Dynamic Talks: Portland Q4 2019
December, 4. Portland, OR, US - Bumped HQ
Come join us at the next event of our free technical meetup series, "Dynamic Talks", in Portland! This is an ongoing meetup series featuring technical talks from some of the leading experts in tech in major cities around the US. Enjoy talks about the most innovative subjects in: AI, ML, voice platforms, the Cloud and search. Every event is free, with complimentary food and drinks.
Head of Practice, Industrial AI, Grid Dynamics
Customer Intelligence: a Machine Learning Approach
In this talk, we will discuss automatic decision-making and AI techniques for customer relationship management. First, we will present a methodology that helps to develop highly automated promotion and loyalty management systems. Next, we will walk through practical examples of how advanced customer and content signals can be generated using predictive models, and how optimization and reinforcement learning techniques can be used for targeting, budgeting, and pricing decisions. This talk is for Data Scientists, Product Owners, and Software Engineers involved into marketing operations or development of marketing automation software and interested in ML-based decision automation techniques.
Ilya 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 managing the Industrial AI consulting practice that helps clients become successful AI adopters and deliver innovative AI solutions. 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).
Lead Data Scientist at Oregon Health and Science University
Challenges for AI in Healthcare
The use of AI in many industries has revolutionized operations and efficiency. In healthcare the progress is just beginning. Despite the promise of AI, why has the development lagged other industries? What issues are unique to healthcare that create challenges for common approaches? How can data scientists overcome these challenges and deliver on the promise of using data to reach multiple goals of improved quality, decreased cost, and greater patient satisfaction.
Dr. Graven is a trained health economist working in advanced analytics and datascience at OHSU. As Data Scientist, Dr. Graven builds and deploys predictive and forecasting models and conducts advanced analytics to support quality improvement operations at the OHSU hospital and system. Additionally he leads the Analytic Services program which performs opportunity discovery analyses on EMR data linked to claims, patient satisfaction, or external benchmark data to aid in population health and value based care. As Affiliate Assistant Professor in the OHSU-PSU School of Public Health, Graven teaches health economics and provides mentorship to students in the masters and PhD programs. Graven received PhD from the University of Minnesota-School of Public Health and performed research with the State Health Access Data Assistance Center (SHADAC). Prior to his operational role OHSU, he was Research Assistant Professor at the Center for Health Systems Effectiveness (CHSE) at OHSU. His research interests include topics of health insurance coverage, behavioral health integration, healthcare workforce, cost effectiveness analysis, program evaluation, and analyses of learning health systems. Recently, he was health economist on awarded NIH grants leading economic evaluation for a randomized control trial of inpatient medical procedure and for employee wellness program for bus drivers.
Director of Product Management & Customer Experience at GoDaddy.
Applications of Big Data, Machine Learning and Artificial Intelligence in HIV Prevention, Treatment and Research
The global HIV pandemic continues, particularly in Sub-Saharan Africa. By 2025, 40 million people will be living with HIV. The global cost of the pandemic is in the hundreds of billions of dollars. Better treatment means more people are living longer and costs will increase. The use of existing and emerging technologies is rare. Research institutions don’t share data. Data that drive US HIV policy in 2019 are from 2017 because of the time it takes for the CDC and NIH to combine data. The are many opportunities for big data, ML and AI to have a broader and continued impact on the HIV crisis. The use of these technologies can identify new avenues of research and help prioritize and focus efforts. We are starting to see these technologies used more and more. Several case studies will be presented. For example, advances in HIV vaccine research by Dr. David Heckerman; research at UCLA and Georgetown University looking at how social media can be used for tracking and predicting the spread of the epidemic; and work by researchers to improve care utilization in South Carolina. The opportunities for commercial and non-profit ventures to apply existing and emerging technologies like big data, ML and AI are countless. Tech4HIV is an organization working to drive these efforts into the tech sector and provides opportunities and resources for engagement.
Kyle is a product leader with 23 years of experience working in the tech sector. He is an expert in delivering software solutions at scale. During his career, he has worked on a broad range of technologies in a variety of customer segments. He brings well-rounded experience that includes platform, server, desktop and mobile app development. Kyle is experienced in leading cross-functional technical teams to build and deliver world-class software solutions. In addition to his work with Tech4HIV, Kyle is Director of Product Management – Customer Experience at GoDaddy.
Dont miss the chance to visitData Science meetup in Portland on Dec, 4
Dont miss the chance to visit
Data Science meetup in Portland on Dec, 4
Dallas, TX, US
Seattle, WA, US