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.
Back to Top ↑2021 Symposium Sessions |
| Monday October 18 |
| 10:30 | Machine Learning for Microscopy |
| 1:30 | AI for Characterization & Manufacturing |
| Tuesday October 19 |
| 1:30 | AI for Biomedical Applications |
| Wednesday October 20 |
| 8:30 | AI Track Keynote |
| 10:30 | AI for Advanced Materials Discovery & Design |
| 4:00 | AI Innovations - Posters |
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2021 Symposium Program |
| Monday October 18 |
|
| 10:30 | Machine Learning for Microscopy | Baltimore 1 |
| Session chair: Greg Haugstad, University of Minnesota |
| 10:30 | 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 |
| 10:55 | Rapid DNA Origami Nanostructure Detection and Classification Using the YOLOv5 Deep Convolutional Neural Network, pp. 134-137
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 |
| 11:15 | Using machine learning to probe classification and correlation AFM images
I. Chakraborty, Stress Engineering Services, Inc, US |
| 11:35 | Machine learning for microstructures classification in functional materials, pp. 114-117
A.K. Choudhary, A. Jansche, Grubesa Tvrtko, T. Bernthaler, G. Schneider, Aalen University, DE |
| 11:55 | Machine learning based detection and deep learning based image inpainting of preparation artefacts in micrographs, pp. 118-121
A. Jansche, A.K. Choudhary, T. Bernthaler, G. Schneider, Aalen University, DE |
|
| 1:30 | AI for Characterization & Manufacturing | Baltimore 1 |
| Session chair: Greg Haugstad, University of Minnesota & Grace Gu, University of California, Berkeley |
| 1:30 | Materials Informatics for Simultaneous Design of Alloy Chemistry and AM Process
S. Broderick, University of Buffalo, US |
| 1:55 | Machine Learning for In-Water Inspection of Submarine Hull Coatings
M. An, J. Cipolla, A. Shakalis, B. Hiriyur, R. Tolimieri, Prometheus Inc., US |
| 2:15 | Gamma-Ray Raster Imaging with Robotic Data Collection, pp. 109-110
W. Wells, T. Aucott, M. Siddiqi, Savannah River National Laboratory, US |
| 2:35 | A New Method for Atmospheric Correction of Satellite Data
D. Groeneveld, Advanced Remote Sensing, Inc., US |
| 2:55 | 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 |
| 3:15 | Forecasting and Decision Impact Analysis from Ripple Effects of Behaviors
B. Frutchey, BigBear.ai, US |
| 3:35 | Leveraging Machine Learning to Predict Public Transportation Arrival Times
P. Reshetova, W. Ruzicka, EastBanc Technologies, US |
| Tuesday October 19 |
|
| 1:30 | AI for Biomedical Applications | Baltimore 4 |
| Session chair: Armada Shehu, George Mason University & Sarah Tao, Sanofi |
| 1:30 | Machine Learning for Automated Hepatic Fat Quantification, pp. 105-108
H. Sagreiya, A. Akhbardeh, I. Durot, D.L. Rubin, University of Pennsylvania, US |
| 1:55 | 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 |
| 2:15 | Kidney Cancer Staging using Deep Learning Neural Network: Comparing Models Trained on Whole Kidney with Cancer and Only the Cancer, pp. 161-164
N. Hadjiyski, Ann Arbor Pioneer High School, US |
| 2:35 | Robust and Trustworthy AI for Brain Tumor Surveillance
G. Rasool, Rowan University, US |
| 2:55 | Preventing Elderly Falling Through Machine Learning, pp. 111-113
P. Hardigan, Nova Southeastern University, US |
| Wednesday October 20 |
|
| 8:30 | AI Track Keynote | Woodrow Wilson D |
| Session chair: Richard Ross, 3M Company & Peter Koenig, Procter & Gamble |
| 8:30 | Closed-loop autonomous combinatorial experimentation for streamlined materials discovery
I. Takeuchi, University of Maryland, US |
| 9:00 | From atoms to emergent mechanisms with information bottleneck and diffusion probabilistic models
P. Tiwary, University of Maryland, US |
| 9:30 | Deep Learning for Linking Chemical and Biological Space in Small Molecules and Macromolecules
A. Shehu, George Mason University, US |
|
| 10:30 | AI for Advanced Materials Discovery & Design | Annapolis 2 |
| Session chair: Richard Ross, 3M Company & Peter Koenig, Procter & Gamble |
| 10:30 | The combination of data-driven and physics-based modeling with application in protein formulations
J.G.E.M. Fraaije, P. Petris, Siemens Culgi, NL |
| 10:55 | Next Generation PCIe Network Fabric for Simulators and Performance Computing
C.T. Reynolds, Technical Systems Integrators, US |
| 11:15 | AI-accelerated materials innovation: From Optoelectronics to Fluorescent Biomarkers
C. Kreisbeck, Kebotix, US |
| 11:35 | Machine Learning for the Exploration of Nanomaterial Synthesis Parameter Spaces
R. Sappington, B. Cornick, Epic Advanced Materials, US |
| 11:55 | 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 |
|
| 4:00 | AI Innovations - Posters | Expo Hall AB |
| Using Robotics to Assemble Graphene Supercapacitor, pp. 122-125
C. Wu, J. Kim, D. Magluyan, D. Kawamoto-Kindred, Y.H. Zhou, N. Cao, H. Zhao, Z. Kuang, T. Kidd, S. Wu, S. Dobbs, Z. Yu, California State Polytechnic University, Pomona, US |
| An eHR Using AI Technology as a Clinical Decision Support Tool
J. Penn, Guidance Foundation Inc,, US |
| Benefits of a Decentralized AI, pp. 130-133
M. Bergstrom, Internet of Everything Corporation, US |
| 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, In2Capital.com, US |
| Benefits of a Decentralized AI
M. Bergstrom, Internet of Everything Corp, US |
| Simulation in Minutes, not Hours
A. Grosvenor, MSBAI, US |
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