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The Rock Stars of Pervasive, Predictive Analytics

Want to know how to avoid the 4 biggest problems of predictive analytics – from the Principal Data Scientist at Microsoft? Take a little time to discover how predictive analytics are used in the real world, like at Lithium. You can stop letting traditional perspectives limit you with help from Mashable’s chief data scientist.

Come to this dynamic one-day symposium.

 

Why Attend Rock Stars of Pervasive, Predictive Analytics?

Predictive analytics is a compelling reality with a lot of scary baggage. Every organization must grapple with it or become obsolete, BUT -

  • How can predictive analytics manage the unknown?
  • How much should predictive analytics know about your company? And you?
  • What if you interpret your analytic data incorrectly?
  • How can you scale it, especially if you can’t find data scientists to help?
  • If your management doesn’t support predictive analytics, how can you succeed?

Most days, predictive analytics has more questions than answers – and you need problem-solving you can rely on.

That’s where Rock Stars comes in. Only in this face-to-face environment can you get real answers and collaboration on your biggest challenges from the leading minds in the field.

What Will You Experience?

Meet the world’s experts in predictive analytics in an intimate environment where you can interact, question, problem-solve, and network. There is no other forum where this kind of future vision meets real-world solutions face-to-face – and you only invest one day of your time.

At Rock Stars you'll -

  • Hear presentations from and meet 6 dynamic C-suite level experts in the field
  • Interact, question and probe
  • Participate in a dynamic technology panel of experts
  • Focus on your challenges
  • Enjoy lunch and cocktails as a great opportunity for comparing notes with your peers
  • Invest only one day of your time - and solve years of problems

 

 

October 18, 2016 - Mountain View, CA

 

 

 

 

 

Speakers

 

Juan Miguel Lavista
Lavista
Haile Owusu
Owusu
Piyanka Jain
Jain
Dr. Florian Neukart
Neukart
Scott Gnau
Gnau

Juan Miguel Lavista

Principal Data Scientist
Microsoft

Juan Miguel Lavista is currently the Principal Data Scientist for Microsoft Data Science team (DnA), where he works with a team of data scientists searching for insights in petabytes of data. Juan joined Microsoft in 2009 to work for the Microsoft Experimentation Platform (EXP) where he designed and ran randomize control experiments across different Microsoft properties. In Microsoft, Juan also worked as part of the Bing Data Mining team. Before joining Microsoft, Juan was the CTO and co-founder of alerts.com. Juan has 2 computer science degrees from the Catholic University in Uruguay, and a graduate degree in Data Mining from Johns Hopkins University. He lives in Kirkland, WA, with his wife and daughter. He has been a speaker at conferences in many countries including the US, Canada, Argentina, Colombia and Uruguay, and he also was a TedX Speaker in 2010. Juan recently delivered a keynote at the Big Data Summit 2014. His talk, "The Good, the Bad, and the Ugly of Big Data and Data Science”, focused on the myths of big data and why the important word in data science is science and not data.

Haile Owusu

Chief Data Scientist
Mashable

Haile Owusu is Chief Data Scientist at Mashable where his main responsibility is the development and refinement of the company's proprietary Velocity technology, which predicts and tracks the viral life-cycle of digital media content. Prior to joining Mashable Haile led all research efforts for SocialFlow, one of the leading social media optimization platforms for leading brands and publishers. Haile specializes in statistical learning as applied to predictive analytics and has a background in theoretical physics, including a Ph.D from Rutgers University, a Masters of Science from King's College, University of London and a B.A. from Yale University.

Piyanka Jain

CEO
Aryng

Piyanka is a highly regarded thought leader in analytics and has been a keynote speaker at business and analytics conferences including American Marketing Association, Predictive Analytics World, and GigaOm. She speaks about data driven decision making to gain competitive advantage. In 15 years as an analytics leader, she has had 150M+ demonstrated impact on business. As a problem solver, she seeks out patterns and insights to drive change in her client’s organizations and impact top levers of business. She considers customer satisfaction, empowerment and positive engagement as the highest rewards, and dollar impact as a natural consequence of these things. Her book, “Behind Every Good Decision,” is a guide for business managers on data driven decision making through business analytics.

Dr. Florian Neukart

Principal Data Scientist
Volkswagen Group of America

Dr. Florian Neukart works as Principal Data Scientist for Volkswagen Group of America, where he is concerned with analyzing and interpreting data over their whole value chain, ranging from sensor data of robots and cars to financial and logistics data. Additionally, he works on solutions in the field of A.I., i.e. related to environment perception and natural language understanding. Before he moved to the U.S., he was responsible for technology and research in one of Volkswagen’s most advanced applied research departments. Apart from that, he is employed by Leiden University as a lecturer for Quantum Computer Science and is concerned with how to leverage quantum physical effects for the advancement of A.I. as well as how to create artificial minds.

Scott Gnau

CTO
Hortonworks

Scott has spent his entire career in the data industry, most recently as president of Teradata Labs where he provided visionary direction for research, development and sales support activities related to Teradata integrated data warehousing, big data analytics, and associated solutions. He also drove the investments and acquisitions in Teradata’s technology related to the solutions from Teradata Labs. Scott holds a BSEE from Drexel University.

Partha Sen
Sen
Tim Persons, PhD
Persons
Michael Wu
Wu
Joshua Greenbaum
Greenbaum

Partha Sen

Founder and Chief Executive Officer
Fuzzy Logix

A passion for solving complex business problems using quantitative methods, data mining and pattern recognition began as a hobby before leading Partha Sen to found Fuzzy Logix and develop its flagship product, DB Lytix, in 2007. Before founding Fuzzy Logix, Partha held senior management positions at Bank of America where his achievements included leading the initiative to build a quantitative model driven credit rating methodology for the entire commercial loan portfolio. In the portfolio strategies group, Partha led a team to devise various strategies for effectively hedging the credit risk for the bank’s commercial loan portfolio and for minimizing the impact of mark-to- market volatility of the portfolio of hedging instruments (Credit Default Swaps, Credit Default Swaptions, and CDS Indexes). Prior to working at Bank of America, Partha held managerial positions at Ernst and Young and Tata Consultancy Services. He has a Bachelor of Engineering, with a major in computer science and a minor in mathematics from the Indian Institute of Technology. He also has an MBA from Wake Forest University.

Tim Persons, PhD

Chief Scientist
United States Government Accountability Office, Advanced Data Analytics

Dr. Timothy M. Persons is a member of the Senior Executive Service of the U.S. federal government and was appointed the Chief Scientist of the United States Government Accountability Office (GAO) in 2008. In addition to leading advanced data analytic activities at GAO, he also serves to direct GAO’s Center for Science, Technology, and Engineering (CSTE), a group of highly specialized scientists, engineers, and operations research staff. In these roles he directs science and technology (S&T) studies (cf., www.gao.gov/technology_assessment/key_reports) and is an expert advisor and chief consultant to the GAO, Congress, and other federal agencies and government programs on cutting-edge S&T, key highly-specialized complex systems, engineering policies and best practices, and original research studies in the fields of engineering, computer, and the physical and biological sciences to ensure strategic and effective use of S&T in the federal sector. Dr. Persons is a 2014 recipient of GAO's Distinguished Service Award, a 2012 recipient of the Arthur S. Flemming award, a 2012 recipient of GAO’s Big Picture Award, a 2007 Director of National Intelligence Science and Technology Fellow, and was selected as the James Madison University (JMU) Physics Alumnus of 2007. He has also served as a radiation physicist at the University of North Carolina at Chapel Hill. He received his B.Sc. (Physics) from JMU, a M.Sc. (Nuclear Physics) from Emory University, and a M.Sc. (Computer Science) and Ph.D. (Biomedical Engineering) degrees from Wake Forest University. He is a senior member of the Institute for Electrical and Electronic Engineers (IEEE), serves as an ex officio council member of the National Academy of Sciences' Government-University-Industry Research Roundtable (GUIRR), serves as a Member of the Board of the Senior Executives Association (SEA), and is a member of the World Future Society Global Advisory Council.

Dr. Michael Wu

Chief Scientist
Lithium

Dr. Michael Wu is the Chief Scientist at Lithium, where he currently applies data-driven methodologies to investigate the complex dynamics of the social web. Michael works with big data and has developed many predictive and prescriptive social analytics with actionable insights. His R&D won him the recognition as a 2010 Influential Leader by CRM Magazine. In addition to purely empirical methods, Michael also leverages social principles that govern human behavior to decipher the intricate human components of social interactions.
Michael believes in knowledge dissemination, and speaks internationally at universities, conferences, and enterprises. His insights are made accessible through “The Science of Social,” and “The Science of Social 2”—two easy-reading e-books for business audience. Prior to industry, Michael received his triple major undergraduate degree in Applied Math, Physics, and Molecular & Cell Biology; and his Ph.D. from UC Berkeley’s Biophysics program, where he studied visual processing within the human brain.”

Joshua Greenbaum

Principal
Enterprise Application Consulting

Joshua has over 30 years of experience in the industry as a computer programmer, systems analyst, author, consultant, and industry analyst. Josh regularly consults with leading public and private enterprise software, database, infrastructure, implementation, and hardware companies, and advises end users on technology infrastructure and applications selection, development, and implementation issues.

 

 

 

Agenda

 

 
 
 
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Morning Session: 9:00 a.m. – 12:30 p.m.

Juan Miguel Lavista

Principal Data Scientist
Microsoft

Everything You Always Wanted to Know About Predictive Analytics* *But Were Afraid to Ask

View Presentation Slides Here

We live in an amazing era – the era of Big Data – where the possibilities of using data seem endless. Where collecting and storing data is considered cheaper than deleting it simply because of the possible potential in that data. Yet despite the possible secrets this data may hold when analyzed correctly, it is even easier to use the data incorrectly and make serious mistakes. And it is these mistakes that are the most dangerous and costly, particularly given the equal amounts of hype on what one could do with the data and the actual confusion felt by most people around. These vast amounts of data and the power they represent mean that we live in a time where it is more important than ever to really understand how to work with data and how to avoid simple mistakes like assuming that correlation implies causation or improperly understanding the biases involved in your data set. In this talk, I will review the lessons learned and the pitfalls I have found with using big data and Predictive Analytics. From data to insights, from machine learning to controlled experiments, I will discuss the power of big data, predictive analytics and data science, the myths surrounding it, and both its problems and successes.
 

Piyanka Jain

CEO
Aryng

Top Myths of Predictive Analytics

View Presentation Slides Here

Predictive Analytics is powerful, it can help predict an event or a behavior at an individual customer level. It can also help spot golden nuggets in the deep-wide-big data ocean. But, it is also a technique that is not well understood. With all the recent buzz about Predictive Analytics, it seems like a new tool. Is that so? In this talk, Piyanka will explain the reality of building and maintaining an impactful Predictive Model and explore questions like: Is Predictive Analytics new? Is it a crystal ball? Is it perfect? Can it be built quickly and cheaply? Is it going to solve all my business problems? Does it always work? Can anybody learn how to build a model?
 

Michael Wu, PhD

Chief Scientist
Lithium

Beyond Prediction: From BI to AI

View Presentation Slides Here

Many modern BI tools are still fairly rudimentary. They merely present descriptive summaries of past data in dashboards and reports. More advance BI systems offer predictive capabilities in addition. These systems can forecast the future and estimate unknown quantities. The most advance BI today can even prescribe a series of recommended actions. Regardless of how advance your BI is, it is often that case that human still make the ultimate decision about what to do. How can BI systems become more intelligent and behave more like true artificial intelligence (AI)?

Dr. Florian Neukart

Principal Data Scientist
Volkswagen Group of America

The Power of Today’s Predictive Analytics and What’s Next

View Presentation Slides Here

Understanding the present to obtain knowledge about the future has always provided significant advantages to those motivated to learn from observations. In our time-critical world, this is even more so, and the power of predictive analytics (PA) makes the future accessible for everyone who is willing to trust data and math. We can hardly find any single branch that would not or has not yet benefitted from applying PA by predicting or analyzing customer churn, risk, stock prices, consumer behavior, clicks, malfunction, sentiment, illnesses, theft, fraud, social interaction, traffic, and a lot more. Advanced use of PA within organizations is not restricted to developing modular solutions but covers the whole value chain, and solutions leverage data from in- and outside the company. The entire data science stack has become an essential technology in our everyday lives, and the analysis of data volumes based on search, pattern recognition, and learning algorithms provides us with insights into the behavior of processes, systems, nature, and ultimately behavior, opening the door to a world of fundamentally new possibilities. What is possible today, and what if we go further and allow for artificial intelligence (A.I.) controlling whole processes, branches, or companies and provide it with the ability to interact with the physical world? Is that something we want to go for and what are the dangers and risks? And we must not forget about science ethics and data privacy...
 

Lunch: 12:30 p.m. - 2:00 p.m.

Afternoon Session: 2:00 p.m. – 5:00 p.m.

Joshua Greenbaum (moderator)

Principal, Enterprise Applications Consulting

Michael Wu, PhD

Chief Scientist, Lithium

Scott Gnau

CTO, Hortonworks

Partha Sen

Founder and Chief Executive Officer, Fuzzy Logix

PANEL: Ensuring Success with Next Generation Predictive Analytics

The technology that can enable a new generation of predictive analytics is here, but the road to creating business models and business processes that can unleash the power of predictive analytics is still unclear for many companies. The questions of which predictive analytics are right for which situations, and how those analytics will be built, deployed, and consumed remain undetermined for many. This panel will dig into the “who”, the “what” and the “how” of predictive, pervasive analytics and help decision makers find the right path to success and avoid the pitfalls of failure.

Tim Persons, PhD

Chief Scientist
United States Government Accountability Office, Advanced Data Analytics

Predictive Analytics: Opportunities, Challenges, and Cross-Sectoral Impacts

View Presentation Slides Here

There has been an unprecedented increase in the quantity and variety of data generated by humankind, with an estimated 90 percent of the world’s data being produced in the past two years. This talk will consider how technological innovation has improved society’s ability to collect, store, and analyze information; and how big data -- large and complex sets of data that cannot be readily managed and analyzed using conventional desktop computers and databases -- influences every sector of society and enables insight on trends from the data itself (i.e., predictive analytics). Leaders across the private, public, academic, and non-profit sectors are grappling with how to understand the opportunities and limitations of these new capabilities. This talk will explore how predictive analytics has the potential to help us better understand the behavior of markets, businesses, populations, and individuals; improve decision-making; target products or services more effectively; manage resources in a more sustainable way; and influence people’s behaviors. These developments will have profound cross-sectoral impacts and will fundamentally change how future business is done.
 

Haile Owusu

Chief Data Scientist
Mashable

Using Data Science to Draw Concrete Behavioral Outcomes

View Presentation Slides Here

Don’t let traditional perspectives limit your company in terms of algorithm development and deployment, statistical modeling and underlying processes that lead to social engagement. Learn strategies to predict the future behavior of your audience.

 

Cocktail Reception 5:00 p.m.–6:30 p.m.

 

 

 

Sponsorships

 

 

 

      Sponsors:
                    

 
Attendee Industries
Industry
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Reach the Decision-makers in Pervasive, Predictive Analytics – Present and Future

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Panel Speaker Slot

Demo Showcase Salon

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Luncheon Sponsor

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Lunch Roundtable Sponsor – Bring a current customer and share your company’s technology story with a captive audience during lunch.

Table Top

Official Program Advertising

Conference Bag Insert

 

Testimonials:
"We are very enthused about all the hot leads and intimate conversations we had with the attendees at the IEEE Rock Star conference. This event was a great opportunity for us because we met the right people  -- the tech decision makers. The quality of the event was excellent for both attendees and vendors. We look forward sponsoring more of IEEE Rock Star events." - Deron E. Tross, Enterprise Account Manager, BAE Systems Applied Intelligence.

"The Rock Star Event generated a lot of great business conversations and was extremely well run. I would recommend the event to anyone looking to attract high quality prospects." -Darin Pendergraft, VP of Marketing, SecureAuth

 

Sponsorship Contact

For more information and to secure your sponsorship and/or exhibit space, please contact:
Helen Scott
helen.scott@computer.org
714-816-2175

 

 

Venue

 

 

The Computer History Museum

 

Rock Stars of Pervasive, Predictive Analytics will be held at the The Computer History Museum. The Museum is dedicated to preserving and presenting the stories and artifacts of the information age and exploring the computing revolution and its impact on society. The Computer History Museum is a nonprofit organization with a four-decade history and in 2003 opened the Mountain View California building previously occupied by Silicon Graphics.
 
Computer History Museum is located just off US 101; Shoreline Blvd Exit. Parking Computer History Museum offers free onsite parking.
 
Distance from San Jose: 20 miles drive time; 15 minutes 
 

Attend the Rock Stars of Pervasive, Predictive Analytics

The Rock Stars of Pervasive & Predictive Analytics will be held on October 18, 2016 at the The Computer History Museum.

Computer History Museum
 

The Computer History Museum
1401 N Shoreline Blvd
Mountain View, CA 94043, USA

Phone: (650) 810-1010
Fax: (650) 810-1055

Directions from San Jose via US-101 North

Take US-101 North toward San Francisco. Take Shoreline Blvd Exit. Turn right onto Shoreline Blvd. Cross through intersection. Museum is on your right.

Distance from San Jose: 20 miles

Drive time: 15 minutes

 

 

Register

 

 
 
 
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Register Now for Rock Stars of Pervasive, Predictive Analytics 2016

 

18 October 2016 Mountain View, CA

IEEE/IEEE Computer Society Member: $329 | SPECIAL DISCOUNT: $79
Non-member: $399 | SPECIAL DISCOUNT: $99
Team: $199 each for groups of three or more | SPECIAL DISCOUNT: $59
Students: $59

Discounts also available for multiple events.