June 17, 9.00-10.00

Kathryn S McKinley, Senior Staff Researcher, Google Cloud

Title: Resource Efficiency and Capacity when Customers are Unpredictable

Abstract: TBC

Bio: Kathryn S. McKinley is a Senior Staff Research Scientist at Google (2017-present). She received her BA, MS, and PhD from Rice University. Her research interests span programming languages, compilers, runtime systems, operating systems, cloud systems, and architecture with a focus on performance, parallelism, and memory systems. She and her collaborators have produced software systems widely used in industry and academia: the DaCapo Java Benchmarks (31,800+ downloads), the TRIPS Compiler, the Hoard memory manager (used by OS X), the MMTk memory management toolkit, the Immix garbage collector (in Jikes RVM, Haxe, Rubinius, and Scala), incremental parallelism in Bing, and the SHIM profiler. Her research papers have garnered five historical impact awards and ten were recognized with best conference and best in area awards. She has graduated 22 PhD students. She and her husband have three sons. Dr. McKinley is an IEEE Fellow and ACM Fellow.

June 18, 9.00-10.00

Indranil Gupta, Professor, & Associate-Head, Department of Computer Science, University of Illinois, Urbana-Champaign

Title: TBC

Abstract: TBC

Bio: TBC

June 19, 9.00-10.00

Patrizio Pelliccione, Associate Professor at Chalmers | University of Gothenburg (Sweden) and University of L’Aquila (Italy)

Title: Software Engineering for ML/AI

Abstract: ML and AI are increasingly dominating the high-tech industry. Organizations and technology companies are leveraging their big data to create new products or improve their processes to reach the next level in their market. However, ML and AI are not a silver bullet and Software 2.0 is not the end of software developers or software engineering.
In this talk I will argument on how software engineering can help ML and AI to become the key technology for (autonomous) systems of the near future. Software engineering best practices and achievements reached in the last decades might help, e.g., (i) democratising the use of ML/AI, (ii) composing, reusing, chaining ML/AI models to solve more complex problems, and (iii) supporting for reasoning about correctness, repeatability, explainability, traceability, fairness, ethics, while building an ML/AI pipeline.

Bio: Patrizio Pelliccione is Associate Professor at the Department of Computer Science and Engineering at Chalmers | University of Gothenburg (Sweden) and Associate Professor at the Department of Information Engineering, Computer Science and Mathematics – University of L’Aquila (Italy) – double affiliation. He got his PhD in 2005 at the University of L’Aquila (Italy) and from February 1, 2014 he is Docent in Software Engineering, title given by the University of Gothenburg (Sweden). His research topics are mainly in software engineering, software architectures modelling and verification, autonomous systems, and formal methods. He has co-authored more than 130 publications in journals and international conferences and workshops in these topics. He has been on the program committees for several top conferences, he is a reviewer for top journals in the software engineering domain, and he organized as program chair international conferences like ICSA2017 and FormaliSE 2018. He is very active in European and National projects. He is the PI for Co4Robots ( H2020 EU project for the University of Gothenburg and he is active researcher and Software Technology cluster co-leader of WASP ( In his research activity he has collaborated with several industries such as Volvo Cars, Volvo AB, Ericsson, Jeppesen, Axis communication, Systemite AB, Thales Italia, Selex Marconi telecommunications, Siemens, Saab, TERMA, etc. More information is available at