Conference Speakers

Divyesh Jadav

IBM Research
Topic: From Blocks to Structures: Blockchain in the Enterprise
Abstract: Blockchain technology is quickly moving from the realm of crypto currency to mainstream. In the last couple of years, there has been widespread discussion about the adoption and deployment of blockchain technology to the enterprise segment. In this talk I will start with some blockchain fundamentals, and then look at some of the challenges blockchain technology faces for widespread adoption. Standardization efforts are afoot that are encouraging. Finally, we will look at some tangible applications of blockchain technology to solve real world problems.

Bio: Dr. Divyesh Jadav manages the Cloud, IoT & Systems Lifecycle Analytics department at IBM's Almaden Research Center. He received a B.E. degree from Mumbai University (India) in 1992 and M.S. and Ph.D. degrees from Syracuse University in 1995 and 1997, respectively, all in Computer Engineering. His research interests are in the area of management and performance analysis of distributed and parallel systems, with an emphasis on storage. Since joining the IBM Research in 1997, Dr. Jadav has led or contributed to innovations in several IBM products such as ServRAID adapters, XIV storage, Tivoli TotalStorage Productivity Center, GPFS, multiple Cloud offerings, Watson Agent Assist, and the IBM GTS Technical Support Appliance. Dr. Jadav's recent work focuses in delivering productivity enhancing analytics to IBM services offerings, and designing and building end-to-end solutions at the confluence of Mobile Computing, Internet-Of-Things, and Blockchain technology. He is a member of the IBM Academy of Technology, and the recipient of multiple IBM Research Division and Outstanding Technical Achievement awards. He has published over 20 technical papers and holds over 25 patents.

Fanjing Meng

IBM Research
Topic: Cognitive Cloud/IT Operations and Management in Real World
Abstract: More and more industries are experiencing digital disruption triggered by new technologies for example cloud, mobile, Internet-of-Things, Big Data, and Artificial Intelligence. Majority of applications are predicted to provide cognitive capabilities to amplify human skills and expertise in coming two years. Information Technology (IT) services industry is also shifting from people-led and technology-assisted model into a technology-led and people-assisted model. However, the ever-changing IT technologies, increasingly complicated IT environments, and ever-shortening IT delivery cycles in real-world pose great challenges towards cognitive IT operations and management. This keynote speech will review the evolution of ITSM and discuss opportunities and challenges of cognitive Cloud/IT operations and management in real-world. We will discuss how AI-driven Cloud/IT operations platform and analytics technologies to address these technical challenges. We will share an industry leading cognitive service delivery platform for large-scale enterprise-level Cloud/IT operations and management.

Bio: Dr. Fanjing(Meg) Meng is a Senior Technical Staff Member (STSM) and research manager at IBM Research. She has 15+ years research experience in various areas including AIOps (AI for IT operations), Cognitive IT Service Management (ITSM), IT Operations Analytics (ITOA), Cloud Transformation and Migration, Software/Solution Engineering, Project Portfolio Management and Optimization (PPMO), Domain Knowledge Management, Computer Integrated Manufacturing System (CIMS). Her current research areas mainly focus on applying advanced analytics techniques (e.g. statistics, machine learning, deep learning) into large-scale system operational data – machine-generated data (e.g. metrics, logs, and events) and service management data (e.g. configurations, tickets, change requests). The research aims to provide predictive/proactive anomaly detection and root cause analysis for mission critical cloud/applications/systems of a thousand of clients.
She received numerous awards including awards of “Patent Plateau Award”, “High Value Patent Award”, “IBM Outstanding Technical Achievement Award” and "IBM Client Value Outstanding Technical Achievement Award". She is serving as the TPC chair and member for top international conferences and the reviewer of international journals. She has published 20+ papers and received the “Best Paper Award” from IEEE CLOUD 2013. She has 20+ patents and patent applications in various innovation areas.

Franz Wotawa

Graz University of Technology, Austria
Topic: AI-Based AI Testing
Abstract: Verifying and validating AI-based systems have become a major issue considering the growing demand from application areas like autonomous driving. In my talk, I will present the foundations for assuring AI-based systems to meet their requirements and discuss some of our recent work in this domain. In particular, I will cover testing autonomous driving function combining combinatorial testing and ontologies, using genetic algorithms for identifying critical driving scenarios, and testing logic-based AI that is used for implementing fail-operational systems. In addition, we will discuss open challenges when testing AI covering also self-adaptive systems.

Bio: Franz Wotawa received a M.Sc. in Computer Science (1994) and a PhD in 1996 both from the Vienna University of Technology. He is currently professor of software engineering at the Graz University of Technology. His research interests include model-based and qualitative reasoning, theorem proving, mobile robots, verification and validation, and software testing and debugging. Beside theoretical foundations he has always been interested in closing the gap between research and practice. Starting from October 2017, Franz Wotawa is the head of the Christian Doppler Laboratory for Quality Assurance Methodologies for Cyber-Physical Systems (QAMCAS). During his career Franz Wotawa has written more than 350 peer-reviewed papers for journals, books, conferences, and workshops.
He supervised 86 master and 35 PhD students. For his work on diagnosis he received the Lifetime Achievement Award of the Intl. Diagnosis Community in 2016. He is a member of the Academia Europaea, the IEEE Computer Society, ACM, the Austrian Computer Society (OCG), and the Austrian Society for Artificial Intelligence and a Senior Member of the AAAI.

Taghi Khoshgoftaar

Florida Atlantic University
Topic: Machine Learning, Big Data and Artificial Intelligence

Bio Taghi M. Khoshgoftaar, M.S., Ph.D. is Motorola Endowed Chair professor of the Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University and the Director of National Science Foundation Big Data Training and Research Laboratory. Dr. Khoshgoftaar brings world-class assets and expertise in Big Data, Advanced Analytics, and Artificial Intelligence to the SIVOTEC team.
Dr. Khoshgoftaar’s research interests are in big data analytics, data mining and machine learning, health informatics and bioinformatics, social network mining, and software engineering. He has published more than 500 refereed journal and conference papers in these areas. Dr. Khoshgoftaar was the conference chair of the IEEE International Conference on Machine Learning and Applications (ICMLA 2016). He was also the conference chair of the IEEE International Conference on Bioinformatics and Bioengineering (BIBE 2014).
Dr. Khoshgoftaar is the Co-Editor-in Chief of the Journal of Big Data. He has served on organizing and technical program committees of various international conferences, symposia, and workshops. Dr. Khoshgoftaar has also served as North American Editor of the Software Quality Journal, and he was on the editorial boards of the journals Multimedia Tools and Applications, Knowledge and Information Systems, and Empirical Software Engineering. He currently serves on the editorial boards of the journals Software Quality, Software Engineering and Knowledge Engineering, and Social Network Analysis and Mining.

Zhiquan (George) Zhou

University of Wollongong, Australia
Topic: Automated testing of real-life self-driving systems and beyond
Abstract: Advances in machine learning have led to a surge in artificial intelligence (AI) applications with substantial investment from industry and governments. The past year has seen the development of innovative customer service solutions powered by a combination of natural language processing and machine learning technologies, as well as an increasing expectation for systems that exhibit high-level intelligence, including medical diagnosis tools and autonomous vehicles. Most of these systems, however, are not easy to test by automated means.
Software testing is essential for the quality assurance of AI systems. To achieve a high standard of testing, the tester needs to generate, execute, and verify a large number of tests. These tasks can hardly be done without test automation, for which the “oracle problem” is a main challenge. In software testing, a “test oracle” is a mechanism against which testers can decide whether the outcomes of test case executions are correct. It is, however, often difficult to design an oracle (automated, if possible) to check the correctness of an AI system’s output. For example, an AI-powered search engine (such as Google) is not easy to test due to the sheer volume of data being processed. Likewise, constructing a fully automated oracle for the testing of autonomous vehicles is especially challenging (except for checking some simple events such as collisions), as it essentially involves recreating the logic of a human driver’s decision making.
In this keynote, I first introduce recent advances in addressing the oracle problem, highlighting metamorphic testing as a practical and cost-effective approach for automated testing of AI systems. I show how we detected fatal software faults in the LiDAR obstacle-perception module of the real-life self-driving car system Baidu Apollo—we reported the alarming results eight days before Uber’s deadly crash in Tempe, Arizona, in March 2018. (Some of the results have been published in CACM, March 2019). I next discuss practical methods of identifying useful metamorphic relations, a core step of metamorphic testing, and introduce the concept of “metamorphic relation patterns.” I then go on to show the usefulness of the “pattern” concept in testing different types of AI applications, including machine translation, Google Maps navigation, web search, image and video analysis, eCommerce websites such as Amazon, and so on. I conclude this keynote speech by playing a short video to show some of the recently detected issues in the planning and vehicle-control modules of the Apollo system.

Bio: Zhi Quan (George) Zhou received the BSc degree in Computer Science from Peking University, China, and the PhD degree in Software Engineering from The University of Hong Kong. He is currently an associate professor and director of the Bachelor of Computer Science degree at the University of Wollongong, Australia. His research interests include software testing and debugging; the interplay among software testing, machine learning, and big data; and self-driving vehicles.
Zhou was a main contributor to some of the earliest research papers on metamorphic testing, and was one of the few pioneers who opened up and established the metamorphic testing research field. In 2016, he co-founded and chaired the IEEE/ACM 1st International Workshop on Metamorphic Testing, in conjunction with the 38th International Conference on Software Engineering (ICSE MET '16) in Austin, Texas. He was an invited keynote speaker at ICSE MET 2017 held in Buenos Aires, Argentina. He was an invited ACM SIGSOFT Webinar speaker, an ICSE '18 Technical Briefings speaker, and an ICSE '16 and ICSE '19 journal-first speaker, introducing metamorphic testing through all these venues. He was selected for a Virtual Earth Award by Microsoft Research, Redmond, USA, and a 2018 Researcher of the Year Gold Disruptor Award by the Australian Computer Society. He has been invited to serve as an advisor/reviewer for the Australian Research Council, the Research Grants Council of Hong Kong, and the Air Force Office of Scientific Research of the United States Air Force.