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 http://yarotsky.info/

Leonid Zhukov joined Boston Consulting Group originally in Boston in September 2015. He is currently leading BCG Gamma team in Moscow and a topic expert in AI and Machine learning. He is focusing on O&G/IG sectors and OPS. He is also a Professor at the Department of Data Analysis and Artificial Intelligence at the Higher School of Economics in Moscow. Prior to BCG, Leonid was a Director of Data Science at Ancestry.com, where he led data science team in development of large scale machine learning and information retrieval systems. Prior to that he was a researcher at Yahoo! Labs and Caltech were he developed big data algorithms for web graph and targeted advertising systems. He has also co-founded and led the engineering team at the information security startup developing data loss prevention DPI system.

Ivan is an Associate Professor at Skoltech since August 2013, where he is a leader of the Scientific Computing Group. Ivan's research focuses on the development breakthrough numerical techniques (matrix and tensor methods) for solving a broad range of high-dimensional problems.

The main tools are linear algebra, singular value decomposition, low rank approximation of tensors. It involves beautiful math, but has a big programming/software development component. The numerical treatment of high-dimensional problems is notoriously difficult due to the curse of dimensionality: the complexity grows exponentially with the number of indices. High-dimensional problems appear in physics, chemistry, biology and still keep coming. The quest is to find something common that unites different numerical methods for high-dimensional problems. And tensors and their decompositions are one of the most promising approaches.

Ivan is the author of more than 40 published/accepted papers in high profile numerical math/computational physics journals, which include SIAM J. Sci. Comput, Computer Physics Communications.

Dr. Andrey Somov is an Assistant Professor at the Skolkovo Institute of Science and Technology (Skoltech), Russia. During his PhD course at the University of Trento Dr. Somov was leading an independent research project on energy harvesting for sensor networks as a principal investigator of the research grant funded by Caritro Foundation, Italy (2009).

Before joining Skoltech (2017), he had worked as a Senior Researcher for CREATE-NET Research Center, Italy (2010-2015) and as a Research Fellow for the University of Exeter, UK (2016-2017). His research interests covered power management for WSN and Internet of Things (IoT) devices, cognitive IoT and associated proof-of-concept implementation.

Dr. Somov has published more than 70 papers for peer-reviewed international journals and conferences. He has delivered a number of invited talks at Berkeley Wireless Research Center, IDTechEx event, Luxembourg SnT Interdisciplinary Centre. He has been General Chair of the 6th International Conference on Sensor Systems and Software (S-Cube'15), the 'IoT360' Summer School on the Internet of Things in 2014 and 2015. Andrey holds some awards in the fields of WSN and IoT including the Google IoT Technology Research Award (2016).

Victor is an associate professor at Skolkovo Institute of Science and Technology and is starting the institute's Computer Vision Group. He has worked as a researcher in various locations including Yandex, the University of Oxford, and at Microsoft Research Cambridge. His interests are in computer vision, visual recognition, and biomedical image analysis.

Victor has published extensively and served as a program committee member/reviewer for top computer vision conferences and journals (ICCV, CVPR, ECCV, NIPS, TPAMI, IJCV). He has served as an area chair for CVPR'18, CVPR'15, ICCV'15, and ECCV'12 conferences. He has also co-authored five US patents. Victor has won the best paper award at the international symposium on Functional Imaging and Modeling of the Heart (FIMH) in 2009.

Since 2007 Evgeny Burnaev carried out a number of successful industrial projects with Airbus, SAFT, IHI, and Sahara Force India Formula 1 team among others. The corresponding data analysis algorithms, developed by Evgeny Burnaev and his scientific group, formed a core of the algorithmic software library for metamodeling and optimization. According to Airbus experts, application of the library "provides the reduction of up to 10% of lead time and cost in several areas of the aircraft design process". Since 2016 Evgeny Burnaev works as Associate Professor of Skoltech and manages his research group for Advanced Data Analytics in Science and Engineering.

For his scientific achievements in the year 2017 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."

My research focus is on using kernel methods to reveal properties and relations in data. A first application is in measuring distances between probability distributions. These distances can be used to determine strength of dependence, for example in measuring how strongly two bodies of text in different languages are related; testing for similarities in two datasets, which can be used in attribute matching for databases (that is, automatically finding which fields of two databases correspond); and testing for conditional dependence, which is useful in detecting redundant variables that carry no additional predictive information, given the variables already observed. I am also working on applications of kernel methods to inference in graphical models, where the relations between variables are learned directly from training data.

Mr. Ovchinnikov received his M.S. and Ph.D. in Biochemistry/Oncology from the Moscow State University n.a. Lomonosov (Virology department). More than 15 years' experience in drug development, clinical trials and leadership of medical affairs including PAREXEL, Johnson&Johnson, Novartis. More than 6 years Dmitry was General Director of Russian branch US-based immunotherapeutical startup SELECTA Biosciences Inc. In 2016 SELECTA made successful IPO (SELB - 75M USD) on NASDAQ. Expert of SKOLKOVO and few other Russian venture funds in area of biotechnology and pharmaceutics.

I have graduated from the Department of Mechanics and Mathematics of Moscow State University in 2012, where I specialized in mathematical linguistics. I have a decade-long experience of brining NLP research and industry closer: applying cutting edge NLP models to industrial cases as well as conducting research in the areas most useful for practical applications. Currently I am the head of NLP in Advanced Research Department at ABBYY and an assistant professor at Moscow Institute of Physics and Technology. At the moment I am most interested in syntactic and semantic parsing, relation extraction and summarization.

Irina Fedulova is currently leading a team of research scientists focused on advancing the technology for medical image and text understanding, multimodal data analysis, and clinical informatics at Philips Research Russia. Before 2017, Irina managed a data science team that was developing analytical solutions for retail at IBM Commerce. Earlier in her career, Irina worked as a senior software engineer and specialized in advanced parallel algorithm design and development in various application domains of high-performance computing at IBM Science and Technology Center.

Irina holds a Ph.D. degree in computer science and mathematical modeling from Lomonosov Moscow State University.

Dmitry is an associate professor of the practice at Skolkovo Institute of Science and Technology and heads the institute's Internet of Things Laboratory. His research interests are Wireless Technologies and Internet of Things. Dmitry is an expert in telecommunication systems and wireless technologies. Also, he is a management professional with 10+ years of experience with extensive experience in building high-tech R&D teams and has an experience in a successful company startup. He worked as Head of the department at IITP and then was the co-founder and CEO of the IITP spin-off company (up to 60 employees and $3mln of annual revenue) for more than six years.
Dmitry heads the Internet of Things Laboratory (IoT Lab). This laboratory added new experience in telecommunications to Skoltech portfolio, and became a bridge between IoT-related disciplines, and forms an ecosystem is committed to understanding how best to evaluate and deploy new IoT opportunities.

Efim Boeru has graduated from Moscow Institute of Physics and Technology and Skolkovo Institute of Science and Technology. At MIT and Skoltech, he did research in recovering electrical properties of human body based on measurements from magnetic resonance imaging. Prior to joining Huawei Research, he built AI research solutions for industrial applications, such as automotive industry and household appliances. He is a leading research engineer at Huawei Technologies applying AI and optimization for transmission and access of optical networks. The name of his topic is "Predictive Analytics and Maintenance of Optical Networks

Danila Doroshin has graduated from the department of Mechanics and Mathematics of Moscow State University in 2008. He has got his PhD in the application of Hidden Markov Models build over State Space Systems to problem of optimal estimation of gravity measurements. His work experience is in the area of AI application to Wireless Technologies, Speech Recognition and Biometrics, and Positioning Technologies. He is a leading engineer at Huawei Technologies.

Irina Basieva is a graduate of department of mechanics and mathematics of Moscow State University where she specialized in using Lie groups for so lving differential equations. She got her PhD in laser physics at Prokhorov General Physics Institute. She has over 60 publications on topics varying from quantum physics to decision making. She is a n honorary researcher at City, University of London, wher e she worked recently following the award of a Marie Curie fellowship. She is a leading engineer at Huawei Technologies.

I am a Senior Research Scientist at the Max Planck Institute for Intelligent Systems in the department of Empirical Inference and project leader in the department of Physiology of Cognitive Processes at the Max Planck Institute for Biological Cybernetics. Neural networks are characterized by dense connectivity at multiple scales that makes challenging to assess their organization and function. We develop machine learning and statistical tools to study the functional organization of these systems at multiple scales. In parallel, the current intensive development of new artificial deep neural networks has lead to impressive successes, but the functioning of these architectures remains largely elusive due to their high dimensional connectivity. This provides us an opportunity to use our network analysis tools to uncover fundamental principles for such systems, and possibly relate them to biology. We are currently investigating causality and invariance principles to understand the structure of deep generative models and in particular assess their modularity.

I am an Associate Professor of Machine Learning at the University of Oxford Computer Science department, and head of the Oxford Applied and Theoretical Machine Learning Group (OATML). I am also the Tutorial Fellow in Computer Science at Christ Church, Oxford, and Fellow at the Alan Turing Institute, the UK's national institute for data science. Prior to my move to Oxford I was a Research Fellow in Computer Science at St Catharine's College at the University of Cambridge. I obtained my PhD from the Cambridge machine learning group, working with Prof Zoubin Ghahramani and funded by the Google Europe Doctoral Fellowship. Prior to that I studied at Oxford Computer Science department for a Master's degree under the supervision of Prof Phil Blunsom. Before my MSc I worked as a software engineer for 3 years at IDesia Biometrics developing code and UI for mobile platforms, and did my undergraduate in mathematics and computer science at the Open University in Israel.

Michael Boguslavsky is Head of AI at a London-based fintech startup Tradeteq. He will share his experience in implementing a Machine Learning system for Credit risk prediction. Predictive quantitative models have been used for credit risk for at least 60 years, but the field then focused a lot on default correlation modelling and got it wrong in the runup to the crisis of 2008. More recently, emergence of new data has enabled a rapid progress in modelling although a lot of the relevant data and models remain private. I will give an overview of pecularities of available datasets, importance of missing data patterns and adversarial attack resilience and will then describe a novel approach based on data-driven credit contagion modelling and graph flows.

I am working in Huawei for more than two years, developing algorithms for LTE and 5G networks. Before I study mathematics in Higher School of Economy and wrote a Ph.D. thesis on Optimal Transport (a.k.a. Monge-Kantorovich) theory. My interests are: optimization, stochastic models, and wireless technology.
My presentation topic is Methods of Machine Learning for Transmission Rate Optimization.

Machine learning and quantum technology: where Bellman's equations meet Bell's inequalities. While quantum machine learning is a rapidly developing and fashionable field, there is no unified definition of what this field encompasses. Is it about using quantum computers to accelerate the training of neural networks? Or, on the contrary, using classical machine learning approaches to find wavefunctions or control quantum experiments? Or quantum generalization of classical neural networks? Without aiming to structure the field or define its boundaries, I will show a few examples of what can be achieved by bringing together machine learning and quantum physics both at the current technology level and in the future with the arrival of fault-tolerant quantum computers.

Lingxi Xie is currently a senior researcher at Noah's Ark Lab, Huawei Inc. He obtained B.E. and Ph.D. in engineering, both from Tsinghua University, in 2010 and 2015, respectively. He also served as a post-doctoral researcher at the CCVL lab from 2015 to 2019, having moved from the University of California, Los Angeles to the Johns Hopkins University.Lingxi's research interests lie in computer vision, in particular the application of deep learning models. His research covers image classification, object detection, semantic segmentation and other vision tasks. He is also interested in medical image analysis, especially object segmentation in CT or MRI scans. Lingxi has published over 40 papers in top-tier international conferences and journals. In 2015, he received the outstanding Ph.D. thesis award from Tsinghua University. He is also the winner of the best paper award at ICMR 2015.

William Clements is a research scientist in reinforcement learning at Unchartech in Paris, France. He graduated from Ecole Polytechnique in physics in 2014, and received a PhD in photonic quantum computing from the University of Oxford in 2018. During his PhD, he designed photonic network architectures with applications in both quantum computing and machine learning. His current research focuses on bridging the gap between reinforcement learning and real world applications, by developing new methods to measure uncertainties, achieve better generalization, and improve data efficiency.