GLSEC 2017 Program & Schedule
May 22nd, 2017

The Eberhard Center

 

7:30 AM Check in

8:00 AM Coffee and Light Breakfast

8:45 AM Opening Remarks

Matt Fletcher: Conference Chair

Main

9:00 AM - 10:00 AM  Featured Speaker

Amen ra Mashariki: How NYC Uses Big Data and Analytics to Assist with Crisis Response

Main

One of the Government's major purposes is to ensure the safety and well-being of its citizens. This discussion will cover how the Mayor's Office of Data Analytics (MODA) uses citywide data and analytics to identify likely cases of tenant harassment throughout NYC. We will also discuss the case study in which MODA used machine learning in order to help lead a coordinated citywide response to the Legionnaire's outbreak during the summer of 2015. This will also include discussion about how we have built a one of a kind data sharing platform and process in NYC in order to be prepared for emergency situations like snow storms or hurricanes.

10:00 AM - 10:15 AM | Short Break

10:15 AM - 11:00 AM | Session Talks 

Joseph Roth: Faces from Academia to Industry

Main

Face recognition has been an important problem in computer vision for many decades.  This talk follows the progression of computer-based face recognition systems from the labs of academia to their widespread use in industry today.  We will see the impact of big data on creating robust, autonomous systems and explore some of the challenges bringing a research idea into production.

Dr. Allaa Hilal: Big Data Powering Connected and Autonomous Vehicles

Auditorium

With people spending about 4.5 years driving, today’s vehicles have the computing power of 20 personal computers, features about 100 million lines of programming code, and processes up to 25 gigabytes of data an hour. Connected and autonomous vehicles are projected to exponentially grow the data sizes. Big data analysis is evitable to enable the future of connected and autonomous vehicles. However, obtaining meaningful information from all of that data is no easy task. Data needs to be extracted, blended and analyzed in real time in order to make transportation smarter, greener and safer. In this talk, Dr. Hilal will discuss the existing challenges facing big data analysis in enabling the connected and autonomous vehicles. Moreover, it will also discuss the various data analytical challenges that needs to be resolved to transition today’s vehicles into intelligent transportation solutions.

11:00 AM - 11: 15 AM | Short Break

11:15 AM - 12:00 PM | Session Talks 

Shannon McCormick: Machine Learning with Google Tensor Flow

Main

The presentation will begin with an explanation of Google Tensor Flow. I will go over the history of the project and what its current features and requirements are. I will then go through a step by step example of building a neural network in the program. This will show how to input data, build then neural network, and use it to score new data. I will also show how to build the tensor flow graph to visualize the network and training process. The presentation will use the Python interface for Tensor Flow but mention other new interfaces that are available.

Jad Abou-Maarouf: Driving Innovation and Change in the Age of Rapid Disruptors

Auditorium

In the last few years many companies attempted to enter the big data space and 40% to 50% have failed.  The reality is that there is a combination of many factors needed to be successful; change, skills, culture, organization maturity, cost and business outcomes.  One failure can put you many steps behind and one win can put you few steps further.  Organizations will need to mature organically to create a foundation for innovation and agility to sustain progressive business outcomes and opportunities to disrupt.

12:00 PM - 1:15 PM | Lunch and Community Discussion

Tech Talks | Meetups and Beyond 

Moderator: Sara Gibbons

Main

Michigan Council for Women in Technology (MCWT)

GiveCamp

SGR Alliance

Girl Develop It

1:30 PM - 2:30 PM |  Featured Speaker

Carolyn Phillips: How Many Anecdotes Make Data?

Main

In our lifetime, the rate of data collection, data movement, and data processing has exploded.  There now exists whole new professions for engineering and detecting patterns in large streams of data.   As a member of a generation of scientists who have migrated from academic careers in the computational sciences to industry careers in data science and engineering, I will reflect on how advances in basic scientific research laid the ground work for the big data revolution driving so many industries today.

2:30 PM - 2:45 PM  Short Break

2:45 PM - 3:30 PM | Session Talk

Venu Vasudevan: Big Data: Bits of History, Words of Advice

Main

Like many grand challenges in computing, Big Data is a problem that pre-dates the term. Today Big Data is thought about in terms of a solution technology stack that addresses data lakes and data streams. However, successful commercial solutions in the space have to grapple with lifecycle related issues that are unique to data problems (vis-a-vis other software) that go beyond the stack. These could be scoping questions such as ‘what is the hard problem, and are there out-of-band business transitions that could make them go away’, or governance questions such as ‘do I really own the data that is critical to the solution’.

This talk will trace evolution of the big data problem and the technology stack in the context of 3 concrete big data products - the Iridium Satellite System (now in the news for launching SpaceX rockets), a big data based rich media product that went from lab to product, and looking at Big Data opportunities from a small company perspective (big ideas with small data ownership).

Rachael Miller: How to Make Data Make Sense: Choosing the Right Visualization

Auditorium

Where words and numbers fail, visualizations can make dense technical information decipherable. We use graphs, images, diagrams, et cetera to understand our data, to explain our points to others, and to attempt to grasp the significance and implications of all of the masses of data that we generate. Those enormous quantities of data tend to have more than two interesting dimensions, so why are we still looking from line graph to line graph to try and unravel the mysteries of complex systems?

We'll be talking about how visualization channels affect viewers' understanding of data, how to take advantage of multidimensional visualizations for multidimensional data, and how to choose the right input and output systems to demonstrate your findings to others. Learn how to make your data make sense!

3:30 PM - 4:00 PM  Afternoon Ice Cream Break

Come and get it! Love's Ice Cream 

4:00 PM - 4:45 PM | Session Talks 

Brian McKeiver-Build a Big Data .Net MVC App in 30 Minutes

Main

Learn how to create an ASP.Net MVC application that communicates with government based Big Data, or Open Data as it is commonly referred to. In 30 mins we will create a .Net solution that will utilize open source libraries to connect to publicly available API endpoints. With the returned datasets we will generate a quick user interface that includes how to filter, query, map, and aggregate the data. Attendees can expect to leave this session with the knowledge on how to unlock the untapped potential that many government entities provide today.

Kevin Kesseler: Creating Custom Cancer Care with Coding

Auditorium

This talk will explain how scientific innovations combined with big data techniques can be technologically implemented to predict the effectiveness of cancer treatment and help develop drugs to make treatment more effective, ultimately leading to cancer drugs tailored to an individual patient's DNA. It will cover the technical hurdles involved in creating tests to measure residual levels of types of cancers such as leukemia; creating a database that can predict the efficacy of chemotherapy or radiation by sequencing a portion of a patient's DNA; and using mathematical models of biochemical pathways to predict combinations of drugs that will achieve a desired therapeutic result allowing pharmaceutical companies to focus physical tests on compounds most likely to yield successful drugs. In addition, it will show how the data and technology from these projects can be combined to allow the development of cancer drugs tailored to the DNA of the patient.

4:50 PM | Wrap up and Closing Discussion

Matt Fletcher

5:00 PM Let's get the party started! 

Post conference mixer

Grand Rapids Public Museum. 

The Grand Rapids Public Museum is a very short walk from the Eberhard Center.  There will be food and beverages. Most importantly, interesting people and conversations.