...the event has started!
We bring together scientists from various fields to address the problems of AI's trustworthiness in numerous AI application, ranging from engineering disciplines to medical diagnostics and legal issues. Progress requires a major step-change, along with the holistic approach.

The conference will be focused on AI trustworthiness in mathematical modelling (surrogate modelling and hybrid modelling), reinforcement learning, adversarial networks, sustainability of AI, including problems of transfer learning and data fusion, and AI's resilience. AI standardisation, verification, testing, ethical issues, and neuromorphic systems will be a part of the conference's general theme.
The emergent trends and global aspects of the AI need a strong foundation. Even a formulation of the trustworthiness of the AI problem requires rigorous scientific language and interdisciplinary expertise.
Maxim Fedorov
Professor, Skoltech Vice-President for Artificial Intelligence and Mathematical Modelling
major topics (sections)
Fundamental limitations of Data-driven AI (Adversarial Networks, AI cyber security, etc.)
Verification and approaches to correct AI errors
Data-driven modelling vs. Mathematical modelling
Problems and risks of AI in medicine, banking, jurisprudence
Emergent trends and fundamental problems of trustworthy AI (AI ethics, AI singularity, etc.)
Global aspects of AI
speakers & topics
conference schedule
Day 1
Day 2
Day 3
09:30–10:00 (GMT) / 11:30–12:00 (MSK)
Opening remark: Trustworthy AI. Problems, approaches and solutions
Prof. Alexander Kuleshov, Skoltech, Academician of RAS

10:00–13:00 (GMT) / 12:00–15:00 (MSK)
Session 1: Fundamental limitations of Data-driven AI (Adversarial Networks, AI cyber security, etc.)
Moderators: Ivan Tyukin, Desmond Higham
  • 10:00–10:50 (GMT) / 12:00–12:50 (MSK) En Route to the Mathematics of Certifiably Trustworthy AI
    Prof. Ivan Tyukin, University of Leicester
  • 10:50–11:40 (GMT) / 12:50–13:40 (MSK) Accuracy and Stability Issues in Deep Learning
    Prof. Desmond Higham FRSE, University of Edinburgh
  • 11.40–12.20 (GMT) / 13:40–14:20 ( MSK)
    Virtual coffee break
  • 12:20–12:40 (GMT) / 14:20–14:40 (MSK) The role of Supporting Technologies in AI development
    Nikita Utkin, Skoltech.
  • 12:40–13:00 (GMT) / 14:40–15:00 MSK) Topic TBD
    Alexey Natekin, Open Data Science

13:00–14:30 (GMT) / 15:00–16:30 (MSK)
Virtual lunch

14:30–17:50 (GMT) / 16:30–19:50 (MSK)
Session 2: Verification and approaches to correct AI errors.

Moderator: Maxim Fedorov
  • 14:30–15:20 (GMT) / 16:30–17:20 (MSK) Conformance Testing for Trustworthy Autonomous Systems
    Prof. Mohammad Mousawi, University of Leicester.
  • 15:20–16:10 (GMT) / 17:20–18:10 (MSK) Adversarial attacks, defenses from adversarial attacks and robustness of deep neural network models
    Prof. Ivan Oseledets, Skoltech.
  • 16:10 – 16:30 (GMT) / 18:10 – 18:30 (MSK)
    Virtual coffee break
  • 16:30 – 16:50 (GMT) / 18:30 –18:50 (MSK) Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers
    Prof. Eugeny Burnaev, Skoltech.
  • 16:50 – 17:10 (GMT) / 18:50 – 19:10 (MSK) Uncertainty estimation: can your neural network provide confidence for its predictions?
    Prof. Maxim Panov, Skoltech
10:00–12:50 (GMT) / 12:00–14:50 (MSK)
Session 3: Data-driven modelling vs. Mathematical modelling.
Moderators: Nikolai Brilliantov, Alexander Shapeev
  • 10:00–10:50 (GMT) / 12:00–12:50 (MSK) AI for Emerging Quantum Technologies: Mathematical Concepts and Applications
    Prof. Catherine Higham, University of Glasgow.
  • 10:50–11:10 (GMT) / 12:50–13:10 (MSK) Features and restrictions of "trust" notion in AI field
    Dr. Sergey Garbuk, Higher School of Economics.
  • 11:10–11:30 (GMT) / 13:10–13:30 (MSK) Reinforcement Learning: safety issues
    Prof. Pavel Osinenko, Skoltech.
  • 11:30–11:50 (GMT) / 13:30–13:50 (MSK) Reinforcement learning view on the collective motion of animals.
    Prof. Nikolai Brilliantov, Skoltech.
  • 11:50–12:10 (GMT) / 13:50–14:10 (MSK)
    Virtual coffee Break
  • 12:10–12:30 (GMT) / 14:10–14:30 (MSK) Deep Fourier expansion.
    Prof. Dmitry Yarotsky, Skoltech.
  • 12:30–12:50 (GMT) / 14:30–14:50 (MSK) Data-driven and mathematical modeling in oil well directional drilling.
    Nikita Klyuchnikov, Skoltech.
  • 12:50–13:40 (GMT) / 14:50–15:40 (MSK) Machine Learning in Material Modelling
    Prof. Alexander Shapeev.

13:40–14:20 (GMT) / 15:40–16:20 (MSK)
Virtual lunch

14:20–17:20 (GMT) / 16:20–19:20 (MSK)
Session 4: Problems and risks of AI in medicine, banking, jurisprudence.

Moderator: Dmitry Dylov
  • 14:20–14:40 (GMT) / 16:20–16:40 (MSK) International Telecommunication Union (ITU) activities on AI and Trust including health
    Dr. Bastiaan Quast, International Telecommunication Union
  • 14:40–15:00 (GMT) / 16:40–17:00 (MSK) Explainable AI in medical imaging: from raw data processing to radiogenomics
    Anna Andreichenko, Government of Moscow
  • 15:00–15:20 (GMT) / 17:00–17:20 (MSK) A Framework for Self-Validation of Medical Diagnostic AI
    Nikolay Pavlov, Government of Moscow

  • 15:20–15:40 (GMT) / 17:20–17:40 (MSK) How to select and seamlessly integrate AI into a regional radiology workflow?
    Prof. Sergey Morozov, Government of Moscow.
  • 15:40–16:00 (GMT) / 17:40–18:40 (MSK) AI for biomedical tasks: trustworthy datasets and labeling.
    Prof. Alexander Bernstein and Maxim Sharaev, Skoltech.
  • 16:00–16:20 (GMT) / 18:00–18:20 (MSK) Imaging Physics as a Sanity Check for AI-based Computer Vision.
    Prof. Dmitry Dylov, Skoltech.
  • 16:20–16:40 (GMT) / 18:20–18:40 (MSK)
    Virtual coffee break + Written Contribution from
    Dr. Dmitry Ogorodov + link
  • 16:40–17:00 (GMT) / 18:40–19:00 (MSK) AI in jurisprudence: challenges and opportunities for one of the oldest and most conservative areas of human life.
    Yury Tsvetkov, Skoltech.
  • 17.00–17.50 (GMT) / 19:00–19:50 (MSK) Machine Learning in the automotive world: from powertrains to autonomous vehicles and beyond.
    Dr. Danil Prokhorov, Toyota North America.
10:00–11:40 (GMT) / 12:00–13:40 (MSK)
Session 5: Fundamental problems of trustworthy AI (AI ethics, AI singularity, and new opportunities).

Moderators: Peter Grindrod, Ivan Oseledets
  • 10:00–10:50 (GMT) / 12:00–12:50 (MSK) Trust, Limitation, Conflation and Hype.
    Prof. Peter Grindrod CBE, University of Oxford.
  • 10:50–11:40 (GMT) / 12:50–13:40 (MSK) An Overview of Artificial Intelligence Ethical Standards: Governments, Corporates and Civil Society Actors.
    Athina Karatzogianni, University of Leicester.

11:40–12:00 (GMT) / 13:40–14:00 (MSK)
Virtual coffee break

12:00–16:00 (GMT) / 14:00–18:00 (MSK)
Session 6: Global aspects of AI.

Moderator: Athina Karatzogianni
  • 12:00–12:20 (GMT) / 14:00–14:40 (MSK) AI influence on international trade developments.
    Prof. Anna Abramova, MGIMO.
  • 12:20–12:40 (GMT) / 14:20–14:40 (MSK) Ethical framework for AI.
    Dr. Alexander Lunkov, RAS.
  • 12:40–13:00 (GMT) / 14:40–15:00 (MSK) Topic TBD.
    Dr. Svetlana Malkarova, State University of Management (GUU).
  • 13:00–13:20 (GMT) / 15:00–15:20 (MSK) Reflecting ethics in AI applications
    Andrey Kuleshov, MIPT and Andrey Ignatyev, Expert of Technical Committee "Artificial Intelligence" (TC 164).
  • 13:20–14:20 (GMT) / 15:20–16:20 (MSK)
    Virtual lunch
  • 14:20–14:40 (GMT) / 16:20–16:40 (MSK) AI in the grips of terminology and principles.
    Prof. Alexander Ageev.
  • 14:40–15:00 (GMT) / 16:40–17:00 (MSK) Ethical aspects of application and regulation of intellectual and autonomous systems.
    Dr. Pavel Gotovtsev, Kurchatov Institute.
  • 15:00–15:20 (GMT) / 17:00–17:20 (MSK)
    Virtual coffee break
  • 15:00–15:20 (GMT) / 17:00–17:20 (MSK) Legal Framework for a Trustworthy AI
    Alexander Tyulkanov, and Sergey Izrailit, Skolkovo Foundation
  • 15:20–15.40 (GMT) / 17:20–17:40 (MSK) Data governance and global aspects of AI
    Wai Min Kwok, United Nations
  • 15.40–16:00 (GMT) / 17:40–18:00 (MSK) Polar views on the sustainable development of AI
    Prof. Maxim Fedorov, Skoltech

16:00–17:00 (GMT) / 18:00–19:00 (MSK)
Round table: Problems of Standardization

Moderator: Nikita Utkin

17:00–… (GMT) / 19:00–... (MSK)
Virtual event
Dr Athina Karatzogianni is an Associate Professor in Media and Communication at the University of Leicester, UK. Her researchportfolio, on the impact of digitization on conflict, economics and security, reflects a commitment to research that is rigorous and innovative, with applied practice that is relevant and internationally influential.She has an extensive record of publications and citations in disciplinary, field-specific and cross-disciplinary research outlets, and has demonstrated sustained success in securing research income from Research Councils UK and the European Commission. Her most recent book is (2018) Platform Economics: Rhetoric and Reality the "Sharing Economy". Athina can be contacted at
Kwok Wai Min is currently Senior Governance and Public Administration Officer in the Division for Public Institutions and Digital Government (DPIDG), United Nations Department of Economic and Social Affairs (UN DESA). He has been with the UN Secretariat since 2008, and after a ten-year stint in the energy and information technology industries as an engineer and a technopreneur. Since the transition to the development field, he has been entrusted with a portfolio of roles in UN DESA. He was one of the lead authors for selected editions of the UN E-Government Survey and the World Public Sector Report. His areas of expertise span a range in sustainable development, public policy, public administration and management, digital government, Internet governance, and information communication technologies for development. For three consecutive years in 2011-2013, he was the Secretary of the UN Committee of Experts on Public Administration (CEPA), a subsidiary body of the Economic and Social Council, and coordinated the annual CEPA sessions and intersessional activities. He also provides secretariat support to the annual Internet Governance Forum, a multistakeholder platform convened by the UN Secretary-General. Wai Min Kwok was formerly a Lee Ka-Shing graduate scholar and Mobil Oil scholar. He has a postgraduate master's in public administration with the Lee Kuan Yew School of Public Policy and an honours degree in electrical engineering at the National University of Singapore.
Evgeny Burnaev obtained his M.Sc. Moscow Institute of Physics and Technology in 2006, and PhD from Institute for Information Transmission Problem in 2008. Currently, he is an Associate Professor in Skolkovo Institute of Science and Technology in Moscow, Russia. His research interests encompass Gaussian processes for multi-fidelity surrogate modelling and optimization, Deep Learning for 3D Data Analysis and manifold learning, on-line learning for prediction and anomaly detection. For his scientific achievements, Evgeny Burnaev was honored with the Moscow Government Prize for Young Scientists in the category for the Transmission, Storage, Processing and Protection of Information for leading the project "The development of methods for predictive analytics for processing industrial, biomedical and financial data'' in 2017.
Ivan Tyukin is a Professor of Applied Mathematics at the University of Leicester. He graduated from Saint-Petersburg State Electrotechnical University (LETI) in 1998, and has been awarded PhD and DSc degrees in 2001 and 2006, respectively. From 2001 to 2007 he has been a Research Scientist at RIKEN Brain Science Institute, Japan, and following 5 years as an RCUK Academic Fellow at Leicester (2007), he was appointed to the posts of Lecturer (2012), Reader (2014), and Professor (2018) at the University of Leicester. In 2019 he has been appointed as an adjunct Professor at the Norwegian University of Science and Technology, Norway, to lead AI and Machine Learning research in the BRU21 Programme. His research interests revolve around challenges of machine intelligence, adaptation, learning, control and dynamical systems theory. Development of these theoretical tools requires combination of techniques from different areas spanning concentration of measure theory, statistical learning theory, analysis, modelling and synthesis of fragile, nonlinear, chaotic, meta-stable dynamics, adaptation and adaptive control theory, synchronization (stable and critical), biologically-inspired systems for processing of the visual information, computer vision, networks of interconnected dynamical systems, analysis of dynamics of the spiking neuron models, their properties and possible functions, algorithms for machine learning and data analysis in high dimensions.
Alexander Kuleshov is a specialist in the field of information technologies and mathematical modeling. In 2006, he was elected Director of the A.A. Kharkevich Institute for Information Transmission Problems (IITP) of the Russian Academy of Sciences. At IITP, he also served as the chairman of the Academic Council, Chairman of the Doctorate Dissertation Council, and Chair of Information Transmission and Processing. Throughout his academic career, he authored and co-authored 54 studies, including 4 monographs. In his speech for 100 days of presidentship, Dr. Kuleshov promised to lead Skoltech to the World Top 10 universities in 5 years which was achieved in advance.
Dr. Dmitry V. Dylov is an Assistant Professor at the Center for Computational and Data-Intensive Science and Engineering at Skoltech. He earned his Ph.D. degree in Electrical Engineering at Princeton University (Princeton, NJ, USA) in 2010, and M.Sc. degree in Applied Physics and Mathematics at Moscow Institute of Physics and Technology (Moscow, Russia) in 2006. Dr. Dylov had been a Lead Scientist at GE Global Research Center (Niskayuna, NY, USA), where he had been leading various projects ranging from bioimaging and computational optics to medical image analytics. Dr. Dylov's innovation record includes IP contributions to GE Healthcare, frequent technical consulting to emerging start-ups, and the foundation of two spin-off companies with clinical validation in major hospitals in the USA (MSKCC, MGH, UCSF, Albany Med). Dr. Dylov has established a new theoretical and computational paradigm for treating noise in imaging systems, resulting in impactful publications in reputable journals, such as Physical Review Letters and Nature Photonics. His career record includes more than 50 peer-reviewed publications, 16 international patents, and more than 80 invited and contributed talks. Dr. Dylov has earned the McGraw Teaching Excellence certificate at Princeton and has been an instructor in the Edison Engineering Development Program at GE. He has served as an avid professional service volunteer, a scientific reviewer, and an advocate for the educational outreach within SPIE, OSA, APS, and IEEE societies.
Prof. Nikolay Brilliantov has graduated Dept. of Physics of the Moscow State University, where he also received the PhD degree and the Doctor of Science degree. From 1995 he was working as a researcher in world-leading universities, including University of Toronto, Humboldt University of Berlin and University of Barcelona. From 2007 he is a Chair of Applied Mathematics at the Department of Mathematics of the University of Leicester (UK). From 2018 he took a position of full professor at Skoltech. He is a recognized expert in Statistical Mechanics and Kinetic Theory, an author of 3 monographs, published in Oxford University Press and Springer. He is also an author of more than 130 papers, including articles in Nature, Nature Comm., PNAS, Phys. Rev. Lett. and Rhys. Rev. X. In 2011-2016 he was on the Editorial Board of Phys. Rev. Lett., and from 2018 on the Editorial Board of Scientific Reports and Fluids. He is a distinguished referee for Europhys. Lett. and Phys. Rev. E. The results of his research had a multiple positive pressure coverage, including "New York Times", "Daily Mail", "Guardian", BBC, etc.
Professor at the Practice Alexander Bernstein works at the Skoltech Center for Computational and Data-Intensive Science and Engineering since May 2016. He received a master degree in Math (1969) at the Department of Mechanics and Mathematics, Moscow State University, a PhD degree in Math (1973) from the Steklov Mathematical Institute of the USSR Academy of Sciences, and a Doctor of Sciences degree in Math (1987) from the Department of Computational Mathematics and Cybernetics, Moscow State University. In 1991, the USSR Higher Attestation Commission awarded Alexander with the academic rank of Professor in the field of Intelligent Technologies and Systems. Prof. Bernstein started his career at the Research Institute of Automatic Equipment in 1969, where he was developing mathematical models and algorithms for computer networks. Then he worked at the Software Engineering Center of the Russian Academy of Sciences (RAS), at the Institute for System Analysis RAS and at the Institute for Information Transmission Problems RAS doing theoretical and applied research in the field of Mathematical modeling, Mathematical and Applied statistics, Intelligent Data Analysis, and their applications. At the same time, he had part-time full professor positions at the Moscow State Institute of Radiotechnics, Electronics and Automation, the National Research University Higher School of Economics and the Moscow Institute of Physics and technology. His current research interests are in Mathematical modeling and Intelligent Data Analysis (including applied geometrical methods and Machine Learning) and their applications for the analysis of neuroimaging biomedical data. He has more than 150 scientific publications.
Professor at the Practice Alexander Bernstein works at the Skoltech Center for Computational and Data-Intensive Science and Engineering since May 2016. He received a master degree in Math (1969) at the Department of Mechanics and Mathematics, Moscow State University, a PhD degree in Math (1973) from the Steklov Mathematical Institute of the USSR Academy of Sciences, and a Doctor of Sciences degree in Math (1987) from the Department of Computational Mathematics and Cybernetics, Moscow State University. In 1991, the USSR Higher Attestation Commission awarded Alexander with the academic rank of Professor in the field of Intelligent Technologies and Systems. Prof. Bernstein started his career at the Research Institute of Automatic Equipment in 1969, where he was developing mathematical models and algorithms for computer networks. Then he worked at the Software Engineering Center of the Russian Academy of Sciences (RAS), at the Institute for System Analysis RAS and at the Institute for Information Transmission Problems RAS doing theoretical and applied research in the field of Mathematical modeling, Mathematical and Applied statistics, Intelligent Data Analysis, and their applications. At the same time, he had part-time full professor positions at the Moscow State Institute of Radiotechnics, Electronics and Automation, the National Research University Higher School of Economics and the Moscow Institute of Physics and technology. His current research interests are in Mathematical modeling and Intelligent Data Analysis (including applied geometrical methods and Machine Learning) and their applications for the analysis of neuroimaging biomedical data. He has more than 150 scientific publications.
Maxim Sharav obtained his MSc in Biochemical Physics, Dept. of Physics of Lomonosov Moscow State University, Moscow, Russia; Department of Biophysics in 2012 and became a PhD in Biochemical Physics at Dept of Physics of Lomonosov Moscow State University, Moscow, Russia; Department of Biophysics since 2016. His main research and scientific interests lie in the field of neuroimaging data analysis, mathematical modelling, neuroimaging methods development as well as the application of Artificial Intelligence (Machine Learning and Data Analysis) to scientific and medical problems in neuroscience, resting-state fMRI, multivariate pattern analysis in neuroimaging EEG, fMRI in attention, memory, cognition, biofeedback and brain-computer interface, statistical analysis/modelling (including Monte-Carlo simulation, nonparametric methods, resampling methods, Bayesian inference), optimization methods (including genetic algorithms, gradient methods, etc.). Maxim Sharaev has experience of working in IHNA RAS, Institute for Higher Nervous Activity and Neurophysiology as an Assistant and in NBICS, National Research Center "Kurchatov Institute" as a Researcher. Now he works in CDISE, Skolkovo Institute of Science and Technology as a Research scientist.
Prof. Maxim V. Fedorov is Skoltech's Vice President for Mathematical Modelling and AI (formerly — Director of Skoltech Center for Data Intensive Science and Engineering, Skoltech CDISE). He belongs to a wide range of international societies. He is known, inter alia, as an organizer of a large number of conferences and seminars, and as a multiple representative of the Russian Federation at conferences organized by UNESCO and other high-level institutions in the area of AI. Prof. Fedorov's educational activity experience is over ten years (2003-2016) of work at the leading research and education institutions of the Republic of Ireland, Germany and the United Kingdom (including Max Planck Society and Cambridge University). In 2012, he founded the Technology & Innovation Supercomputer Centre in Glasgow, which was led by him from 2012 to 2016. At the same time, Prof. Fedorov headed the independent scientific laboratory in the University of Strathclyde in Glasgow. As a research cluster leader, he also coordinated the activities of a number of the University's laboratories in several application areas of high-performance computing and information technologies for Big Data processing. Under Maxim Fedorov's personal guidance 10 PhD theses were successfully defended at University College Dublin (Ireland), the University of Duisburg-Essen (Germany) and the University of Strathclyde in Glasgow (UK).
Sergey P. Morozov, MD, MPH, PhD, Professor, Chief External Officer for Radiology and Instrumental Diagnostics, Moscow Health Care Department and RF Ministry of Health in the Central Federal District; Chief Executive Officer at the Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow Health Care Department. Main focus of activity: development of teleradiology; promotion of artificial intelligence in diagnostics; development and implementation of screening programs; development of information technology in radiology; creation of radiology reference centers.

Major achievements:
  • Development of techniques for coronary CT angiography, CT colonoscopy, knee MRI, CT arthrography, endorectal coil MRI of the pelvis, functional MRI of the brain;
  • Research on the accuracy of various chemotherapy response assessment systems using CT, PET-CT, and MRI.
Under the guidance of Sergey P. Morozov:
  • The Unified Radiological Information Service (ERIS) has been developed and introduced in Moscow;
  • The pilot projects Breast Cancer Screening Using Mammography and Low-Dose Computed Tomography of the Chest as a Screening Method to Diagnose Lung Cancer and Other Chest Diseases are being implemented.
Info: TBD
Des Higham is Professor of Numerical Analysis at the University of Edinburgh. He works on the development, analysis and implementation of computational algorithms. He holds an EPSRC/RCUK Digital Economy Programme Established Career Fellowship (2015--2020) and is institutional lead on an EPSRC Mathemtical Sciences Programme Grant (2017--2021). He received the Germund Dahlquist Prize from the Society for Industrial and Applied Mathematics (SIAM) for his research contributions in stochastic computation. He was elected Fellow of the Royal Society of Edinburgh in 2006, and received a Royal Society Wolfson Research Merit Award in 2012. Higham is Editor-in-Chief of SIAM Review, a journal that is consistently ranked number one in applied mathematics, by citation index. Web-site:
Catherine's current position as a senior member of Prof. Rod Murray-Smith's Inference, Dynamics and Interaction group involves applying state-of-the-art machine learning and statistical methods to inverse problems in image processing arising from recent advances in Quantum Optics. The research associate position is supported by the EPSRC UK Quantum Technology Programme under grant P/M01326X/1. After a graduating with a mathematics degree from Oxford, she worked for Novaction, a brand/marketing consultancy based in Paris, France, who sponsored me through a business MBA at City University, London. After moving to Scotland, she worked as a strategic analyst at Scottish Hydro-Electric. Following a career break, she independently prepared and submitted a research proposal to the Daphne Jackson Trust, and obtained a two-year Fellowship 2006-2008. This was followed by a Lord Kelvin/Adam Smith PhD scholarship from the University of Glasgow, 2008-2012. Catherine's PhD work took place in Professor Darren Monckton's lab, which has accumulated one of the most comprehensive datasets concerned with unstable human DNA mutations. Website:
Mohammad got his bachelors and masters degree in Computer Engineering and Software Engineering, respectively in 1999 and 2001, from Sharif University of Technology, Iran. Subsequently, he obtained his Ph.D. in Computer Science from Eindhoven University of Technology, The Netherlands in 2005. Since then he held positions at Reykjavik University (postdoctoral researcher), Eindhoven University of Technology (assistant and associate professor), Delft University of Technology (guest faculty member), Halmstad University (professor of Computer Systems Engineering), and Chalmers / University of Gothenburg (guest professor of Software Engineering). He currently holds the chair of Data-Driven Software Engineering at the Department of Informatics, University of Leicester. He has had various leadership positions in his past appointments, such as managing educational programs, leading research teams, and leading research-centre-building initiatives. Mohammad's main research area is in model-based testing, particularly applied to software product lines and cyber-physical systems. He has been leading several research initiatives and industrial collaboration projects on healthcare and automotive systems their validation, verification, and certification. Web page:
Dr. Danil Prokhorov started his research career in Russia. He studied system engineering which included courses in math, physics, mechatronics and computer technologies, as well as aerospace and robotics. He received his M.S. with Honors in St. Petersburg, Russia, in 1992. After receiving Ph.D. in 1997, he joined the staff of Ford Scientific Research Laboratory, Dearborn, Michigan. While at Ford he pursued machine learning research focusing on neural networks with applications to system modeling, powertrain control, diagnostics and optimization. He has been involved in research and planning for various intelligent technologies, such as highly automated vehicles, AI and other futuristic systems at Toyota Tech Center (TTC), Ann Arbor, MI since 2005. Since 2011 he is in charge of future research department in Toyota Motor North America R & D. He has been serving as a panel expert for NSF, DOE, ARPA, Senior and Associate Editor of several scientific journals for over 20 years. He has been involved with several professional societies including IEEE Intelligent Transportation Systems (ITS) and IEEE Computational Intelligence (CI), as well as International Neural Network Society (INNS) as its former Board member, President and recently elected Fellow. He has authored lots of publications and patents. Having shown feasibility of autonomous driving and personal flying mobility, his department continues research of complex multi-disciplinary problems while exploring opportunities for the next big thing.
Pavel Osinenko studied control engineering at the Bauman Moscow State Technical University from 2003 through 2009. He started his postgraduate education in vehicle control systems at the same university. In 2011, Pavel moved to the Dresden University of Technology after receiving a Presidential Grant for studying abroad. He obtained a Ph.D. degree in 2014 after having successfully defended his dissertation on vehicle optimal control and identification. Pavel has work experience in the German private sector and at the Fraunhofer Institute for Transportation and Infrastructure Systems. In 2016, he made a transition to the Chemnitz University of Technology as a group leader and research coordinator. He was actively involved in project coordination, doctorate supervision, teaching, administration etc. Pavel's major focus of research has been on reinforcement learning, especially its safety, and computational aspects of dynamical systems. Currently, he addresses the connections between control theory and machine learning, and, in particular, the matters of meeting specified certificates on reinforcement learning methods in dynamical context.
Dr. Utkin is the Chairman of the Technical Committee 194 "Cyber-Physical Systems" and specialist for standardization of Skoltech Center for Computational and Data-Intensive Science and Engineering (CDISE). His experience in the field of the technology business is very large and include investment, consulting, technology analytics and regulation. He is a leading expert in the national regulatory formation in the field of digital technologies, and also represents the Russian Federation at international regulatory platforms, especially ISO/IEC. Being a part of advisory bodies of federal executive bodies, he often participates in the development and approval of national and international standards: IoT, Digital Twins, AI, Big Data, Smart Manufacturing, Smart Cities etc.
Ivan Oseledets graduated from Moscow Institute of Physics and Technology in 2006, got Candidate of Sciences degree in 2007, and Doctor of Sciences in 2012, both from Marchuk Institute of Numerical Mathematics of Russian Academy of Sciences. He joined Skoltech CDISE in 2013. Ivan's research covers a broad range of topics. He proposed a new decomposition of high-dimensional arrays (tensors) – tensor-train decomposition, and developed many efficient algorithms for solving high-dimensional problems. These algorithms are used in different areas of chemistry, biology, data analysis and machine learning. His current research focuses on development of new algorithms in machine learning and artificial intelligence such as construction of adversarial examples, theory of generative adversarial networks and compression of neural networks. It resulted in publications in top computer science conferences such as ICML, NIPS, ICLR, CVPR, RecSys, ACL and ICDM. Professor Oseledets is an Associate Editor of SIAM Journal on Mathematics in Data Science, SIAM Journal on Scientific Computing, Advances in Computational Mathematics (Springer). He is also an area chair of ICLR 2020 conference. He got several awards for his research and industrial cooperation, including two gold medals of Russian academy of Sciences (for students in 2005 and young researchers in 2009), Dynasty Foundation award (2012), SIAM Outstanding Paper Prize (2018), Russian President Award for young researchers in science and innovation (2018), Ilya Segalovich award for Best PhD thesis supervisor (2019), Best Professor award from Skoltech (2019), the best cooperation project leader award from Huawei (2015, 2017). He also has been a Pi and Co-Pi of several grants and industrial projects (230 million of rubles since 2017).

Pavel Gotovtsev is the Deputy Head of the Department of Biotechnology and Bioenergy at the Research Center "Kurchatov Institute", and also concurrently holds the position of MIPT Associate Professor. His main research interests are the creation of intelligent control and management systems in biotechnology and bioenergy, the Internet of Things and biotechnology. Since 2017, Dr. Gotovtsev has been the coordinator of the Russian working group under the auspices of the IEEE global initiative on the ethics of autonomous and intelligent systems.

Dmitry graduated from the Department of Mechanics and Mathematics of Moscow State University in 1998, where he also obtained his Candidate of Sciences degree in 2002. Later on, he worked at the Institute for Information Transmission Problems (IITP), Dublin Institute for Advanced Studies, and Munich University. In 2015, he obtained his Doctor of Sciences degree from IITP. Dmitry's interests cover a wide range of topics in Applied Mathematics. He started his research career with studies of stochastic processes and quantum lattice systems, gradually shifting to topics related to engineering, data analysis, and optimization. You can find more details about his research on his personal web page.
Prof. Shapeev has graduated from Novosibirsk State University in 2001 with Bachelor degree and in 2003 with Master degree. PhD from National University of Singapore in 2009 on this topic of computational fluid mechanics. Through his two postdocs, in EPFL (Lausanne, Swizerland) and the University of Minnesota (USA) he was working on mathematical analysis of coupling of atomistic and continuum description of solids, my work has been award the 2013 SIAM Outstanding Paper Prize. After assuming an Assistant Professor position at Skoltech, Alexander's main interest is in development and practical applications of models of interatomic interaction (aka interatomic potentials) with a multidisciplinary approach that combines ideas from computational mathematics, machine learning and physics and has applications in materials science, physics, and chemistry. Personal website:
Maxim received his Bachelor and Master degrees from the Moscow Institute of Physics and Technology in 2010 and 2012, respectively. His Bachelor thesis addressed the problem of filling missing data values in the support vector machine classification framework. The Master thesis focused on the Gaussian processes regression and adaptive design of experiments. In 2012, Maxim started his postgraduate studies and switched the direction to the field of Mathematical Statistics. He concentrated on obtaining tight bounds for the Gaussian approximation of posterior distribution (Bernstein-von Mises phenomenon). The research resulted in a series of publications in peer-reviewed journals and formed the core of Maxim's Candidate of Sciences thesis defended at the Institute for Information Transmission Problems in January 2016. Starting in 2010, Maxim also worked part time as a research scientist in DATADVANCE Company, which is a resident of Skolkovo Innovation Center. There he participated in developing the library of data analysis methods for engineering applications. This library, pSeven, is now used by a number of companies worldwide, including Airbus, Porsche, Mitsubishi, Toyota, Limagrain and many others.
Alexander is an AI and Data Regulation lawyer at the Skolkovo Foundation's Centre of Excellence for Digital Economy Regulation. He is an Edinburgh University graduate (LLM in Innovation, Technology and Law) with an extensive legal experience which includes in-house legal advisory work and legal practice at large multinational firms. Alexander publicly advocates for sustainable, ethical technology development, including human-centred artificial intelligence and responsible personal data processing.
Founder of the largest CIS community of data specialists, Open Data Science. CEO of Data Souls ML Competition Platform. Organizer of the largest specialized conference Data Fest in the CIS and Eastern Europe. Alexey has completed over 20 projects at DM Labs, Deloitte, Diginiteca and Siemens.
Director General of the International Research Institute of Management Problems (MNIIPU) and the Institute for Economic Strategies of the Russian Academy of Sciences (INES). Doctor of Economics, professor, academician of the Russian and European Academy of Natural Sciences and several leading Moscow universities. Head of Working Group 01, Fundamental Standards, Technical Committee No. 164, Artificial Intelligence. Ageev specializes in the issues of economic growth, the global economy as a whole, integration processes, digital transformation, foreign regional studies, management of various sectors of the high-tech complex and energy, entrepreneurship, development of competition, creation of information systems, development and application of international standards in the field of artificial intelligence, investments, risk management, integrated reporting, etc. He is the author of more than 700 scientific papers, including 30 monographs. More than 30 scientific papers published in the USA, Germany, Portugal, Finland, Italy, China and several other foreign countries in English, German, Chinese and other languages. Editor-in-chief of the journal Economic Strategies and the international journal Partnership of Civilizations. Dr. Ageev is the Member of the Union of Writers of Russia and the Union of Journalists of Russia.
Peter Grindrod is Professor in the Mathematical Institute, University of Oxford. His research interests are:
  • The theory and applications of dynamically evolving networks, including nonlinear node-based dynamics, fully coupled through time dependent network dynamics.
  • Stochastic modelling and classification of behaviour within evolving peer-to-peer communication and social networks.
  • Applications of mathematics to social media, digital media and marketing
  • Dynamical systems and Delay Differential Equations Analysis of fMRI scans of human brains, including measures of network "fragility" as predictors of future performance and early cognitive degradation.
  • Modelling, analysis and forecasting of domestic and small business energy consumption on low voltage networks including dynamic behaviour driven segmentations of consumers via smart meter data
  • Inference and forecasting problems for the retail, consumer goods, and telecommunications sectors. Behaviour-based risk measures and targetted-marketing applications.
  • Models for counter terrorism and real time recognition of anomalies within vast communications data sets.
  • Strategy for investment in science and technology research and innovation. Knowledge exchange and balancing open public research with confidential commercial interests through open innovation.
Yury D. Tsvetkov is a career diplomat with 12 years of experience on various positions within the Ministry of Foreign Affairs of the Russian Federation. In 2019-2020 served as team leader on international aspects of AI regulation in global digital environment at Press and Information Department of the Russian MFA; represented Russia in the Council of Europe Steering Committee on Media and Information Society. 2016-2019 - Head of Political Section, Head of Press-Service, Embassy of the Russian Federation to the Republic of Namibia. 2013-2016 - European Cooperation Department (European Union Division) of the Russian Foreign Ministry. 2014 - Permanent Mission of the Russian Federation to the European Union and Euroatom (Kingdom of Belgium). 2008-2013 - Russian Embassy in the Republic of Estonia.
Alexander Lunkov was educated as a teacher of history, sociology and philosophy in 2003. Since 2004, he has been teaching postgraduate students of the Russian Academy of Sciences in the subject of history and philosophy of science, research methodology, etc. In 2008, he defended a dissertation on the history of Russia during the Great Patriotic War. His main scientific interest is the philosophy of war and peace and philosophical issues of technology. Since 2019, he is an expert in the Technical Committee 164 "Artificial Intelligence". In this Committee, he deals with the ethical issues of the development of AI technology in terms of academic research and technical standardization. His main goal in this area is to create a comprehensive ethics for people dealing with AI or involved in situations, which were determined by AI technologies.
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The professional and social activities of Svetlana Malkarova are related to information and international activities in order to strengthen communication and understanding between peoples in the field of education and culture. From the very beginning of her research and professional activities, Malkarova chose the topic of value orientations and moral values in culture, science and education. The dissertation "A Comparative Analysis of the Value Orientations of Students in the North-East of Russia and the North of the USA" was prepared and successfully defended. All her subsequent activities are related to the promotion of spiritual and cultural values. Since 2019, Svetlana Malkarova headed the UNESCO Chair at the State University of Management "Social, Legal and Ethical Foundations of the Knowledge Society (Information Society)" as Executive Director. Svetlana is acknowledged by the President of the Russian Federation V.V. Putin for her contribution to the development of education. She was awarded by the thanks of the Minister of Culture of the Russian Federation for her contribution to the development of culture. In 2007-2008 she became Fulbright Program Graduate (USA)
Anna Abramova is Head of the Department of Digital Economy and Artificial Intelligence ADV group at MGIMO-University, Academic Director for the Masters' Programme on Artificial intelligence and Associate Professor at School of International Economic Relations (MGIMO-University). She has more than 10 years of experience in research and lecturing on e-commerce, ICT market and digital trade developments, as well as official development assistance in ICT sector. She has worked as a consultant for UNCTAD and World Bank.
Dr Bastiaan Quast is a data scientist and author of the popular machine learning library rnn, which implements recurrent neural network architectures in the R language, making these algorithms accessible to statisticians, as well as the author of the datasets GUI: datasets.load, and the author of the upcoming book: "Machine Learning Algorithms from Scratch in R" with Manning.
event details
The Conference will take place at the heart of Moscow's Skolkovo Innovation Center – Skoltech Institute of Science and Technology, rated globally as one of TOP-100 young universities (by Nature Index). Skoltech was created during 2011 in collaboration with the Massachusetts Institute of Technology (MIT). Central to the campus are its multi-story laboratories that accommodate lab space, work and teaching areas, as well as the associated offices, which include 12 laboratory buildings within the insitute.
Moscow is the capital of Russia and one of the largest cities in Europe. A historic center with modern infrastructure easily reachable from all major cities of Russia by air or train travel. Located on the river Moskva, in the west of the country, Moscow's landmarks include the Red Square, the Bolshoi Theatre, the Gorky Park, VDNH and the Tretyakov gallery. As the conference will be held in online format in 2020, we will be pleased to present you a virtual tour.
Zoom will be used as the main tool for online communication: sections, discussions, networking. Google Drive will be used to store and exchange presentation files and topical charts. All registered participants, as well as speakers, will also get the access to special webpage with useful links to files, chats, conference rooms, and other event-related content.
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organizers / contact
Maxim Fedorov
Chair, Skoltech Vice President for Mathematical Modelling and AI
Anna Zhdanova
Ksenia Poliakova
Ivan Khlebnikov
Skolkovo Institute of Science & TechnologyCenter for Computational and Data-Intensive Science and Engineering

Do not hesitate to drop by:

30с1 Bolshoi boulevard, Skolkovo, 121205, Russian Federation
For directions on how to reach us, please see here