AI & MACHINE LEARNING
ACCELERATING INDUSTRY SOLUTIONS.
Connecting emerging tools and platforms with the global innovation ecosystem. Join us for the inaugural AI TechConnect program in Boston. Submit your abstract and join the program.
AI TechConnect Program2019 Program: At-A-Glance |
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Monday June 17 | ||
10:30 | AWS AI & Cyber Innovation Programs | |
10:30 | AI for Advanced Manufacturing | |
1:30 | Amazon Web Services - AWS Innovation Programs & Workshop | |
1:30 | AI for Biomaterials, Medical and Biotech Applications | |
3:00 | Data/AI/Cyber Innovation Spotlights: DOD/USINDOPACOM & MIT Innovation Initiative | |
Tuesday June 18 | ||
10:30 | AI for Materials Development | |
1:30 | AI for Materials Development | |
Wednesday June 19 | ||
8:30 | AI Keynotes | |
10:30 | AI for Materials Development | |
1:30 | AI Innovations & Implementation | |
3:30 | TechConnect Innovation Leaders Networking Reception - Poster Session II | |
3:30 | Materials Modeling, Informatics & Machine Learning: Posters | |
3:30 | AI & Machine Learning Applications: Posters | |
Detailed Program: | ||
Monday June 17 | ||
10:30 | AWS AI & Cyber Innovation Programs | 308 |
10:30 | Artificial Intelligence and Machine Learning on AWS Amazon Web Services, US | |
11:15 | Cybersecurity Innovation in the Cloud Amazon Web Services, US | |
10:30 | AI for Advanced Manufacturing | 304 |
Session chair: Brent Segal, Lockheed Martin, US | ||
10:30 | AI at Scale: Real World Industrial Applications C. Lefebvre, nDimensional, US | |
10:50 | Science-Guided AI for Development of New Biofuels and Bioenergy Production Technologies M. Urgun-Demirtas, Y. Lin, P. Laible, Argonne National Laboratory, US | |
11:10 | Rapid Artificially Intelligent Design S. Guerin, Z. Rogers, Additive Rocket Corporation, US | |
11:30 | Robot Axis Control Using a Differential Learning Algorithm B. Abegaz, Loyola University of Chicago, US | |
11:50 | Tension prediction using web moving speed and natural vibration frequency X. Du, J. Yan, University of Massachusetts, Amherst, US | |
1:30 | Amazon Web Services - AWS Innovation Programs & Workshop | 308 |
1:30 | Startup Resources for Accelerated Growth Amazon Web Services, US | |
2:15 | AWS GovCloud, Compliance and Security Amazon Web Services, US | |
3:15 | Startup Success Stories in Highly Regulated Markets Amazon Web Services, US | |
4:00 | Aerospace and the Cloud Amazon Web Services, US | |
1:30 | AI for Biomaterials, Medical and Biotech Applications | 304 |
Session chair: Sarah Tao, Sanofi, US | ||
1:30 | Automating Chemical Synthesis using AI and Automated Systems P. Madrid, J.P. Malerich, M. Latendresse, M. Krummenacker, D. Stout, J-P. Lim, Vi-Anh Vu, J. Szeto, K. Rucker, J. White, N. Collins, SRI, US | |
1:55 | The Concept of “Sniper Shoot” in Discovery of a New Drugs A. Yu Rogachev, Illinois Institute of Technology, US | |
2:15 | Next-Generation Cheminformatics Approaches for Rational Drug Discovery D. Fourches, North Carolina State University, US | |
2:35 | Deep convolutional neural network and image prior based super resolution for X-ray nano-tomography K.C. Prabhat, V. DeAndrade, N. Kasturi, X. Yang, Argonne National Laboratory; The University of Chicago, US | |
2:55 | Effective Radiation Therapy Using Accurate Tumor Segmentation G. Rasool, Rowan University, US | |
3:00 | Data/AI/Cyber Innovation Spotlights: DOD/USINDOPACOM & MIT Innovation Initiative | 303 |
Session chair: Jennifer Rocha, TechConnect, US | ||
3:00 | MIT Innovation Initiative Spotlight G. Keselman, MIT Innovation Initiative, US | |
3:15 | USINDOPACOM Innovation Spotlight R. Roley, USINDOPACOM, US | |
3:30 | Social Media Environment and Internet Replication (SMEIR) A. Furuta, IDS International Government Services, US | |
3:37 | rasdaman P. Baumann, rasdaman GmbH, DE | |
3:44 | Artificial intelligence, machine learning, machine vision for infrastructure and road surface assessments B. Utter, RoadBotics, US | |
3:51 | Skylight - A Big Data Platform for Buildings J. Hahn, Site 1001, US | |
3:58 | HADES - High-Fidelity Adaptive Deception & Emulation System V. Urias, Sandia National Laboratories, US | |
4:05 | Geoanalytics Platform for Special Operations Mission Readiness S. Moola, EPIC Ready, US | |
4:12 | Curve3d M. Script, vClick3d, US | |
Review Panelist N. Rao, US Small Business Administration, US | ||
Review Panelist G. Keselman, MIT Innovation Initiative, US | ||
Review Panelist R. Roley, USINDOPACOM, US | ||
Review Panelist B. Segal, Lockheed Martin, US | ||
Review Panelist S. Stella, Electric Power Research Institute (EPRI), US | ||
Tuesday June 18 | ||
10:30 | AI for Materials Development | 304 |
Session chair: Peter Koenig, Procter & Gamble, US | ||
10:30 | Learning from Small Data: Optimization of Complex Material Systems with Hierarchical Machine Learning A. Menon, N. Washburn, Carnegie Mellon University, US | |
10:55 | AI and Machine Learning for Accelerating Materials Design and Discovery S. Sankaranarayanan, Argonne National Laboratory, US | |
11:20 | Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals C. Chen, University of California, San Diego, US | |
11:45 | Combining data-driven models with physics-based models for industrial materials discovery, design, and manufacturing L. Subramanian, Dassault Systems, US | |
12:10 | Accelerating Materials Development with Uncertainty-Aware Sequential Learning M. Hutchinson, Citrine Informatics, CA | |
1:30 | AI for Materials Development | 304 |
Session chair: Keith Brown, Boston University, US | ||
1:30 | Towards Autonomous Materials Research Systems J. Hattrick-Simpers, NIST, US | |
1:55 | Autonomous Research for Carbon Nanotube Synthesis Benji Maruyama, Air Force Research Laboratory, US | |
2:20 | Polymer Genome: An Informatics Platform for Rational Polymer Design H. Tran, Georgia Institute of Technology, US | |
2:45 | A Self-Driving Laboratory for Accelerating Materials Discovery C.P. Berlinguette, J.E. Hein, A. Aspuru-Guzik, B.P. MacLeod, F.G.L. Parlane, B. Lam, University of British Columbia, CA | |
3:05 | Smart design of organic and organometallic materials using automated machine learning methods and workflows H.S. Kwak, T. Robertson, C.M. Krauter, K. Leswing, M.D. Halls, Schrodinger, Inc., US | |
3:25 | Computational screening of Li and Na fast ion conductors using high-throughput bond-valence calculations and machine-learning analysis Javier Carrasco, CIC Energigune, ES | |
Wednesday June 19 | ||
8:30 | AI Keynotes | 304 |
Session chair: Brent Segal, Lockheed Martin, US | ||
8:30 | Welcome - Sector Expanding AI Innovation Impact B.M. Segal, Lockheed Martin, US | |
8:55 | AI and Robotics for Rapid Innovation of Materials J.S. Becker, Kebotix, US | |
9:20 | Advances in AI for Design and Discovery J. Kautz, NVIDIA, US | |
10:30 | AI for Materials Development | 304 |
Session chair: Peter Koenig, Procter & Gamble, US | ||
10:30 | The Search for Ground Truth: Machine Learning for Mechanical Design K. Brown, Boston University, US | |
10:55 | Towards Trusted AI For Advancing Science and Innovation P. Das, IBM Thomas J Watson Research Center, US | |
11:20 | Predicting potential energy surfaces with machine learning M. Hellström, Software for Chemistry & Materials BV, NL | |
11:40 | Coarse-grained modeling of polycrystalline ice in supercooled water H. Chan, M. Cherukara, B. Narayanan, T. Loeffler, C. Benmore, S. Gray, S. Sankaranarayanan, Argonne National Laboratory, US | |
12:00 | Machine Learning for Glass Science and Engineering M. Bauchy, University of California, Los Angeles, US | |
1:30 | AI Innovations & Implementation | 304 |
Session chair: Brent Segal, Lockheed Martin, US | ||
1:30 | 3 Tiers of Cyber Security - The Future of Cyber Science M.H. Nance, C. Johnson-Bey, Lockheed Martin, US | |
1:50 | MiniZinc - a constraint modeling language D. Hemmi, Monash University, AU | |
2:10 | Telling the story of AI internally - the missing piece of implementation E. Thoresen, Midwest Capital Group, US | |
3:30 | TechConnect Innovation Leaders Networking Reception - Poster Session II | Boylston Hallway |
3:30 | Materials Modeling, Informatics & Machine Learning: Posters | Boylston Hallway |
Analysis of Gate-Length Dependence of Lags and Current Collapse in Field-Plate AlGaN/GaN HEMTs T. Chiba, Y. Saito, R. Tsurumaki, K. Horio, Shibaura Institute of Technology, JP | ||
Analysis of Breakdown Characteristics in Field-Plate AlGaN/GaN HEMTs: Dependence on Deep-Acceptor Density in Buffer Layer S. Akiyama, M. Kondo, L. Wada, K. Horio, Shibaura Institute of Technology, JP | ||
Improved Electric Field Decomposition Capacitance Model with 3-D Terminal and Fringe Components in Sub-28nm Interconnect S. Ueda, R. Tomita, Y. Kawada, K. Horio, Shibaura Institute of Technology, JP | ||
Interaction modeling of interfacial surfaces with molecular agents: an approach to the problem of bioaccumulation of lead in fish O. Torres, E. González, Pontificia Universidad Javeriana, CO | ||
Molecular Dynamics Simulation Study on Nanoelectromechanical Oscillator based on Graphene Nanoflake O.K. Kwon, J.W. Kang, Semyung University, KR | ||
Violation of the Zeroth Law of Thermodynamics -- Thermodynamic Properties of a long-range Interacting System Z-Y. Yang, Duke University, US | ||
3:30 | AI & Machine Learning Applications: Posters | Boylston Hallway |
Intelligent Transportation Asset Management System P. Bhavsar, N. Bouaynaya, Y. Mehta, G. Rasool, CREATEs at Rowan University, US | ||
A Learning-based Approach to Cover Short-term Camera Failure in a Monocular Visual Inertial Odometry System Y. Tian, Embry-Riddle Aeronautical University, US | ||
Feedback Control of an ROS-Enabled Autonomous Vehicle B. Abegaz, Loyola University of Chicago, US | ||
Smart Control of an Electric Power Assisted Steering (EPAS) B. Abegaz, Loyola University of Chicago, US | ||
Fader Axis D. Hemmi, Monash University, AU | ||
Bayesian Reasoning For Better Thinking D. Hemmi, Monash University, AU | ||
Image-based damage conditional assessment of large-scale infrastructure systems using remote sensing and deep learning approaches H. Pan, Z. Zhang, X. Wang, Z. Lin, North Dakota State University, US | ||
Real World Optimization of Traffic Flow Inside a Large Manufacturing Facility M. Griffin, Insight, US | ||
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