Defining the New Era in Software Development

Tuesday, November 7th
DoubleTree by Hilton McLean Tysons
1960 Chain Bridge Rd, McLean, VA

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Just as the emergence of steam engines and the telegraph changed the world during the industrial revolution, artificial intelligence technologies such as machine learning are reinventing the way we live and work. Engineers and organizations alike must adapt or risk becoming obsolete. is a community and event series where developers come together to explore the digital machinery of the new era.

At our upcoming conference, experience a day of learning from industry experts to look “under the hood” at the building blocks of machine learning.

Code of Conduct

Modev is committed to creating diverse and inclusive events and encourages people to participate regardless of gender identity or expression, age, family or marital status, national origin, physical and mental ability, race, ethnicity, religion, socio-economic status, veteran status, or sexual orientation. We believe the best events are ones where all types of people feel welcome and included, and are represented in both the audience and the speakers. Please join us!


“The next 10,000 business plans are easy to forecast: Take X and add AI. This is a big deal, and now it’s here.” — Kevin Kelly, founder wired magazine


Make your mark as an expert where human touch and technology collide. Share your perspective with top companies, leading developers and individuals that are helping define the future of AI.


Adam Wenchel

VP AI & Data Innovation

Capital One

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Experienced machine learning, big data and cybersecurity leader.

Occasional speaker, talks include the 2016 Hadoop Summit keynote, AI Summit 2016 keynote, and "Universal Authentication in K12 Education" delivered at the White House.

Jonathan Altman

Distinguished Engineer

Tech Fellows Program at Capital One


Olya Flores

Machine Learning Product R&D Lead

Leidos Cyber

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A product R&D lead with a coding habit and extensive management expertise in complex products and projects. A big fan of design thinking. Always looking for interesting and fun puzzles to solve.

Mark McGovern

VP Product Management

CA Technologies

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Mark McGovern is VP of Product Management at CA Technologies. He joined CA in 2016 when they acquired Mobile System 7, a leading analytics company that Mark founded and led as CEO. His previous positions include: VP Technology at In-Q-Tel, where he led security investments for the US Intelligence Community; Director of Technology at Cigital, where he led Cigital’s Software Security Group; and building covert communications systems for the CIA. He holds a BSEE from WPI and an MS Engineering from Virginia Tech.

Amrinder Arora

The George Washington University


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Amrinder Arora
Dr. Arora is a leading expert in risk targeting and data analysis, with 15+ years of experience in design and development of enterprise software applications. He serves as a faculty member in the Department of Computer Science at the George Washington University. Dr. Arora is also the co-founder and CEO of BizMerlin where he leads the design and delivery of HCM software solutions, and the author of the book “Analysis and Design of Algorithms.

Elizabeth Haubert

Data Architect and Relevancy Engineer


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Elizabeth Haubert is a relevancy consultant with OpenSource Connections. She is interested in the data problems created and solved by machine learning.

Robert Dempsey


Atlantic Dominion Solutions

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For more than 17 years Robert Dempsey has been delivering technology solutions that solve difficult business challenges. He's the author of the Python Business Intelligence Cookbook and co-author of the upcoming Building Machine Learning Pipelines. In addition to leading agile development teams and teaching data science courses, Robert specializes in building distributed data gathering, processing and visualization systems using Python. Connect with him at

Vicky Fu

Data Solutions Architect


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Data Solution Architect in Microsoft focusing on Machine Learning and AI. Previous Data Scientist@Bing Ads Team

Holly Ferguson

Lead Technologist

Booz Allen Hamilton

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Recently I have graduated from the University of Notre Dame with a Ph.D. in Semantic Knowledge Engineering. I am actively working on Big Data integration methodologies and semantically enhanced cyber-infrastructures utilizing Linked Data Principles and Semantic Ontologies leading to more accurate Decision Support. My experience and interests includes a blend of applied research and implementation with Semantics/Linked Data, Resilience, Geo-Spatial challenge areas, and Automation technologies.

Ashish Jaiman

Director of Civic Tech and Services



Ethan Chumley

Campaign Technology Advisor


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Ethan Chumley is a Campaign Technology Advisor and Tech Evangelist for Microsoft’s Tech & Corporate Responsibility team. A hands-on technologist, he has worked on tech, data, and security strategy in the political and civic spaces, most recently focusing heavily on the 2016 Presidential cycle. Ethan works across a broad suite of Microsoft technology, including “big data” analytics, visualizations, Azure, and Office 365. Formerly, Ethan was a field consultant in Microsoft Consulting Services (MCS), specializing in the Internet of Things and IoT analytics. Prior to Microsoft, he worked in energy modeling for an Engineering firm in New York City. Ethan received a B.S. in Engineering from Cornell University, grew up near Denver, CO, and is an avid skier.

Anna Cheney


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With a BS in Applied Mathematics, and a MS in computer science, Anna launched her career in engineering in 2002 working on the Thirty Meter Telescope project. Over the next 12 years, she specialized in remote sensing algorithms, culminating as the principle investigator in an Office of Naval Research contract on the classification of signals. In 2014 she took her breadth of knowledge in applied research to the IBM Watson group. Within IBM Watson she has defined and measured key cognitive metrics necessary to track the improvement and value of the machine learning training cycle, and continues to be passionate about quantifying and improving systems that can improve people’s lives.


Doors open, registration begins, light breakfast
Welcome Remarks
Opening Speaker
with Adam Wenchel of Capital One
"Getting Started with Machine Learning"
with Ashish Jaiman & Ethan Chumley of Microsoft
"Ensemble Models in Machine Learning - Theory vs. Practice"
with Amrinder Arora of The George Washington University

Ensemble Models in Machine Learning are a bit of a polarizing topic. People either love them, or hate them. A common criticism is that they are not mathematically rigorous. However, nothing could be further from the truth, as one of the earliest foundations of Machine Learning was in form of theoretical validity of ensemble models. Nevertheless, there are noticeable differences between theory and practice when it comes to ensemble models. This talk will attempt to compare the theory and practice and present suggestions to narrow the gap between the both.

"Getting Started With Learning To Rank"
with Elizabeth Haubert of Open Source Connections

Overview, prerequisites, and implementation details of getting started with Learning To Rank. Why relevancy tuning will be taken over by machine learning, but not yet.

"Building a Production-Level Machine Learning Pipeline"
with Robert Dempsey of Atlantic Dominion Solutions

With so many options to choose from how do you select the right technologies to use for your machine learning pipeline? Do you purchase bare metal and hire a devops team, install Spark on EC2 instances, use EMR and other AWS services, combine Spark and Elasticsearch?! Attend this talk to hear first-hand experience of building ML pipelines: what options were looked at, how the final solution was selected, the tradeoffs made and the final results.

"Applying Machine Learning to Small Data Sets"
with Olya Flores of Leidos Cyber

Machine learning has delivered great results in domains where data is easily available. But what about real-world domains where large amounts of data are hard to find? Often, the industries that could benefit from AI and ML most - energy, finance, medicine - are also most constrained in their data availability and access. What can we do when finding large amounts of labeled data to train with is near impossible? What if all we have is small data? How can we still apply ML techniques to these domains? In this talk, I will present an overview of approaches like transfer learning, adaptive sampling and semi-supervised learning and show how these techniques can shine a ray of hope in a barren data landscape. :)

"Using CNTK Train Reinforcement Learning Model play game with GPUs in Azure"
with Vicky Fu of Microsoft

Using CNTK Train Reinforcement Learning Model play game with GPUs in Azure

"Rinse and Repeat: The Spiral of Applied Machine Learning"
with Anna Cheney of IBM

The output of machine learning system can alwaysbe improved. Better training data, algorithms more suited to your use case, and system improvements based on threshold setting can all be employed.However, you will find that after each iteration, the system will improve less and less...much like the radius of a spiral as it makes rotations around the origin. In this talk, I will describe the process to improve two different types of problems: sentiment detection and question answer.

"If Only Algorithms were Products: Critical Capabilities for ML Success"
with Mark McGovern of CA Technologies

Analytics, machine learning, data models and neural networks are valuable tools, but customers buy functionality and usability. Deriving and delivering value from data requires dev teams to consider far more than the efficiency of their code and efficacy of their algorithm. This session will highlight key capabilities that can make the difference between a successful machine learning product and interesting "science project".

"The Semantic Web in Industry: Learning Instead with Intelligent Data"
with Holly Ferguson of Booz Allen Hamilton

This talk ultimately focuses on the ways in which Semantics and Linked Data are an essential part of Industry AI solutions for Big Data in the future, illustrated with examples and case studies (language interpretations, lifting Relational DB schema, sentiment analysis, automation, IoT, and other knowledge engineering areas). This discussion includes a brief introduction into the field, how data can be effectively used in a semantic framework, how building context awareness from a data perspective can be useful and exciting, and most importantly how Semantics/Linked Data are not solutions in and of themselves to reach true AI, but rarely are analytics truly intelligent without them.

Closing Keynote
with Jonathan Altman of Fellows Group
Post Event Networking - Hotel Bar


Blaze the trail and solidify your brand as a leader in the AI domain. Position your company to end 2017 strong and start 2018 ready.

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1960 Chain Bridge Rd, McLean, VA [MAP]

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