• Machine Learning Live!

    Praxis Engineering Technologies Inc

    Join us in January for a live machine learning work through based on collected data from our members about their interests. Please complete a quick survey [ https://esurv.org/?s=MNJLFK_2fc3e25f ] to help make this event a success! Agenda ------------------------------------------------- 6:30 PM -- Networking & Food 7:00 PM -- Greetings 7:05 PM -- Machine Learning Live - John Hebeler Location ------------------------------------------------- Praxis 135 National Business Pkwy, Annapolis Junction, MD 20701 Directions ------------------------------------------------- There is ample parking around the building. Please enter through the first or second floor and take the elevator to the third floor. Upon exit, turn right to proceed to the meeting room. Talks ------------------------------------------------- Machine Learning Live! See machine learning surprises revealed live - from the hypotheses to the validations (or failures) directly built upon our meetup’s survey data. This includes clustering on unsupervised data, classification on hypothesized labeled data, and regression analysis using both traditional and deep learning machine learning methods. We explore the code to preprocess the data, format the data for machine learning, perform machine learning with multiple methods, and then validate, determine performance, and possibly visualize the outcome. All based on your live suppositions. You see how or how not your data science hunches perform. Code and data will be available for you to take machine learning adventures even further. Speakers ------------------------------------------------- John Hebeler, PhD is a Lockheed Martin Fellow focusing on abnormalities across large data sets via multiple machine learning methods. Formally, he led a five-year program to analyze large data streams to form complex policies in an event-driven architecture. John holds three patents and coauthored two technical books and multiple journal articles on networking, data semantics, and machine learning. He also teaches graduate technology courses for University of Maryland. John holds a BS in Electrical Engineering, an MBA, and a PhD in Information Systems. In his free time, he’s an avid tennis player, beer brewer, and amateur audiophile, usually not simultaneously

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  • GraphQL is the Better REST

    Loyola Columbia

    Please join us in December to learn why you should replace your old busted REST API with a new fresh GraphQL endpoint! Agenda ------------------------------------------------- 6:30 PM -- Networking & Food 7:00 PM -- Greetings 7:05 PM -- GraphQL is the Better REST - Michael Smolyak 9:00 PM -- Post event drinks at Green Turtle Location ------------------------------------------------- Loyola University Room 210/[masked] McGaw Rd Columbia, MD 21045 Please proceed to the second floor once you enter the building. Parking ------------------------------------------------- There is ample free parking surrounding the building. Talks ------------------------------------------------- GraphQL is the Better REST GraphQL is a data query language developed by Facebook and released under MIT license. It offers a number of advantages for the development of Web services compared to the traditional REST approach. The talk will describe what REST pain points GraphQL is designed to address and will illustrate GraphQL basic concepts and the ways to develop GraphQL-based Web services. Speakers ------------------------------------------------- Michael Smolyak As a software engineer, Michael has been fortunate to find a career he truly enjoys. During his 20+ career he has been a full-stack developer, a technical lead, a software architect, a college instructor, a book editor and a mentor. Regardless of his position and a role at work Michael never ceases to be a student with interests ranging from software engineering methodologies, to functional reactive programming, to machine learning, to deploying to the cloud. For the last 7 years of his career he found a good home at Next Century Corporation. In his free time, Michael loves to read, listen to audiobooks and podcasts. He enjoys traveling with his family, running and portrait photography. Post-event Drinks ------------------------------------------------- Green Turtle 8872 McGaw Rd Suite C Columbia, MD 21045

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  • Effective Cybersecurity Analytics with Quantum Computing

    JHU APL, Building 200

    As quantum computing becomes more accessible, what will the impacts be on cybersecurity and data scientist? Can a quantum computers help solve the hard problems facing cybersecurity experts? Please join us in October to learn how D-Waves quantum computer works and how it is changing cybersecurity analysis. Agenda ------------------------------------------------- 6:30 PM -- Networking & Food 7:00 PM -- Greetings 7:05 PM -- Cybersecurity Analytics on a D-Wave Quantum Computer - Steve Reinhardt Location ------------------------------------------------- JHU APL Building[masked] Johns Hopkins Rd Laurel, MD 20723 Directions ------------------------------------------------- Building 200 is in the South campus. There is ample free parking that surrounds the building. Talks ------------------------------------------------- Cybersecurity Analytics on a D-Wave Quantum Computer Effective cybersecurity analysis requires frequent exploration of graphs of many types and sizes, the computational cost of which can be overwhelming if not carefully chosen. After briefly introducing the D-Wave quantum computing system, we describe an analytic for finding “lateral movement” in an enterprise network, i.e., an intruder or insider threat hopping from system to system to gain access to more information. This analytic depends on maximum independent set, an NP-hard graph kernel whose computational cost grows exponentially with the size of the graph and so has not been widely used in cyber analysis. The growing strength of D-Wave’s quantum computers on such NP-hard problems will enable new analytics. We discuss practicalities of the current implementation and implications of this approach. Speakers ------------------------------------------------- Steve Reinhardt has built hardware/software systems that deliver new levels of performance usable via conceptually simple interfaces, including Cray Research’s T3E distributed-memory systems, ISC’s Star-P parallel-MATLAB software, and YarcData/Cray’s Urika graph-analytic systems. He now leads D-Wave’s efforts working with customers to map early applications to D-Wave systems. Steve can be emailed at [masked] Company ------------------------------------------------- D-Wave Systems is the world's first quantum computing company and the leader in the development and delivery of quantum computing systems and software. Our mission is to unlock the power of quantum computing to solve the world's most challenging problems. Our systems are being used by world-class organizations and institutions including Lockheed Martin, Google, NASA, USC, USRA, Los Alamos National Laboratory, Oak Ridge National Laboratory, Volkswagen, and many others. D-Wave has been granted over 160 U.S. patents and has published over 100 peer-reviewed papers in leading scientific journals. More information can be found at https://www.dwavesys.com

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  • Predictive Analytics and Neighborhood Health

    Bloomberg Center for Physics & Astronomy, Johns Hopkins University

    Using predictive analytics, GovEx helped to keep neighborhoods in Kansas City healthy. Please join us in September to learn about how data science is helping to change government. Agenda ------------------------------------------------- 6:30 PM -- Networking & Food 7:00 PM -- Greetings 7:05 PM -- Predictive Analytics and Neighborhood Health - Matt Pazoles, Chief Data Scientist, GovEx Location ------------------------------------------------- Shafler Auditorium Bloomberg Center for Physics and Astronomy San Martin Dr, Baltimore, MD 21210 The event will be held in the Bloomberg Center (interactive campus map can be found at https://www.jhu.edu/maps-directions/) in the Shafler Auditorium (https://krieger.jhu.edu/pa-intranet/facilities/schafler-auditorium/), which is next to the main entrance on Floor 2. For parking, the best option is the San Martin Garage near the intersection of San Martin Drive and Bowman Drive. It’s pretty close to the Bloomberg Center. If you walk north up San Martin Drive to the Space Telescope Science Institute, there’s a staircase between the Bloomberg Center and the STSI parking garage that leads you right to the front door. Talk ------------------------------------------------- Predictive Analytics and Neighborhood Health After the 2008 recession, Kansas City, MO, experienced waves of unemployment and foreclosures that led many properties to fall into disrepair. Faced with this growing issue during a period of decreased funding, the city’s code enforcement officials were unable to keep up with the workload, creating an enormous backlog and doubling the workload for each inspector. Together with the JHU Center for Government Excellence (GovEx), the city developed an algorithm to predict how long a given violation will take to resolve based on internal and public data that will help inspectors proactively schedule follow-up inspections and connect more serious cases to community programs earlier. Speakers ------------------------------------------------- Matt is the Chief Data Scientist at the Johns Hopkins University Center for Government Excellence, where he and his team help governments apply data to performance challenges and improve the quality of life of their constituents. Prior to joining GovEx, Matt led the data, GIS, and targeting programs for national and state political campaigns, labor unions, and non-profits as they sought to register, persuade, and motivate voters. He was also the lead GIS analyst for Delaware’s State House of Representatives redistricting project in 2010. Matt can be emailed at [masked]. Company ------------------------------------------------- GovEx’s mission is to help governments use data to make informed decisions and improve people’s quality of life. More information can be found at https://govex.jhu.edu Newsletter ------------------------------------------------- Sign-up for the new Data Works MD newsletter at http://eepurl.com/dDIfL1

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  • Finding Bad Actors in Big Cyber Data

    BrainTrust Holdings

    As cybersecurity attacks increase daily, automated solutions are needed to keep up with the growing amount of data. Please join us this August to learn how data scientists are using machine learning to build scalable solutions to fight cyber attackers. Agenda ------------------------------------------------- 6:30 PM -- Networking & Food 7:00 PM -- Greetings 7:05 PM -- Finding the Needle in the Haystack: Using Data science to Find Bad Actors in Big Cyber Data - Dr. Chris Morris Location ------------------------------------------------- BrainTrust 420 National Business Parkway, Suite 150 Annapolis Junction, MD There is ample free parking surrounding the building. Please note that doors may be locked. We will have members periodically coming to the door to let folks in. Talks ------------------------------------------------- Finding the Needle in the Haystack: Using Data science to Find Bad Actors in Big Cyber Data Nearly every day we hear about a new, high-impact cyber incident where sensitive personal data is stolen from companies like Target or Equifax. These breaches have made cybersecurity one of the hottest areas of technology, with problems to be solved that affect millions of people. Large scale machine learning techniques have the potential to revolutionize this field. We’ll discuss some of the cutting-edge techniques being developed to find and catch bad actors in these petabyte scale datasets at research programs in private companies, universities, and government organizations like DARPA. Don’t worry if you don’t know an IP from a port, we’ll go over everything you need to know in order to understand how machine learning is helping this rapidly advancing field. Speakers ------------------------------------------------- Dr. Chris Morris is a Senior Data Scientist at KeyW, where he works on the application of machine learning to detection of malicious activities in large, cloud-scale cybersecurity data. He is currently the principal investigator for a DARPA program aimed at detecting botnets at internet scales for KeyW. Additionally, he heads an internal research effort on using deep neural networks to perform automated object detection across several imaging modalities. Chris has over 10 years of experience conducting scientific research across several fields for organizations including Johns Hopkins, UCSB, and DARPA. Chis can be found on LinkedIn at https://www.linkedin.com/in/morrischrism/

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  • The Xs and Ys Behind The Xs and Os: Data Science in Football

    Betamore at City Garage

    Please join us this July as we kick off a new meetup with a discussion on using data science in football! Agenda ------------------------------------------------- 6:30 PM -- Networking, Pizza, and Beer 7:00 PM -- Greetings 7:05 PM -- The Xs and Ys Behind The Xs and Os: Data Science in Football Strategy and Business - Daniel Stern and Jack Nicastro Location ------------------------------------------------- Betamore City Garage There is ample free parking surrounding the building. Food & Drink ------------------------------------------------- HomeSlyce pizza, Natty Boh and other beverages will be available to members. Talks ------------------------------------------------- The Xs and Ys Behind The Xs and Os: Data Science in Football Strategy and Business From in-game coaching to opponent research and player evaluation, and from ticket and concession pricing to fan analysis and targeted marketing, big data has affected professional football both on and off the field. In this two-part presentation, two analysts from the Baltimore Ravens (one from coaching, one from business) will discuss how teams around the NFL and other levels of the sport have used data science to aid in both (1) football strategy and (2) football business. Speakers ------------------------------------------------- Daniel Stern, entering his third season, works for the Baltimore Ravens coaching staff as a Football Analyst. In his role, he aids the coaches with statistical research related to opponents, in-game strategy, and more. Prior to joining the Ravens, Daniel earned his B.A. in Cognitive Science from Yale University, where he also spent three seasons as an undergraduate assistant to the football team – helping with coaches’ video, opponent game breakdowns, and football operations. He grew up in Baltimore and coached youth football while in high school. Jack Nicastro is the Business Intelligence Coordinator for the Ravens, tasked with gathering insights from data that will be helpful for the business side of the franchise, including for the Ticketing, Marketing, and Media departments. Jack graduated from UNC-Chapel Hill in 2017 with a degree in Statistics and Analytics and a minor in Information Science. He is passionate about football and basketball analytics on both the sports and business sides.