KDD 2019 | Applied Data Science Invited Speakers

Applied Data Science Invited Speakers

The Applied Data Science Invited Talks will provide a venue for leading experts in the world of applied data mining and knowledge discovery. These invited talks will feature highly influential speakers who have directly contributed to successful data mining applications in their respective fields. The talks and discussions will focus on innovative and leading-edge, large-scale industry or government applications of data mining in areas such as finance, health-care, bio-informatics, public policy, infrastructure, telecommunications, social media and computational advertising.

Addressing Challenges in Data Science: Scale, Skill Sets and Complexity — Addressing Challenges in Data Science: Scale, Skill Sets and Complexity
Joseph Bradley
Databricks, Inc.

Friends Don’t Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Lear — Friends Don’t Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning
Richard Caruana
Microsoft

Building a Better Self-Driving Car: Hardware, Software, and Knowledge — Building a Better Self-Driving Car: Hardware, Software, and Knowledge
Kumar Chellapilla
Lyft

Preventing Rhino Poaching through Machine Learning — Preventing Rhino Poaching through Machine Learning
Gabriel Domínguez Conde
Microsoft

Welfare Maximization in Online Two-sided Marketplaces — Welfare Maximization in Online Two-sided Marketplaces
Sreenivas Gollapudi
Google Research

Seven Years of Data Science at Airbnb — Seven Years of Data Science at Airbnb
Elena Grewal
Airbnb

4 Perspectives in Human-Centered Machine Learning — 4 Perspectives in Human-Centered Machine Learning
Carlos Guestrin
Apple

Towards ML Engineering with TensorFlow Extended (TFX) — Towards ML Engineering with TensorFlow Extended (TFX)
Kevin Haas
Google

Exploiting High Dimensionality in Big Data — Exploiting High Dimensionality in Big Data
David Heckerman
Amazon

Towards ML Engineering with TensorFlow Extended (TFX) — Towards ML Engineering with TensorFlow Extended (TFX)
Konstantinos Katsiapis
Google

Preventing Rhino Poaching through Machine Learning — Preventing Rhino Poaching through Machine Learning
Olga Liakhovich
Microsoft

Facebook Disaster Maps: Aggregate Insights for Crisis Response & Recovery — Facebook Disaster Maps: Aggregate Insights for Crisis Response & Recovery
Paige Maas
Facebook

Earth Observations from a New Generation of Geostationary Satellites — Earth Observations from a New Generation of Geostationary Satellites
Rama N
NASA

Spinning the AI Pinwheel — Spinning the AI Pinwheel
Jai Ranganathan
KeepTruckin

Product Ecosystem Optimization at LinkedIn — Product Ecosystem Optimization at LinkedIn
Romer Rosales-Delmoral
LinkedIn

Integrating Domain-Knowledge into Deep Learning — Integrating Domain-Knowledge into Deep Learning
Ruslan Salakhutdinov
Apple

Applications of AI/ML in Established and New Industries — Applications of AI/ML in Established and New Industries
Hassan Sawaf
Amazon Web Services

AI for Small Businesses and Consumers: Applications and Innovations — AI for Small Businesses and Consumers: Applications and Innovations
Ashok Srivastava
Intuit Inc.

From Code to Data: AI at Scale for Developer Productivity — From Code to Data: AI at Scale for Developer Productivity
Neel Sundaresan
Microsoft

Analytics Journey Map: An approach enable to ML at scale — Analytics Journey Map: An approach enable to ML at scale
Ganesh Thondikulam
Kaiser Permanente

Role of Unified Graph Analysis Platforms for Smart Cities — Role of Unified Graph Analysis Platforms for Smart Cities
Yinglong Xia
Facebook

Data Science Challenges @LinkedIn — Data Science Challenges @LinkedIn
Ya Xu
LinkedIn

Aligraph: A Comprehensive Graph Neural Network Platform — Aligraph: A Comprehensive Graph Neural Network Platform
Hongxia Yang
Alibaba

Transportation: A Data Driven Approach — Transportation: A Data Driven Approach
Jieping Ye
Didi Chuxing & University of Michigan, Ann Arbor