Title: Software Debugging: Past, Present, and Future
Speaker: Dr. Alessandro Orso, Professor in the College of Computing at the Georgia Institute of Technology
Time:8:45 AM- 10:00AM, April 19, 2019
Place: Room 400
Abstract: Software debugging, which involves localizing, understanding, and removing the cause of a failure, is a notoriously difficult, extremely time consuming, and human-intensive activity. For this reason, researchers have invested a great deal of effort in developing automated techniques and tools for supporting various debugging tasks. Although potentially useful, most of these techniques have yet to fully demonstrate their practical effectiveness. Moreover, many current debugging approaches suffer from some common limitations and rely on several strong assumptions on both the characteristics of the code being debugged and how developers behave when debugging such code. In this talk, I first provide an overview of the state of the art in the broader area of software debugging. I then present our research on assessing the weaknesses of popular debugging approaches and on developing techniques that aim to overcome such weaknesses. Finally, I discuss a set of open challenges in this area and future research directions that may help address these challenges.
Bio: Alessandro Orso is a Professor and former Associate School Chair in the College of Computing at the Georgia Institute of Technology. He received his M.S. degree in Electrical Engineering (1995) and his Ph.D. in Computer Science (1999) from Politecnico di Milano, Italy. From March 2000, he has been at Georgia Tech. His area of research is software engineering, with emphasis on software testing and program analysis. His interests include the development of techniques and tools for improving software reliability, security, and trustworthiness, and the validation of such techniques on real-world systems. Dr. Orso has received funding for his research from both government agencies, such as DARPA, DHS, NSF, and ONR, and industry, such as Fujitsu Labs, Google, IBM, and Microsoft. He served on the editorial boards of ACM TOSEM and on the Advisory Board of Reflective Corp, served as program chair for ACM-SIGSOFT ISSTA 2010 and program co-chair for IEEE ICST 2013, ACM-SIGSOFT FSE 2014, and ACM-SIGSOFT/IEEE ICSE 2017. He has also served as a technical consultant to DARPA. Dr. Orso is a senior member of the ACM and of the IEEE Computer Society.
Title: Recent Developments in Deep Learning Research
Speaker: Dr. Yi Pan, Regents' Professor and Chair in the Department of Computer Science, Georgia State University
Time: 6:00 PM, April 19, 2019
Place: Room 400
Due to improvements in mathematical formulas, availability of big data and increasingly powerful computers, we can now model many more layers of virtual neurons (deep neural networks or deep learning) than ever before. Deep learning is now producing many remarkable recent successes in computer vision, automatic speech recognition, natural language processing, audio recognition, and medical imaging processing. Although various deep learning architectures and novel algorithms have been applied to many big data applications, extending deep learning into more complicated applications such as bioinformatics or medical images will require more conceptual and software breakthroughs, not to mention many more advances in processing power. In this talk, I will outline the challenges and problems in deep learning research. They include design of new architectures, handling high dimensional data, encoding schemes, mathematical proofs, optimization of hyperparameters, logic and reasoning, result explanation and hardware support for deep learning. Some of our solutions and preliminary results in these areas will be presented in this talk.
Dr. Yi Pan is currently a Regents’ Professor and Chair of Computer Science at Georgia State University, USA. He has served as an Associate Dean and Chair of Biology Department during 2013-2017 and Chair of Computer Science during 2006-2013. Dr. Pan received his B.Eng. and M.Eng. degrees in computer engineering from Tsinghua University, China, in 1982 and 1984, respectively, and his Ph.D. degree in computer science from the University of Pittsburgh, USA, in 1991. His profile has been featured as a distinguished alumnus in both Tsinghua Alumni Newsletter and University of Pittsburgh CS Alumni Newsletter. Dr. Pan's research interests include parallel and cloud computing, wireless networks, and bioinformatics. Dr. Pan has published more than 250 journal papers with over 80 papers published in various IEEE journals. In addition, he has published over 150 papers in refereed conferences. He has also co-authored/co-edited 43 books. His work has been cited more than 9900 times. Dr. Pan has served as an editor-in-chief or editorial board member for 17 journals including 7 IEEE Transactions. He is the recipient of many awards including IEEE Transactions Best Paper Award, several other conference and journal best paper awards, 4 IBM Faculty Awards, 2 JSPS Senior Invitation Fellowships, IEEE BIBE Outstanding Achievement Award, NSF Research Opportunity Award, and AFOSR Summer Faculty Research Fellowship. He has organized many international conferences and delivered keynote speeches at over 60 international conferences around the world.
Title: Computing in the Post von Neumann Age - Artificial Intelligence, Security,
and Quantum Computing
Speaker: Dr. Flavio Villanustre, CISO & Vice President Technology, LexisNexis Risk Solutions
Time: 18:00 PM, April 18, 2019
Place: Room 400
Abstract: Long gone are the days of the dawn of software engineering. Even the idea of a programmer dealing with a stack of punched cards seems archaic, even though the time elapsed represents a blink of an eye in human history. The rate of change in technology and science continues to accelerate and new paradigms emerge almost every day. Computer science and engineering have seen significant breakthroughs in the past two decades, with some of these disruptive technologies having the potential to reshape the way we think about computers.
During this keynote, we will focus on three paradigm-disrupting new technologies: computers that learn from data and non-deterministically manage their own storage and recall, covering Deep Learning, AI and the Differentiable Neural Controller architecture. We will also review distributed computing on the Blockchain, and, last but not least, the way Quantum computers change the way we think about computational complexity. Because, as Abraham Lincoln said, “The best way to predict the future is to create it.”