AI TechConnect Program
2020 Program: At-A-Glance |
| Monday June 29 |
| 8:15 | TechConnect World Innovation Conference - Keynotes |
| 10:30 | AI, Modeling and Simulation for Materials Design |
| 10:30 | Machine Learning for Optical and Radiative Microscopy |
| 1:30 | AI, Modeling and Simulation for Materials Design |
| 1:30 | Machine Learning for Probe and Electron Microscopy |
| Tuesday June 30 |
| 8:30 | AI and Machine Learning Fireside Chat |
| 10:30 | AI, Modeling and Simulation for Materials Design |
| 10:30 | AI & Sensors Innovations |
| 1:30 | AI for Biomaterials and Drug Design |
| 4:00 | Machine Learning for Materials Characterization & Imaging - Posters |
| Wednesday July 1 |
| 10:30 | Design, Modeling, Simulation & Software Innovation |
| 10:30 | Innovations for Next-gen AI |
| 10:30 | AI Design & Manufacturing |
| 1:30 | Machine Learning for Medical Diagnostics |
| 1:30 | AI for Manufacturing Inspection and Control |
| 4:00 | Innovations in AI - Posters |
| 4:00 | AI for Advanced Manufacturing - Posters |
Detailed Program: |
| Monday June 29 |
|
| 8:15 | TechConnect World Innovation Conference - Keynotes | |
| A. Goel, Nanobiosym, Chairman & CEO, US |
| J. Clarke, Intel Corporation, Director of Quantum Hardware, US |
| A. Fischer, DARPA, AI Program Manager, US |
|
| 10:30 | AI, Modeling and Simulation for Materials Design | |
| Session chair: Rick Ross, 3M Company, US; Peter Koenig, Procter & Gamble, US |
| Accelerating Materials Discovery and Design using AI and Machine Learning
S. Sankaranarayanan, University of Illinois at Chicago, US |
| Molecular Models Empowering Data-Driven Approaches to Materials Discovery
J. Wu, University of California, Riverside, US |
| Combining High-throughput Atomic Scale Simulation and Deep Reinforcement Learning in the Discovery of Novel OLED Materials with Targeted Optoelectronic Properties
Y. An, T.F. Hughes, D.J. Giesen, A. Chandrasekaran, M.A.F. Afzal, H.S. Kawk, K. Leswing, K. Marshall, T. Robertson, M.D. Halls, Schrodinger, Inc., US |
| An Improved Sampling Technique For Accelerated Numerical Simulations with Hybrid Uncertainties
F. Pourkamali-Anaraki, M.A. Hariri-Ardebili, S. Sattar, University of Massachusetts Lowell, US |
| Adaptive Machine Learning enabled Search for Functional Materials with Targeted Properties
P.V. Balachandran, University of Virginia, US |
| Deep Materials Informatics: Illustrative Applications of Deep Learning in Materials Science
A. Agrawal, Northwestern University, US |
|
| 10:30 | Machine Learning for Optical and Radiative Microscopy | |
| Session chair: Greg Haugstad, University of Minnesota, Dalia Yablon, TechConnect, US |
| Autonomous Synchrotron X-ray Diffraction for Phase Mapping and Materials Optimization
A.G. Kusne, National Institute of Standards & Technology, US |
| Real-time 3D Coherent Diffraction Data Inversion Through Deep Learning
M. Cherukara, H. Chan, T. Zhou, Y. Nashed, S. Sankaranarayanan, M. Holt, R. Harder, Argonne National Lab, US |
| 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 |
| Improvement of Oil Spill Mapping from Satellite Image Using Directional Median Filtering with Articicial Neural Network
S.H. Park, H.S. Jung, University of Seoul, KR |
| Application of deep convolutional neural networks (DCNN) in materials microscopy for the automated detection of defects
O. Badmos, A. Kopp, D. Hohs, R. Büttner, T. Bernthaler, G. Schneider, Hochschule Aalen, DE |
| Machine Learning for Materials Characterization and Imaging
M.K.Y. Chan, Argonne National Laboratory, US |
|
| 1:30 | AI, Modeling and Simulation for Materials Design | |
| Session chair: Rick Ross, 3M Company, US; Peter Koenig, Procter & Gamble, US |
| Materials Informatics Applications for Structural Adhesives at 3M
C. Lipscomb, 3M, US |
| Multiscale modeling and machine learning for accelerated decision making for formulation and packaging materials
L. Subramanian, Dassault Systemes, US |
| Machine Learning for the Optimization of Optical Nano-Materials
A.-P. Blanchard-Dionne, O.J.F. Martin, Ecole Polytechnique Federale de Lausanne, CH |
| Identifying Crystal Structure from Open and Accessible Materials
J. Tate, J. Aguiar, M.L. Gong, T. Tasdizen, University of Utah, US |
| SMILES-X: Autonomous Molecular Compounds Characterisation For Small Datasets Without Descriptors
G. Lambard, E. Gracheva, National Institute for Materials Science, JP |
|
| 1:30 | Machine Learning for Probe and Electron Microscopy | |
| Session chair: Dalia Yablon, TechConnect, US, Greg Haugstad, University of Minnesota, US |
| TBA
M. Scott, University of California, Berkeley, US |
| Artificially Intelligent Transmission Electron Microscopy
H. Xin, University of California, Irvine, US |
| Correlative and causal machine learning in scanning probe and electron microscopy
M. Ziatdinov, Oak Ridge National Laboratory, US |
| Opportunities in Machine Learning for Atomic Force Microscopy
I. Chakraborty, D. Yablon, Stress Engineering Services, Inc., US |
| Intermodulation AFM a novel multifrequency technique for material insight
D. Forchheimer, Intermodulation Products AB, SE |
| Fourier-reconstructed force fingerprints in AFM: machine learning for novel contrast
G. Haugstad, A. Avery, R. Rahn, S. Hubig, B. Luo, H.-S. Lee, A. McCormick, D. Forschheimer, University of Minnesota, US |
| Tuesday June 30 |
|
| 8:30 | AI and Machine Learning Fireside Chat | |
| Session chair: Brent M. Segal, Lockheed Martin, US |
|
| 10:30 | AI, Modeling and Simulation for Materials Design | |
| Session chair: Rick Ross, 3M Company, US; Peter Koenig, Procter & Gamble, US |
| Data-Driven Discovery of New Materials for Solid-State Batteries
Y. Mo, University of Maryland, US |
| TBA
C. Kreisbeck, Kebotix, Inc., US |
| Self-driving laboratory for accelerated discovery of thin-film materials
C.P. Berlinguette, J.E. Hein, A. Aspuru-Guzik, B.P. MacLeod, F.G.L. Parlane, University of British Columbia, CA |
| Topology-Informed Machine Learning for the Prediction of Glass Properties
M. Bauchy, University of California, Los Angeles, US |
| Materials Design by Integrated Computational Materials Engineering (ICME) and AI
C. Niu, A. Saboo, Y. Lin, S. Sorkin, P. Lu, J. Gong, QuesTek Innovations LLC, US |
|
| 10:30 | AI & 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 |
|
| 1:30 | AI for Biomaterials and Drug Design | |
| Session chair: Payel Das, IBM. Thomas J. Watson Research Center, US, Sarah Tao, Sanofi, US |
| Chemical Discovery and AI-Assisted Chemical Synthesis
C. Coley, Massachusetts Institute of Technology, US |
| Combining machine learning with other computational methods for drug design
G. Butterfoss, ProteinQure, CA |
| TBA
D. Marks, Harvard University Medical School, US |
| Machine learning methods for the de-novo design of proteins and antibodies
P. Kim, University of Toronto, CA |
| Accelerating Drug Discovery With Outcome-based Data Science and AI Application
L. Subramanian, S. Schweizer, 3DS, US |
| Explainable Deep Models for Compound-Protein Binding Affinity Prediction and Deep Generative Models for Protein Design
Y. Shen, Texas A&M University, US |
|
| 4:00 | Machine 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 |
|
| 10:30 | Design, Modeling, Simulation & Software Innovation | |
| Session chair: Nanci Hardwick, MELD. Manufacturing Corporation, US |
| Additive manufacturing needs validated machine learning for simulation-based part quantification
W.K. Liu, Northwestern University, US |
| Large Generative Designs Optimized for Production Through Simulation and Produced with Multi-Axis Hybrid Metal Additive
S. Gardner, Big Metal Additive, LLC, US |
| Integrated process-structure-property modeling framework and methods for process design and performance prediction of additively manufactured material systems
W.K. Liu, Z. Gan, C. Yu, O.L. Kafka, K.K. Jones, Y. Lu, Northwestern University, US |
| Prediction and optimization of surface roughness in additive manufacturing with data-driven multiphysics models
Z. Gan, K.K. Jones, Y. Lu, L. Cheng, J. Lua, G. Wagner, W.K. Liu, Northwestern University, US |
| High Fidelity vs Low Fidelity Physics-Based Modeling of Laser Powderbed Fusion Processes: Accuracy vs. Speed
C. Katinas, Y.C. Shin, Purdue University, US |
| AM computational tech: Software for multiscale simulation and process modeling of additive manufacturing
H. Hosseinzadeh, Rowan University, US |
| Modeling Optical, Reaction, and Transport Effects in Continuous Stereolithographic 3D Printing
Z.D. Pritchard, M.P. de Beer, R.J. Whelan, T.F. Scott, M.A. Burns, University of Michigan, US |
|
| 10:30 | Innovations for Next-gen AI | |
| TBA
C. Milroy, NVIDIA, US |
| TBA
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 |
|
| 10:30 | AI Design & Manufacturing | |
| Session chair: Ishita Chakraborty, Stress Engineering Services, US |
| A Bayesian Experimental Autonomous Researcher for Mechanical Design
K. Brown, Boston University, US |
| Preform Design Prediction Model Based on Convolutional Neural Network Deep Learning in Piston Forging
S. Lee, L. Quagliato, J. Sun, N. Kim, Sogang University, KR |
| Semi-supervised and Reinforcement Learning Methods for VLSI Chip Design
J. Obert, Sandia National Labs, US |
| A Deep Convolutional Neural Network for Predicting the Failure Response of High-pressure Gas Pipes subject to Pitting Corrosion
S. Soghrati, Ohio State University, US |
| A Holistic, Digital-Twin Approach to Performance Simulation of Automated Fiber Placement Manufactured Parts
R. Cook, P.-Y. Lavertu, MSC Software, US |
| Machine Learning for Glass Manufacturing
M. Bauchy, University of California, Los Angeles, US |
|
| 1:30 | Machine Learning for Medical Diagnostics | |
| Session chair: Prakash D. Nallathamby, Notre Dame University, Eric Gale, Harvard Medical School |
| Point-of-care serodiagnostic test using a multiplexed paper-based immunoassay and machine learning
Z. Ballard, University of California, Los Angeles, US |
| Machine Learning for Automated Hepatic Fat Quantification
H. Sagreiya, A. Akhbardeh, University of Pennsylvania, US |
| An Unsupervised Approach for Treatment Effectiveness Monitoring Using Curvature Learning
A. Akhbardeh, H. Sagreiya, Stanford University, US |
| Novel respiratory monitoring using ultrasound signaling
G. Bilen-Rosas, I. Ong, H. Rosas, University of Wisconsin School of Medicine and Public Health Madison, US |
| Microbubbling Digital Assay
P. Wang, University of Pennsylvania, US |
| AI Platform for DNA Diagnostics, Therapeutics & Ancestry
R. Peterson, J. Kahn, DNA Analtytics, US |
| Kidney Cancer Staging using Deep Learning Neural Network
N. Hadjiyski, Ann Arbor Pioneer High School, US |
|
| 1:30 | AI for Manufacturing Inspection and Control | |
| Session chair: Keith Brown, Boston University, US |
| TBA
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 |
|
| 4:00 | Innovations 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 |
|
| 4:00 | AI 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 |
|
