Seminar: Improving Decision Making with Machine Learning, Provably

Automated decision support systems promise to help human experts solve tasks more efficiently and accurately. However, existing systems typically require experts to understand when to cede agency to the system or when to exercise their own agency. Moreover, if the experts develop a misplaced trust in the system, their performance may worsen. In this talk, Manuel Gomez Rodriguez will discuss the development of automated decision support systems that, by design, do not require experts to understand when each of their recommendations is accurate to improve their performance.

Moreover, he will present both theoretical and experimental results that demonstrate that such decision support systems are robust to the accuracy of the machine learning algorithms they rely on, however, the amount of human effort they help reducing does depend on their accuracy.

Manuel Gomez Rodriguez is a tenured faculty at the Max Planck Institute for Software Systems (MPI-SWS). His research interests lie in the development of human-centric machine learning models and algorithms. He has co-authored over 50 publications in top-tier conferences (NeurIPS, ICML, UAI, AISTATS, AAAI, KDD, WWW) and journals (PNAS, Nature Communications, JMLR, TMLR).

Manuel is an ELLIS Fellow and has received several recognitions for his research, including an ERC Starting Grant, an outstanding Paper Award at NeurIPS and a Best Research Paper Honorable Mention at KDD and WWW. He has served as Track Chair for FAccT 2020, Workshop Chair at ICML 2022 and as Area Chair and/or Senior Area Chair for every major conference in machine learning, data mining and the Web.
He is also an Action Editor for JMLR. Manuel holds a M.S. and a Ph.D. in Electrical Engineering from Stanford University (2009 and 2013) and a B.S. in Electrical Engineering from University Carlos III in Madrid, Spain (2006).

 
About the EuroCC-2 project

EuroCC 2 will work to identify and address the skills gaps in the European High Performance Computing (HPC) ecosystem and coordinate cooperation across Europe to ensure a consistent skills base.

The role of EuroCC 2 is to establish and run a network of more than 30 NCCs across the EuroHPC Participating States. The NCCs act as single points of access in each country between stakeholders and national and EuroHPC systems. They operate on a regional and national level to liaise with local communities, in particular SMEs, map HPC competencies and facilitate access to European HPC resources for users from the private and public sector.

EuroCC 2 delivers training, interacts with industry, develops competence mapping and communication materials and activities, and supports the adoption of HPC services in other related fields, such as quantum computing, artificial intelligence (AI), high performance data analytics (HPDA) to expand the HPC user base.

This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101101903. The JU receives support from the European Union’s programme Digital Europe and Germany, Bulgaria, Austria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, France, Netherlands, Belgium, Luxembourg , Slovakia, Norway, Switzerland, Turkey, the Republic of North Macedonia, Iceland, Montenegro and Serbia.