Lecturers


Davide Bacciu
University of Pisa, Italy

Biography

Davide Bacciu is Associate Professor at the Computer Science Department, University of Pisa. The core of his research is on Machine Learning (ML) and deep learning models for structured data processing, including sequences, trees and graphs. He is the PI of an Italian National project on ML for structured data and the Coordinator of the H2020-RIA project TEACHING (2020-2022).  He is an IEEE Senior Member, the founder and chair of the IEEE Task Force on learning for structured data (www.learning4graphs.org), a member of the IEEE NN Technical Committee and of the IEEE CIS Task Force on Deep Learning. He is an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems. Since 2017 he is the Secretary of the Italian Association for Artificial Intelligence (AI*IA). He coordinates the task force on Bioinformatics and Drug Repurposing of the CLAIRE-COVID-19 European initiative (covid19.claire-ai.org).



Topics

Artificial Intelligence, Deep Learning.

Biography

Pierre Baldi is a chancellor’s professor of computer science at University of California Irvineand the director of its Institute for Genomics and Bioinformatics.

Pierre Baldi received his Bachelor of Science and Master of Science degrees at the University of Paris, in France. He then obtained his Ph.D. degree in mathematics at the California Institute of Technology in 1986 supervised by R. M. Wilson.

From 1986 to 1988, he was a postdoctoral fellow at the University of California, San Diego. From 1988 to 1995, he held faculty and member of the technical staff positions at the California Institute of Technology and at the Jet Propulsion Laboratory, where he was given the Lew Allen Award for Research Excellence in 1993. He was CEO of a start up company called Net-ID from 1995 to 1999 and joined University of California, Irvine in 1999.

Baldi’s research interests include artificial intelligence, statistical machine learning, and data mining, and their applications to problems in the life sciences in genomics, proteomics, systems biology, computational neuroscience, and, recently, deep learning.

Baldi has over 250 publications in his field of research and five books including

  • Bioinformatics: the Machine Learning Approach” (MIT Press, 1998; 2nd Edition, 2001, ISBN 978-0262025065) a worldwide best-seller
  • Modeling the Internet and the Web. Probabilistic Methods and Algorithms“, by Pierre Baldi, Paolo Frasconi and Padhraic Smyth. Wiley editors, 2003.
  • The Shattered Self—The End of Natural Evolution“, by Pierre Baldi. MIT Press, 2001.
  • DNA Microarrays and Gene Regulation“, Pierre Baldi and G. Wesley Hatfield. Cambridge University Press, 2002.
  • “Deep Learning in Science”, Pierre Baldi, Cambridge University press, 2021.

Baldi is a fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the AAAS, the IEEE,and the Association for Computing Machinery (ACM). He is also the recipient of the 2010 Eduardo R. Caianiello Prize for Scientific Contributions to the field of Neural Networks and a fellow of the International Society for Computational Biology (ISCB).

Deep learning algorithm solves Rubik’s Cube faster than any human.

https://news.uci.edu/2019/07/15/uci-researchers-deep-learning-algorithm-solves-rubiks-cube-faster-than-any-human/

AI solves Rubik’s Cube in one second

https://www.bbc.com/news/technology-49003996

https://scholar.google.com/citations?user=RhFhIIgAAAAJ&hl=it

 

Lectures



Topics

Information Theory, Mathematics for Machine Learning

Biography

Roman Belavkin is a Reader in Informatics at the Department of Computer Science, Middlesex University,  UK.  He has MSc degree in Physics from the Moscow State University and PhD in Computer Science from the University of Nottingham, UK.   In his PhD thesis, Roman combined cognitive science and information theory to study the role of emotion in decision-making, learning and problem solving.  His main research interests are in mathematical theory of dynamics of information and optimization of learning, adaptive and evolving systems.  He used information value theory to give novel explanations of some common decision-making paradoxes.  His work on optimal transition kernels showed non-existence of optimal deterministic strategies in a broad class of problems with information constraints.

Roman’s theoretical work on optimal parameter control in algorithms has found applications to computer science and biology.  From 2009, Roman lead a collaboration between four UK universities involving mathematics, computer science and experimental biology on optimal mutation rate control, which lead to the discovery in 2014 of mutation rate control in bacteria (reported in Nature Communications http://doi.org/skb  and PLOS Biology http://doi.org/cb9s).  He also contributed to research projects on neural cell-assemblies, independent component analysis and anomaly detection, such as cyber attacks.

Lectures



Topics

Critical Data Science, Data Science, Ethics and AI, Privacy/Data Protection, Discrimination and Fairness.

Biography

Bettina Berendt is Professor for Internet and Society at the Faculty of Electrical Engineering and Computer Science at Technische Universität Berlin, Germany, Director of the Weizenbaum Institute for the Networked Society, Germany, and guest professor at KU Leuven, Belgium. She previously held positions as professor in the Artificial Intelligence group (Department of Computer Science at KU Leuven) and in the Information Systems group (School of Business and Economics at Humboldt-Universität zu Berlin). Her research centres on data science and critical data science, including privacy/data protection, discrimination and fairness, and ethics and AI, with a focus on textual and web-related data.

Lectures



Jacob D. Biamonte
Skolkovo Institute of Science and Technology, Russian Federation
 

Topics

Quantum Machine Learning

Biography

Quantum machine learning, Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe & Seth Lloyd, Nature, volume 549, pages 195–202 (2017).

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 LaboratoryBiamonte’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: 

  1. A 2019 proof that variational quantum computation can be used as a computationally universal model of  quantum computation [arXiv:1903.04500].
  2. 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)].
  3. 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)].
  4. 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).

International Awards. 

  1. Usern Medal Laureate in Formal Sciences (2018)
  2. Shapiro Lecture in Mathematical Physics, Pennsylvania State University (2014)
  3. Invited lifelong member (from 2013) of the Foundational Questions Institute (FQXi)
  4. Longuet-Higgins Paper Prize [jointly with JD Whitfield and AA Guzik for Molecular Physics 109, 735 (2011)]

Lectures



Topics

Machine learning

Biography

Christopher Michael Bishop FRS FRSE FREng is the Laboratory Director at Microsoft Research Cambridge, Professor of Computer Science at the University of Edinburgh and a Fellow of Darwin College, Cambridge.

Author of Pattern Recognition and Machine Learning (PRML) book.

Bishop obtained a Bachelor of Arts degree in Physics from St Catherine’s College, Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory supervised by David Wallace and Peter Higgs.

Bishop’s research investigates machine learning by allowing computers to learn from data and experience.

Awards and Honours

Chris Bishop at the Royal Society admissions day in London, July 2017 Bishop was awarded the Tam Dalyell prize in 2009 and the Rooke Medal from the Royal Academy of Engineering in 2011.  He gave the Royal Institution Christmas Lectures in 2008 and the Turing Lecture in 2010. Bishop was elected a Fellow of the Royal Academy of Engineering (FREng) in 2004,  a Fellow of the Royal Society of Edinburgh (FRSE) in 2007,  and Fellow of the Royal Society (FRS) in 2017.

https://en.wikipedia.org/wiki/Christopher_Bishop

https://scholar.google.co.uk/citations?user=gsr-K3ADUvAC&hl=en

Lectures



Silvia Chiappa
 

Topics

Bayesian & causal reasoning, graphical models and variational inference

Biography

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.

Lectures



Oren Etzioni
CEO at Allen Institute for AI

Topics

AI, Meta-Search, Machine Reading, Open Information Extraction

Biography

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.

Lectures



Topics

Learning with constraints, Vision

Biography

Marco Gori received the Ph.D. degree in 1990 from Università di Bologna, Italy, while working partly as a visiting student at the School of Computer Science, McGill University – Montréal. In 1992, he became an associate professor of Computer Science at Università di Firenze and, in November 1995, he joint the Università di Siena, where he is currently full professor of computer science.  His main interests are in machine learning, computer vision, and natural language processing. He was the leader of the WebCrow project supported by Google for automatic solving of crosswords, that  outperformed human competitors in an official competition within the ECAI-06 conference.  He has just published the book “Machine Learning: A Constrained-Based Approach,” where you can find his view on the field.

He has been an Associated Editor of a number of journals in his area of expertise, including The IEEE Transactions on Neural Networks and Neural Networks, and he has been the Chairman of the Italian Chapter of the IEEE Computational Intelligence Society and the President of the Italian Association for Artificial Intelligence. He is a fellow of the ECCAI (EurAI) (European Coordinating Committee for Artificial Intelligence), a fellow of the IEEE, and of IAPR.  He is in the list of top Italian scientists kept by  VIA-Academy.

Lectures



Topics

Knowledge Processing, Logic, AI

Biography

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.

Lectures



Topics

machine learning, computer science, statistics, artificial intelligence, optimization

Biography

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive and biological sciences. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He was a Plenary Lecturer at the International Congress of Mathematicians in 2018. He received the Ulf Grenander Prize from the American Mathematical Society in 2021, the IEEE John von Neumann Medal in 2020, the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM. In 2016, Professor Jordan was named the “most influential computer scientist” worldwide in an article in Science, based on rankings from the Semantic Scholar search engine.

https://people.eecs.berkeley.edu/~jordan/

https://en.wikipedia.org/wiki/Michael_I._Jordan

https://scholar.google.com/citations?user=yxUduqMAAAAJ&hl=en

Lectures



Topics

Probabilistic reasoning, Deep Learning, Safety and Trust for Mobile Autonomous Robots

Biography

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 PRISMmodel 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.

Lectures



Topics

Data Science, Optimization, Networks

Biography

Panos M. Pardalos serves as distinguished professor of industrial and systems engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor of industrial and systems engineering. He is also an affiliated faculty member of the computer and information science Department, the Hellenic Studies Center, and the biomedical engineering program. He is also the director of the Center for Applied Optimization. Pardalos is a world leading expert in global and combinatorial optimization. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing.

https://en.wikipedia.org/wiki/Panos_M._Pardalos

https://scholar.google.com/citations?user=4e_KEdUAAAAJ&hl=en

Lectures



Daniela Rus
Director of CSAIL

Topics

Science of Autonomy, AI & ML, Robotics, Systems & Networking

Biography

Daniela Rus is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Rus’s research interests are in robotics, mobile computing, and data science. Rus is a Class of 2002 MacArthur Fellow, a fellow of ACM, AAAI and IEEE, and a member of the National Academy of Engineering, and the American Academy for Arts and Science. She earned her PhD in Computer Science from Cornell University.

Awards

Constellation Research Award, Business Transformation 150 (BT150) 2019
Pioneer in Robotics and Automation Award, IEEE Robotics and Automation Society, 2018

Woman in STEM Award, Wheaton College, 2018

Member, American Academy of Arts and Sciences, 2017

Robotic Industries Association: Joseph F Engelberger Robotics Award for Education, 2017

Member of the National Academy of Engineering (NAE)

Fellow of the Association for Computing Machinery (ACM)

Fellow of the Institute of Electrical and Electronics Engineers (IEEE)

Fellow of the Association for the Advancement of Artificial Intelligence (AAAI)

MacArthur Fellow, Class of 2002

Andrew (1956) and Erna Viterbi Chair

Best Paper Award Finalist, ICRA 2015

Best Manipulation Paper Finalist, ICRA 2015

Best Paper Award, ROBIO 2014

Best Paper Award, IROS 2014

Best Presented Paper, Mobicom 2014

Best Paper, Robotics Systems&Science 2014

Curiosity Award, Cambridge Science Festival

1st Place Hardware & Curriculum Categories for Seg robot at the 2014 AFRON Robot

Best Paper for Entertainment Robot Ent. Systems, IROS 2013

Most Societally Beneficial Video, IJCAI  2013

Best Automation Paper Finalist, ICRA  2013

Best Robot Actor for Seraph, Robot Film Festival 2012

Best Paper Finalist, BIOROB  2012

Best Paper Award, ACM Sensys  2004

http://danielarus.csail.mit.edu/index.php/about-daniela-2/press-2/

http://danielarus.csail.mit.edu

https://youtu.be/CBbiDBJSNXM

Lectures



Cristina Savin
New York University, USA (TBC)

Topics

Computational Neuroscience, Data Science, Probabilistic Computation, Learning & Plasticity

Biography

After a PhD at Goethe University in Frankfurt, studying the role of different forms of plasticity in unsupervised learning,  Cristina worked as postdoctoral researcher at Cambridge U. developing normative models of memory. This was followed by a short stint at ENS in Paris, modeling probabilistic computation in spiking neurons, and an independent research fellowship at IST Austria, building statistical tools for quantifying learning in multiunit recordings. Since 2017 she is an Assistant Professor in Neural Science and Data Science at NYU.

Lectures



Isabel Valera
Saarland University, Germany
Max Planck Institute for Intelligent Systems, Tübingen, Germany
 

Topics

Machine Learning, Probabilistic Methods, Ethical Machine Learning

Biography

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).

Lectures



Topics

Machine Learning for Medicine, Data Science and decisions, Artificial Intelligence

Biography

Professor van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Turing Fellow at The Alan Turing Institute in London, where she leads the effort on data science and machine learning for personalised medicine. She is an IEEE Fellow (2009). She has received the Oon Prize on Preventative Medicine from the University of Cambridge (2018).  She has also been the recipient of an NSF Career Award, 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. She holds 35 granted USA patents.

The current emphasis of her research is on machine learning with applications to medicine, finance and education. She has also worked on data science, network science, game theory, signal processing, communications, and multimedia.

http://www.vanderschaar-lab.com/NewWebsite/Publications_ML.html

9 papers @ NeurIPS 2020.

7 papers accepted at ICLM 2020.

2 papers @ ICLR 2020.

4 papers @ AISTATS 2020.

5 papers accepted at NeurIPS 2019.

 

 

Lectures




Each Lecturer will hold three lessons on a specific topic.

Past Lecturers