David A. Bader

Distinguished Professor

Director, Institute for Data Science

New Jersey Institute of Technology

Solving Global Grand Challenges with High Performance Data Analytics

Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of the electric power grid. Unlike traditional applications in computational science and engineering, solving these social problems at scale often raises new challenges because of the sparsity and lack of locality in the data, the need for research on scalable algorithms and architectures, and development of frameworks for solving these real-world problems on high performance computers, and for improved models that capture the noise and bias inherent in the torrential data streams. In this talk, Bader will discuss the opportunities and challenges in massive data science for applications in social sciences, physical sciences, and engineering.


David A. Bader is a Distinguished Professor in the Department of Computer Science and founder of the Department of Data Science and inaugural Director of the Institute for Data Science at New Jersey Institute of Technology. Prior to this, he served as founding Professor and Chair of the School of Computational Science and Engineering, College of Computing, at Georgia Institute of Technology. Dr. Bader is a Fellow of the IEEE, AAAS, and SIAM, and advises the White House, most recently on the National Strategic Computing Initiative (NSCI) and Future Advanced Computing Ecosystem (FACE). Bader is a leading expert in solving global grand challenges in science, engineering, computing, and data science. His interests are at the intersection of high-performance computing and real-world applications, including cybersecurity, massive-scale analytics, and computational genomics, and he has co-authored over 300 scholarly papers and has best paper.

Tarek Abdelzaher

Sohaib and Sara Abbasi Professor and Willett Faculty Scholar at the Department of Computer Science, the University of Illinois at Urbana Champaign

IEEE Fellow, ACM Fellow

Real-Time Intelligent Services for Internet of Things Applications

Advances in neural network revolutionized modern machine intelligence, but important challenges remain when applying these solutions in IoT contexts; specifically, in cost-sensitive applications on lower-end embedded devices. The talk discusses challenges in offering real-time machine intelligence services at the edge to support applications in resource constrained environments. The intersection of IoT applications, real-time requirements, and AI capabilities motivates several important research directions. For example, how to support efficient execution of machine learning components on low-cost edge devices while retaining inference quality and offering confidence estimates in results? How to reduce the need for expensive manual labeling of IoT application data? How to improve the responsiveness of AI components to critical real-time stimuli in their physical environment? How to prioritize and schedule the execution of intelligent data processing workflows on edge-device GPUs? How to exploit data transformations that lead to sparser representations of external physical phenomena to attain more efficient learning and inference? The talk discusses recent advances and presents evaluation results in the context of different real-time edge AI applications.


Tarek Abdelzaher received his Ph.D. in Computer Science from the University of Michigan in 1999. He is currently a Sohaib and Sara Abbasi Professor and Willett Faculty Scholar at the Department of Computer Science, the University of Illinois at Urbana Champaign. He has authored/coauthored more than 300 refereed publications in real-time computing, distributed systems, sensor networks, and control. He serves as an Editor-in-Chief of the Journal of Real-Time Systems for over 10 years, and has served as Associate Editor of the IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE Embedded Systems Letters, the ACM Transaction on Sensor Networks, and the Ad Hoc Networks Journal, among others. Abdelzaher's research interests lie broadly in understanding and influencing performance and temporal properties of networked embedded, social and software systems in the face of increasing complexity, distribution, and degree of interaction with an external physical environment. Tarek Abdelzaher is a recipient of the IEEE Outstanding Technical Achievement and Leadership Award in Real-time Systems (2012), the Xerox Award for Faculty Research (2011), as well as several best paper awards. He is a fellow of IEEE and ACM.

Edwin R. Hancock

Fellow of Royal Academy of Engineering

Emeritus Professor at the University of York, Adjunct Professor at Beihang University, Distinguished Chair Professor at Xiamen University

IAPR Fellow, IEEE Fellow

BMVA Distinguished Fellow

Using Archival Polar Images and Computer Vision to Measure Historical Changes in Glacier Extent and Volume

In direct response to global warming, almost all of the world’s ice-masses are shrinking, and melting glaciers contribute to sea-level rise. There are still many unknowns regarding the precise relationship between glacier volume change and climatic drivers such as air temperature. Improved understanding of this link will come from observations of how glaciers have responded to climate, but requires long-term records of how glaciers have changed. A powerful tool at our disposal for making such observations is satellite imagery and since the launch of the first earth observation satellite in 1972 it has been possible to monitor and measure glacier change. However, there is limited measurement of glacier extent prior to this, and long-term records of glacier change are needed to fully understand the role of climate in affecting glaciers. This talk describes how computer vision and machine learning techniques can be used to help expand the record of glacier extent and volume change using archival images from the Scott Polar Research Institute (SPRI) at Cambridge University. The SPRI Archive is one of the most comprehensive collections of polar imagery in the world, dating back to the nineteenth century and covering the expeditions of Shackleton and Scott. These images are not only of great historical value but are also glaciologically valuable too because they present snapshots of past glaciers and ice sheet margins. Using the SPRI images we have been able to use structure-from-motion to make detailed analysis of ice margin change and glacier volume change from the 1930's.


Edwin R. Hancock (FREng) holds a BSc degree in physics (1977), a PhD degree in high-energy physics (1981) and a D.Sc. degree (2008) from the University of Durham, and a doctorate Honoris Causa from the University of Alicante in 2015.

He is currently Emeritus Professor at the University of York, Adjunct Professor at Beihang University and Distinguished Chair Professor at Xiamen University. He has published more than 200 journal papers and 650 refereed conference publications. He was awarded the Pattern Recognition Society medal in 1991 and an outstanding paper award in 1997 by the journal Pattern Recognition. In 2009 he was awarded a Royal Society Wolfson Research Merit Award. He is a Fellow of the International Association for Pattern Recognition and the IEEE, and was a Distinguished Fellow by the British Machine Vision Association. He was elected a Fellow of Royal Academy of Engineering in 2021. In 2018 he received the Pierre Devijver Award from the IAPR. He is currently Editor-in-Chief of the journal Pattern Recognition, and was founding Editor-in-Chief of IET Computer Vision from 2006 until 2012. He was Second Vice President of the IAPR (2016-2018) and is an IEEE Computer Society Distinguished Visitor 2021-2023.

Shengyong Chen

Distinguished Professor,

IET Fellow,

Vice President,

Tianjin University of Technology

Machine Vision and Intelligent Systems

This talk presents the principle of vision perception and some ongoing projects on machine vision for robots and aerial vehicles, including object localization, segmentation, recognition, reconstruction, representation, feature extraction, target tracking, pattern analysis, etc. The questions which need to answer in an intelligent system include: where is the target? who is who? what is meaningful? what represents the object? what happen? These questions are inevitable for implementation of practical intelligent systems, e.g. for self-localization of the robots, planning a path for tracking a person, classification of objects in industrial production, or interpretation of traffic events. Variety of examples and videos are shown in the talk to show the recent results of the research group.


Prof. Shengyong Chen received the Ph.D. degree from City University of Hong Kong in 2003. He worked as a guest researcher at University of Hamburg, Germany, where he received a fellowship from the Alexander von Humboldt Foundation in 2006. He was a visiting professor at Imperial College London, from 2008 to 2009. He is currently a full Professor and Vice-President in Tianjin University of Technology. He is an IET Fellow and an IEEE senior member. His research interests include machine vision and robotics. He received the National Outstanding Youth Foundation Award of NSFC. He has applied over 100 patents and published over 400 scientific papers, including 50 in IEEE Transactions, and 5 Best Paper Awards from international organizations. His work received over 12000 citations in Google Scholar.