Economics

Datacast Episode 129: Early-Stage Product Management, Product-Led Revenue, and Startup Monologues with Diana Hsieh

Datacast Episode 129: Early-Stage Product Management, Product-Led Revenue, and Startup Monologues with Diana Hsieh

Diana Hsieh is the Head of Product and Co-Founder at Correlated. She thrives on working with enterprise software startups. She was previously the first PM at infrastructure startups, including Cockroach Labs and Timescale. Before that, she was a VC at Norwest, focused on investing in early-stage enterprise software companies. Diana is always on the hunt for her next favorite coffee shop on weekends.

Datacast Episode 109: Developer Productivity, Real-Time Data Infrastructure, and The Fat-Tailed Nature of Enterprise Software with Nnamdi Iregbulem

Datacast Episode 109: Developer Productivity, Real-Time Data Infrastructure, and The Fat-Tailed Nature of Enterprise Software with Nnamdi Iregbulem

Nnamdi Iregbulem, a Partner at Lightspeed Venture Partners, is a self-taught programmer and lifelong technology nerd. His mission is to increase total software output by supporting entrepreneurs building technical tools for technical people. He focuses on investments in technical enterprise software such as developer tools, application infrastructure, and machine learning.

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 92: Analytics Engineering, Locally Optimistic, and Marketing-Mix Modeling with Michael Kaminsky

Datacast Episode 92: Analytics Engineering, Locally Optimistic, and Marketing-Mix Modeling with Michael Kaminsky

Michael Kaminsky is the co-founder of Recast, a marketing optimization platform, and the co-founder of Analytics Engineers Club, a training course for data analysts looking to improve their engineering skills. He is passionate about helping organizations “make better decisions faster.” He has experience applying econometric research methods to environmental economics, child welfare policy, and medical treatment efficacy. He studies Spanish, reads, and pets dogs around Mexico City in his spare time.

Datacast Episode 86: Risk Management, Open-Source Governance, and Negative Engineering with Jeremiah Lowin

Datacast Episode 86: Risk Management, Open-Source Governance, and Negative Engineering with Jeremiah Lowin

Jeremiah Lowin is the Founder & CEO of Prefect, a dataflow automation company. Before starting Prefect, Jeremiah gained extensive experience in all aspects of the modern data stack as a director of risk management, machine learning researcher, and data scientist at a number of institutional investment firms. Today, he lives with his wife and two sons in Washington, DC.

Datacast Episode 43: From Economics and Operations Management to Data Science with Francesca Lazzeri

Datacast Episode 43: From Economics and Operations Management to Data Science with Francesca Lazzeri

Francesca Lazzeri, Ph.D., is an experienced scientist and machine learning practitioner with over 12 years of academic and industry experience. She is the author of several publications, including technology journals, conferences, and books. She currently leads an international team of cloud advocates and developers at Microsoft, managing an extensive portfolio of customers in the academic/education sector, and building intelligent automated solutions on the Cloud.

Before joining Microsoft, she was a research fellow at Harvard University in the Technology and Operations Management Unit. She is also an advisory board member of the Global Women in Data Science (WiDS) initiative, a machine learning mentor at the Massachusetts Institute of Technology and Columbia University, and an active member of the AI community.

Datacast Episode 32: Economics, Data For Good, and AI Research with Sara Hooker

Datacast Episode 32: Economics, Data For Good, and AI Research with Sara Hooker

Sara Hooker is a researcher at Google Brain doing deep learning research on reliable explanations of model predictions for black-box models. Her main research interests gravitate towards interpretability, model compression, and security. In 2014, she founded Delta Analytics, a non-profit dedicated to bringing technical capacity to help non-profits across the world use machine learning for good. She grew up in Africa, in Mozambique, Lesotho, Swaziland, South Africa, and Kenya. Her family now lives in Monrovia, Liberia.

Datacast Episode 23: Machine Learning for Finance with Jannes Klaas

Datacast Episode 23: Machine Learning for Finance with Jannes Klaas

Jannes is a data scientist at QuantumBlack and author of the book "Machine Learning For Finance". He previously studied financial economics at Oxford University where he wrote his thesis on the predictability of banking stress tests.