speakers
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.
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.
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.
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.
University of Montpellier
Title:
Formal Concept Analysis, A framework for Knowledge Structuring and Exploration.
--Applications to Component/Service Directories and Product Lines.
Bio: Marianne Huchard is Full Professor of computer science at the University of Montpellier since 2004, where she teaches courses in software engineering and knowledge engineering. She develops her research at LIRMM (Laboratory of Informatics, Robotics and Microelectronics at Montpellier). She obtained a PhD in computer Science in 1992, during which she investigated algorithmic questions connected to the management of multiple inheritance in various object- oriented programming languages. She conducts research in two areas: in Formal Concept Analysis (FCA), where she studies theoretical and applied aspects, and in Software Engineering, where she contributes to model-driven engineering, component-based and service-based software development, as well as migration towards software product lines. She has been deputy director of the LIRMM, and she also participated to the creation and supervision of the Master degree in Software Engineering at the University of Montpellier, where she is currently responsible for the Doctoral studies in Computer Science. She recently served as the program chair of the 13th International Conference on Concept Lattices and their Applications (CLA 2016), as the general conference chair of the 27th European Conference on Object-Oriented Programming edition, the 9th European Conference on Modelling Foundations and Applications edition and the 7th European Conference on Software Architecture (ECMFA, ECOOP, ECSA 2013), and as the general chair of the 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018).
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.