UNC Research Computing along with Nvidia and SAS are hosting a two day symposium on Deep Learning (DL), Machine Learning (ML), and Artificial Intelligence (AI) at the SAS campus in Cary, NC, on September 27-28, 2018. Deep Learning, Machine Learning, and Artificial Intelligence, technologies are transforming diverse scientific, engineering and business domains. This is a call for presentations that characterize the use of these technologies in research.

We are soliciting calls for presentations. To be considered for a 30 minute speaking slot please submit a short abstract (1 page maximum) to

https://www.surveygizmo.com/s3/4432729/UNC-Deep-Learning-Symposium-Call-for-Presentations

The deadline for submission is August 15, 2018. All contributions will be acknowledged and notification will be made by August 29.

The symposium has two components:

  1. Educational Workshops—led by Nvidia instructors on topics including Deep Learning for healthcare and image analysis, genomics, image segmentation and classification, neural networks and more.
  2. Research Usage Presentations—by researchers in the triangle and beyond, on how DL, ML and AI methods are being used to further their research. This includes faculty and students from regional universities such as Carolina, Duke and State, and researchers from the commercial sector such as SAS and Nvidia.

We will share ideas and knowledge and hope to generate new avenues of inquiry and collaboration.

Topics of Interest

Use of deep learning, machine learning and artificial intelligence in research including but not limited to applications including health care, robotics, language processing, speech recognition, image recognition, drug discovery, neural networks, recommendation systems, bioinformatics as well as challenges faced, and new avenues of inquiry into the methodology and frameworks.

Symposium Audience

UNC, Duke, and NC State faculty, students and researchers interested in DL, ML and AI as well as other university researchers across the state.

Invited Speakers (alphabetically)

Mohit Bansal, Assistant Professor in Computer Science at UNC Chapel Hill.

Mohit Bansal’s research interests include statistical natural language processing and machine learning, with a focus on multimodal, grounded, and embodied semantics (i.e., language with vision and speech, for robotics), human-like language generation and Q&A/dialogue, and interpretable and structured deep learning.

Lawrence Carin, Professor of Electrical and Computer Engineering at Duke University

Lawrence Carin joined the Electrical Engineering Department at Duke University in 1995 where he is now a Professor, and Vice Provost for Research. From 2003-2014 he held the William H. Younger Distinguished Professorship, and he was ECE Department Chair from 2011-2014. Dr Carin’s early research was in the area of electromagentics and sensing, and over the last 15 years his research has moved to applied statistics and machine learning. He has recently served on the Program Committee for the following machine learning conferences: International Conf. on Machine Learning (ICML), Neural and Information Processing Systems (NIPS), Artificial Intelligence and Statistics (AISTATS), and Uncertainty in Artificial Intelligence (UAI). He was previously an Associate Editor (AE) of the IEEE Trans. on Antennas and Propagation, the IEEE Trans. on Signal Processing, and the SIAM J. of Imaging Science. He is currently an AE for the J. of Machine Learning Research. He is an IEEE Fellow.

Olexandr Isayev, Assistant Professor at the UNC Eshelman School of Pharmacy, UNC Chapel Hill

Olexandr Isayev’s research interests focus on making sense of chemical data with molecular modeling and deep learning. Previously, Olexandr was a post-doctoral research fellow at Case Western Reserve University, and a scientist at a government research lab. He is a recipient of the Emerging Technology Award from the American Chemical Society, the 2016 Eshelman Institute for Innovation Fellowship and the GPU computing award from NVIDIA.

Jeff Layton, Senior Solution Architect, Nvidia

Jeff Layton is a Senior Solution Architect at NVIDIA for Deep Learning and HPC. In his 30+ years of experience he has been a professor, engineer, cluster builder, cluster user and cluster admin, code writer, system architect/engineer, manager, and benchmark/IO engineer. He’s a big proponent of GPUs, MPI, OpenACC, CUDA, Python, Deep Learning, and Fortran and loves helping people solve problems and help people use new technologies.

Karl Ricanek, Jr., Associate Professor of Computer Science, UNC-Wilmington

Karl Ricanek, Jr. is a Professor of Computer Science at University of North Carolina at Wilmington, where he founded the world renowned Face Aging Group Research Lab. He is the Director for the Institute for Interdisciplinary Identity Sciences (I3S) and affiliated with multiple International Scientific Working Groups on the subject of face recognition and facial analytics. He has authored more than 80 scientific articles and multiple book chapters in the areas of machine learning, face recognition, and facial analytics. In addition Karl is Co-Founder & Chief Data Scientist of Lapetus Solutions Inc. He leads the facial analytics, machine learning, and data science group, which focuses on developing novel solutions for the prediction of life events through deep analysis of the face and application data.

Guillermo Sapiro, Edmund T. Pratt, Jr. School Professor of Electrical and Computer Engineering at Duke University

Dr. Sapiro works on theory and applications in computer vision, computer graphics, medical imaging, image analysis, and machine learning. He has authored and co-authored over 300 papers in these areas and has written a book published by Cambridge University Press, January 2001. Dr. Sapiro was awarded the Gutwirth Scholarship for Special Excellence in Graduate Studies in 1991, the Ollendorff Fellowship for Excellence in Vision and Image Understanding Work in 1992, the Rothschild Fellowship for Post-Doctoral Studies in 1993, the Office of Naval Research Young Investigator Award in 1998, the Presidential Early Career Awards for Scientist and Engineers (PECASE) in 1998, the National Science Foundation Career Award in 1999, and the National Security Science and Engineering Faculty Fellowship in 2010. He received the test of time award at ICCV 2011.

Wayne Thompson, SAS Senior Manager, Product Management

Wayne Thompson leads SAS Data Science Technologies. He is one of the early pioneers of business predictive analytics, and he is a globally recognized presenter, teacher, practitioner, and innovator in the field of predictive analytics technology. He has worked alongside the world’s biggest and most challenging companies to help them harness analytics to build high-performing organizations. Over the course of his 20-year career at SAS, he has been credited with bringing to market landmark SAS analytic technologies (SAS® Text Miner, SAS® Credit Scoring for SAS® Enterprise MinerTM, SAS® Model Manager, SAS® Rapid Predictive Modeler, SAS® Scoring Accelerator for Teradata, SAS® Analytics Accelerator for Teradata, and SAS® Visual Statistics). His current focus initiatives include easy-to-use, self-service data mining tools for business analysts, deep learning and cognitive computing.

Raju Vatsavai, Chancellor’s Faculty Excellence Program Associate Professor, CSC Dept. NCSU.

Dr. Raju works at the intersection of spatial and temporal big data management, analytics, and high performance computing with applications in the national security, geospatial intelligence, natural resources, agriculture, climate change, location-based services, and human terrain mapping. Before joining NCSU, Raju was the Lead Data Scientist for the Computational Sciences and Engineering Division (CSED) at the Oak Ridge National Laboratory (ORNL). He has published more than 100 peer-reviewed articles in conferences and journals and edited two books on “Knowledge Discovery from Sensor Data.” He served on program committees of leading international conference including ACM KDD, AAAI, ECML/PKDD, SDM, ACM SIGSPATIAL GIS, CIKM, and IEEE BigData.

Andre Violante, SAS Staff Scientist

Andre is part of the Technology and Industry Products and supports Product Management and R&D concerning the machine learning and artificial intelligence products offered by SAS.

Andre has over 8 years of digital analytics experience and specializes in the retail industry and customer analytics. Andre has worked with several different data platforms (Oracle, Hadoop, AWS) using a variety of open source tools including R and Python. Andre is very intellectually curious with a passion for solving real world business problems that make impact.