AI Spring Conference & Expo

June 17-19, 2019 | Boston, MA | Co-Located with:

Co-Located with TechConnect World Innovation Conference & Expo SBIR/STTR Spring Innovation Conference Nanotech 2019

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 Program - Program still growing - Submit abstract to speak

2019 Program: At-A-Glance

Monday June 17

10:30AI for Advanced Manufacturing
1:30Machine Learning - AWS Workshop
1:30AI for Biomaterials, Medical and Biotech Applications

Tuesday June 18

10:30AI for Materials Development I
1:30AI for Materials Development II

Wednesday June 19

8:30AI Keynotes
10:30AI for Materials Development
1:30AI Innovations & Implementation
4:00Materials Modeling, Informatics & Machine Learning: Posters
4:00AI & Machine Learning Applications: Posters

Detailed Program:

Monday June 17

10:30AI for Advanced Manufacturing
AI at Scale: Real World Industrial Applications
C. Lefebvre, nDimensional, US
Science-Guided AI for Development of New Biofuels and Bioenergy Production Technologies
M. Urgun-Demirtas, Y. Lin, P. Laible, Argonne National Laboratory, US
Rapid Artificially Intelligent Design
S. Guerin, Z. Rogers, Additive Rocket Corporation, US
Robot Axis Control Using a Differential Learning Algorithm
B. Abegaz, M. Pecherek, Loyola University of Chicago, US
Tension prediction using web moving speed and natural vibration frequency
X. Du, J. Yan, University of Massachusetts, Amherst, US
1:30Machine Learning - AWS Workshop
1:30AI for Biomaterials, Medical and Biotech Applications
TBA
P. Madrid, SRI, US
The Concept of “Sniper Shoot” in Discovery of a New Drugs
A. Yu. Rogachev, Illinois Institute of Technology, US
Next-Generation Cheminformatics Approaches for Rational Drug Discovery
D. Fourches, North Carolina State University, US
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
Effective Radiation Therapy Using Accurate Tumor Segmentation
G. Rasool, Rowan University, US
Cartesian Neural Network Constitutive Models for 3-D Elasticity Imaging
C. Hoerig, J. Ghaboussi, M.F. Insana, University of Illinois at Urbana-Champaign, US
Reaching the Potential of Artificial Intelligence (AI) in Healthcare Data
B. Scarpelli, Connected Health Initiative, US

Tuesday June 18

10:30AI for Materials Development I
Learning from Small Data: Optimization of Complex Material Systems with Hierarchical Machine Learning
N. Washburn, Carnegie Mellon University, US
Combining data-driven models with physics-based models for industrial materials discovery, design, and manufacturing
S. Sankaranarayanan, Argonne National Laboratory, US
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
C. Chen, University of California, San Diego, US
AI guided defect dynamics in nanoscale materials
L. Subramanian, Scienomics, US
Accelerating Materials Development with Uncertainty-Aware Sequential Learning
E. Kim, Citrine Informatics, CA
1:30AI for Materials Development II
Towards Autonomous Materials Research Systems
J. Hattrick-Simpers, NIST, US
Polymer Genome: An Informatics Platform for Rational Polymer Design
H. Tran, Georgia Institute of Technology, US
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
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

Wednesday June 19

8:30AI Keynotes
Welcome - Sector Expanding AI Innovation Impact
B.M. Segal, Lockheed Martin, US
Multispectral cross-modal machine learning for material characterization and property predictioncharacterization and property prediction
W. Li, Aramco Research Center, US
Advances in AI for Design and Discovery
J. Kautz, NVIDIA, US
TBA
TBA, Sanofi, US
10:30AI for Materials Development
The Search for Ground Truth: Machine Learning for Mechanical Design
K. Brown, Boston University, US
AI For Material and Molecule Discovery
P. Das, IBM Thomas J Watson Research Center, US
Deep Learning of Input/Label Images for a Convolutional Neural Network (CNN) Utilizing Plasmonic Simulation Response of a Nanoparticle Sensor Substrate
G.R. Gallegos, New Mexico Highlands University, US
Predicting potential energy surfaces with machine learning
M. Hellström, Software for Chemistry & Materials BV, NL
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
Machine Learning for Glass Science and Engineering
M. Bauchy, University of California, Los Angeles, US
1:30AI Innovations & Implementation
Knowledge Graphs for Data Science & Machine Learning
A. Prasad, T. Cook, Cambridge Semantics Inc., US
Interactive Deep Learning Tool to Discover Disruptive Technologies
M. Price, Vector Analytics, US
A compelling need for a national Trusted AI and Smart Autonomy Test Bed
J.R. Mills, J. Clarke, The Aerospace Corporation, US
3 Tiers of Cyber Security - The Future of Cyber Science
M.H. Nance, C. Johnson-Bey, Lockheed Martin, US
Artificial Immune System Based Approach to Cyber Attack Detection
T. Saadawi, CIty University of New York, US
Telling the story of AI internally - the missing piece of implementation
E. Thoresen, Midwest Capital Group, US
4:00Materials Modeling, Informatics & Machine Learning: Posters
Using DAMASK for multi scale modeling of TRIP Steel Mg-PSZ Metal Matrix Composites
F. Qayyum, Technische Universität Bergakademie Freiberg, Germany, DE
High-speed contact mechanics between amorphous carbon nanoparticles
W.F. Sun, P.W. Chen, Beijing Institute of Technology, CN
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
Analysis of Breakdown Voltage of AlGaN/GaN HEMTs with High-k Passivation Layer and High Acceptor Density in Buffer Layer
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
Automatic Leather Species Identification using Machine Learning Techniques
M. Jawahar, Central Leather Research Institute, IN
Artificial Intelligence and Patents in Indian Healthcare Sector: Legal Issues and Challenges
K. Sharma, M Padmavati, Rajiv Gandhi School of Intellectual Property Law, Indian Institute of Technology, Kharagpur, IN
4:00AI & Machine Learning Applications: Posters
Multimodal Convolutional Neural Network for Music-Video Emotion Analysis
Y.R. Pandeya, Chonbuk National University, KR
Applying Machine Learning to Cyber
A. Kam, Lockheed Martin, US
Intelligent Transportation Asset Management System
P. Bhavsar, N. Bouaynaya, Y. Mehta, G. Rasool, CREATEs at Rowan University, US
Process plants: digitize for operational excellence
MP. Sukumaran Nair, Center for Green Technology & Management, IN
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, J. Gamilla, Loyola University of Chicago, US
Smart Control of an Electric Power Assisted Steering (EPAS)
B. Abegaz, A. Share, D. Paper, Loyola University of Chicago, US
Ai as a conduit to efficiency and safety of private airline travel
E. Kesselman, TAPJETS, US
 

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