Events

Join us for a talk plus Q&A with Dr. Alonso Bucarey, Senior Data Scientist, Airbnb.

Speaker: At Airbnb, Dr. Bucarey focuses on the marketing space. Until recently he was a Sr Economist at Amazon where his work informed leadership across multiple organizations such as Amazon Web Services, Human Resources, and Groceries. He holds a PhD in economics from MIT.

Talk: Dr Bucarey will share his experience working as an economist in academia and at tech companies and present a case study on which he worked and for which he and his team developed and applied state of the art causal inference methodologies.

Audience: Students trained in fields such as economics, data science, statistics, and computer science who have an interest in causal inference and are considering a career in the tech industry.

Goal: When students transition from academia to working, they often experience a big gap between how causal inference is taught in the classroom and how it’s applied in the workplace. In the tech industry: (1) The causal question rarely comes fully formed—researchers must disambiguate the business problems. (2) Company data is messier and larger than what’s used in coursework. (3) Implementation choices depend on stakeholders, scale, and costs, not just technical correctness. (4) Communicating results effectively is as important as getting them right. This talk’s goal is to bridge the gap between theory and practice through real-world case studies.

Want more of this? This talk is one of seven talks with tech industry experts who have wide-ranging experience applying causal analysis at tech companies such as Airbnb, Amazon, Google, Meta, Twitter, Zillow, and Wayfair. This series of talks  is offered in conjunction with ECMA 31370 “Causal Analysis for Industry” taught by Dr. Melissa Tartari at the Kenneth C. Griffin Department of Economics.

Join us for a talk plus Q&A with Dr. Thomas Quan, Principal Economist, Wayfair.

Speaker: At Wayfair, Dr. Quan is the tech lead on Wayfair’s core pricing model. He is a subject matter expert on price optimization and demand modeling, estimation, and simulation. Formerly he was an Assistant Professor at the University of Georgia. He holds a PhD in economics from the University of Minnesota.

Talk: Dr Quan will share his experience working as an economist in academia and at tech companies and present a case study on which he worked and for which he and his team developed and applied state of the art causal inference methodologies.

Audience: Students trained in fields such as economics, data science, statistics, and computer science who have an interest in causal inference and are considering a career in the tech industry.

Goal: When students transition from academia to working, they often experience a big gap between how causal inference is taught in the classroom and how it’s applied in the workplace. In the tech industry: (1) The causal question rarely comes fully formed—researchers must disambiguate the business problems. (2) Company data is messier and larger than what’s used in coursework. (3) Implementation choices depend on stakeholders, scale, and costs, not just technical correctness. (4) Communicating results effectively is as important as getting them right. This talk’s goal is to bridge the gap between theory and practice through real-world case studies.

Want more of this? This talk is one of seven talks with tech industry experts who have wide-ranging experience applying causal analysis at tech companies such as Airbnb, Amazon, Google, Meta, Twitter, Zillow, and Wayfair. This series of talks  is offered in conjunction with ECMA 31370 “Causal Analysis for Industry” taught by Dr. Melissa Tartari at the Kenneth C. Griffin Department of Economics.

Join us for a talk plus Q&A with Dr. Liquan Huang, Head of Ad Ranking AI Ds-Core, Meta

Speaker: Dr Huang and her team leverage cutting-edge ML/AI technologies and influence Meta’s Ads revenue by enhancing the ads ranking systems to drive personalized and relevant ad experiences for billions of users. Formerly she was a Data Scientist at Google, an Economist at YouTube, and a Sr Economist at Amazon. She holds a PhD in economics from the University of Rochester.

Talk: Dr Quan will share her experience working as an economist at multiple tech companies and present a case study on which she worked and for which she and her team developed and applied state of the art causal inference methodologies.

Audience: Students trained in fields such as economics, data science, statistics, and computer science who have an interest in causal inference and are considering a career in the tech industry.

Goal: When students transition from academia to working, they often experience a big gap between how causal inference is taught in the classroom and how it’s applied in the workplace. In the tech industry: (1) The causal question rarely comes fully formed—researchers must disambiguate the business problems. (2) Company data is messier and larger than what’s used in coursework. (3) Implementation choices depend on stakeholders, scale, and costs, not just technical correctness. (4) Communicating results effectively is as important as getting them right. This talk’s goal is to bridge the gap between theory and practice through real-world case studies.

Want more of this? This talk is one of seven talks with tech industry experts who have wide-ranging experience applying causal analysis at tech companies such as Airbnb, Amazon, Google, Meta, Twitter, Zillow, and Wayfair. This series of talks  is offered in conjunction with ECMA 31370 “Causal Analysis for Industry” taught by Dr. Melissa Tartari at the Kenneth C. Griffin Department of Economics.

Join us for a talk plus Q&A with Dr. Adler Xie, Sr Data Science Manager, Airbnb.

Speaker: Dr Xie is a science leader with experience building high performance data science organization. Formerly she was a Sr Economist Manager at Amazon, a Sr Data Science Manager at Twitter, a Manager/Sr Principal at Keystone AI. She holds a PhD in economics from the University of Illinois Urbana-Champaign.

Talk: Dr Xie will share her experience working as an economist and data science manager at several tech companies and present a case study on which she worked and for which she and her team developed and applied state of the art causal inference methodologies.

Audience: Students trained in fields such as economics, data science, statistics, and computer science who have an interest in causal inference and are considering a career in the tech industry.

Goal: When students transition from academia to working, they often experience a big gap between how causal inference is taught in the classroom and how it’s applied in the workplace. In the tech industry: (1) The causal question rarely comes fully formed—researchers must disambiguate the business problems. (2) Company data is messier and larger than what’s used in coursework. (3) Implementation choices depend on stakeholders, scale, and costs, not just technical correctness. (4) Communicating results effectively is as important as getting them right. This talk’s goal is to bridge the gap between theory and practice through real-world case studies.

Want more of this? This talk is one of seven talks with tech industry experts who have wide-ranging experience applying causal analysis at tech companies such as Airbnb, Amazon, Google, Meta, Twitter, Zillow, and Wayfair. This series of talks  is offered in conjunction with ECMA 31370 “Causal Analysis for Industry” taught by Dr. Melissa Tartari at the Kenneth C. Griffin Department of Economics.