Sandra Wachter, PhD

Professor Sandra Wachter is an Associate Professor and Senior Research Fellow focusing on law and ethics of AI, Big Data, and robotics as well as Internet regulation at the Oxford Internet Institute at the University of Oxford. Professor Wachter is specialising in technology-, IP-, data protection and non-discrimination law as well as European-, International-, (online) human rights,- and medical law. Her current research focuses on the legal and ethical implications of AI, Big Data, and robotics as well as profiling, inferential analytics, explainable AI, algorithmic bias, diversity, and fairness, governmental surveillance, predictive policing, and human rights online.At the OII, Professor Sandra Wachter also coordinates the Governance of Emerging Technologies (GET) Research Programme that investigates legal, ethical, and technical aspects of AI, machine learning, and other emerging technologies.Professor Wachter is also a Fellow at the Alan Turing Institute in London, a Fellow of the World Economic Forum’s Global Futures Council on Values, Ethics and Innovation, a Faculty Associate at The Berkman Klein Center for Internet & Society at Harvard University, an Academic Affiliate at the Bonavero Institute of Human Rights at Oxford’s Law Faculty, a Member of the European Commission’s Expert Group on Autonomous Cars, a member of the Law Committee of the IEEE and a Member of the World Bank’s task force on access to justice and technology.

Dr. Fabian Theis

Fabian Theis is director of the Institute of Computational Biology at the Helmholtz Center Munich and scientific director of the Helmholtz Artificial Intelligence Cooperation Unit (HelmholtzAI) which was launched in 2019. He is a full professor at the Technical University of Munich, holding the chair ‘Mathematical Modelling of Biological Systems’, associate faculty at the Wellcome Trust Sanger Institute as well as adjunct faculty at the Northwestern University. Fabian Theis holds a Master’s degree in Mathematics and Physics and Ph.D. Degrees in Physics and Computer Science. After different research stays. He worked as visiting researcher at the Department of Architecture and Computer Technology (University of Granada, Spain), at the RIKEN Brain Science Institute (Wako, Japan), at FAMU-FSU (Florida State University, USA), and TUAT’s Laboratory for Signal and Image Processing (Tokyo, Japan), and headed the ‘signal processing & information theory’ group at the Institute of Biophysics (Regensburg, Germany). In 2006, he started working as a Bernstein fellow leading a junior research group at the Bernstein Center for Computational Neuroscience, located at the Max Planck Institute for Dynamics and Self-Organisation at Göttingen. In summer 2007, Fabian Theis became working group head of CMB at the Institute of Bioinformatics at the Helmholtz Center Munich. In spring 2009, he became associate Professor for Mathematics in Systems Biology at the Math Department of the TU Munich. 2009-2014 he was a member of the ‘Young Academy’ (founded by the Berlin-Brandenburg Academy of Sciences and Humanities and the German Academy of Natural Scientists Leopoldina) and was awarded an ERC starting grant in 2010. In 2017 he was awarded the Erwin Schrödinger prize together within an interdisciplinary team at the ETH Zürich. Fabian Theis is part of and also coordinates various consortia (i.e. sparse2big involving 8 Helmholtz Centers) and founded the network SingleCellOmics Germany (SCOG). Furthermore, he coordinates 2019 launched Munich School for Data Science (MUDS) and is co-directing the ELLIS Munich Unit, the local hub of the European Machine Learning network ELLIS. Since 2020, he holds the position of co-chair of the Bavarian AI Council of the Bavarian Ministry for Science and Art and supports the TUM with his expertise as start-up Ambassador.

Thomas Wiecki, PhD

Thomas Wiecki is the VP of Data Science at Quantopian, where he uses probabilistic programming and machine learning to help build the world’s first crowdsourced hedge fund. Among other open source projects, he is involved in the development of PyMC3—a probabilistic programming framework written in Python. A recognized international speaker, Thomas has given talks at various conferences and meetups across the US, Europe, and Asia. He holds a PhD from Brown University

Machine Learning and Statistics: Don’t Mind the Gap(Track Keynote)

Frank Hutter, PhD

Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), as well as Chief Expert AutoML at the Bosch Center for Artificial Intelligence.
Frank holds a PhD from the University of British Columbia (UBC, 2009) and a Diplom (eq. MSc) from TU Darmstadt (2004). He received the 2010 CAIAC doctoral dissertation award for the best thesis in AI in Canada, and with his coauthors, several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. He is the recipient of a 2013 Emmy Noether Fellowship, a 2016 ERC Starting Grant, a 2018 Google Faculty Research Award, a 2020 ERC PoC Award, and he is a Fellow of ELLIS. Frank’s recent research focuses on automated machine learning (AutoML), where he co-organized the ICML workshop series on AutoML every year since its inception in 2014, co-authored the prominent AutoML tools Auto-WEKA, Auto-sklearn, and Auto-PyTorch, won the first two AutoML challenges with his team, co-authored the first book on AutoML, worked extensively on efficient hyperparameter optimization and neural architecture search, and gave a NeurIPS 2018 tutorial with over 3000 attendees.

Alessio Lomuscio, PhD

Alessio Lomuscio is Professor in Logic for Multi-Agent Systems in the Department of Computing, where he directs the Verification of Autonomous Systems (VAS) research group. He currently holds a Royal Academy of Engineering Fellowship in Emerging Technology, and was previously the recipient of the EPSRC Leadership Fellowship “Trusted Autonomous Systems”. His research interests concern the principled analysis of autonomous agents. Since early 2000 Lomuscio pioneered the topic of verification of autonomous systems via model checking and was one of the early proponents of verifiable AI.

The VAS group developed and still maintains MCMAS, a leading model checking tool for multi-agent systems, and has put forward various verification methods for unbounded multi-agent systems, such as robotic swarms, including probabilistic approaches.

He has recently focused his attention on autonomous systems synthesized from data. The VAS group develops and maintains state-of-the-art toolkits for the verification of neural networks. He has published approximately 150 papers in top-tier AI and verification conferences and journals; he is a fellow of the EurAI. In addition to his research work, he frequently gives graduate courses on Safe AI (e.g., Stanford, 2019) and regularly engages with the wider community (e.g., World Economic Forum 2017-2018, Sotif Automotive Conference 2019).