Confirmed Speakers now include:
Dr. Michelle Gregory
SVP, Data Science
Data Science & Engineering Lead
VP IT and Chief Digital Health Officer
VP and Chief Data Officer
Global Head of Data Science and Analytics
Director, Head of Data Architecture
Principal Data Scientist
Ella Alkalay Schreiber
VP, Data Science
Carly Brown Buxton
Director, Marketing Insights
Head of AI Initiatives / CAIO
Chief Data Scientist
VP, Data Strategy, Analytics and Machine Learning
Manager, Data Science
Innovation Technology Leader
We co-develop the Summit Agenda with our speakers so you can expect a variety of activities, sessions and networking opportunities that will really maximise what you gain.
Event highlights including:
- Artificial Intelligence in usage: cases of day to day applications and management
- Rewriting life: data science in the health industry
- Leading the Way - Start-ups that are advancing and applying AI and machine learning technologies
- Advanced analytics and IT governance And many more...
08:30 – 09:20
09:20 – 09:30
09:30 – 10:00
Artificial intelligence—AI—is already making healthcare more efficient. It’s not about robots replacing nurses; it’s about rerouting the many daily aggravations that get in the way of patient care. How can you start using AI to your advantage?
Join Girish Venkatachaliah, the VP of AI, Data Strategy, Analytics, and Machine Learning of athenahealth for an interactive online discussion. He’ll guide us through 5 steps of getting started with AI, sharing real-life examples of how AI is saving millions of hours of work per year for athenahealth’s customers.
10:00 – 10:30
The next phase of Elsevier’s content strategy is a transformation into information analytics. To accomplish this calls for the application of big data techniques and expertise in data structures and transformation. And the success is defined by product metrics which requires a strong collaborative relationships with product and technology.
10:30 – 11:00
Before the lunch of the day, let's break the ice and do a speed networking.
Task 1 - Introduce Yourself
Task 2 - What do you hope to learn from today's event?
Task 3 - What analytics challenges have your team experienced in the past 6 months? Were you able to find a solution?
Task 4 - Are there any good resources you'd like to recommend to people in your field?
Task 5 - What advice would you give to machine learning early adopters?
11:00 – 11:30
AM Coffee Break
11:30 – 12:00
MindMaze builds intuitive human machine interfaces through its breakthrough neuro-inspired computing platform. The innovations at the intersection of neuroscience, mixed reality and artificial intelligence are poised to transform multiple industries. As the Head of AI Initiatives and an highly experienced machine learning expert, Martin will present about the journey integrating AI & VR into the neurological healthcare applications.
12:00 – 12:30
According the Transportation Research Group, bad roads cost each US driver over $2,000 annually in unnecessary costs with an annual US cost of well over $100B and the costs of bad globally is estimated to be 5X that amount. A central challenge to maintaining good roads – that has existed since the Romans built the Appian Way over 2,000 years ago – is regular, thorough inspection of those roads. Two millennia ago a ‘liktor’ or road inspector sat on the back of a chariot looking for and making notes of imperfections and damage on the surface and along the edges of the road. He would then share those notes with the local road crew to fix. That process was tedious, expensive, dangerous and highly subjective. Regrettably little has changed in two thousand years, save for the fact that the chariot is now a Ford150 or Toyota truck.
Visual inspection is still the most popular method of road inspection, and not just for roads but for the inspection of large infrastructure. Fortunately, rapid advances in both AI/deep learning and the availability inexpensive, yet precise, sensors is transforming infrastructure monitoring and maintenance, with the result of lower costs and far greater transparency. My presentation will focus on how this revolution in 'asset transparency' is happening now by, among other things, using the presenter’s company, RoadBotics, as a prime example of this transformation. The presenter will highlight the opportunities and challenges of deploying this technology not only through the example of his own company but other similar companies assessing other infrastructure. We use deep learning and standard smartphones to assess road surfaces and roadways. We were spun out of Carnegie Mellon Robotics Institute in 2016 and we serve over 100 cities in 16 US states and 4 countries.
12:30 – 14:00
14:00 – 14:30
This presentation will be delivered by the Data Science and Engineering Lead at Wells Fargo.
14:30 – 15:00
Analytics & Behavior Change (A&BC) is a central component in Aetna’s mission to building a healthier world. The group leverages machine learning, predictive analytics and statistical modeling to not only help improve the quality of health care and manage costs but also to better understand individuals and help them on their personal health journeys. In this talk, Himanshu will share two examples to demonstrate how Big Data and AI are helping to create solutions at a bigger scale and with greater speed.
15:30 – 17:00
Given the resurgence of neural network-based techniques in recent years, it is important for data science practitioner to understand how to apply these techniques and the trade-offs between neural network-based and traditional statistical methods. This lecture discusses two specific techniques, real-world applications and their advantages and disadvantages, and demonstrations of exploratory time series data analysis.
- Vector Autoregressive (VAR) Models – one of the most important class of multivariate time series statistical models applied in finance.
- Recurrent Neural Network (RNN) - a neural network architecture suitable for time series forecasting.
17:15 – 18:30
08:00 – 08:50
08:50 – 09:00
09:00 – 09:30
Clinical trials are fraught with missteps and data quality issues that often create costly delays in bringing life-saving drugs and diagnoses to the market. Often times, data quality issues and data integration challenges cause clinical trials to fail. Today’s complex clinical studies, which are also distributed around the globe, generate vast amounts of data from disparate sources, mobile heath devices, Internet of Things (IOTs), systems, vendors and clinical endpoints. Not only is the clinical data voluminous, it’s often comes at tremendous velocity commingled with data variety including a combination of structured, unstructured and binary data format. On top, these disparate systems don’t talk to each other in a standard manner, creating data silos and inhibiting real time integrated clinical trial information for real time decision making, risk analysis and regulatory compliance monitoring. This presentation will be focusing on the architecture of a modern, cloud-based real time data integration and analytics platform that ingests any type of clinical data (structured, unstructured, binary, lab values, etc.) at scale from any data sources. The presentation will focus on laying out the architectural building blocks of this modern data platform including explaining our “serverless” data pipeline, cloud architecture and deployment, continuous integration and deployment pipeline, platform scalability, data governance and master data management.
09:30 – 10:00
Unleash Data Power with the Right Data Architecture
10:00 – 10:30
Hopper was built on the premise that the combination of big data + machine learning could empower travellers in a completely unprecedented way.
The prediction algorithm upon which the app is based was engineered to take in massive amounts of data (~1 trillion price points/month, with five years of historical data and many trillions of archived prices) and consistently learn based on feedback from user behaviour to offer the most accurate predictions year after year.
The strength of our predictions has created a relationship of trust between Hopper and its users. We’re able to capture our users’ intent in an unprecedented way in the industry because users start watching their trips (i.e. enable push notifications) 4-5 months in advance of departure. During that period, we build a relationship through an ongoing conversation about their trip, which primarily takes place via push notifications. The conversation could be telling the user the best time to buy, but it could also be making recommendations and learning more about their preferences/intent. We’ve sent over two billion notifications to date, and about 90% of our sales come directly from push notifications.
10:30 – 11:00
Traditional marketing strategies target customers who are most likely to respond to ads. This can cause wasted marketing investment since those customers may buy even without seeing an ad. Uplift modeling predicts the causal effect of marketing campaigns by comparing the conversion rates of both treated and control groups, and thus selects the most “persuadable” customers for targeting. In this presentation Jen will discuss how data scientists at Wayfair take uplift modeling approaches to drive incremental revenue and improve marketing RoI.
11:00 – 11:30
How can data people implement strategy work? As Director of Market Insights for EF Education First, Carly leads market research and user research, uncovering actionable insights and putting data to work. In this talk, Carly shares best practices in how to motivate cross-functional teams to apply insights in ways that move the business forward.
I. Introduction: Familiar formats of how we receive information
II. A challenge for data scientists, quant/qual research teams: how to impart our insights across the company, to compel people to take action
III. This is a barrier that it often feels like we should not cross (data people should not be salesmen); here’s why we should
IV. Examples of putting data to work
11:30 – 12:00
This is a session dedicated to women technology leaders, scientists and engineers in the data science world. Extraordinary women leaders who are pushing the boundary of the AI technology and business world will sit together to have an in-depth communication around topics like AI technologies, the future of data science, women power in technology leadership, women's career development as well as tips they'd like to share with peer women leaders and young ladies who regard them as role models.
Director, Marketing Insights, EF Education First
VP, Data Science, Hopper
Manager, Data Science, Wayfair
12:00 – 13:00
Day 2 Lunch Break & End of the Event
Don't just take our word for it
We’re always looking for go-ahead, fascinating sponsors to align with our global leadership Summits which are packed full of senior connectivity potential. Our delegates are very select so we limit the number of sponsors for each of our Summits but if your values connect with ours, please reach out to Allan. We’d love to welcome you to our tribe!
Previous sponsors include:
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The summit will take place in The Rotunda, 3rd Floor of the conference centre.Book your stay