Agenda (Pacific Time)

8:55 – 9:00: Welcome, Agenda and Opening Remarks (Shaili Jain,  Sofus Macskassy)

9:00 – 9:45: Franziska Bell, VP Data & Analytics, BP 

Invited Talk: How data science powers energy production

Abstract: In this talk I am going to discuss data science applications that resulted in step function changes to the energy industry.  The energy industry is filled with rich data-driven problems and opportunities to change the world through data science, and advanced data science techniques have been able to drive transformative changes to the operations and evolution of the energy industry.

Bio: Dr. Franziska Bell is the vice president, data & analytics at bp. She heads the data & analytics discipline which is comprised of data science, AI, data engineering, data management and data analytics.  Before joining bp, Fran was an executive at Toyota Research Institute, where she focused on two areas: (i) novel battery and fuel cell materials using AI and computational chemistry for a low-emission future and (ii) a human-centered AI. Previously, Fran was the head of data science platforms at Uber. At Uber, Fran founded and built several digital platform teams with the mission to transform anyone in the company into a data scientist at a push-of-a-button. Before Uber, Fran was a postdoc at Caltech where she developed a novel, highly accurate approximate quantum molecular dynamics theory to calculate chemical reactions for large, complex systems, such as enzymes. Fran earned her Ph.D. in theoretical chemistry from UC Berkeley focusing on developing highly accurate, yet computationally efficient approaches which helped unravel the mechanism of non-silicon-based solar cells and properties of organic conductors.

9:45 – 10:45: Data Collection and Learning (Session Chair: Shaili Jain)

  • Challenges in Data Production for AI with Human-in-the-Loop, Dmitry Ustalov, Toloka (Yandex)
  • AI & Public Data for Humanitarian and Emergency Response, Alex Jaimes (Dataminr)
  • Scalable Attribute Extraction at Instacart, Shih-Ting Lin (Instacart)

10:45 – 11:00: 15-min BREAK

11:00 – 11:45: Barr Moses, Co-founder and CEO,  Monte Carlo.

Invited Talk: The Rise of Data Observability: Architecting the Future of Data Trust

Abstract: As companies become increasingly data driven, the technologies underlying these rich insights have grown more and more nuanced and complex. While our ability to collect, store, aggregate, and visualize this data has largely kept up with the needs of modern data teams (think: domain-oriented data meshes, cloud warehouses, data visualization tools, and data modeling solutions), the mechanics behind data quality and integrity has lagged. To keep pace with data’s clock speed of innovation, data engineers need to invest not only in the latest modeling and analytics tools, but also technologies that can increase data accuracy and prevent broken pipelines. The solution? Data observability, the next frontier of data engineering. I’ll discuss why data observability matters to building a better data quality strategy and tactics best-in-class organizations use to address it — including org structure, culture, and technology.

Bio: Barr Moses is CEO & Co-Founder of Monte Carlo, a data reliability company and creator of the industry’s first Data Observability Platform, backed by Accel, GGV, Redpoint, ICONIQ Growth, Salesforce Ventures, and other top Silicon Valley investors. Previously, she was VP Customer Operations at customer success company Gainsight, where she helped scale the company 10x in revenue and, among other functions, built the data/analytics team. Prior to that, she was a management consultant at Bain & Company and a research assistant at the Statistics Department at Stanford University. She also served in the Israeli Air Force as a commander of an intelligence data analyst unit. Barr graduated from Stanford with a B.Sc. in Mathematical and Computational Science.

11:45 – 12:25: Knowledge Representation (Session Chair:  Sofus Macskassy)

  • Mining Frequent Patterns on the Tax Knowledge Graph, Lalla Mouatadid (Intuit)
  • Graph Neural Networks for the Global Economy with Microsoft DeepGraph, Jaewon Yang, Alex Samylkin, Baoxu Shi  (LinkedIn, Microsoft)

12:30 – 1:30pm: LUNCH

1:30 – 2:15: Vijay K Narayanan, Chief AI officer, ServiceNow

Invited Talk: Successes and opportunities in Enterprise AI

Abstract: The advances in AI over the last decade has led to significantly better outcomes and improved experiences in consumer applications. Meanwhile, successful applications of AI in enterprises have been modest during this period. In this talk, I will provide an overview of the scope of Enterprise AI, how it is similar and different from AI for consumer applications, a few successful applications, open problems and challenges and the enormous opportunity to create better outcomes and transform the experiences for employees and customers of enterprises.  

Bio: Vijay Narayanan founded and leads the Advanced Technology group (ATG), a customer-focused innovation group of researchers, applied scientists and engineers in ServiceNow building smart user experiences using AI and related advanced technologies. Previously, he led the sciences and engineering group in Pinterest for all organic products, and earlier led teams building Cloud AI platform and solutions in Microsoft. Even earlier he worked on machine learning platforms, products and solutions at Yahoo Labs and in FICO. He has deep and wide experience leveraging statistical analysis, machine learning, and scalable systems to drive innovative breakthroughs in new and existing product lines and services in different domains. 

2:15 – 3:15: User Preferences and Behavior (Session Chair: Lei Tang)

  • Experiments with predictive long term guardrail metrics, Sri Sri Perangur (Lyft)
  • Near real time AI personalization for notifications at LinkedIn, Ajith Muralidharan (LinkedIn)
  • Studying Long-Term User Behaviour Using Dynamic Time Warping for Customer Retention, Harsha Gwalani (Twitter)

3:15 – 3:30: 15-min BREAK

3:30 – 4:15: Haixun Wang, VP Engineering and Distinguished Scientist, Instacart

Abstract: The quality of the search experience on an e-commerce site plays a critical role in customer conversion and the growth of the e-commerce business. In this talk, I will discuss the current status and challenges of product search. In particular, I will highlight the significant amount of effort it takes to create a high-quality product search engine using classical information retrieval methods. Then, I will discuss how recent advances in NLP and deep learning, especially the advent of large pre-trained language models, may change the status quo. While embedding-based retrieval has the potential to improve classical information retrieval methods, creating a machine learning-based, end-to-end system for general-purpose, web search is still extremely difficult. Nevertheless, I will argue that product search for e-commerce may prove to be an area where deep learning can create the first disruption to classical information retrieval systems.

Bio: Haixun Wang is currently an IEEE fellow, editor in chief of IEEE Data Engineering Bulletin, and a VP of Engineering and Distinguished Scientist at Instacart. Before Instacart, he was a VP of Engineering and Distinguished Scientist at WeWork, a Director of Natural Language Processing at Amazon, and he led the NLP team working on Query and Document Understanding at Facebook. From 2013 to 2015, he was with Google Research working on natural language processing. From 2009 to 2013, he led research in semantic search, graph data processing systems, and distributed query processing at Microsoft Research Asia. He had been a research staff member at IBM T. J. Watson Research Center from 2000 to 2009. He received the Ph.D. degree in Computer Science from the University of California, Los Angeles in 2000. He has published more than 150 research papers in referred international journals and conference proceedings. He served as PC Chairs of conferences such as SIGKDD'21, and he is on the editorial board of journals such as IEEE Transactions of Knowledge and Data Engineering (TKDE) and Journal of Computer Science and Technology (JCST). He won the best paper award in ICDE 2015, 10-year best paper award in ICDM 2013, and the best paper award of ER 2009.

4:15 – 5:15: Optimization and Learning Systems (Session Chair: Lei Tang)

  • The Incentives Platform at Lyft, Alex Wood-Doughty, Cam Bruggeman (Lyft)
  • Exploration in Recommender Systems, Minmin Chen (Google)
  • A Practical Guide to Robust Multimodal Machine Learning and Its Application in Education, Zitao Liu (TAL Education Group)

5:15-5:30: Closing Remarks (Lei Tang, Shaili Jain)