Applying Machine Learning & AI to Finance, Healthcare & Technology

AUSTIN, TX | FEBRUARY 18-19

Presented by

Get the Bigger Picture in Texas

Covering all major applications of AI and Machine Learning in all Data Science Salon verticals.

This flagship edition of the Data Science Salon sets the tone of the year ahead in data science. The Data Science Salon series is a unique vertical focused conference which brings together specialists face-to-face to educate each other, illuminate best practices, and innovate new solutions in a casual atmosphere with food, great coffee, and entertainment.

About

Get access to powerful decision makers in data science in an intimate setting at Data Science Salon Austin, the flagship conference in the DSS series around all major applications AI and Machine Learning. Learn from practitioners, technical experts and executives how to solve real-world problems by harnessing disruptions in data, artificial intelligence, machine learning, and cutting-edge technologies. At DSS Austin, we connect you with our powerful community face-to-face and digitally – each ticket comes with one year of access to DSS Insider, the content repository for all Data Science Salons.

Data Science Salon Austin has two dedicated tracks. Learn the latest technical skills from the tool creators themselves in our technical track. Or join executives and top machine learning practitioners in our uses cases track as they share exclusive success stories and paint a roadmap to ML success.

Data Science Salons are one or two day events hosted at Blue Chip companies. Over 50% of our 200-500 attendees at each conference are data decisionmakers (Sr. Data Scientists and above). And we are the only data science conference with a gender balance in our speaking roster.

A Sample of Topics Covered

PUTTING ML APPS INTO PRODUCTION

PERSONALIZATION AT SCALE WITH AI

CLOUD AUTOMATION AND MACHINE LEARNING

NATURAL LANGUAGE PROCESSING & DEEP LEARNING

ENGINEERING FOR DATA SCIENCE

SCALING ML PRODUCTION

IMPROVING DATA QUALITY

MACHINE LEARNING BEST PRACTICES

DATA STRATEGY AND GOVERNANCE

DATA ETHICS AND BIAS

RECOMMENDATION ENGINES

DESIGNING ML PIPELINES EFFICIENTLY

DEEP NEURAL NETWORKS

IMAGE RECOGNITION WITH ML

USING LARGE SCALE DATA SETS

DATA SCIENCE TEAMS: MANAGING, BUILDING, COLLABORATION

INTEGRATING OPEN SOURCE TOOLS INTO YOUR WORKFLOWS

ENHANCING THE VIEWER EXPERIENCE WITH MACHINE LEARNING & AI

CONTENT PERSONALIZATION AND MONETIZATION

AUDIENCE TARGETING AND SEGMENTATION (ACROSS PLATFORMS)

DATA AND AI FOR EMERGING PLATFORMS

DATA GOVERNANCE

AND MORE!

Immersive Days

Attendees

Speakers

Blue chip companies represented

dedicated tracks for practitioners + managers

Year access to DSS Insider + the DSS Online Community

Speakers

Elizabeth Chabot

Lead Data Scientist at ScaleFactor

Priscilla Boyd

Senior Manager, Data Analytics at Siemens Mobility

Andy Terrel

President at NumFocus

Amy DaalI

Founder & CEO at Lucea AI

Michael Zelenetz

Analytics Project Leader at New York-Presbyterian Hospital

Alex Schwarm

VP/Head of Data Science at Dun & Bradstreet

Boryana Manz

Manager, Data Science at Capital One

Caitlin Hudon

Lead Data Scientist at OnlineMedEd

Nancy Hornberger

Executive Vice President – Healthcare at ElectrifAi

Randi Ludwig

Sr. Manager, Applied Data Science at Dell Technologies

Fatih Akici

Manager, Risk Analytics and Data Science at Populus Financial Group

Moody Hadi

Group Manager – Financial Engineering at S&P Global Market Intelligence

Jacob Claussen

Manager, Analytics/Data Science at Zynga

Gerald Fahner

Senior Director, Data Science at FICO

Raffael Marty

VP Research and Intelligence at Forcepoint

Meltem Ballan

Data Science Lead at GM

Jesse Barbour

Chief Data Scientist at Q2ebanking

Jordan Birdsell

Chief Machine Learning Architect at phData

Ying Ding

Professor, School of Information & Dell Medical School at UT Austin

Eero Laaksonen

CEO at Valohai

Dhruv Bhargava

VP Data Science at Square Panda

Linnette Attai

President at PlayWell LLC

Darby Laffoon

Sr Manager of Data Science Engineering at Charles Schwab

Dedy Kredo

Head of Customer Facing Data Science Organization at Explorium

Kelsey Redman

AVP, Data Science at Comerica Bank

Chris Lindner

Manager, Product Science at Indeed

Raktim Saha

Director, Digital Insights at CGI

Patrick McGarry

Head of Strategic Partnerships at Data.World

Micah Price

Principal Associate Data Scientist at Capital One

Ilya Katsov

Head of Data Science at GridDynamics

Seemit Sheth

Head of Data Science at Capital One

Peter Guerra

North America Chief Data Scientist at Accenture AI

Joe Ray

Data Science & Engineering at Dell Technologies

Alyssa Simpson Rochwerger

VP, Data and AI at Appen

Marcelo Casas

SVP Data Science at GM Financial

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Schedule

Tap on the Day 1 and Day 2 tabs to switch between the schedule for each day

Day 1 schedule

8:00 am – 8:50 am – Registration & Coffee
Coffee, Healthy Snacks, and Networking

8:50 am – 9:10 am – Introduction & Morning Meditation

9:10 am – 9:40 am – Leveraging Entity-Resolution to Identify Customers in 3rd Party Data
Kelsey Redman – AVP, Data Science at Comerica Bank

9:40 am – 10:10 am – Model Monitoring Pipelines
Boryana Manz – Manager, Data Science at Capital One

10:15 am – 10:35 am – The Next Frontier of Data Science: Automated Feature Generation
Dedy Kredo – Head of Customer Facing Data Science Organization

10:35 am – 11:05 am – Behaviors in Bookkeeping
Elizabeth Chabot – Lead Data Scientist at ScaleFactor

11:05 am – 11:25am – Coffee & Entertainment
Networking, Exhibitor Meet & Greet

11:25 am – 11:55 am – Keys to Leading Highly Effective Data Science Teams
Darby Laffoon – Sr Manager – Big Data Platform Engineering at Charles Schwab

11:55 am – 12:25 pm – All Models Are Wrong, But Some Are Useful… Especially if you have the Right Data
Alex Schwarm – VP/Head of Data Science at Dun & Bradstreet

12:30 pm – 12:50 pm – Building a Data-Driven Price Management System: Lessons Learned and Best Practices
Ilya Katsov – Head of Data Science at GridDynamics

12:50 pm – 1:10 pm – 10 Questions Before You set-Up Your Data Science Team
Marcelo Casas – SVP Data Science at GM Financial

1:10 pm – 2:10 – Lunch & Sponsor Table Crawl
Networking, Exhibitor Meet & Greet

2:10 pm – 2:55 pm – DSSe Panel
Randi Ludwig – Sr. Manager, Applied Data Science at DELL
Meltem Ballan – Data Science Lead at GM
Ying Ding – Professor, School of Information and Dell Medical School at UT Austin
Nancy Hornberger – Executive Vice President – Healthcare at ElectrifAi

2:55 pm – 3:25 pm – Building a Recommender System from Node Embeddings on a Transaction Graph
Jesse Barbour – Chief Data Scientist at Q2ebanking

3:25 pm – 3:55 pm – Applied Machine Learning for Spreading Financial Statements
Moody Hadi – Group Manager – Financial Engineering at S&P Global Market Intelligence

3:55 pm – 4:25 pm – Afternoon Coffee
Networking, Exhibitor Meet & Greet

4:25 pm – 4:55 pm – A Hands-on Inquiry into Algorithmic Bias and Machine Learning Interpretability
Fatih Akici – Manager, Risk Analytics and Data Science at Populus Financial Group

4:55 pm – 5:25 – Bias In, Bias Out? How to Fight Biases in AI and Create Trust
Gerald Fahner – Senior Director, Data Science at FICO

5:25 pm – 5:55 pm – New Frontiers for Data Science in Banking
Seemit Sheth – Head of Data Science at Capital One
Micah Price – Principal Associate Data Scientist at Capital One

6:00 pm – 8:00 pm – Opening reception

At Penny Lane Street Bar, 115B San Jacinto Blvd, Austin, TX 78701

Make sure to get a drink token from the Formulatedby table, good for two drinks of your choice!

Day 2 schedule

8:00 am – 8:50 am – Registration & Coffee
Coffee, Healthy Snacks, and Networking

8:50 am – 9:10 am – Introduction & Morning Meditation

9:10 am – 9:40 am – Minority Report: Can we predict emergencies?
Michael Zelenetz – Analytics Project Leader at New York-Presbyterian Hospital

9:40 am – 10:10 am – Leveraging Machine Learning and Open Data for Smart Cities
Priscilla Boyd – Senior Manager, Data Analytics at Siemens Mobility

10:15 am – 10:35 am – Talk Title TBD
Joe Ray – Data Science and Engineering at Dell Technologies

10:35 am – 11:05 am – Process Automation using Machine Learning
Raktim Saha – Director, Digital Insights, CGI

11:05 am – 11:25am – Coffee & Entertainment
Networking, Exhibitor Meet & Greet

11:25 am – 11:55 am – Who will fight for AI in Healthcare
Amy Daali – Founder & CEO at Lucea AI

11:55 am – 12:25 pm – Discovering What Our Players Will Thank Us For
Jacob Claussen – Manager, Analytics/Data Science at Zynga

12:30 pm – 12:50 pm – MLOps: The Assembly Line of Machine Learning
Jordan Birdsell – Chief Machine Learning Architect at phData

12:50 pm – 1:10 pm – The Joys and Peril of AI at Scale
Peter Guerra – North America Chief Data Scientist at Accenture AI

1:10 pm – 2:10 – Lunch & Sponsor Table Crawl
Networking, Exhibitor Meet & Greet

2:10 pm – 2:55 pm – Panel: AI/ML Privacy and Compliance
Dhruv Bhargava – VP Data Science at Square Panda
Raffael Marty – VP Research and Intelligence at Forcepoint
Linnette Attai – President at PlayWell LLC
Andy Terrel – President at NumFocus

2:55 pm – 3:15 pm – How to Organize a Team to Build an AI Product That Works
Alyssa Simpson Rochwerger – VP, Data and AI at Appen

3:15 pm – 3:45 pm – What Indeed’s Job Market Data can tell us about Demographics and Trends in Data Science Careers
Chris Lindner – Manager, Product Science at Indeed

3:45 pm – 4:15 pm – Afternoon Coffee
Networking, Exhibitor Meet & Greet

4:15 pm – 4:35 pm – Asynchronous Model Training & Version Control for Jupyter Notebooks
Eero Laaksonen – CEO at Valohai

4:35 pm – 5:05 – Building Data Science Infrastructure
Caitlin Hudon – Lead Data Scientist at OnlineMedEd

5:05 pm – 6:05 pm – The Roadmap to Beautiful and Meaningful Data
Patrick McGarry – Head of Strategic Partnerships at data.world

6:05 pm – 6:10 pm – Closing remarks

attendees from

And many more!

DATE AND TIME

February 18, 2020 | 8 AM – 7 PM CST

February 19, 2020 | 8 AM – 7 PM CST

LOCATION

Brazos Hall, 204 E 4th St,

Austin, TX 78701

Contact

e: info@formulated.by

t: (415) 322-0760

Priscilla Boyd – Senior Manager, Data Analytics at Siemens Mobility

Leveraging Machine Learning and Open Data for Smart Cities

This presentation discusses how AI, machine learning and data publicly available is playing a role in smart cities, walking through applications developed using ML to solve practical transportation for smart cities globally.

ABOUT THE SPEAKER

Priscilla Nagashima Boyd is a Senior Manager of Data Analytics at Siemens Mobility based in Austin, TX. With expertise in transportation and machine learning, she leads a team of data scientists and developers that are seeking solutions for mobility problems through novel data science techniques and design thinking. With over 10 years of transportation experience as a practitioner, Priscilla has worked with public and private sectors in the Americas and Europe, advising on new technologies and related policy to deliver more efficient, accessible and sustainable transportation for urban communities. She has a Bachelor of Science in Computing and Information Systems from the University of London and a Master of Science in Software Engineering with emphasis in machine learning from the University of Oxford. She is also a STEM ambassador, the former honorary secretary of the Women in ITS (Intelligent Transportation Systems) UK and has acted as an evangelist for connected vehicles and big data during her TEDx talk in 2017.

Moody Hadi – Group Manager – Financial Engineering at S&P Global Market Intelligence

Applied machine learning for spreading financial statements

Counterparty financial statements, particularly for small and medium enterprises can be difficult to handle. Financial analysts need to be able to distill out relevant line items in order to calculate their credit exposure to a counterparty for lending purposes. The solution solves a labor intensive, expert driven inefficient process and frees up the analysts to focus on their high value add operations. This involves combining Optical Character Recognition using pre-trained language neural networks, with context sensitive semantic matching. We will go over the developed ML pipeline and architecture.

ABOUT THE SPEAKER

Moody is a Senior Director of Financial Engineering at S&P Global – Market Intelligence. As a Group Manager in New Product Development within Market Intelligence, he leads a team focusing on applying modelling techniques, such as machine learning and data sciences to extract information value for risk management. Previously, he was Co-Head of Research and Development at Credit Market Analysis (CMA), where he lead the model development and research on Credit Default Swaps pricing and risk management. Prior to CMA, Moody was a Senior Quantitative Analyst at the Chicago Mercantile Exchange (CME) Group, where we worked on Over-The-Counter (OTC) Clearing of Interest Rate and Credit Derivatives and the SPAN Margining Algorithm. Prior to that he had several senior roles in analytical & technical consulting, spanning diverse areas from Asset-Liability Management (ALM) to Business Intelligence (BI).

Moody holds a Bachelors of Science in Computer Science from Georgia Institute of Technology, Masters of Science in Operations Research from Columbia University and MBA from the University of Chicago – Booth School of Business.

Jacob Claussen – Manager, Analytics/Data Science at Zynga

Discovering What Our Players Will Thank Us For

The gaming industry, and the entertainment industry as a whole, is a relentless battle for the human attention span. From acquiring the “right” audience to providing the “right” content, a game’s success hinges on identifying what is “right” better than your competition. Applying proper research techniques, identifying opportunities within this research, and devising smarter delivery systems to users require constant evolution in technology and methodology. In this talk, I will discuss how Zynga’s data science team has risen to this challenge, incorporating interdisciplinary techniques to fit the needs of our live-game operations.

ABOUT THE SPEAKER

Jacob Claussen has been at Zynga for the past three years, where his innovative work has redefined the role of data science in live-game operations. Past projects have focused on predictive modeling, deep learning, anomaly detection, segmentation, experimentation, simulation, recommendation, and optimization, among other -tion words. As a manager, he has continued to contribute to live operations while building growth and development opportunities for a team of top analytics talent.

Prior to starting at Zynga, Jacob completed his Physics PhD at The University of Texas at Austin, where his research focused on quantum physics/string theory.

Jesse Barbour – Chief Data Scientist at Q2ebanking

Improving Information Retrieval with Neural Language Models

Traditional information retrieval systems, built upon inverted indices and text vectorization techniques like TF/IDF, have served us well for some time, but advancements in neural language modeling present opportunities for significant improvement. We will discuss what traditional information retrieval systems do well and what can be improved, along with how some of those improvements might be implemented. Along the way, we will explore the role of semantics, the criticality of performance, and how to balance these two concerns in a production system.

ABOUT THE SPEAKER

Jesse Barbour is the Chief Data Scientist at Q2ebanking. He uses machine learning to discover hidden structure, insight, and value from data. His work has paved the way for multiple ground-breaking products including Sentinel, Q2’s flagship security offering, and SMART, the company’s targeting and messaging platform. Jesse’s 10 years in the financial services industry have been driven by a relentless passion to deliver technologies that have a fundamental, positive impact on individuals and communities.

Jordan Birdsell – Chief Machine Learning Architect at phData

MLOps: The Assembly Line of Machine Learning

The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation, we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.

ABOUT THE SPEAKER

With more than 10 years of experience launching successful Analytics and Data Science organizations at large enterprises, Jordan Birdsell now brings his expertise and knowledge to our clients around the globe. As head of machine learning services at phData, Jordan and his team help companies escape from the endless loop of machine learning POCs and move them into driving real value.

Eero Laaksonen – CEO at Valohai

Asynchronous Model Training & Version Control for Jupyter Notebooks

While notebooks are great for early experimentation with ML models, they are not ideal to facilitate company-wide, transparent and production-scale ML development. During this presentation, you’ll learn how to run your notebook experiments asynchronously on the cloud while having automatic versioning for code, data, hyperparameters, and more.

ABOUT THE SPEAKER

Eero is a San Francisco based startup founder and CEO. Their company Valohai focuses on large-scale, in-production machine learning and deep learning tooling. Eero is a frequent speaker in ML events around the world.

Dedy Kredo – Head of Customer Facing Data Science Organization at Explorium

Engineering new, relevant features that move the needle on your predictive models is one of the hardest problems in data science. Finding and integrating external data to test out those features is, arguably, an even bigger challenge. In this session, Maor Shlomo, CEO of Explorium, will talk about how the next frontier of data science takes feature generation from an art to a science by opening up additional data sources to achieve breakthroughs in predictive models.

ABOUT THE SPEAKER

Dedy leads the customer-facing data science organization at Explorium, enabling customers to deliver real business impact by discovering the most relevant data and features to supercharge their predictive models.

Prior to Explorium, Dedy held a number of product and leadership roles, the most recent was Co-Founder and Chief Product Officer of a big data analytics platform which enabled customers to radically simplify purchasing decisions and optimize online shopping experiences.

Dedy is an avid rock climber who is always on the lookout for the next big challenge.

Chris Lindner – Manager, Product Science at Indeed

What Indeed’s Job Market Data can tell us about Demographics and Trends in Data Science Careers

Over the past decade, data science has exploded as a lucrative, high-demand career. During this time, we’ve seen rapid expansion in both the demand for data scientists, and for the number of individuals trying to get into the field. But what exactly does the title “data scientist” even mean? Who are these “data scientists,” and where do they come from? Are we becoming overly flooded with data science candidates? What emerging trends do we see as more and more jobseekers enter the market to meet this growing demand? Indeed is the world’s #1 job site. Using our data on job postings, searches, and resumes, I’ll try to explore the answers to some of these questions, and paint a picture of what the job market looks like today, and where it is going in 2020 and beyond.

ABOUT THE SPEAKER

Chris Lindner is a Product Science Manager at Indeed, the world’s #1 job site. He became a professional data scientist in 2014, and has conducted hundreds of interviews of data science job candidates and sifted through countless resumes. Chris is also a PhD astrophysicist and semi-professional poker player.

Raktim Saha – Director, Digital Insights at CGI

Process Automation using Machine Learning

The focus of this talk is to showcase how Machine learning and AI is leveraged to radically outperform traditional loan-underwriting acceptance process for one of the largest lenders in US by utilizing a broader set of market, demographic, and various events data. Implementing an automated data-driven process in a large Enterprise and especially in a highly regulated industry has its own challenge spanning from data collection to accuracy of insights, and finally streamline the overall process. The speaker will cover the business, data, and technical aspects of this implementation.

ABOUT THE SPEAKER

Mr. Raktim Saha is a Senior Advisor and Director with CGI Technology and Solutions Inc, he represents the Emerging Technology Practice business unit within CGI US. He specializes in Data and Analytics Product Development and modernization of Data Management practices.
Raktim has more than 20 years of experience with consulting and industry in developing Technology Strategy & Solutions, and leading Engineering & Analytics organizations towards Integrated Data Solutions and Data Products re-invention. Working with CGI’s customers, he is helping evangelize the transition of various industry patterns from traditional data warehousing to sophisticated Big Data Analytics driven culture and Big Data AI solutions, which has improved business process and customer engagement. Raktim has graduated from Florida International University on MIS and has an undergrad in Engineering from India, and he lives at North Austin with his wife and two teenage daughters.

Micah Price – Principal Associate Data Scientist at Capital One

New Frontiers for Data Science in Banking

Capital One is one of the pioneers of ‘Information based strategy’ which is essentially what we call today as data science. The world of data science in banking has evolved over time and now data science powers strategies in many diverse areas of a bank, aided by cutting-edge technologies. In this talk, we will go over a glimpse of its past, present and how it is changing banking for good as we move toward the future. In particular, we will also explore how cutting-edge algorithms and technologies are improving customer experiences.

ABOUT THE SPEAKER

Micah Price is a principle data scientist within a tech innovation team at Capital One Financial Services specializing in computer vision and natural language understanding problems. His team has brought multiple products from prototype to market, including a car shopping experience powered by machine learning and augmented reality, showcased at SXSW.

Micah graduated with an M.S. in Physics from Montana State University, where he studied Physics Education Research and managed an outreach program for NASA’s Montana Space Grant Consortium, then attended the Signal Data Science bootcamp in Berkeley, California. His interests include fairness in AI, effective altruism, and being outside.

Ilya Katsov – Head of Data Science at GridDynamics

Building a Data-Driven Price Management System: Lessons Learned and Best Practices

Revenue management in manufacturing and retail is a complex problem that requires to make thousands of decisions, both strategic and tactical. In this talk, we discuss the lessons learned from building several ML-driven price management systems. We will present case studies on how predictive models were used to improve decision support tools for price managers, and how prescriptive and optimization models were used to automate certain classes of pricing decisions. We will also discuss how to identify price management tasks that can be solved correctly using ML methods, and what is a reasonable boundary between decision automation and decision support.

ABOUT THE SPEAKER

Ilya Katsov is a Head of Data Science at Grid Dynamics. He joined Grid Dynamics in 2009, and since then has been leading engagements with a number of major retail and technology companies, focusing primarily on Big Data, Machine Learning, and Economic Modeling. He is currently leading the consulting practice that helps clients to become successful AI adopters and deliver innovate AI solutions. Prior to joining Grid Dynamics, Ilya worked at Intel Research on emerging wireless communication technologies. He is the author of several scientific articles and international patents, and also authored a book, “Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations” (2017).

Seemit Sheth – Head of Data Science at Capital One

New Frontiers for Data Science in Banking

ABOUT THE SPEAKER

Seemit Sheth is a senior director and head of data science for Capital One Financial Services. Over the last 18 years, Seemit has built world class data science organizations for several lending businesses and developed multiple industry-leading data science products. He has been twice awarded Capital One’s top award from their CEO.

Prior to Capital One, Seemit worked as a research biostatistician at the eastern Virginia medical school for one year with focus on survival analysis in oncology studies.

Seemit has Master’s degrees in both mathematics as well as statistics. He also has a bachelor’s degree in classic percussion music and plays Indian drums, Tabla.