Building A Smarter Media Marketplace: Machine Learning & AI Demystified for TV Advertising
Yuling Ma – Chief Technology Officer at FreeWheel
With data as the connective tissue of the media advertising business, a new way of thinking is needed to solve the unique challenges of applying data, machine learning and artificial intelligence across the TV advertising ecosystem. In this special presentation, Yuling Ma, Chief Technology Officer, FreeWheel, will walk through the latest thinking from FreeWheel.
Chief Technology Officer at FreeWheel
Yuling Ma is chief technology officer for FreeWheel, a Comcast company and global technology platform that is powering the future of the television marketplace. As CTO, she leads the development and operation of FreeWheel’s technology platform and network operations as well as the company’s global engineering team spanning the United States, Europe and China. Yuling partners with FreeWheel’s teams to fuel the expansion of the company’s technology solutions across the ecosystem to make FreeWheel the connective tissue between buyers and sellers, transforming how the industry works today and its future direction. Previously, Yuling served as senior vice president of engineering and general manager overseeing FreeWheel’s Beijing office. In this role, she led an engineering team that achieved significant product development and innovation milestones, including the creation of a new marketplace technology and programmatic trading capabilities. She also collaborated with FreeWheel disciplines spanning engineering, product marketing and revenue globally to pioneer and deliver innovative, customer-focused ad management solutions. Yuling has more than 20 years of engineering experience and has successfully built and scaled engineering, technology, research and development and operations teams at global and start-up organizations. Yuling holds a bachelor’s and master’s degree in computer science from Tsinghua University in China and is a PhD candidate in Computer Science at the University of Maryland, College Park. Yuling currently resides in Beijing, China and plans to move to New York later this year.
Considerations for Successful Model Management
Saira Kazmi – Enterprise Data Strategy and Engineering at CVS Health
AI/ML is top of mind for leaders across academia and the industry. Many have tried and failed several times before successfully deploying models that bring real business value or have an impact. Even after a model is deployed, constant maintenance and monitoring is required to ensure that the model is relevant and makes good decisions that are still applicable to the changing business environment. Key factors leading to successful deployments include: * Availability and maturity of data to build a model and availability of this data when making real-time decisions * Thorough understanding of the business problem * Understanding of nuances leading to data variability * We can often overestimate what models can do – a simple test “Is the Candidate task simple for a human to solve?” can help. * Automation will lead to ROI (the problem is large enough to automate) * Mechanisms are in place to track the algorithm performance * There is a way to provide feedback on the solution and model decisions * The model is kept alive (refreshed with new data at regular intervals.
Enterprise Data Strategy and Engineering at CVS Health
Saira Kazmi, Ph.D., is an Engineering leader in Data Science with the ability to align cross-functional technology, business, and research teams to deliver advanced analytics capabilities into business workflows. She received her Ph.D. in Computer Science, focusing on Bioinformatics from the University of Connecticut, and her post-doctoral training in Medical Informatics from Yale University. Saira loves working with complex data and enjoys designing and implementing solutions for problems associated with generating, storing, and analyzing large amounts of data. Saira has extensive technical and leadership expertise in delivering novel solutions for complex business problems from inception to production. Domains of experience include Bioinformatics, Medical Informatics, Healthcare, Insurance, Business Analytics, Text Search, Patent, and Intellectual Property Analytics. She advocates metadata best practices and establishes standards and business processes to enable high-quality data-driven metrics and insights.
Innovate with AI & ML: Achieving the Data Driven Enterprise with DataStax & Cassandra
Reed Peterson – Field CTO – Telecom Strategy at DataStax
AI systems exhibit learning, planning, reasoning, decision making, and problem-solving. AI is a step deeper than Machine Learning and, when supported by a modern data stack, brings tremendous value to both enterprises and their customers. Join this session to: * Learn the ideal architecture & features required to deliver AI/ML solutions – including real time data, scalable infrastructure, intelligent replication, relevant data & dynamic provisioning *Discuss how the process should work along with some of the key challenges & pitfalls that limit success *Walk through ML & AI use cases and the key ways take advantage of them in your business
Field CTO - Telecom Strategy at DataStax
Reed is a dynamic and results-driven leader with global operational experience in 80+ countries. He has a strong 20-year track record in Management, Corporate Strategy, Strategic Partnerships, Business Development and Operations with deep domain expertise in the convergence of wireless innovation, 5G, IoT, Data, AI and Edge with a global network of trusted CXO relationships.
Scaling and Transforming Stitch Fix’s Visibility into What People Will Love
June Andrews – Senior Director of the Search Sciences Team at Nike
A central component of Stitch Fix’s ability to match clients with clothing they will love–is the data-driven curation of our expansive inventory. Knowing ahead of time how well our inventory will perform not only reduces costs associated with missing the mark on what clients will love; it also provides great insight into the inventory that will be most successful with a growing client base in expanding sales channels. Here we present the history, lessons learned, adjustments in the face of 2020’s historic challenges, and important milestones in developing a recommender system focused on inventory curation. This recommender system, Style Explorer, predicts what items our clients will love–often before those items have even been fabricated. Providing Style Explorer as a tool available at all stages of the Stitch Fix vertical supply chain has de-risked and augmented processes ranging from design and fabrication to purchasing. In the process, it has transformed and scaled our visibility into what people will love.
Senior Director of the Search Sciences Team at Nike
Dr. June Andrews recently transitioned to Senior Director of the Search Sciences Team at Nike. Previously, she led the Style Discovery Team at Stitch Fix to scale and transform Stitch Fix’s visibility into what people will love. She also led building a Monitoring & Diagnostics platform for GE’s airplane engines in use today. The platform has since been extended to turbines in renewable energy and power plants. At Pinterest, June created the Signals Program, a feature store, supporting over 50 ML engineers. At LinkedIn, she supported growth and engagement during a 50% gain in membership. June holds degrees in applied mathematics, computer science, and electrical engineering from UC Berkeley and Cornell. Her hobbies include teaching at Berkeley and serving on advisory boards.
Improving ML systems Beyond First A/B Test
Vijay Pappu – Senior ML Engineering Manager, Personalization Lead at Peloton
Senior ML Engineering Manager, Personalization Lead at Peloton
Vijay is an experienced ML leader with strong communication skills who is focused on building high quality engineering teams that deliver reliable, and highly scalable ML solutions with broad impact. Vijay’s specialties include over 10 years of experience in training and deploying scalable machine learning models, solid programming experience, and experience in high performance computing. Vijay currently leading personalization efforts at Peloton Interactive Inc.
Towards Mass Adoption of Computer Vision Application
Appu Shaji – CEO & Co Founder at Mobius Labs
Appu will discuss how next-gen computer vision has removed boundaries to adoption and is now readily available to everyone, including non-technical users.
During this talk you can see real life use cases of companies that have built AI-powered business applications without a staff of AI experts, complicated integrations or the risk of having their data exposed.
CEO & Co Founder at Mobius Labs
Appu Shaji Appu has over 18 years experience in Computer Vision and AI. He has started 3 companies, and founded Mobius Labs in 2018, with the goal of taking computer vision to every application. He previously headed AI teams at EyeEm, and has also co-authored highly cited academic publications.
Using Data to Optimize the Content Acquisition Lifecycle
Tim Yoo – Sr. Director, Head of Analytics at Roku Inc
The content acquisition lifecycle is complex and multi-faceted. Streaming services that strive to be both a competitive service and a meaningful source of entertainment to consumers need to efficiently value content as well as understand the complex relationship between value to the company and to consumers. Discover how analytics and modeling can be used to navigate through the various stages of the content acquisition lifecycle.
Sr. Director, Head of Analytics at Roku Inc
Tim Yoo is the Head of Analytics at Roku, America’s #1 TV Streaming Platform. He joined Roku in 2018 to oversee a rapidly growing team of analytics professionals and build out Roku’s analytics organization to provide data driven insights to help drive strategy and decision making across various functions at Roku including hardware products, marketing, engagement, content licensing, and advertising. Prior to joining Roku, Tim held various data science and product management leadership roles at Facebook, Playdom and Intel. Tim began his career in data science in the world of gaming startups. He holds a B.S. in Mathematics and M.S. in Statistics from Stanford University.
ESG as the signal in the noise: Using NLP and verified data assets to create a holistic measure of company resiliency
Rochelle March – Head of ESG Product at Dun & Bradstreet
Global changes have impacted countries and companies everywhere. From climate change, the Covid-19 pandemic, resource constraints and demographic fluctuations challenge the stability of even the longest-established enterprises. Traditional financial data is not enough today to provide a clear enough picture on sourcing, investment and insurance decisions. ESG data and metrics can serve as valued information and tools for competitive advantage. For Dun & Bradstreet, this means extending its efforts around business transparency to generate ESG intel that can help customers, investors and other stakeholders identify which companies are actively moving towards a different, and hopefully, more sustainable future. This presentation will showcase analysis that explores the relationship between ESG and financial performance, and will provide a deep dive into the NLP machine learning and analytical techniques used to create Dun & Bradstreet’s new ESG Rankings dataset and model.
Head of ESG Product at Dun & Bradstreet
Rochelle leads product strategy, development and delivery as Head of ESG Product at Dun & Bradstreet. She specializes in impact analysis related to carbon, water and the Sustainable Development Goals and in applying machine learning to ESG products. She also teaches data and analytics in Bard College’s MBA program, sits on the advisory board for USL Technology, Inc., a minority and women-owned sustainable building and engineering company, and is a mentor for fellows in the Environmental Defense Fund’s Climate Corps program and for STEM talent participating in the New York Academy of Science’s Junior Academy. Previously, Rochelle directed ESG innovation and analytics at S&P Global and worked as a sustainability consultant for SustainAbility (now part of ERM Group), Dunkin’ Donuts and the NRDC. Rochelle has had work cited by The Economist, The Guardian, IR Magazine and GreenBiz, and is a regular speaker on sustainability and data science topics. Before entering the sustainability field, Rochelle worked on cancer research at Weill Cornell Medical College and founded a graphic design company.
Recommendation Strategies for engaging with audiences of Conde Nast brands
Sriram Subramanian – Head of Data Science & Engineering at Condé Nast
Conde Nast is home to many iconic brands with wide ranging and influential content that engages audiences around the world. Audiences find our content via many channels: social, email, organic, and search. In this talk, we outline the machine learning-based recommendation and personalization strategy employed across all of these channels. Key elements of our strategy include: * Social: The most relevant content is promoted on social media *Email: Newsletters are personalized with content of interest to the recipient *On-site: Consumer experiences are enhanced by recommendations based on not only reader interest but also the context *Search: Editorial topic recommendations based on search trends. We walk through several of these use-cases and their positive impact on audience engagement.
Head of Data Science & Engineering at Condé Nast
Sriram is the Head of Data Sciences & Engineering at Condé Nast, the publisher of some of the most iconic titles in media such as The New Yorker, Vogue, and GQ. Before joining Condé Nast, Sriram founded Lighthouse Datalab, which focused on creating Artificial Intelligence solutions for major media and entertainment firms, and was eventually acquired by Conde Nast. He has also taught at the Sy Syms School of Business at Yeshiva University in New York. Sriram holds a PhD in Operations Research from Georgia Institute of Technology.