Mike Shores – Sr. Director – Data Science at Vista

Despite some companies still approaching data science as a pure research field, data science has become a time-tested, value-driving method for companies to solve customer problems. And for some companies that already recognize that data science is a value-creator, the path to unlocking that potential remains unclear. In this talk, we’ll discuss how embedding data scientists in customer-oriented product teams is a win-win-win recipe for customers, companies, and employees alike. We’ll talk through two specific use cases and how having clearly aligned customer problems, empowered teams and team members with the right technical skills is a recipe for success.

Ying Ding – Bill & Lewis Suit Professor at University of Texas at Austin

This talk will outline the current advances about applying AI technologies in medical imaging diagnosis, medical notes, and health risk prediction. The latest AI technologies include transformer, knowledge graph, and explainable AI. This talk summarizes the opportunities and challenges related to AI in health.

Kelly Tsao – UX Designer at Q2ebanking
Jesse Barbour – Chief Data Scientist at Q2ebanking

Intelligence is more than the human brain. This case study delves into the philosophical underpinnings of how artificial intelligence applied to financial technology problems at the enterprise or platform level reveals new powers of scale. Cognitive science and UX best practices enable effective human-computer-AI interaction at the individual level, which creates an emergent intelligence greater than the sum of its parts once connected to a diverse network of financial institutions.

Jean-Leah Njoroge – Director Conversational AI/NLP at Lowe’s Home Improvement

Recent years have seen important advances in the quality of state-of-the-art models; however, this has come at the expense of models becoming less interpretable. In this talk, I will present an overview of the current state of Explainable AI (XAI), within the domain of Natural Language Processing (NLP); the operations and explainability techniques currently available for generating explanations for NLP model predictions, to serve as a resource for model developers in the community.

Sumayah Rahman – Director of Data Science – Machine Learning and Infrastructure at Cedar

Discounting is a pricing technique that can be used to help customers (in our case, patients) to pay their bill. At Cedar, we believe that if discounting is done right at the point of engagement, there is an opportunity to make the cost of care more affordable, while also improving financial outcomes for providers. We applied ML to identify which patients would benefit from receiving a discount on their bill, but, like many ML projects, it was not as straightforward as it seemed. For example, we realized that the target variable for our model did not fully capture the outcome we are trying to optimize for: financial results. By digging into the data to more deeply understand trends in payment behavior, we took steps to build a more robust solution that works for both patients and providers—a journey that is still ongoing!

Astha Puri – Senior Data Scientist at Twilio

Most advanced ML techniques are applied in the B2C space to understand how customers behave, what their preferences are and how they are enjoying a particular product. But what if your business model is B2B? How do you understand customer satisfaction when your customer is a 50,000 people business entity? This talk focuses on leveraging ML to improve your business if you’re B2B (or even a small growing B2C).

Graham Ganssle – Head of Data Science, Sales and Customer Service at Wayfair

Wayfair may be a home goods retailer, but our customer service representatives are taxi drivers. Our customers are on the street wishing they could catch a ride. In this talk, I’ll describe the Uber of the customer service world: Wayfair’s Algorithmic Contact Assignment (ACA) product. ACA is an adaptive machine learning product that connects customer phone calls and chats to customer service agents. For each customer contact, ACA makes rider/driver-like connections in a hyper-personalized assignment that ensures the customer’s intent, the contact’s complexity, and the value-at-stake of the interaction are handled by the goldilocks agent (the agent with the single best natural proclivities to handle that contact juuuust right). The result is the customer gets what they need faster, happier, and with better resolutions. When customer Sarika got from the parking lot to the soccer field just in time to see her daughter’s first goal because she got off her call with a Wayfair returns specialist in 98 seconds, we knew ACA was perfect!

Randi Ludwig – Director, Data Science at Dell Technologies

Agile software development practices are great tools to increase visibility and productivity of any team writing code. However, the data science lifecycle has some particular differences from traditional software development that make it tricky to implement. Randi has led multiple teams of data scientists through the transition of adopting then thriving using modified Agile methodologies She will share tips and tricks for how to get the most value out of Agile in the ways that are most beneficial to the uncertain process of data science. She will also share an adoption roadmap to go from wild west data science to a well-oiled value factory!

Atindriyo Sanyal – Co-Founder at Galileo

Kim Martin – Director, Software Engineering at Indeed

Hiring is a time-consuming part of a manager’s job. Hiring well is hard and impacts a company’s success. What if managers spent less time hiring really great people? This talk will explore some of the experiments and considerations at Indeed towards finding really great job applicants for any hiring manager.

Zach Childers – Manager, NLP & Machine Learning at Press Ganey

Investigation of attention mechanisms of BERT and other language models – neural and otherwise – have found that these models are often, to various degrees, implicitly learning details of syntactic structure and other linguistic features in the performance of natural language processing tasks (Clark et al. 2019, Goldberg 2019, Shi et al. 2016, inter alia).

While this might be heartening news to linguists, it raises the questions of whether these frameworks might be partially reinventing the wheel with respect to the scientific knowledge of language, and also of what further linguistic structures and phenomena might be exploited to supplement the statistical models.

In this talk, I discuss various ways of leveraging linguistic structure to improve and expand NLP performance, particularly in a pipeline architecture, with a focus on experience data in the healthcare domain.

Sweta Sinha – Director Data Science at Ascend Learning

AI has been a key driver in innovation in every industry Organizations have ramped up their effort on leveraging AI to gain a competitive advantage. However, AI solution comes with its own challenges and risk, particularly in regulated industries. There have been numerous instances when AI introduced bias, ethical and legal risks. Organizations must use a balanced approach to accelerating the adoption of AI and prioritize AI governance to ensure trust in the AI system. While AI regulation landscape is still evolving, now is the time for organizations to start taking steps to understand and mitigate AI risks. In this session you will learn about how organizations can embed AI governance principle into AI strategy and the platform for identifying and mitigating AI risk.

Graham Ganssle – Head of Data Science, Sales and Customer Service at Wayfair
Sweta Sinha – Director, Data Science at Ascend Learning
Alec Coughlin – Client Partner, Financial Services at LivePerson

Kshetrajna Raghavan – Staff Data Scientist at Shopify

Peter Grabowski – AUS Site Lead – Core Enterprise Machine Learning at Google

Companies are frequently faced with large amounts of unstructured text data, like forum comments or product reviews. Important trends can emerge in these datasets, but it can be time-consuming to read through comments, and keyword matching frequently misses critical nuances.

We’ll discuss how we’ve approached this problem at Google using Natural Language Processing, with examples of the approach applied to open datasets.

We’ll explore how this fits into the ML project lifecycle, with examples of common pitfalls. Finally, we’ll highlight how to use this technology as part of a “”human in the loop”” approach to supercharge your existing team members.