TechConnect World 2020
Co-Located with Nanotech 2020Co-Located with Nanotech 2020 SBIR/STTRSBIR/STTR AI TechConnectAI TechConnect
Nanotech 2020

AI Innovations

AI Innovations

Symposium Co-Chairs

Brent M. SegalBrent M. Segal
Director of Technology Collaboration and Commercialization
Lockheed Martin

Fiona CaseFiona Case
Program Chair

Key Speakers

Chris MilroyChris Milroy
Senior Solution Architect

Christoph KreisbeckChristoph Kreisbeck
Chief Product Officer
Kebotix, Inc.

This symposium highlights developments in AI, machine learning, data analytics and robotics that will enable multiple application areas. These innovations will have broad impact - but they will revolutionize manufacturing, advanced materials, biomaterials, and drug design and development - the focus areas for this conference. Submit an abstract describing your contributions to this exciting field and plan to join us in Washington DC in June.

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Symposium Sessions

Tuesday June 30

8:30AI and Machine Learning Fireside Chat
10:30AI & Sensors Innovations
4:00Machine Learning for Materials Characterization & Imaging - Posters

Wednesday July 1

10:30Innovations for Next-gen AI
1:30AI for Manufacturing Inspection and Control
4:00Innovations in AI - Posters
4:00AI for Advanced Manufacturing - Posters

Symposium Program

Tuesday June 30

8:30AI and Machine Learning Fireside Chat
Session chair: Brent M. Segal, Lockheed Martin, US
10:30AI & Sensors Innovations
Forecasting and Decision Impact Analysis from Ripple Effects of Behaviors
B. Frutchey, NuWave Solutions, LLC, US
LinkStar-X and the QuickSAT/Autonomy System: An AI Based System Supporting Tactical Intelligence, Surveillance, and Reconnaissance Functions For Small Satellites
A. Santangelo, sci_Zone, US
Utilizing Machine Learning to Predict Public Transportation Times
P. Reshetova, EastBanc Technologies, US
Portable sensor platform for fuel analysis using smart phones, machine learning, and miniature infrared sensors
V. Hanagandi, A. Metcalf, D. Landay, Optimal Solutions, Inc., US
Using AI to Improve Safety at Grade Crossings
C. McGlynn, H. Zhang, Rowan University, US
4:00Machine Learning for Materials Characterization & Imaging - Posters
Machine learning for microstructures classification in functional materials
A.K. Choudhary, A. Jansche, O. Badmos, T. Bernthaler, G. Schneider, Aalen University, DE
A Machine Learning Driven Damage Quantification Algorithm in moisture-contaminated composites.
R.D. Guha, North Carolina State University, US
Application of Savitzky-Golay(SG) filter in image processing
S. Karmakar, S. Karmakar, Farmingdale State College- State University of New York, US

Wednesday July 1

10:30Innovations for Next-gen AI
C. Milroy, NVIDIA, US
P. Das, IBM Thomas J Watson Research Center, US
How Can the DoD Leverage Big Data, AI and Machine Learning to Accelerate UxS Integration and Decision Making
G. Galdorisi, Naval Information Warfare Center Pacific, US
In-Storage Distributed Machine Learning for the Edge
V. Alves, NGD Systems, Inc., US
Graph-Centric Machine Learning: Algorithms, Systems, and Cybersecurity Applications
H. Huang, George Washington University, US
A Study of Famous CNN Architectures to Have Descent Base Models
M. Bari, T-Mobile, US
Concept for a Natural Language Processing (NLP) Application: Artificial Intelligence (AI) Technology for Text and Language Search (ATTLS)
M. Niv, N. Kumar, E. Henry, T&T Consulting Services, Inc, US
1:30AI for Manufacturing Inspection and Control
Session chair: Keith Brown, Boston University, US
Y. Liu, Cardiff University, UK
Manufacturing Quality Inspection Using AI and Edge Computing
C. Ouyang, T. Cook, C. Lu, IBM, US
Artificial Intelligence and Machine Learning for Weld Modeling and Quality Monitoring
J.E. Jones, V.L. Rhoades, M.D. Mann, T. Surrufka, EnergynTech, Inc., US
A Data-Driven Approach for Selecting Critical Process Parameters in Material Extrusion Additive Manufacturing
F. Pourkamali-Anaraki, A. Peterson, R. Jensen, University of Massachusetts Lowell, US
Laser Dissimilar Material Quality Assessment by Deep Learning
T. Kim, C. Han, H. Choi, Keimyung University, KR
Semantic Segmentation for 3D Feature Detection in the Automation of High Mix Industrial Processes
M. Powelson, Southwest Research Institute, US
Machine Learning for In-Water Inspection of Submarine Hull Coatings
M. An, J. Cipolla, A. Shakalis, B. Hiriyur, R. Tolimieri, Prometheus Inc., US
Gamma-Ray Raster Imaging with Robotic Data Collection
W. Wells, T. Aucott, Savannah River National Laboratory, US
4:00Innovations in AI - Posters
An EHR using AI technology as a Clinical Decision Support Tool
J.M. Penn, Guidance Founation Inc., US
IoT + DDoS = Disruptive (Business + Cyber) Risk!
A. Pabrai, ecfirst, US
Improve Health Outcomes and Maximize Quality Improvement: Using Artificial Intelligence Models
V. Melenez, HealthEC, US
A Review of AI Influence in Intellectual Property Law
D. Mottley, Howard University, School of Law, US
ABSCA - Boost Converter Switching Controller using Machine Learning Algorithms
B. Abegaz, Loyola University of Chicago, US
AROSV - An ROS based Self-Driving Vehicle Controller using Unsupervised Machine Learning Methods
B. Abegaz, Loyola University of Chicago, US
Dropping 500 Feet in 20 Seconds: Simulating the Cockpit Experience of an Airliner with a Trim Control Failure
A. Redei, Central Michigan University, US
Artificial Intelligence Trends Based on the Patents Granted by the United States Patent and Trademark Office
H.H.N. Abadi, M. Pecht, University of Maryland - Center for Advanced Life Cycle Engineering (CALCE), US
A Survey of Artificial Intelligence Funding in China
Z. He, W. Diao, M.G. Pecht, University of Maryland, US
Next Generation PCIe Network Fabric for High Performance AI Computing
C. Reynolds, Technical Systems Integrators, US
AI - Lack of data characterization significantly reduces accuracy of AI results
M. Gilger, Modus Operandi, US
4:00AI for Advanced Manufacturing - Posters
Residual Distortion Prediction through an Artificial Intelligence Approach in Additive Manufactured Components
A. Imanian, TDA, US
Bayesian Networks Connecting Processing and Product Features in Additive Manufacturing
A. Malmberg, K. Chandra, A. Peterson, J. Mead, University of Massachusetts Lowell, US
Using Robotics to Assemble Graphene Supercapacitor
C. Wu, J. Kim, D. Magluyan, D.K. Kindred, Y. Zhou, N. Cao, H. Zhao, Z. Kuang, T. Kidd, S. Dobbs, Z. Yu, California State Polytechnic University, Pomona, US
2020 Sponsors & Partners

2019 SBIR/STTR Agency Partners:

SBIR/STTR Agency Partners