Welcome by Jenny Lundberg, Senior Director, Global Head of Strategy Developer Relations at SAP

• History & Motivation
• Plan for the day & Logistics
• Warm-up / Ice breaker / Meet your neighbor

Welcome to WiDS: Inspiring Diversity in Data Science.

• Judy Logan, Co-Director, Women in Data Science (WiDS) Conference
  Stanford ICM
  
• Michelle Grover, SVP of Development Concur, and board member for Techtonica

Panel Abstract: The introduction of AI technology in products affects the way companies operate and its culture. Often, Data Scientists develop models for PoCs. However, a very small percentage of these models reach the production phase. The gap between a good Machine Learning (ML) model and a production-ready ML application is still widely misunderstood. Ideally, deploying ML models should be a collaboration between Data Science, Engineering, Product Management, Design and the Legal team. Yet, often the engineering organization is tasked with turning ML models to a software product.  As part of a data-driven organization how do Data Scientist deal with topics such as sample bias, user privacy and the legal implications associated with their models? This panel will highlight the risks that come along with putting Data Science in production.

Panelist: Krithika Swaminathan, Intuit; Michelle Grover, SAP; Srujana Kaddevarmuth, Walmart Labs

Moderator: Sally Lawler Kennedy, SAP

Panel Abstract: Showcase the culture of SAP, the work of a data scientists, and ways to enter or grow a DS career in SAP

Panelist: Cynthia Wood, SAP (Senior Data Scientist); Ran Zhou (Data Scientist); Other Panelists TBC

Moderator: Caitie Sullivan (Program Manager, SVNT)

12:30 – 1:00pm: Productize Machine Learning and Drive Customer Success in CRM

Speaker: Jing Ye and Sylvie Huang, SAP

Abstract: One way to leverage ML is to productize it. However building a successful model offline is not enough to be successful in production. What are the gaps between offline and production and what are the steps to take? Let’s share our ML stories from CX and make your model valuable to customers too!

1:00 – 1:30pm: Like a Pro: Making Your Data Science Code Production-Ready

Speaker: Richa Namballa & Shruti Bhargava, SAP

Abstract: PoCs are a great first step in showcasing your latest machine learning model. But what good is that model if it’s never used? When making your model generally available, your code must be robust and easily delivered. This talk contains tips for making your data science code “production-ready.”

1:30 – 2:00pm: Research to Production with Open Source Software: High performance model inferencing using ONNX

Speaker: Faith Xu & Emma Ning, Microsoft

Abstract: As ML developments shift from research to real world applications, we encounter a host of deployment challenges. Using the interoperable ONNX model format and the cross platform ONNX Runtime inference engine, we can streamline the process to deploy models performantly across a variety of hardware.

12:30 – 1:00pm: How do algorithms become biased?

Speaker: Eva Sasson, Persona

Abstract: There’s bias in algorithms - how does this happen? In this talk, we walk through a step by step example of building a prediction algorithm, focused on areas where bias could be inadvertently introduced. We then look at when algorithms were used to make biased decisions, and what we can do about it.

1:00 – 1:30pm: Fueling Machine Learning with Feature Engineering

Speaker: Jigyasa Grover, Twitter

Abstract: Modern times are buzzing with Machine Learning & for these state-of-the-art algorithms to work their magic it’s important to lay a strong foundation via Feature Engineering. This talk reviews the what is Feature Engineering & how to actually harmonize valuable information from volumes of crude data.

1:30 – 2:00pm: Making Prophecies with Data: Introduction to Time-Series Forecasting and Recent Developments

Speaker: Serena Peruzzo, Bardess

Abstract: In this session, we’ll demonstrate the fundamentals of time series forecasting, using Python. We’ll cover typical data prep tasks, univariate and multivariate forecasting techniques, and more recent developments which improve the accuracy and ease of building forecast models.

2:00 – 2:30pm: Human Centered Machine Learning

Speaker: Michelle Carney, Google

Abstract: How can you start from scratch to create a personalized, user-centered product if you don’t yet have any data? We’ll bridge the gap between UX design and ML by showing you when and how to bring humans into an ML project with battle tested methods that work whether you're just getting started or building on a strong foundation. ML insights to inform your design and designing delightful ML experiences.

12:30 – 1:00pm: Why your project needs to be an app

Speaker: Amanda Kelly, Streamlit

Abstract: Before you get to production, you must explain and sell your work to teammates and customers. Ensure your success by creating apps that demo your work and allow others to explore your project. Streamlit is an open-source tool that allows you to create beautiful data apps in just a few lines of code.

1:00 – 1:30pm: SAP Ariba Reinforcement Learning Approach Towards Recommendations in a Procurement Marketplace

Speaker: Cynthia Wood, SAP

Abstract: Recommendation systems are a standard element of marketplaces. At SAP Ariba we use a Reinforcement Learning to provide recommendations to new users with minimum data while staying compliant to GDPR. Join us to learn the techniques used to capture user behavior and provide relevant recommendations.

1:30 – 2:00pm: NLP as a powerful tool in Health Data Science: Unstructured Text Analysis in Electronic Health Records to Characterize Sepsis Presentation

Speaker: Meghana Bhimarao, Kaiser Permanente’s Division of Research

Abstract: With the realization of the value of unstructured data in a wide range of sectors, NLP has become essential to the data science toolkit. We will explore NLP as a tool in health DS, and how Kaiser Permanente research is using NLP to characterize diseases that are tough to identify, such as sepsis.

2:00 – 2:30pm: What Makes Data Science "Fast" ?

Speaker: Tanya Piplani, SAP

Abstract: ML algorithms often need to execute several billion operations per second. For them to be viable solutions for real time applications, one usually needs to exploit: efficient architecture search, full utilization of GPU cores and maximum CPU parallelism. Let’s explore what makes data science “fast”.

12:30 – 1:00pm: Understanding and Addressing Bias in AI Diagnostic Systems

Speaker: Julie Vaughn, MIT

Abstract: Experts predict that artificial intelligence (AI) will completely revolutionize the field of medical diagnostics. However, we must consider the impact of bias. How do we recognize and address different kinds of bias, and avoid models that perpetuate bias in healthcare and other fields?

1:00 – 1:30pm: Data Science in Cloud Data Protection

Speaker: Shmily Wang, SAP

Abstract: In this talk, it will be talking about how to implement data science techniques to protect data on public cloud. In the meanwhile, it will also discuss how to deal with risks come along with that, given the needs to comply with various data-protection regulations like GDPR.

1:30 – 2:00pm: Laying the Foundation for Successful Model Deployment

Speaker: Hiranmayi Duvvuri, Vacasa

Abstract: You’ve started a new project, but have you thought through everything that could go wrong? Any new project needs an issue mitigation plan. The initial work sets up the foundation that ensures all parties are on board; with their responsibilities, uses of the model, and long/short-term plans.

2:00 – 2:30pm: Adverse Impact - a data science perspective on a legal data-evaluation process

Speaker: Elinor Brondwine, Workday

Abstract: Legal requirements are becoming a critical part of data science productization. We will examine current legal requirements for evaluating unintentional discriminatory effects via adverse impact, to build a better understanding of what the future may hold for algorithm evaluation.

12:30 – 1:30pm: Open Neural Network eXchange (ONNX): Accelerate and operationalize models for deployment

Speaker: Vinitra Swamy, Ashwini Khade & Cecilia Liu. Microsoft

Abstract: The modern data scientist has a large toolbox, from frameworks like PyTorch and TF to Scikit-Learn and Keras. These are useful for model training but not optimized for latency-sensitive deployment with real customers. The solution? ONNX. Come to our hands-on workshop to learn more.

1:30 – 2:30pm: Turn Python Scripts into Beautiful Data Apps

Speaker: Amanda Kelly, Streamlit

Abstract: Quickly create your own custom data tools for visualization, debugging, comparing models and presenting data - all in Python. This tutorial covers how to use Streamlit, a new open-source app framework built specifically for data scientists to create beautiful, performant tools.

Panel Abstract: There are 9 key issues in AI ethics according to the World Economic Forum that span the entire lifecycle AI: how do we build AI, for what purpose do we use AI, what impacts does AI have on society? We’ll dig into these questions, as well as how to incorporate these questions in your own work, with a diverse group of women.

Panelist: Helen Arnold, SAP; Lisa Nash,  IDEO
Moderator: Nicole Helmer, SAP

Panel Abstract: According to the World Economic Forum, 1.2 billion employees worldwide will be affected by the adaptation of automation technologies and AI over the next 10 years. While automation will change 50% of jobs, it is not expected to eliminate more than 5%. What impacts does AI have on hiring and developing talent? How can we proactively manage the transition to a new future?

Panelist: 

• Jo Riley, CEO & Founder of Censia – SAP.iO Foundry portfolio. Censia is an SAP endorsed app, available on the App Center
• Margaret Tomaszczuk, Head of Product and Customer Experience at Cultivate – SAP.iO Fund portfolio. SAP Partner available on the App Center
• Sirisha Ayyagari, Development Lead at Success Factors

Moderator: Domitilla Caputo

Closing Keynote: Technology and Humanity: The Future of Data Science
Abstract: TBD

by Allie K. Miller, Amazon