Professor of Computer Science and Computational Neuroscientist at the Hebrew University of Jerusalem.
Ruth & Stan Flinkman Family Endowment Fund Chair in Brain Research.
Awards: Israel Defense Prize, Landau Prize in Computer Science, The 2019 IBT Award in Mathematical Neuroscience.
He is one of the leaders in machine learning research and computational neuroscience, and his numerous former students serve in key academic and industrial research positions all over the world. Tishby was the founding chair of the new computer-engineering program, and a director of the Leibnitz Center for Research in Computer Science at Hebrew University. Tishby received his PhD in theoretical physics from Hebrew University in 1985, and was a research staff member at MIT and Bell Labs from 1985 to 1991. Tishby has been a visiting professor at Princeton NECI, the University of Pennsylvania, UCSB, and IBM Research.
He works at the interfaces between computer science, physics, and biology which provide some of the most challenging problems in today’s science and technology. We focus on organizing computational principles that govern information processing in biology, at all levels. To this end, we employ and develop methods that stem from statistical physics, information theory and computational learning theory, to analyze biological data and develop biologically inspired algorithms that can account for the observed performance of biological systems. We hope to find simple yet powerful computational mechanisms that may characterize evolved and adaptive systems, from the molecular level to the whole computational brain and interacting populations.
Marta Kwiatkowska, Computer Science Dept., University of Oxford, UK
Marta Kwiatkowska is Professor of Computing Systems and Fellow of Trinity College, University of Oxford, and Associate Head of MPLS. Prior to this she was Professor in the School of Computer Science at the University of Birmingham, Lecturer at the University of Leicester and Assistant Professor at the Jagiellonian University in Cracow, Poland. She holds a BSc/MSc in Computer Science from the Jagiellonian University, MA from Oxford and a PhD from the University of Leicester. In 2014 she was awarded an honorary doctorate from KTH Royal Institute of Technology in Stockholm.
Marta Kwiatkowska spearheaded the development of probabilistic and quantitative methods in verification on the international scene and is currently working on safety and robustness for machine learning and AI. She led the development of the PRISM model checker, the leading software tool in the area and widely used for research and teaching and winner of the HVC 2016 Award. Applications of probabilistic model checking have spanned communication and security protocols, nanotechnology designs, power management, game theory, planning and systems biology, with genuine flaws found and corrected in real-world protocols. Kwiatkowska gave the Milner Lecture in 2012 in recognition of “excellent and original theoretical work which has a perceived significance for practical computing”. She is the first female winner of the 2018 Royal Society Milner Award and Lecture, see her lecture here, and won the BCS Lovelace Medal in 2019. Marta Kwiatkowska was invited to give keynotes at the LICS 2003, ESEC/FSE 2007 and 2019, ETAPS/FASE 2011, ATVA 2013, ICALP 2016, CAV 2017, CONCUR 2019 and UbiComp 2019 conferences.
She is a Fellow of the Royal Society, Fellow of ACM, member of Academia Europea, Fellow of EATCS, Fellow of the BCS and Fellow of Polish Society of Arts & Sciences Abroad. She serves on editorial boards of several journals, including Information and Computation, Formal Methods in System Design, Logical Methods in Computer Science, Science of Computer Programming and Royal Society Open Science journal. Kwiatkowska’s research has been supported by grant funding from EPSRC, ERC, EU, DARPA and Microsoft Research Cambridge, including two prestigious ERC Advanced Grants, VERIWARE (“From software verification to everyware verification”) and FUN2MODEL (“From FUNction-based TO MOdel-based automated probabilistic reasoning for DEep Learning”), and the EPSRC Programme Grant on Mobile Autonomy.
Georg Gottlob F.R.S., Computer Science Dept, University of Oxford, UK
Georg Gottlob is a Royal Society Research Professor and a Professor of Informatics at Oxford University. and at TU Wien. At Oxford he is a Fellow of St John’s College. His interests include knowledge representation, logic and complexity, and database and Web querying. He has received various awards, among which the Wittgenstein Award (Austria) and the Ada Lovelace Medal (UK). He is a Fellow of the Royal Society, of the Austrian Academy of Science, the Leopoldina National Academyof Sciences (Germany), and of the Academia Europaea. He was a founder of Lixto, a company specialised in semi-automatic web data extraction which was acquired by McKinsey in 2013. Gottlob was awarded an ERC Advanced Investigator’s Grant for the project “DIADEM: Domain-centric Intelligent Automated Data Extraction Methodology”. Based on the results of this project, he co-founded Wrapidity Ltd, a company that specialised in fully automated web data extraction, which was acquired in 2016 by Meltwater. He recently co-founded DeepReason.ai, which puts the logic-based VADALOG system into practice and applies it with banks and other corporate customers.
Jacob D. Biamonte, Head of Skoltech’s Laboratory for Quantum Information Processing
Center for Photonics and Quantum Materials (CPQM)
Skolkovo Institute of Science and Technology
Moscow, Russian Federation
After his undergraduate studies (Bachelor of Science from Portland State University), Biamonte was employed as one of the world’s first quantum software programmers at D-Wave Systems Inc. in Vancouver B.C., Canada (2004-2007). His subsequent Doctorate from Oxford (2010) earned a Chancellors award. Biamonte worked as a research fellow at Harvard and as part of a joint Oxford/Singapore postdoctoral program before joining the Institute for Scientific Interchange (ISI Foundation) in Torino Italy to direct the institute’s Quantum Science Division (2012-2017). Biamonte joined Skoltech in 2017, while Skoltech’s Laboratory for Quantum Information Processing was officially founded in 2019 with Biamonte appointed Head of Laboratory.
Biamonte’s research focuses broadly on the theory and implementation of modern quantum algorithms and employs various mathematical techniques, particularly group-algebraic techniques, tensor networks and the formal theory of computation and information.
Biamonte is best known for several results:
- A 2019 proof that variational quantum computation can be used as a computationally universal model of quantum computation [arXiv:1903.04500].
- A definition given in 2016 of a spectral graph function which provably satisfies both (i) the definition of an entropy and (ii) subadditivity [with Domenico in PRX 6, 041062 (2016)].
- A 2015 proof that #P-hard counting problems (and hence 2, 3-SAT decision problems) can be solved efficiently when their tensor network expression has at most O(log c) COPY-tensors and polynomial bounded fan-out [with Turner and Morton in J. Stat. Phys. 160, 1389 (2015)].
- A 2008 proof that the two-body model Hamiltonian with tunable XX, ZZ terms is (i) computationally universal for adiabatic quantum computation and (ii) admits a QMA-complete ground state energy decision problem [with Love in PRA 78, 012352 (2008)]
Biamonte is also credited for pioneering work developing quantum algorithms for electronic structure calculations and more recently for work uniting quantum information processing with machine learning.
Biamonte has further provided theoretical support to enable milestone quantum information processing experimental demonstrations. The list includes the first quantum algorithmic demonstration of quantum chemistry [Nature Chemistry 2, 106 (2009)] (linear optics), the first experimental implementation of optimal control [Nature Communications 5, 3371 (2014)] (creating a quantum random access memory using NV-centers in diamond) as well as the first demonstration of neural network quantum state tomography on actual experimental data [npj Quantum Information 6:20 (2020)] (linear optics).
- Usern Medal Laureate in Formal Sciences (2018)
- Shapiro Lecture in Mathematical Physics, Pennsylvania State University (2014)
- Invited lifelong member (from 2013) of the Foundational Questions Institute (FQXi)
- Longuet-Higgins Paper Prize [jointly with JD Whitfield and AA Guzik for Molecular Physics 109, 735 (2011)]
Skoltech’s Laboratory for Quantum Information Processing (affectionately called, Deep Quantum Lab) contributes primarily to the theory, development and implementation of quantum enhanced algorithms, a topic underrepresented inside the Russian Federation and globally. The research capacity of the laboratory responds critically to inquiries from the public and private sectors and the research output has established Skoltech’s role internationally in quantum science and technology. The Laboratory was officially created as an autonomous laboratory structure by order number 44 enacted on 19 February 2019.
The laboratory currently participates in several national initiatives and industrial projects, including
- The large-scale national Digital Economy project, Leading Research Center on Quantum Computing (agreement No. 014/20)
- Ongoing multi-year collaboration agreements sponsored by Huawei Technologies Co., Ltd.
- Quantum algorithms research and consulting projects sponsored by Gaspromneft PJSC
Silvia Chiappa, DeepMind London, UK
Senior Staff Research Scientist in Machine Learning at DeepMind
She received a Diploma di Laurea in Mathematics from University of Bologna and a PhD in Machine Learning from École Polytechnique Fédérale de Lausanne (IDIAP Research Institute). Before joining DeepMind, she worked in the Empirical Inference Department at the Max-Planck Institute for Intelligent Systems (Prof. Dr. Bernhard Schölkopf), in the Machine Intelligence and Perception Group at Microsoft Research Cambridge (Prof. Christopher Bishop) and in the Statistical Laboratory at the University of Cambridge (Prof. Philip Dawid).
Her research interests are based around Bayesian & causal reasoning, graphical models, variational inference, time-series models, deep learning, and ML fairness and bias.
Saarland University & Max Planck Institute for Intelligent Systems, Tübingen, Germany
Isabel Valera is a full Professor on Machine Learning at the Department of Computer Science of Saarland University in Saarbrücken (Germany), and an independent group leader at the MPI for Intelligent Systems in Tübingen (Germany) until the end of the year.
She is a fellow of the European Laboratory for Learning and Intelligent Systems ( ELLIS), where she is part of the Robust Machine Learning Program and of the Saarbrücken Artificial Intelligence & Machine learning (Sam) Unit.
Prior to this, she has held a German Humboldt Post-Doctoral Fellowship, and a “Minerva fast track” fellowship from the Max Planck Society. She obtained thePhD in 2014 and MSc degree in 2012 from the University Carlos III in Madrid (Spain), and worked as postdoctoral researcher at the MPI for Software Systems (Germany) and at the University of Cambridge (UK).
Oren Etzioni, CEO at Allen Institute for AI
Dr. Oren Etzioni is Chief Executive Officer at AI2. He is Professor Emeritus, University of Washington as of October 2020 and a Venture Partner at the Madrona Venture Group since 2000. His awards include Seattle’s Geek of the Year (2013), and he has founded or co-founded several companies, including Farecast (acquired by Microsoft). He has written over 100 technical papers, as well as commentary on AI for The New York Times, Wired, and Nature. He helped to pioneer meta-search, online comparison shopping, machine reading, and Open Information Extraction.
New Keynote Speaker: Pierre Baldi, University of California Irvine, USA
Author of the book: Deep Learning in Science – Cambridge University Press, 2021.