AI EXPO & DEMO HALL

Graham Neubig is an associate professor at the Language Technologies Institute of Carnegie Mellon University. His research focuses on multilingual natural language processing, natural language interfaces to computers, and machine learning methods for NLP, with the final goal of every person in the world being able to communicate with each-other, and with computers in their own language. He also contributes to making NLP research more accessible through open publishing of research papers, advanced NLP course materials and video lectures, and open-source software, all of which are available on his web site.

Jun Zeng is HP’s Distinguished Technologist and founding manager of the 3D Digital Twin group. Jun has 20 years of industrial experiences in creating and commercializing software for improving cyber-physical systems. ​His publications include a co-edited book on computer-aided Design and a co-authored book on digital factory, and 50+ peer-reviewed papers.​ He has 58 U.S. patents granted and more pending. His academic training includes Ph.D. in mechanical engineering and M.S. in computer science, both from Johns Hopkins University. ​ He is ACM member, and IEEE senior member. ​

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive, and biological sciences; in recent years, he has focused on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines, and applications to problems in distributed computing systems, natural language processing, signal processing, and statistical genetics. Previously, he was a professor at MIT. Michael is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences and a fellow of the American Association for the Advancement of Science, the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA, and SIAM. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. Michael holds a master’s degree in mathematics from Arizona State University and a PhD in cognitive science from the University of California, San Diego.

Ion Stoica is a Professor in the EECS Department at University of California at Berkeley. He does research on cloud computing and networked computer systems. Past work includes Apache Spark, Apache Mesos, Tachyon, Chord DHT, and Dynamic Packet State (DPS). He is an ACM Fellow and has received numerous awards, including the SIGOPS Hall of Fame Award (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001). In 2013, he co-founded Databricks a startup to commercialize technologies for Big Data processing, and in 2006 he co-founded and Conviva, a startup to commercialize technologies for large scale video distribution.

Kay Firth-Butterfield is Head of Artificial Intelligence and a member of the Executive Committee at the World Economic Forum and is one of the foremost experts in the world on the governance of AI. She is a Barrister, former Judge and Professor, technologist and entrepreneur who has an abiding interest in how humanity can equitably benefit from new technologies, especially AI. Kay is an Associate Barrister (Doughty Street Chambers), Master of the Inner Temple, London and serves on the Lord Chief Justice’s Advisory Panel on AI and Law. She co-founded AI Global and was the world’s first Chief AI Ethics officer in 2014 and created the AIEthics twitter hashtag. Kay is Vice-Chair of The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems and was part of the group which met at Asilomar to create the Asilomar AI Ethical Principles. She is on the Polaris Council for the Government Accountability Office (USA), the Advisory Board for UNESCO International Research Centre on AI and AI4All. Kay has advanced degrees in Law and International Relations and regularly speaks to international audiences addressing many aspects of the beneficial and challenging technical, economic and social changes arising from the use of AI. She has been consistently recognized as a leading woman in AI since 2018 and was featured in the New York Times as one of 10 Women Changing the Landscape of Leadership.

Dr. Inchiosa’s passion for AI drives his work as Principal Data Scientist Manager in Azure Data’s Advanced Workload Engineering team, where he leads a team of data scientists focused on AI-led co-innovation engagements with strategic customers and partners. Previously, Mario served as Revolution Analytics’ Chief Scientist and as Analytics Architect in IBM’s Big Data organization, where he worked on advanced analytics in Hadoop, Teradata, and R. Prior to that, Mario was US Chief Scientist in Netezza Labs, bringing advanced analytics and R integration to Netezza’s SQL-based data warehouse appliances. He also served as US Chief Science Officer at NuTech Solutions, a computer science consultancy specializing in simulation, optimization, and data mining, and Senior Scientist at BiosGroup, a complexity science spin-off of the Santa Fe Institute. Mario holds Bachelor’s, Master’s, and PhD degrees in Physics from Harvard University. He has been awarded four patents and has published over 30 research papers, earning Publication of the Year and Open Literature Publication Excellence awards.

Dr. Inchiosa’s passion for AI drives his work as Principal Data Scientist Manager in Azure Data’s Advanced Workload Engineering team, where he leads a team of data scientists focused on AI-led co-innovation engagements with strategic customers and partners. Previously, Mario served as Revolution Analytics’ Chief Scientist and as Analytics Architect in IBM’s Big Data organization, where he worked on advanced analytics in Hadoop, Teradata, and R. Prior to that, Mario was US Chief Scientist in Netezza Labs, bringing advanced analytics and R integration to Netezza’s SQL-based data warehouse appliances. He also served as US Chief Science Officer at NuTech Solutions, a computer science consultancy specializing in simulation, optimization, and data mining, and Senior Scientist at BiosGroup, a complexity science spin-off of the Santa Fe Institute. Mario holds Bachelor’s, Master’s, and PhD degrees in Physics from Harvard University. He has been awarded four patents and has published over 30 research papers, earning Publication of the Year and Open Literature Publication Excellence awards.

Monica Lam is a Professor in the Computer Science Department at Stanford University since 1988. She is the faculty director of the Open Virtual Assistant Lab (OVAL). She received a B.Sc. from University of British Columbia in 1980 and a Ph.D. in Computer Science from Carnegie Mellon University in 1987. Monica is a Member of the National Academy of Engineering and an ACM Fellow. She is a co-author of the popular text Compilers, Principles, Techniques, and Tools (2nd Edition), also known as the Dragon book. Professor Lam’s current research is on conversational virtual assistants with an emphasis on privacy protection. Her research uses deep learning to map task-oriented natural language dialogues into formal semantics, represented by a new executable programming language called ThingTalk. Her Almond virtual assistant, trained on open knowledge graphs and IoT API standards, can be easily customized to perform new tasks. She is leading an Open Virtual Assistant Initiative to create the largest, open, crowdsourced language semantics model to promote open access in all languages. Her decentralized Almond virtual assistant that supports fine-grain sharing with privacy has received Popular Science’s Best of What’s New Award in Security in 2019.

Prof. Lam is also an expert in compilers for high-performance machines. Her pioneering work of affine partitioning provides a unifying theory to the field of loop transformations for parallelism and locality. Her software pipelining algorithm is used in commercial systems for instruction level parallelism. Her research team created the first, widely adopted research compiler, SUIF. Her contributions in computer architecture include the CMU Warp Systolic Array and the Stanford DASH Distributed Memory Multiprocessor. She was on the founding team of Tensilica, now a part of Cadence.

She received an NSF Young Investigator award in 1992, the ACM Most Influential Programming Language Design and Implementation Paper Award in 2001, an ACM SIGSOFT Distinguished Paper Award in 2002, the ACM Programming Language Design and Implementation Best Paper Award in 2004, the ACM SIGARCH/SIGPLAN/SIGOPS ASPLOS Influential Paper Awards in two consecutive years, 2021 and 2022. She was the author of two of the papers in “20 Years of PLDI–a Selection (1979-1999)”, and one paper in the “25 Years of the International Symposia on Computer Architecture”. She received the University of British Columbia Computer Science 50th Anniversary Research Award in 2018.

Zoe Steinkamp is a developer Advocate for influxData. She was a front end software engineer for over 6 years before she moved into a developer advocate role. She has been with InfluxDB for over 3 years and she looks forward to sharing her knowledge of the platform and databases. She enjoys learning about awesome new technologies and doing at home tech projects to help make her life as well as other people’s lives easier. Her passions besides new technology include traveling and gardening.

Alex first joined HP as a program manager on the Advanced Compute Solutions OEM team, providing extended life hardware solutions to critical partners across industry. Now, Alex is the product manager for Data Science Workstations. Alex manages Data Science Hardware, operating systems, and the Z by HP Data Science Stack Manager. Prior to HP, Alex received his MBA from the University of Texas-Austin and served in the United States Army.

Steve is the Software Engineering Lead for HP’s Data Science Solutions Team. For two years he’s been curating HP’s Data Science Stack and building processes to ensure compatibility across HP workstations. He studied Computer Science at Colorado State University, and no matter the season – he tries his hardest to get lost in the Rocky Mountains.

Jun Zeng is HP’s Distinguished Technologist and founding manager of the 3D Digital Twin group. Jun has 20 years of industrial experiences in creating and commercializing software for improving cyber-physical systems. ​His publications include a co-edited book on computer-aided Design and a co-authored book on digital factory, and 50+ peer-reviewed papers.​ He has 58 U.S. patents granted and more pending. His academic training includes Ph.D. in mechanical engineering and M.S. in computer science, both from Johns Hopkins University. ​ He is ACM member, and IEEE senior member. ​

Nick is a passionate machine learning, data science, and MLOps enthusiast with experience across multiple domains including fraud detection, natural language processing, computer vision, and data mining. Nick holds a BSc. in Cognitive Science with a specialization in ML and Neural Computation from University of California, San Diego. He is an AWS Certified Solutions Architect, and has earned certifications in Python, Pytorch, Apache Airflow, PySpark and other frameworks. Currently, Nick acts as pre-sales MLOps Engineer at Iguazio, where he specializes in helping enterprises create real-world impact with their data science initiatives, with expertise in deployments on AWS, GCP, and Azure as well as on-premise Kubernetes architecture. Nick speaks at global industry events and blogs about MLOps, data science and ML Engineering.

Savita is Data & AI evangelist based out of San Francisco Bay Area, USA. Savita brings 15 years of experience as a Technology professional during which she worked on Microsoft platform architecting and developing applications, automating solutions and integrations across Azure, M365, Power Platform and Teams.

She believes AI is a game changing development in human history which can solve some of the most daunting challenges humanity faces today. This idea drives her to relentlessly engage with customers, educate them on the potential of AI, showcase practical use cases and ultimately get them excited and thinking about how they can use AI to solve their organization’s pressing challenges.

Sandeep Agrawal leads the HeatWave Machine Learning (HeatWave ML) project within MySQL HeatWave. HeatWave ML is the product of years of research and advanced development, and aims to help both data scientists and non-data scientists quickly apply ML to a given problem. Prior to HeatWave, Sandeep led the Oracle AutoML project within Oracle labs, creating a state-of-the-art distributed AutoML engine. He is passionate about Machine Learning and Systems Architecture, and a project like HeatWave ML that combines the two is heaven for him. Prior to Oracle, he completed his PhD in Computer Science from Duke University in 2015.

Salil Pradhan is a Product Manager in MySQL HeatWave team. His interests include distributed data processing, machine learning, cloud computing, middleware technologies as well as application areas such as Marketing Automation and Supply Chain Management.

Sandeep Agrawal leads the HeatWave Machine Learning (HeatWave ML) project within MySQL HeatWave. HeatWave ML is the product of years of research and advanced development, and aims to help both data scientists and non-data scientists quickly apply ML to a given problem. Prior to HeatWave, Sandeep led the Oracle AutoML project within Oracle labs, creating a state-of-the-art distributed AutoML engine. He is passionate about Machine Learning and Systems Architecture, and a project like HeatWave ML that combines the two is heaven for him. Prior to Oracle, he completed his PhD in Computer Science from Duke University in 2015.

Salil Pradhan is a Product Manager in MySQL HeatWave team. His interests include distributed data processing, machine learning, cloud computing, middleware technologies as well as application areas such as Marketing Automation and Supply Chain Management.

Alex first joined HP as a program manager on the Advanced Compute Solutions OEM team, providing extended life hardware solutions to critical partners across industry. Now, Alex is the product manager for Data Science Workstations. Alex manages Data Science Hardware, operating systems, and the Z by HP Data Science Stack Manager. Prior to HP, Alex received his MBA from the University of Texas-Austin and served in the United States Army.

Steve is the Software Engineering Lead for HP’s Data Science Solutions Team. For two years he’s been curating HP’s Data Science Stack and building processes to ensure compatibility across HP workstations. He studied Computer Science at Colorado State University, and no matter the season – he tries his hardest to get lost in the Rocky Mountains.

Yotam is a machine learning and deep learning expert with extensive hands-on experience in neural network development. Prior to co-founding Tensorleap, Yotam developed and led AI and Big Data projects from research to production for companies in the automotive and other sectors, as well as developing machine learning algorithms for large government projects, including the Soreq Nuclear Research Center (Israel).

Dmitry Petrov is an ex-Data Scientist at Microsoft with Ph.D. in Computer Science and active open source contributor. He has written and open sourced the first version of DVC.org – machine learning workflow management tool. Also he implemented Wavelet-based image hashing algorithm (wHash) in open source library ImageHash for Python. Now Dmitry is working on tools for machine learning and ML workflow management as a co-founder and CEO of Iterative in San Francisco.

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.

Mr. Yurchisin has over ten years’ experience applying operations research, machine learning, statistics, and data visualization to improve decision making. Before joining Gurobi, Jerry (who also goes by Jerome) was a Senior Consultant at OnLocation, Inc. where he customized several linear programming models within the National Energy Modeling System (NEMS) to analyze implementing specific energy policies and utilizing new technologies.

Prior to OnLocation, Jerry was an Operations Research Analyst & Data Scientist at Booz Allen Hamilton for over seven years. There he formulated scheduling and staffing integer programming models for the US Coast Guard, as well as led a project to quantify the maritime risks of offshore energy installations with the Research & Development Center. Further, Jerry was the technical lead on several Coast Guard studies including Living Marine Resources and Maritime Domain Awareness, providing statistical analysis and building supervised and unsupervised machine learning models. He also performed statistical analyses, machine learning modeling, and data visualization for cyberspace directorates at DoD and DHS.

Jerry has several years of experience teaching a wide variety of college-level mathematics and statistics courses and has a passion for education. He also enjoys golfing, biking, and writing about sports from an analytics point of view. He lives in Alexandria, Virginia with his wife, son, and two dogs.

Jerry holds B.S., Ed. and M.S., Mathematics degrees from Ohio University and an M.S. in Operations Research and Statistics from The University of North Carolina at Chapel Hill.

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.

Pete spent more than two decades on Wall Street, growing, and running automated trading groups. In 2005, he was the founding CEO of Walleye Capital, a multi-billion-dollar quant fund that derives value at the intersection of real-time data and automated applications. In 2017, Pete and some engineers spun a proprietary data engine out of Walleye, forming an independent company called Deephaven Data Labs. Deephaven is an open-first software shop, delivering a real-time query engine, APIs, UIs, and integrations to the community via open projects designed for diverse teams. Deephaven complements streaming technologies and makes dynamic data easy and accessible.

Bio Coming Soon!

As a member of the Neo4j Field Engineering team, Stuart brings 15 years of experience helping many Global 2000 organizations solve their business challenges leveraging semantic technologies, natural language processing, search and graphs. In addition, he has experience across a wide range of industries, including healthcare, finance, manufacturing and retail. Based in the Bay Area, Stuart works with large enterprise companies including Wells Fargo, eBay, Visa, Adobe, Genentech, Kaiser and Cisco.

Justin Emerson is a Principal Technology Evangelist at Pure Storage focused on the FlashBlade product portfolio. He joined Pure in 2020 as a FlashBlade Data Architect for the San Francisco Bay Area. Prior to that, he worked at storage-focused reseller partners for more than a decade.

Jake is currently working as a Senior Product Marketing Manager over ML Lifecycle products at Cloudera. Before joining Cloudera, Jake worked as a Data Scientist and Solution Architect at ExxonMobil. Additionally, he worked as a Senior Data Scientist at FarmersEdge. Before starting his professional career, Jake obtained his bachelor’s and master’s degree from Brigham Young University. When he isn’t working, Jake enjoys skiing, golfing, and spending time with his family in the mountains.

Zoe Steinkamp is a developer Advocate for influxData. She was a front end software engineer for over 6 years before she moved into a developer advocate role. She has been with InfluxDB for over 3 years and she looks forward to sharing her knowledge of the platform and databases. She enjoys learning about awesome new technologies and doing at home tech projects to help make her life as well as other people’s lives easier. Her passions besides new technology include traveling and gardening.

Audrey Reznik has been in the IT industry (private and public sectors) for 27 years in multiple verticals. In the last 4 years, she worked as a Data Scientist at ExxonMobil where she created a Data Science Enablement team to help data scientists easily deploy ML models in a Hybrid Cloud environment. Audrey was instrumental in educating scientists about what the OpenShift platform was and how to use OpenShift containers (images) to organize, run, and visualize data analysis results. Audrey now works as a Data Scientist with the Red Hat Data Science Team where she is focused on next-generation applications. She is passionate about Data Science and in particular the current opportunities with ML and Federated Data.

John is a Data Architect at Airbyte where he enjoys helping companies move data from where it’s created to where they want it to live. Before AIrbyte he worked as a Global Solutions Architect at LiveRamp where he helped companies activate data to transform customer experiences. Besides being in the weeds about data, John is an avid bike rider and golfer.

Hugo Shi a data science leader with 15 years of experience with data science and software projects at companies ranging from JP Morgan to the Chicago Trading Company. He is the CTO and co-founder of Saturn Cloud where he helps to make sure that Saturn Cloud is secure, scalable and easy to use for all data science teams. Hugo has a PhD in Signal Processing and his academic research focused on iterative reconstruction algorithms in medical imaging.

Aaron is our Director of Solutions Engineering at Appen. He works closely with the Sales and Solutions teams to manage Fortune 500 deals through the pipeline. Aaron has lived in 7 cities around the world and is a geek at heart. He loves solving problems, breaking new technologies and identifying opportunities where technology can have a real impact on how we get things done.

Peter is VP of Engineering at Mindtech. Peter has many years of experience in semiconductors, with expertise in AI, GPU and VR/AR.  Working at companies including Highwai, Imagination Technologies and ST.  Peter has also been highly active in Khronos, including chairing the NNEF working group.

Vincent has 30+ years as AI specialist with ILOG and IBM. He has mentored several Data Science teams. Vincent has designed/modeled several major AI projects for customers such as Samsung. Electronics, McDonald’s, Dassault Aviation, Carhartt, Toyota, TSMC, Disney, etc. He is skilled in Mathematical Modeling, Machine Learning, Time Series prediction. He has strong experience in Manufacturing, Retail & Logistics industries. His main objective is to “Help companies go beyond AI pilots and be successful in bringing AI to their end-users”. He received his Msc in Comp. Science & AI from Paris-Saclay University.

Martin has over 30 years of experience in Data Science, AI, Decision Optimization. He worked as Consulting Project Manager, Technical Sales, Data Scientist with organizations including ILOG, IBM, Manhattan Associates, Emptoris. He has strong modeling skills in constraint programming, mathematical programming, machine learning. He is skilled in C++, Java, Python. Martin’s main objective is to help organizations identify and deploy analytics that maximize ROI. He was selected as INFORMS Franz Edelman Award finalist. He has studied M.S. in Operations Research from Massachusetts Institute of Technology.

Florian Jacta is a specialist of Taipy, a low-code open-source Python package enabling any Python developers to easily develop a production-ready AI application. Package pre-sales and after-sales functions. He is data Scientist for Groupe Les Mousquetaires (Intermarche) and ATOS. He developed several Predictive Models as part of strategic AI projects. Also, Florian got his master’s degree in Applied Mathematics from INSA, Major in Data Science and Mathematical Optimization.

Mike Wong is a Solutions Engineer at Unravel Data helping customers navigate the challenges of the modern data economy and optimize complex data stack. Previously, he spent nearly 20 years as a solution architect in a range of technology roles from PLM to Hadoop. His robust experience in the DataOps domain allows Mike to help customers achieve their vision with data applications and infrastructure.

As a member of the Neo4j Field Engineering team, Stuart brings 15 years of experience helping many Global 2000 organizations solve their business challenges leveraging semantic technologies, natural language processing, search and graphs. In addition, he has experience across a wide range of industries, including healthcare, finance, manufacturing and retail. Based in the Bay Area, Stuart works with large enterprise companies including Wells Fargo, eBay, Visa, Adobe, Genentech, Kaiser and Cisco.

Bio Coming Soon!

Pete spent more than two decades on Wall Street, growing, and running automated trading groups. In 2005, he was the founding CEO of Walleye Capital, a multi-billion-dollar quant fund that derives value at the intersection of real-time data and automated applications. In 2017, Pete and some engineers spun a proprietary data engine out of Walleye, forming an independent company called Deephaven Data Labs. Deephaven is an open-first software shop, delivering a real-time query engine, APIs, UIs, and integrations to the community via open projects designed for diverse teams. Deephaven complements streaming technologies and makes dynamic data easy and accessible.

Ben is a machine learning solutions consultant with W&B. He trains our customers to use W&B and works with them to improve their machine learning workflow. Prior to joining W&B he was training models and developing ml infrastructure for Samsung Research.

Oryan is a ֿLead Software Engineer with a passion for Machine Learning and DevOps, with 7 years of experience developing services for production and development environments and leading teams.

Bob has worked with the HPCC Systems technology platform and the ECL programming language for over a decade and has been a technical trainer for over 30 years. He is the developer and designer of the HPCC Systems Online Training Courses and is the Senior Instructor for all classroom and remote based training.

Bob has worked with the HPCC Systems technology platform and the ECL programming language for over a decade and has been a technical trainer for over 30 years. He is the developer and designer of the HPCC Systems Online Training Courses and is the Senior Instructor for all classroom and remote based training.

Hugo Shi a data science leader with 15 years of experience with data science and software projects at companies ranging from JP Morgan to the Chicago Trading Company. He is the CTO and co-founder of Saturn Cloud where he helps to make sure that Saturn Cloud is secure, scalable and easy to use for all data science teams. Hugo has a PhD in Signal Processing and his academic research focused on iterative reconstruction algorithms in medical imaging.

Chase is a solutions architect at Arrikto with a passion for connecting people to technical solutions that can prevent them from wasting precious time and mental energy- solving the same problems over and over. Chase is a certified Kubernetes Administrator, Developer, and Security Specialist who works to help clients reduce MLOps friction and toil while ensuring the “non-negotiables” are enforced to provide the best return on their production models.

Souheil is the Head of Field Data Science at Arrikto where he helps build machine learning solutions for clients. Previously, Souheil worked at Freddie Mac and Capital One where he built models and machine learning platforms. Prior to becoming a data scientist, he spent 15 years in academia working on MRI and Brain Imaging. Souheil holds a BS and PhD in Physics from Yale and MIT respectively.

Bio Coming Soon!

Bio Coming Soon!

Radu is an engineering manager in Azure Machine Learning at Microsoft, where he works on AI infrastructure for Deep Learning. Most recently, he has been leading the team that develops Azure AutoML’s computer vision capabilities – PyTorch deep learning models for image classification, object detection and segmentation. Prior to this, he designed and led the implementation of HyperDrive, Azure ML’s distributed hyperparameter tuning system. In previous roles, Radu has worked on different projects, ranging from search engine infrastructure to information retrieval and data mining. He holds a PhD in compilers and programing language design from INRIA Nancy, France.

Phani is a Senior Software Engineer at Microsoft. He has been working with Azure Machine Learning team for the past 5 years working on services for Hyperparameter tuning and Automated Machine Learning.

Danny D. Leybzon has worn many hats, all of them related to data. He studied computational statistics at UCLA and has worked in the data and ML space ever since. In his role as MLOps architect, he has worked to evangelize machine learning best practices, talking on subjects such as distributed deep learning, productionizing machine learning models, automated machine learning, and lately has been talking about AI observability and data logging. When Danny’s not researching, practicing, or talking about data science, he’s usually doing one of his numerous outside hobbies: rock climbing, backcountry backpacking, skiing, etc.

Shayan Mohanty is the CEO and Co-Founder of Watchful, a company that largely automates the process of creating labeled training data. He’s spent over a decade of leading data engineering teams at various companies including Facebook, where he served as lead for the stream processing team responsible for processing 100% of the ads metrics data for all FB products. He is also a Guest Scientist at Los Alamos National Laboratory and has given talks on topics ranging from Automata Theory to Machine Teaching.

John is a Data Architect at Airbyte where he enjoys helping companies move data from where it’s created to where they want it to live. Before AIrbyte he worked as a Global Solutions Architect at LiveRamp where he helped companies activate data to transform customer experiences. Besides being in the weeds about data, John is an avid bike rider and golfer.

Bio Coming Soon!