Research

Datacast Episode 130: Towards Accessible Data Analysis with Emanuel Zgraggen

Datacast Episode 130: Towards Accessible Data Analysis with Emanuel Zgraggen

Before founding Einblick in 2020, Emanuel was a postdoc in the database group at MIT and got his Ph.D. in Computer Science from Brown University. He worked on various interactive tools for visual data exploration and analysis during this time. Most of them either influenced or are direct predecessors of Einblick.

Before coming to the US, Emanuel worked as a Software and Data Engineer for various financial companies in Zurich. He tried his luck as a freelancer building in-studio touchscreen installations for Swiss National TV and developing a Spotify clone that failed miserably.

Datacast Episode 118: Overcoming Hardships, Confident Learning, Dataset Improvement, and The Ph.D. Rapper with Curtis Northcutt

Datacast Episode 118: Overcoming Hardships, Confident Learning, Dataset Improvement, and The Ph.D. Rapper with Curtis Northcutt

Curtis Northcutt is an American computer scientist and entrepreneur focusing on AI to empower people. He is the CEO and Co-Founder of Cleanlab, building next-generation data-centric AI and open-source technologies that enable AI to work with real-world, messy data.

He completed his Ph.D. at MIT, where he invented confident learning to automatically find label issues in any dataset. Curtis received the MIT thesis award, NSF Fellowship, and Goldwater Scholarship for his work. Before Cleanlab, he worked in AI research teams at Google, Oculus, Amazon, Facebook, Microsoft, and NASA.

Datacast Episode 112: Distributed Systems Research, The Philosophy of Computational Complexity, and Modern Streaming Database with Arjun Narayan

Datacast Episode 112: Distributed Systems Research, The Philosophy of Computational Complexity, and Modern Streaming Database with Arjun Narayan

Arjun Narayan is the co-founder and CEO of Materialize. Materialize is a streaming database for real-time applications and analytics, built on top of a next-generation stream processor – Timely Dataflow. He was previously an engineer at Cockroach Labs and held a Ph.D. in Computer Science from the University of Pennsylvania.

Datacast Episode 111: Astrophysics, Visualization Recommendation, and Scalable Data Science with Doris Lee

Datacast Episode 111: Astrophysics, Visualization Recommendation, and Scalable Data Science with Doris Lee

Doris Lee is the co-founder and CEO of Ponder, a startup delivering scalable, enterprise-ready pandas that improve the productivity of data teams. She graduated with her Ph.D. from UC Berkeley RISE Lab in 2021, where she developed data science tools to accelerate insight discovery.

Datacast Episode 103: Computational Economics, Statistical Arbitrage, and Adaptable Data Consolidation with Eric Daimler

Datacast Episode 103: Computational Economics, Statistical Arbitrage, and Adaptable Data Consolidation with Eric Daimler

Dr. Eric Daimler is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Eric has co-founded six technology companies that have done pioneering work in areas ranging from software systems to statistical arbitrage.

As a Presidential Innovation Fellow during the Obama Administration, Eric helped drive the agenda for U.S. leadership in research, commercialization, and public adoption of AI. He has also served as the Assistant Dean and an Assistant Professor of Software Engineering at Carnegie Mellon’s School of Computer Science. He specializes in public policy and economics, helped launch Carnegie Mellon’s Silicon Valley Campus, and founded its Entrepreneurial Management program. His academic research focuses on the intersection of Machine Learning, Computational Linguistics, and Network Science.

As a frequent keynote speaker, Eric has presented at venues including the engineering schools of MIT, Stanford, and Harvard. He studied at Stanford University, the University of Washington-Seattle, and Carnegie Mellon University, where he earned his Ph.D. in its School of Computer Science.

Datacast Episode 100: Data-Centric Computer Vision, Productizing AI, and Scaling a Global Startup with Hyun Kim

Datacast Episode 100: Data-Centric Computer Vision, Productizing AI, and Scaling a Global Startup with Hyun Kim

Hyun Kim is the co-founder and CEO of Superb AI, an ML DataOps platform that helps computer vision teams automate and manage the entire data pipeline: from ingestion and labeling to data quality assessment and delivery. He initially studied Biomedical Engineering and Electrical Engineering at Duke but shifted from genetic engineering to robotics and deep learning. He then pursued a Ph.D. in computer science at Duke with a focus on Robotics and Deep Learning but ended up taking leave to further immerse himself in the world of AI R&D at a corporate research lab. During this time, he started to experience the bottlenecks and obstacles that many companies still face to this day: data labeling and management were very manual, and the available solutions were nowhere near sufficient.

Datacast Episode 97: Escaping Poverty, Embracing Digital Learning, Benchmarking ML Systems, and Advancing Data-Centric AI with Cody Coleman

Datacast Episode 97: Escaping Poverty, Embracing Digital Learning, Benchmarking ML Systems, and Advancing Data-Centric AI with Cody Coleman

Cody Coleman is the Founder and CEO of Coactive AI. He is also a co-creator of DAWNBench and MLPerf and a founding member of MLCommons. His work spans from performance benchmarking of hardware and software systems to computationally efficient methods for active learning and core-set selection. He holds a Ph.D. in Computer Science from Stanford University, where Professors Matei Zaharia and Peter Bailis advised him, and an MEng and BS from MIT.

Datacast Episode 94: Modern Metadata Management, Open-Source Adoption, and Early-Stage Culture with Mars Lan

Datacast Episode 94: Modern Metadata Management, Open-Source Adoption, and Early-Stage Culture with Mars Lan

Mars Lan is the co-founder and CTO of Metaphor Data, a startup that offers a Modern Metadata Platform to solve many complex organizational data challenges, such as data discovery and data governance. He also co-created the popular open-source project DataHub while working as the Tech Lead of the metadata team at LinkedIn. Mars received his Ph.D. in Computer Science from the University of California, Los Angeles.

What I Learned From Convergence 2022

What I Learned From Convergence 2022

Last week, I attended Comet ML’s Convergence virtual event. The event features presentations from data science and machine learning experts, who shared their best practices and insights on developing and implementing enterprise ML strategies. There were talks discussing emerging tools, approaches, and workflows that can help you effectively manage an ML project from start to finish.

In this blog recap, I will dissect content from the event’s technical talks, covering a wide range of topics from testing models in production and data quality assessment to operational ML and minimum viable model.

Datacast Episode 82: Enabling AI-Powered AR Navigation For Driving with Chen-Ping Yu

Datacast Episode 82: Enabling AI-Powered AR Navigation For Driving with Chen-Ping Yu

Dr. Chen-Ping Yu is the co-founder and CEO of Phiar, a company that is bringing human-like perception to every vehicle with its advanced lightweight spatial AI. Prior to founding Phiar, he was a postdoctoral fellow at Harvard University, researching neuro-inspired deep learning.

Chen-Ping received his Ph.D. from Stony Brook University in Computer Vision and Machine Learning and his MS from Penn State University. He was the recipient of numerous honors and awards, including from the NSF, and has published more than 15 scientific publications at top computer vision, AI, and cognitive science conferences and journals.