Sachs’ Merchant Banking Division. Pavan graduated summa cum laude from the University of California, Los Angeles with a BS in Electrical Engineering and a BA in Economics, and received an MBA from the Stanford University Graduate School of Business.
Pavan's Sessions
Current and Future State of ML DataOps Landscape
As enterprises dive deeper into commercializing AI applications to improve business efficiencies, many realize the massive transformation and increasing complexity of the machine learning data operations landscape. Catch this session to hear seed stage and growth stage venture capitalists share their perspectives on the current and future state of the machine learning data operations landscape.
Lucas is the Product Manager for Ground Control, iMerit’s single source of truth platform for managing data annotation workflows through reporting, analytics, and insights. Prior to iMerit, he designed and launched mapping technology for self-driving cars and developed electronics systems for high-performance vehicles. When not working in the trenches of machine learning, either as an engineer or Product Manager, you can find Lucas experimenting with ML in a variety of side projects, like using computer vision to optimize human biomechanics.
Lucas's Sessions
Overcoming the Obstacles of Achieving High Quality Data
Ready to reinvent ML DataOps 2.0? Join iMerit experts as they talk industry trends, insights, and challenges that hundreds of companies face as they work towards production ready AI. Learn about the 6 obstacles holding companies back from achieving high quality data and the innovative solutions needed to overcome them
Viddu Devigere is the Head of Algorithms for Overstock.com and a veteran leader with a deep expertise in technology architecture, algorithms, and machine learning. In his current role, Devigere directs multiple teams of machine learning scientists, machine learning engineers, and software engineers, who work on problems spanning a diverse set of domains such as search relevancy, product ranking, pricing and promotion optimizations, personalization, merchandizing attribution, logistics and customer support. Devigere’s teams, which are geographically dispersed across the U.S., and in Ireland, work with cutting edge machine learning and data transformation tools deployed both on premise and in the cloud. Before joining Overstock, Devigere held leadership positions at Shopify, Affirm, and Macy’s. Besides data and machine learning, his expertise spans enterprise cloud and PaaS strategy to e- commerce search. Devigere holds a master’s degree in management of information systems from Texas A&M and a bachelor’s degree in computer science.
Vidyaranya's Sessions
Convergence of ML Ops and Data Pipelines
Machine learning has grown exponentially in the last decade, drastically transforming machine learning data operation efforts. Data scientists and professionals alike realize the daunting effort of converging healthy data pipelines with machine learning models to produce successful AI applications. Join this session to hear ML experts share how they are transforming their data pipelines across collection, preparation, management and development to better manage their data, improve ML throughput and create meaningful AI applications for their business.
Itai joined Autobrains in 2022 as VP of Technology leading the L4 business unit and to tackle innovative topics and technologies such as self-supervised learning, sensor fusion and radar perception. Itai has an extensive and diversified experience of over 15 years in machine learning, computer vision, signal processing, autonomous driving and aerospace fields from several automotive and defense companies. Prior to joining Autobrains, Itai was the CTO and Head of AI at Wisense Technologies working on high resolution 4D imaging radar and co-founded and lead AerialGuard as a CEO, developing advanced autonomous navigation systems for drones. Itai holds a PhD in artificial intelligence, researching deep learning approaches for radar signal processing and perception, from Bar-Ilan University. M.Sc. in Physics researching nonlinear optics and B.Sc. in Aerospace Engineering, both from Technion University. He is currently conducting research in the field of DNA storage in his Postdoc position in Computer Science department at the Technion University. Itai holds multiple patents and publications in top tier journals such as Science and Nature on the topics of sensors, AV and AI and has received several technical as well as innovation awards.
Dr. Itai's Sessions
Why Solving Data Edge Cases is Key to Accelerating AI
It’s no secret that AI systems are being used in more and more high-stakes applications – self-driving cars, robotic surgery and more. As AI advances, it’s becoming critical to ensure that AI systems navigate real-world anomalies successfully. Join this session to hear veteran machine learning experts discuss what it takes to identify and solve data edge cases to maximize AI performance and achieve widespread adoption.