2020 Conference on Artificial Intelligence, Machine Learning, and Business Analytics

December 10-11, 2020

 Virtual on Zoom

Registration Link

Co-organized by

Stern School of Business, New York University
Heinz College, Carnegie Mellon University
Fox School of Business, Temple University

Over five billion people worldwide actively engage with AI, bots, machine-to-machine connected solutions, wearables, Internet-of-Things, 5G, AR/VR, Fintech, Mooc, and blockchain. This conference will explore how digital, social, and mobile technologies affect business models, customer behavior, public policy, and social changes at large. Exemplar topics include:

AI automation/ Robotics AI adoption and user behavior/AI chatbot voice-mining for promo and recommendations/ Bot trading and AI advisor in financial markets/AI for ad creatives and publishers/ AI applications in worker training, hiring, and supervising/ ML applications in fintech, pharma, and e-commerce/ Privacy and new technologies/ Data breach and security/ Blockchain applications/ Future of work and unemployment/ Big data IoT, 5G, AR, and VR applications/ Public policy and regulation of AI technologies/ AI algorithm bias, interpretable ML/ Machine learning for causal inference/ ML and deep learning for statistics methods/ Machine learning for empirical IO/ Deep reinforcement learning for microeconomics theory / Healthcare applications of ML/ Multi-armed bandits for online advertising and pricing/ Contextual MAB for personalized dynamic recommendations/ MOOC education online with ML and AI NLP with social media text data for targeting/ ML for images, voice, and video data B2B markets with ML and AI

Confirmed Keynote Speakers: Alessandro Acquisti (CMU), Jonah Berger (Wharton), Anindya Ghose (NYU), John Hauser (MIT), Kartik Hosanagar (Wharton), Dokyun Lee (CMU), Harikesh Nair (Stanford), Beibei Li (CMU), Sridhar Narayan (Stanford), Xueming Luo (Temple), Puneet Manchanda (UMich), PK Kannan (UMaryland), Bin Gu (BostonU),  Roland Rust (UMaryland), Ravi Bapna (UMN), Param Singh (CMU), Catherine Tucker (MIT), Olivier Toubia (Columbia), Shunyuan Zhang (Harvard)

Submission logistics: Submit either a 3-page abstract, full paper, or 10 PPT slides to AIML2020Conf@gmail.com; and cc the conference co-chairs: AGhose@stern.nyu.edu; BeibeiLi@andrew.cmu.edu; Xueming.Luo@temple.edu;

Submission deadline: October 26, 2020. Acceptance notification date: November 2, 2020 (Acceptance of submission requires one co-author to register and present at the conference)

This annual conference was hosted at Chicago Booth in 2014, NYU Stern in 2015 and 2017, Stanford GSB in 2016, CMU Tepper in 2018, and Temple in 2019. It has attracted a vibrant group of professors, industry people, and PhD students (each year max 150 people) working on cutting-edge ML AI models and data in inter-disciplinary fields. This conference serves as an intellectual bridge between computer science, economics, statistics, marketing, management, finance, strategy, IS, healthcare, education, public policy, and others.

Conference Schedule

Thursday, December 10, 2020

Welcome by Dean: Ron Anderson
Welcome by Conference Chairs (Xueming Luo, Beibei Li, and Anindya Ghose)

Session Chair: Yang Wang


Presenter  Title 
Catherine Tucker (MIT)


Does Black-Box Profiling Lead To Disadvantages For Less Privileged and Unhealthy People


PK Kannan (Maryland)


An AI-based Adaptive Personalization System for Online Learning
Ravi Bapna (Minnesota) Social Learning in Prosumption: Evidence from a Randomized Field Experiment
10 min Break 
Olivier Toubia (Columbia) A Poisson Factorization Topic Model for the Study of Creative Documents
Anindya Ghose (NYU) Empowering Patients Using Smart Mobile Health Platforms


Concurrent Sessions 1


Session Chair: Marco Qin


Presenter  Title
Harikesh Nair (Stanford) Online Inference for Advertising Auctions
DK Lee (CMU) Patents, Generative Algorithms, and Innovation Frontiers
John Hauser (MIT)


Identifying Profitable and Feasible Design Gaps for New Products
10 min Break 
Sridhar Narayanan (Stanford) Behavioral Targeting, Machine Learning and Causal Effects using a Regression Discontinuity Design
Xueming Luo (Temple) AI Coach for Sales Agents


Concurrent Sessions 2

Panel Discussion on “Challenges and Opportunities of Publishing AIML Topics in Top Journals” (Olivier Toubia at Columbia, Anindya Ghose at NYU, Param Singh at CMU, Xueming Luo at Temple).

Each panelist will first talk about one project or research themes on AIML topics at very high level for 5 min, then open up to conference attendees for Q&A on challenges and opportunities of publishing impactful AIML research at top journals such as Management ScienceMarketing ScienceJournal of Marketing ResearchInformation Systems Research, and others

Friday, December 11, 2020

Session Chair: Yang Wang


Presenter Title
Puneet Manchanda (Michigan)


The Race for Data: Who Gained from Re-permission E-mails in the Enforcement of GDPR
Alessandro Acquisti (CMU) The Impact of the GDPR on Content Providers
Bin Gu (Boston) TBD
10 min Break 
Shunyuan Zhang (Harvard) Unmasking with face masks: Analyzing customers’ risk-perception and purchase decisions with in-store shopping video
Beibei Li (CMU) Trading Privacy for the Greater Social Good: How Did America React During COVID-19?

Coffee Break

Concurrent Session 3

Join via Zoom Link

Session Chair: Marco Qin

Presenter  Title
Kartik Hosanagar (Wharton) Trust and Adoption of AI
Nan Jia (USC)


AI Supervision: A Battle of Quality and Trust
Param Singh (CMU) “Un”fair Machine Learning Algorithms
10 min Break 
Roland Rust (UMD)/Ming-Hui Huang (NTU) The Managerial Side of AI
Jonah Berger (Wharton) Quantifying the Shape of Narratives


Concurrent Sessions 4

Panel Discussion on “Best Practices of Working with Industry Companies on AIML Topics” (John Hauser at MIT, Harikesh Nair at Stanford, Vashist Avadhanula at Facebook, Brett Gordon at Northwestern, moderated by Xueming Luo)

Each panelist will first share the experience of working with industry tech leaders such as Facebook, JD, and others on AIML applications at very high level for 5 min, then open up to conference attendees for Q&A on best practices and pitfalls of industry-academic collaboration on AIML topics.