AI Fall Conference & Expo

OCTOBER 18-20, 2021 | WASHINGTON, DC | Co-Located with:

Co-Located with TechConnect World Innovation Conference & Expo SBIR/STTR Spring Innovation Conference Nanotech 2021



Connecting emerging tools and platforms with the global innovation ecosystem. Join us for the AI TechConnect conference in Washington DC. The event is open for poster submissions. Submit your abstract and join the program.


AI TechConnect Program

2021 Program: At-A-Glance

Monday October 18

10:30Machine Learning for Characterization
1:30Machine Learning for Microscopy

Tuesday October 19

10:30AI Innovations
1:30AI for Biomedical Applications
4:00AI for Materials - Posters

Wednesday October 20

8:30AI Track Keynote
10:30AI for Advanced Materials Discovery & Design
1:30AI for Advanced Manufacturing
4:00AI Innovations - Posters

Detailed Program:

Monday October 18

10:30Machine Learning for Characterization
Session chair: Greg Haugstad, University of Minnesota
Machine Learning for In-Water Inspection of Submarine Hull Coatings
M. An, J. Cipolla, A. Shakalis, B. Hiriyur, R. Tolimieri, Prometheus Inc., US
A Machine Learning Driven Damage Quantification Algorithm in moisture-contaminated composites
R.D. Guha, North Carolina State University, US
Gamma-Ray Raster Imaging with Robotic Data Collection
W. Wells, T. Aucott, M. Siddiqi, Savannah River National Laboratory, US
A New Method for Atmospheric Correction of Satellite Data
D. Groeneveld, Advanced Remote Sensing, Inc., US
1:30Machine Learning for Microscopy
Session chair: Greg Haugstad, University of Minnesota
Machine Learning for Automated Experiments in Charged Particle Beam Tools
A. Belianinov, Oak Ridge National Laboratory, US
Variational Autoencoders for Physics Extraction: Latent View of Complex Processes
M. Ziatdinov, Oak Ridge National Laboratory, US
Automated Analysis of Transmission Electron Microscopy Images for Characterization of Dynamic Material Systems
J.P. Horwath, D.J. Groom, P.J. Ferreira, E.A. Stach, University of Pennsylvania, US
Using machine learning to probe classification and correlation AFM images
I. Chakraborty, Stress Engineering Services, Inc, US
Rapid DNA Origami Nanostructure Detection and Classification Using the YOLOv5 Deep Convolutional Neural Network
M. Chiriboga, C.M. Green, D.A. Hastman, D. Mathur, Q. Wei, I.L. Medintz, S.A. Díaz, R. Veneziano, United States Naval Research Laboratory, US
Machine learning for microstructures classification in functional materials
A.K. Choudhary, A. Jansche, Grubesa Tvrtko, T. Bernthaler, G. Schneider, Aalen University, DE
Machine learning based detection and deep learning based image inpainting of preparation artefacts in micrographs
A. Jansche, A.K. Choudhary, T. Bernthaler, G. Schneider, Aalen University, DE

Tuesday October 19

10:30AI Innovations
Next Generation PCIe Network Fabric for Simulators and Performance Computing
C.T. Reynolds, Technical Systems Integrators, 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 and T consulting services, inc, US
Forecasting and Decision Impact Analysis from Ripple Effects of Behaviors
B. Frutchey, NuWave Solutions, US
Leveraging Machine Learning to Predict Public Transportation Arrival Times
P. Reshetova, W. Ruzicka, EastBanc Technologies, US
Towards an accessible mathematics and programming learning platform using Artificial Intelligence.
D. Marghitu, M. Das, A. Jariwala, Auburn University, US
How Can the DoD Leverage Big Data, AI and Machine Learning to Accelerate UxS Integration and Decision Making?
G. Galdorisi, S. Tangredi, N. Zerbe, Naval Information Warfare Center Pacific, US
1:30AI for Biomedical Applications
Session chair: Payel Das, IBM & Sarah Tao, Sanofi
DeepChrome: Deep-learning for predicting gene expression from histone modifications
Y. Qi, University of Virginia, US
A. Shehu, George Mason University, US
AI Platform for Gene Therapy & Gene Diagnostics
R. Peterson, J. Kahn, DNA Analytics, Inc, US
Accelerating Drug Design, Optimization and Development With Goal-based Machine Learning and 3D Modeling
L. Subramanian, S. Schweizer, A. Polavarpu, Dassault Systemes, US
Machine Learning for Automated Hepatic Fat Quantification
H. Sagreiya, A. Akhbardeh, I. Durot, D.L. Rubin, University of Pennsylvania, US
Point-of-care serodiagnostic test using a multiplexed paper-based immunoassay and machine learning
Z.S. Ballard, H-A Joung, A. Goncharov, J. Liang, K. Nugroho, J. Wu, D.K. Tseng, H. Teshome, L. Zhang, E.J. Horn, P.M. Arnaboldi, R.J. Dattwyler, O.B. Garner, D. Di Carlo, University of California, Los Angeles, US
Kidney Cancer Staging using Deep Learning Neural Network: Comparing Models Trained on Whole Kidney with Cancer and Only the Cancer
N. Hadjiyski, Ann Arbor Pioneer High School, US
Robust and Trustworthy AI for Brain Tumor Surveillance
G. Rasool, Rowan University, US
Preventing Elderly Falling Through Machine Learning
P. Hardigan, Nova Southeastern University, US
4:00AI for Materials - PostersExpo Hall
Potentiometric Investigation on Complex Formations and Stabilities of Some Divalent Metal Ions with L-Cysteine and Glycine as Ligands in Aqueous Solutions
A.M. Radalla, Beni Suef University, EG
Simultaneous Multiplexing and Classification of N Electrochemical Sensors
K. Conley, J. Ray, E. Brown, G.K. Kosgei, P.U. Ashvin, I. Fernando, L.C. Moores, A. Netchaev, US Army Corps of Engineers - ERDC, US

Wednesday October 20

8:30AI Track Keynote
Session chair: Richard Ross, 3M Company
Materials Informatics: Building a Data Infrastructure Foundation for the future of Materials Development
C. Lipscomb, 3M Company, US
Detecting Malaria Parasites with Machine Learning
S. Jaeger, U.S. National Library of Medicine / NIH, US
AI-enabled additive manufacturing platform for extreme environments
G. Gu, University of California, Berkeley, US
P. Tiwary, University of Maryland, US
10:30AI for Advanced Materials Discovery & Design
Session chair: Richard Ross, 3M Company
Autonomous Research Systems
B. Maruyama, U.S. Air Force Research Laboratory, US
The combination of data-driven and physics-based modeling with application in protein formulations
J.G.E.M. Fraaije, P. Petris, Siemens Culgi, NL
Closed-loop autonomous combinatorial experimentation for streamlined materials discovery
I. Takeuchi, University of Maryland, US
Combining High-throughput Atomic Scale Simulation and Deep Reinforcement Learning in the Discovery of Novel OLED Materials with Targeted Optoelectronic Properties
Y. An, H.S. Kawk, D.J. Giesen, P. Winget, T.F. Hughes, M.D. Halls, Schrodinger, Inc, US
AI-accelerated materials innovation: From Optoelectronics to Fluorescent Biomarkers
C. Kreisbeck, Kebotix, US
Machine Learning for the Exploration of Nanomaterial Synthesis Parameter Spaces
R. Sappington, B. Cornick, Epic Advanced Materials, US
A Self-Driving Laboratory for Accelerated Materials Discovery
C.P. Berlinguette, J.E. Hein, A. Aspuru-Guzik, B.P. MacLeod, F.G.L. Parlane, University of British Columbia, CA
1:30AI for Advanced Manufacturing
Session chair: Grace Gu, University of California, Berkeley
Materials Informatics for Simultaneous Design of Alloy Chemistry and AM Process
S. Broderick, University of Buffalo, US
Manufacturing Quality Inspection Using AI and Edge Computing
C. Ouyang, C. Lu, T. Cook, IBM, US
Semi-supervised and Reinforcement Learning Methods for VLSI Chip Design
J. Obert, Sandia National Labs, US
Real-Time Porosity Prediction for Metal Additive Manufacturing using Convolutional Neural Networks
W. Young, S. Ho, S. Al Jufout, M. Mozumdar, M. Buchholz, W. Zhang, K. Dajani, California State University, Long Beach, US
Manufacturing Informatics: Embracing Machine Learning for Smart Manufacturing
Y. Liu, Cardiff University, UK
Laser Dissimilar Material Joining and Quality Assessment by Deep Learning
J.H. Cha, S.H. Choi, T.W. Kim, H.W. Choi, Keimyung University, KR
4:00AI Innovations - PostersExpo Hall
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
IoT + DDoS = Disruptive (Business + Cyber) Risk!
A. Pabrai, ecfirst, US
Application of Savitzky-Golay (SG) Filter in Image Processing
S. Karmakar, S. Karmakar, Farmingdale State college - SUNY, US
An eHR Using AI Technology as a Clinical Decision Support Tool
J. Penn, Guidance Foundation Inc,, US
A Review of AI Influence in Intellectual Property Law
D.G. Mottley, Howard University School of Law, US
A Study of Famous CNN Architectures to Have Descent Base Models
M. Bari, T-Mobile, US
Benefits of a Decentralized AI
M. Bergstrom, Quantum1Net, ES
Automatic deep-learning classification models for breast lesions
S. Hasan, A. Hasan, Princeton Day School, US
Zero Bandwidth, Zero Storage, Full Evidentiary Data
M. Script,, US

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