John is an international technology executive with over 30 years of experience in the business intelligence and advanced analytics fields. Currently, John is responsible for the global Advanced Analytics & Artificial Intelligence team and efforts at CSL.
Prior to CSL, John was an Executive Partner at Gartner, where he was management consultant to market leading companies in the areas of digital transformation, data monetization and advanced analytics. Before Gartner, John was responsible for the advanced analytics business unit of the Dell Software Group.
John is the author of the new book – Analytics Teams: Leveraging analytics and artificial intelligence for business improvement. The book was published in June 2020 and outlines how to hire and manage high performance advanced analytics teams. The book outlines how to engage with executives and senior managers. How to select and undertake analytics projects that change and improve how a business operates.
John is co-author of the bestselling book – Analytics: How to win with Intelligence, which debuted on Amazon as the #1 new book in Analytics in 2017. Analytics is a book that guides non-technical executives through the journey of creating an analytics function, funding initiatives and driving change in business operations through data and applied analytical applications.
Mr. Thompson’s technology expertise includes all aspects of advanced analytics and information management including – descriptive, predictive and prescriptive analytics, artificial intelligence, analytical applications, deep learning, cognitive computing, big data, data warehousing, business intelligence systems, and high performance computing.
One of John’s primary areas of focus and interest has been to create innovative technologies to increase the value derived by organizations around the world.
John has built start-up organizations from the ground up and he has reengineered business units of Fortune 500 firms to reach their potential. He has directly managed and run – sales, marketing, consulting, support and product development organizations.
He is a technology leader with expertise and experience spanning all operational areas with a focus on strategy, product innovation, growth and efficient execution.
Thompson holds a Bachelor of Science degree in Computer Science from Ferris State University and a MBA in Marketing from DePaul University.
Srujana is a Data Science & Analytics evangelist and advocate of using data science for social good. She has expertise in using applied intelligence techniques of machine learning for marketing & customer analytics. As a Director of Data Science & Value Realization at Walmart Labs, she leads data science portfolio to solve for unique business use cases to drive quantifiable data value. Srujana has held positions with Accenture, Google, and Hewlett Packard all in data science leadership capacities. She has had numerous articles in data science published in The Harvard Business Review (HBR) and INFORMS, among others. She earned her MBA in Operational Research, an Executive graduation in Analytics Strategy Management from Harvard University, and completed the Executive Program for Women Leaders at Stanford University. She is on the governing council of Analytical Society of India and cofounder of Women in Machine Learning and Data Science (WiMLDS) Bengaluru chapter. She is also a Women in Data Science (WiDS) ambassador. She enjoys fostering the communities of data science professionals and actively collaborating with United Nations Association (UNA) in attaining sustainable development goals through responsible usage of artificial intelligence technologies.
Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), as well as Chief Expert AutoML at the Bosch Center for Artificial Intelligence.
Frank holds a PhD from the University of British Columbia (UBC, 2009) and a Diplom (eq. MSc) from TU Darmstadt (2004). He received the 2010 CAIAC doctoral dissertation award for the best thesis in AI in Canada, and with his coauthors, several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. He is the recipient of a 2013 Emmy Noether Fellowship, a 2016 ERC Starting Grant, a 2018 Google Faculty Research Award, a 2020 ERC PoC Award, and he is a Fellow of ELLIS. Frank’s recent research focuses on automated machine learning (AutoML), where he co-organized the ICML workshop series on AutoML every year since its inception in 2014, co-authored the prominent AutoML tools Auto-WEKA, Auto-sklearn, and Auto-PyTorch, won the first two AutoML challenges with his team, co-authored the first book on AutoML, worked extensively on efficient hyperparameter optimization and neural architecture search, and gave a NeurIPS 2018 tutorial with over 3000 attendees.
Professor Sandra Wachter is an Associate Professor and Senior Research Fellow focusing on law and ethics of AI, Big Data, and robotics as well as Internet regulation at the Oxford Internet Institute at the University of Oxford. Professor Wachter is specialising in technology-, IP-, data protection and non-discrimination law as well as European-, International-, (online) human rights,- and medical law. Her current research focuses on the legal and ethical implications of AI, Big Data, and robotics as well as profiling, inferential analytics, explainable AI, algorithmic bias, diversity, and fairness, governmental surveillance, predictive policing, and human rights online.At the OII, Professor Sandra Wachter also coordinates the Governance of Emerging Technologies (GET) Research Programme that investigates legal, ethical, and technical aspects of AI, machine learning, and other emerging technologies.Professor Wachter is also a Fellow at the Alan Turing Institute in London, a Fellow of the World Economic Forum’s Global Futures Council on Values, Ethics and Innovation, a Faculty Associate at The Berkman Klein Center for Internet & Society at Harvard University, an Academic Affiliate at the Bonavero Institute of Human Rights at Oxford’s Law Faculty, a Member of the European Commission’s Expert Group on Autonomous Cars, a member of the Law Committee of the IEEE and a Member of the World Bank’s task force on access to justice and technology.
Dr. Duccio Medini is a Quantitative Scientist at the interface between Life Sciences, Data Science and Complex Systems Theory, and a Pharma Executive with 20 years’ experience, currently serving as Head of Data Science and Digital Innovation for GSK Vaccines R&D.
He dedicated his activity at solving biological problems that impact human health globally, by designing data strategies and extracting knowledge from genomic, epidemiological, preclinical and clinical data with advanced analytics and data-driven computing.
Dr. Medini studied the diversity of bacterial populations leading to the discovery of the pan-genome concept, solving the pan-genome structure and dynamics of several pathogens; contributed to the discovery and development of the first universal vaccine against serogroup B meningitis, and led the Meningococcal Antigen Typing System (MATS) platform worldwide, managed large teams of data scientists contributing to the successful development of bacterial, viral, prophylactic and therapeutic vaccines. More recently he focused on elucidating the mechanisms of action of vaccines and their impact on infectious diseases through complex systems methodologies. He has published 40+ scientific articles, books and patents, on the population genomics of bacteria and on mathematical modelling of vaccine effects.
Dr. Medini is Full Professor of Molecular Biology; member of international PhD school committees at the Perugia and Turin Universities in Italy; honorary member of the Cuban Immunology Society; Research Fellow of the ISI Foundation; Oversees Fellow of the Royal Society of Medicine; member of the International Society for Computational Biology.
Data Science for Vaccines Research and Development(Business Talk)
Daniel Voigt Godoy has20+ years experience in developing solutions, programs and models using analytical skills across different industries: software development, government, fintech, retail and mobility. 7+ years experience with data processing, data analysis, machine learning and statistical tools: Python (numpy, scipy, pandas, scikit-learn), Spark, R Studio, MatLab and Statistica. Experience in stochastic simulation and agent-based modeling. Experienced programmer in SQL, Python, Java, R, PowerBuilder, PHP. Strong programming skills and eagerness to learn different languages, frameworks and tools. Solid background in statistics, economics, capital markets, debt management and financial instruments.
Sara is a Senior Research Associate in Biomedical Data Science and University Research Lecturer at the University of Oxford, where she is the Machine Learning Lead in the Centre for Statistics in Medicine. She has 12 years of experience in machine learning, signal processing, and intelligent remote monitoring research, with applications in biomedical and planetary health informatics. Sara has served on the NASA Frontier Development Lab Artificial Intelligence Panel and the NASA Climate Challenge Big Think. She is a National Geographic Society Explorer in Tracking Plastic Pollution with Remote Monitoring and Machine Learning. Sara is also a University of Oxford Ambassador for Women in Data Science.
David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a Ph.D. in computer science and master’s degrees in both computer science and business administration.
Thomas Wiecki is the VP of Data Science at Quantopian, where he uses probabilistic programming and machine learning to help build the world’s first crowdsourced hedge fund. Among other open source projects, he is involved in the development of PyMC3—a probabilistic programming framework written in Python. A recognized international speaker, Thomas has given talks at various conferences and meetups across the US, Europe, and Asia. He holds a PhD from Brown University
Machine Learning and Statistics: Don’t Mind the Gap(Track Keynote)
Judy Wawira Gichoya, MD, MS, is an Assistant Professor in the Department of Radiology and Imaging Sciences at Emory University School of Medicine. An interventional radiologist, Dr. Gichoya specializes in ??? interventional radiology procedures.
Dr. Gichoya is a member of the Cancer Prevention and Control Research Program at Winship Cancer Institute. She holds professional memberships with the Radiological Society of North America, the American College of Radiology, the Society of Interventional Radiology, the Society of Imaging Informatics in Medicine, and the American Medical Informatics Association.
Guglielmo is part of MSD (Merck & Co. in North America). He is currently busy unlocking business value through Computer Vision and other ML/DL/AI applications to the biotech manufacturing space. He has an extensive background in Software Engineering and Data Science across other big organizations including IBM, Optum and FAO of the UN in diverse contexts (such as Healthcare, DevOps, Cyber Security). Guglielmo has been recognized as DataOps Champion at the Streamsets DataOps Summit 2019 and awarded as one of the Top 50 Tech Visionaries at the 2019 Dubai Intercon Conference. Since 2018 he is also an international speaker (almost 30 international conferences so far, including Big Things 2019 and 2020, Spark+AI Summit 2019, Annual Cyber Security and AI Summit 2019 and 2020), author of a tech book on distributed Deep Learning with Apache Spark and planning about a second tech book which should be probably released at the end of 2021.
Daria Stepanova is a research scientist at Bosch Center for Artificial Intelligence. Her research interests include Knowledge Representation and Reasoning with a special focus on the automatic acquisition of rules from structured knowledge. Previously Daria was a senior researcher at Max Plank Institute for Informatics (Germany), where she was heading a group on Semantic Data. Daria got her diploma degree in Applied Computer Science from the Department of Mathematics and Mechanics of St. Petersburg State University (Russia) in 2010 and a PhD in Computational Logic from Vienna University of Technology (Austria) in 2015. Before starting her PhD she worked as a visiting researcher at the School of Computing Science at Newcastle University (UK) in an industrially-oriented project.
Alex Peattie is the co-founder and CTO of Peg, a technology platform helping multinational brands and agencies to find and work with top YouTubers. Peg is used by over 1500 organisations worldwide including Coca-Cola, L’Oreal and Google.
An experienced digital entrepreneur, Alex spent six years as a developer and consultant for the likes of Grubwithus, Huckberry, UNICEF and Nike, before joining coding bootcamp Makers Academy as senior coach, where he trained hundreds of junior developers. Alex was also a technical judge at this year’s TechCrunch Disrupt conference.
Currently Senior (Big) Data Scientist at InPost and Lecturer at Wroclaw University of Economics and Business, previously Head of Data Science at Objectivity, with background in Mathematical Statistics. For almost 10 years, she has been discovering the potential of data in various business domains, from medical data, through retail, HR, finance, aviation, real estate, logistics, … She deeply believes in the power of data in every area of life. Articles’ writer, conference speaker and privately – passionate dancer and hand-made jewellery creator.
The Colours of Cleaning(Talk)
Dr. Diego Galar is a Full Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering at LTU, Luleå University of Technology where he is coordinating several H2020 projects related to different aspects of cyber-physical systems, Industry 4.0, IoT or Industrial AI and Big Data. He was also involved in the SKF UTC center located in Lulea focused on SMART bearings and also actively involved in national projects with the Swedish industry or funded by Swedish national agencies like Vinnova. He is also a principal researcher in Tecnalia (Spain), heading the Maintenance and Reliability research group within the Division of Industry and Transport. He has authored more than five hundred journal and conference papers, books and technical reports in the field of maintenance, working also as a member of editorial boards, scientific committees and chairing international journals and conferences and actively participating in national and international committees for standardization and R&D in the topics of reliability and maintenance. In the international arena, he has been visiting Professor in the Polytechnic of Braganza (Portugal), University of Valencia and NIU (USA), and the Universidad Pontificia Católica de Chile. Currently, he is visiting professor at the University of Sunderland (UK), the University of Maryland (USA), and Chongqing University in China.
Gabriel is the founder of Scalar Research, a full-service artificial intelligence & data science consulting firm. Scalar helps companies tackle complex business challenges with data-driven solutions leveraging cutting-edge machine learning and advanced analytics. Previously, Gabriel was a B.S. & M.S. student in computer science at Stanford, where he conducted research on computer vision, deep learning, and quantum computing. He’s also spent time at Google, Facebook, startups, and investment firms.
Brad is passionate about educating the world about artificial intelligence both by empowering developers and improving societal understanding. He is currently a Developer Programs Engineer at Google where he specializes in machine learning and big data solutions. Outside of work, Brad can be found singing, climbing, playing board games, and locating the best restaurants in New York City.
Nisha Muktewar is a Research Engineer at Cloudera Fast Forward Labs, where she spends time researching latest ideas in machine learning, builds prototypes that showcase these capabilities when applied to real-world use cases, and advises clients in this space. Prior to joining Cloudera, she worked as a Manager in Deloitte’s Actuarial & Modeling practice leading teams in designing, building, and implementing predictive modeling solutions for pricing, consumer behavior, marketing mix, and customer segmentation use cases for insurance and retail/consumer businesses.
Hugo Bowne-Anderson is Head of Data Science Evangelism and VP of Marketing at Coiled, a company that makes it simple for organizations to scale their data science and machine learning in Python. He has extensive experience as a data scientist, educator, evangelist, content marketer, and data strategy consultant at DataCamp, the online education platform for all things data. He also has experience teaching basic to advanced data science topics at institutions such as Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and ODSC and with organizations such as Data Carpentry. He has developed over 30 courses on the DataCamp platform, impacting over 500,000 learners worldwide through his own courses. He also created the weekly data industry podcast DataFramed, which he hosted and produced for 2 years. He is committed to spreading data skills, access to data science tooling, and open source software, both for individuals and the enterprise.
Bayesian Data Science: Probabilistic Programming(Half-Day Training)
Anuj is a seasoned Machine Learning leader, having incubated and led multiple ML teams. During his career, he has worked in academia, early stage startups as well as Fortune 100. He has successfully led efforts for building commercially viable products in a wide spectrum of verticals and functions. He has authored over a dozen research papers and patents.
Anuj is a major speaker at various international forums. He has delivered technical talks, bootcamps and participated in panel discussions at prestigious forums like ODSC, Anthill inside, PyData DC, Fifth Elephant, ICDCN, PODC, to name a few. He was Co-editor of “Anthill inside 2018”. He is a well-known name in the Machine Learning ecosystem.
Advanced NLP: From Essentials to Deep Transfer Learning(Full-Day Training)
Dr. Jiahang Zhong is the leader of the data science team at Zopa, one of the UK’s earliest fintech company. He has broad experience of data science projects in credit risk, operational optimization and marketing, with keen interests in machine learning, optimization algorithms and big data technologies. Prior to Zopa, he worked as a PhD and Postdoctoral researcher on the Large Hadron Collider Project at CERN, with a focus on data analysis, statistics and distributed computing.
Jo-fai (or Joe) has multiple roles (data scientist / evangelist / community manager) at H2O.ai. Since joining the company in 2016, Joe has delivered H2O talks/workshops in 40+ cities around Europe, US, and Asia. Nowadays, he is best known as the H2O #360Selfie guy. He is also the co-organiser of H2O’s EMEA meetup groups including London Artificial Intelligence & Deep Learning – one of the biggest data science communities in the world with more than 11,000 members (https://www.meetup.com/London-Artificial-Intelligence-Deep-Learning/).
Alessio Lomuscio is Professor in Logic for Multi-Agent Systems in the Department of Computing, where he directs the Verification of Autonomous Systems (VAS) research group. He currently holds a Royal Academy of Engineering Fellowship in Emerging Technology, and was previously the recipient of the EPSRC Leadership Fellowship “Trusted Autonomous Systems”. His research interests concern the principled analysis of autonomous agents. Since early 2000 Lomuscio pioneered the topic of verification of autonomous systems via model checking and was one of the early proponents of verifiable AI.
The VAS group developed and still maintains MCMAS, a leading model checking tool for multi-agent systems, and has put forward various verification methods for unbounded multi-agent systems, such as robotic swarms, including probabilistic approaches.
He has recently focused his attention on autonomous systems synthesized from data. The VAS group develops and maintains state-of-the-art toolkits for the verification of neural networks. He has published approximately 150 papers in top-tier AI and verification conferences and journals; he is a fellow of the EurAI. In addition to his research work, he frequently gives graduate courses on Safe AI (e.g., Stanford, 2019) and regularly engages with the wider community (e.g., World Economic Forum 2017-2018, Sotif Automotive Conference 2019).
Max Novelli is a Software Engineer, Data Architect, and Data Manager with a “Laurea” degree from Politecnico di Milano, Italy. He currently works as “Head of Informatics and Data” at Rehab Neural Engineering Lab at the University of Pittsburgh.
His latest interests are in Big Data and its management, structure, visualization, and curation applied to research data — although he never lets go any opportunity to play with hardware and customized experimental equipment. In his current position, he is responsible for the entire lab’s IT infrastructure and the safety, integrity, validation, and curation of experimental data. He is also leading R&D projects spanning from data visualization to data analysis and translating them into viable production tools. His focus is in developing visual tools to explore data structure and to assess the integrity of complex experimental data as well as using neural networks to further study and prove specific experimental results. He has been heavily involved in publishing open-access large datasets into public domain under the Open Science initiative of National Institutes of Health.
When Max is not lost in “computer land,” he enjoys spending time with his family and friends, mountain–biking, hiking trails, swimming, walking (better on the beach), cross-country skiing, eating good food, sipping good wines, and drinking good espresso. He also invests a considerable amount of energy practicing, teaching, and experimenting with yoga and body movement. Lately, he has discovered rock climbing and is trying to perfect his climbing skills.
Dr. Mohamed Sayed is a Data Science Lead at Jaguar Land Rover’s Corporate Analytics Centre of Excellence where he leads business transformation activities utilising machine learning and statistical modelling techniques. He has led projects ranging from demand forecasting and assortment optimisation to warranty and quality management. Prior to that he was a Senior Research Associate with Loughborough University where he led a major European Union funded collaborative research project developing machine learning and decision support tools in the manufacturing domain.
Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. Before his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. He also was a senior executive at a global fintech company with operations in 15 markets.
Earlier, he advised Central Banks in emerging markets, worked for the World Bank, raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He is the author for ‘Machine Learning for Algorithmic Trading’ and has been teaching data science at Datacamp and General Assembly.
How to Build and Test a Trading Strategy Using ML(Half-Day Training)
A Teaching Associate Professor in the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern work force to wisely communicate and handle a data-driven future at the nation’s first Master of Science in analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management. Previously, he was Director and Senior Scientist at Elder Research, where he mentored and led a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government. Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.
Olivier Grisel is a machine learning engineer at Inria. He is a member of the team of maintainers of the scikit-learn project. Scikit-learn is an Open Source machine learning library written in Python. His work is supported by the Fondation Inria and its partners.
Hands-on Machine Learning Engineer with scikit-learn(Full-Day Training)
Anna Veronika Dorogush graduated from the Faculty of Computational Mathematics and Cybernetics of Lomonosov Moscow State University and from Yandex School of Data Analysis. She used to work at ABBYY, Microsoft, Bing and Google, and has been working at Yandex since 2015, where she currently holds the position of the head of Machine Learning Systems group and is leading the efforts in development of the CatBoost library.
Daan Knoope works as an AI Engineer at de Volksbank, the Dutch parent company of several banks and mortgage providers. He has a background in Computer Science (MSc) and has specialized in Algorithmic Data Analysis. During his studies, he researched the application of Dynamic Bayesian Networks on practical use cases to help further the development of explainable AI. Currently, he is focusing on developing AI-models for the bank as well as providing fellow AI Engineers the tools they need to efficiently explore data and build production-ready models
Kamila Hankiewicz is a Managing Director of Untrite, an AI company helping companies make better use of data they already have; we provide an AI engine which pulls data from silos and understands the links and relevance between them. Kamila is a vivid advocate for diversity and empowering women in technology; she co-founded NGO Girls in Tech London and Poland. The local chapter has an active member base of more than 9 000 people, with more than 125 000 globally.
Kamila’s past life includes working as a Management Consultant with a focus on banking, where she was involved in digital transformation projects (such as Santander’s £1.65bn worth project Rainbow). Kamila is a frequent speaker on the subject of humanising work with use of AI. Her latest talks include those for BigDataLDN, Rasa, Future of AI and Women in AI. She hosts “Humans of AI” video interviews with prominent people solving some of the world’s toughest problems with the use of AI – https://www.youtube.com/channel/UC7qPUVnjrzb4oFwtAmDOTtw. Some of her guests include: Lord Tim Clement-Jones from House of Lords, David Barber from UCL, Rana el Kaliouby from Affectiva and many more.
Ramon van den Akker works as a data scientist at the AI Center of Excellence and the Risk Modelling departments of de Volksbank, a Dutch retail bank located in Utrecht. He also works, as an associate professor, at the econometrics group of Tilburg University. His research interests cover various fields in data science, machine learning, econometrics and statistics and his research findings have been published in leading journals in econometrics and statistics. Ramon has taught courses in data science, econometrics, life insurance, machine learning, mathematics, probability theory, quantitative finance, and statistics at Tilburg University, Tias business school, Tilburg Professional Learning, the Jheronimus Academy for Data Science (JADS), and the Dutch Actuarial Institute. In his work at de Volksbank, Ramon mainly works on projects related to data-driven innovation, but also on governance aspects like frameworks for responsible AI & data science and the use of techniques for privacy-preserving data analytics.
Joris Krijger (1991) works as an Ethics & AI specialist at the Dutch bank de Volksbank while also holding a PhD position at the Erasmus University Rotterdam on that topic. He has a background in Economic Psychology (MA), Philosophy (MA) and Film and Literary Studies (BA) and studied in Glasgow, Buenos Aires and Leiden, where he was awarded a national thesis prize in 2017 by the Royal Dutch Society of the Sciences for his graduation thesis on technology ethics and the financial crisis of 2008. He co-founded high-tech startup Condi Food (Rabobank Wijffels Innovation Award 2014) and was involved in various biomedical initiatives related to bacteriophages. He presently works on bridging the gap between principle and practice in AI Ethics by studying the operationalization of ethical principles from an academic and practical perspective and is reviewer for the AI Ethics Journal, Subject Matter Expert for CertNexus’ ‘Certified Ethical Emerging Technologist’ and Editorial Board Member for Springer’s AI and Ethics Journal.
Pieter Gijsbers is a PhD student at Eindhoven University of Technology. His areas of interest are automated machine learning and meta-learning. He is the main author of GAMA, a research-focused open source AutoML tool. While GAMA is easy to use for end-users, it also allows researchers to try different search techniques and visualize the optimization process. He is a co-author of the Open Source AutoML Benchmark. He is also part of the team working on the openml-python package, providing an easy to use Python interface to OpenML.
Automated Machine Learning(Workshop)
Dipanjan (DJ) Sarkar is a Data Science Lead at Applied Materials, leading advanced analytics efforts around computer vision, natural language processing and deep learning. He is also a Google Developer Expert in Machine Learning. He has consulted and worked with several startups as well as Fortune 500 companies like Intel and Open Source organizations like Red Hat. He primarily works on leveraging data science, machine learning and deep learning to build large- scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. Dipanjan has been an analytics practitioner for several years now, specializing in machine learning, natural language processing, computer vision and deep learning. Having a passion for data science and education, he also acts as an AI Consultant and Mentor at various organizations like Springboard, where he helps people build their skills on areas like Data Science and Machine Learning. Dipanjan is also a published author, having authored several books on R, Python, Machine Learning, Social Media Analytics, Natural Language Processing, and Deep Learning. In his spare time he loves reading, gaming, watching popular sitcoms and football and writing interesting articles on https://email@example.com and https://www.linkedin.com/in/dipanzan. He is also a strong supporter of open-source and publishes his code and analyses from his books and articles on GitHub at https://github.com/dipanjanS.
Advanced NLP: From Essentials to Deep Transfer Learning(Full-Day Training)
Fabian Theis is director of the Institute of Computational Biology at the Helmholtz Center Munich and scientific director of the Helmholtz Artificial Intelligence Cooperation Unit (HelmholtzAI) which was launched in 2019. He is a full professor at the Technical University of Munich, holding the chair ‘Mathematical Modelling of Biological Systems’, associate faculty at the Wellcome Trust Sanger Institute as well as adjunct faculty at the Northwestern University. Fabian Theis holds a Master’s degree in Mathematics and Physics and Ph.D. Degrees in Physics and Computer Science. After different research stays. He worked as visiting researcher at the Department of Architecture and Computer Technology (University of Granada, Spain), at the RIKEN Brain Science Institute (Wako, Japan), at FAMU-FSU (Florida State University, USA), and TUAT’s Laboratory for Signal and Image Processing (Tokyo, Japan), and headed the ‘signal processing & information theory’ group at the Institute of Biophysics (Regensburg, Germany). In 2006, he started working as a Bernstein fellow leading a junior research group at the Bernstein Center for Computational Neuroscience, located at the Max Planck Institute for Dynamics and Self-Organisation at Göttingen. In summer 2007, Fabian Theis became working group head of CMB at the Institute of Bioinformatics at the Helmholtz Center Munich. In spring 2009, he became associate Professor for Mathematics in Systems Biology at the Math Department of the TU Munich. 2009-2014 he was a member of the ‘Young Academy’ (founded by the Berlin-Brandenburg Academy of Sciences and Humanities and the German Academy of Natural Scientists Leopoldina) and was awarded an ERC starting grant in 2010. In 2017 he was awarded the Erwin Schrödinger prize together within an interdisciplinary team at the ETH Zürich. Fabian Theis is part of and also coordinates various consortia (i.e. sparse2big involving 8 Helmholtz Centers) and founded the network SingleCellOmics Germany (SCOG). Furthermore, he coordinates 2019 launched Munich School for Data Science (MUDS) and is co-directing the ELLIS Munich Unit, the local hub of the European Machine Learning network ELLIS. Since 2020, he holds the position of co-chair of the Bavarian AI Council of the Bavarian Ministry for Science and Art and supports the TUM with his expertise as start-up Ambassador.
Azin Asgarian is currently an applied research scientist on Georgian’s R&D team where she works with companies to help adopt applied research techniques to overcome business challenges. Azin holds a Master of Science in Computer Science from University of Toronto and a Bachelor of Computer Science from University of Tehran. Prior to joining Georgian, Azin was a research assistant at the University of Toronto and part of the Computer Vision Group where she was working on the intersection of Machine Learning, Transfer Learning, and Computer Vision. Due to her interest in HealthCare, she has worked on various healthcare projects as a research assistant at University Health Network (UHN).
Joaquin Vanschoren is Assistant Professor in Machine Learning at the Eindhoven University of Technology. His research focuses on machine learning, meta-learning, and understanding and automating learning. He founded and leads OpenML.org, an open science platform for machine learning. He received several demo and open data awards, has been tutorial speaker at NeurIPS and ECMLPKDD, and invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NIPS, and many other occasions. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and meta-learning workshop series at NIPS and ICML. He is also co-editor of the book ’Automatic Machine Learning: Methods, Systems, Challenges’.
Tutorial on Automated Machine Learning(Workshop)
Franziska Kirschner is the Research and Product Lead of Car Inspection at Tractable. Her team uses machine learning to automate car damage appraisal across a range of applications. Her research interests include domain adaptation, and multitask- and multi-instance learning. In a previous life, she did a PhD in Physics at the University of Oxford. In her spare time, she enjoys cooking and making bad puns.
Radovan Kavicky is Macroeconomist (academically) and Data Science Consultant (professionally). He is President and Principal Data Scientist at GapData Institute and the founder of PyData Bratislava, R <- Slovakia, and skczTUG (SK & CZ Tableau User Group). Radovan is also the author of Practical Data Science and a mentor in the R for Data Science (#R4DS) online learning community. Find out more about Radovan on LinkedIn. You can also follow him on Twitter at @radovankavicky.
Alejandro is the Director of Machine Learning Engineering at Seldon Technologies, where he leads large scale projects implementing open source and enterprise infrastructure for Machine Learning Orchestration and Explainability. Alejandro is also the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he leads thedevelopment of industry standards on machine learning bias, adversarial attacks and differential privacy. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and has delivered multi-national projects with top tier investment banks, magic circle law firms and global insurance companies. He has a strong track record building departments of machine learning engineers from scratch, and leading the delivery of large-scale machine learning system across the financial, insurance, legal, transport, manufacturing and construction sectors (in Europe, US and Latin America).
Mikhail is a Research Staff Member at IBM Research and MIT-IBM Watson AI Lab in Cambridge, Massachusetts. His research interests are Model fusion and federated learning; Algorithmic fairness; Applications of optimal transport in machine learning; Bayesian (nonparametric) modeling and inference. Before joining IBM, he completed Ph.D. in Statistics at the University of Michigan, where he worked with Long Nguyen. He received his bachelor’s degree in applied mathematics and physics from the Moscow Institute of Physics and Technology.
At Scaleout, we are solving the data access challenge in AI. We are developing a world leading solution for federated learning. In federated learning, you distribute the training of machine learning models to the data. You avoid collecting all data in one place.
Daniel is the CEO and co-founder of Scaleout and has a long background as an entrepreneur and leader in deep tech companies. He co-founded Scandinavia’s first personal DNA-testing company in 2008, was CTO at a multinational growing medtech company for 7 years and then co-founded the first international accelerator for blockchain startups. As CTO and CEO, he has many years of experience in leading deep tech projects and taking them to market.
Jaime Buelta has been a professional programmer since 2002 and a full-time Python developer since 2010. He has developed software for a variety of fields, focusing, in the last 10 years, on developing web services in Python in the gaming and finance industries. He is a strong proponent of automating everything to make computers do most of the heavy lifting, so humans can focus on the important stuff. He published his first book, “Python Automation Cookbook”, in 2018 (now updated recently with an extended second edition), followed by “Hands-On Docker for Microservices with Python” the following year. He is currently working as Software Architect in Double Yard in Dublin, Ireland, and is a regular speaker at PyCon Ireland.
I am Laurens, Machine Learning Engineer at Dataworkz. I have a background in Artificial Intelligence and worked for several years as lead data scientist (at ProRail). I like to be on the edge where business meets tech, where AI and machine learning make an impact. Currently I am working as a data engineer / data architect at the Port of Rotterdam, via Dataworkz. I am intrigued by the creative side of artificial intelligence; can a machine show symptoms of creativity or can it only reproduce what it has seen before? How is that different from what we can do? The subject we are talking about is inspired by David’s love for music and my urge to do cool stuff in the field of data science.
I’m David, I work as a teacher, teaching artificial intelligence, at the applied university of Utrecht. Before I started teaching I’ve worked as a prototyping developer for AI research at Philips Research developing intelligent agents using machine learning and reinforcement learning for consumer healthcare products. It was very interesting but my need for more social engagement has driven me towards teaching. A step that has made me a very happy person so far. I love how artificial intelligence is like a lens through which we can see the world. Whether the models we make of the world are realistic or not – do brains really work like a neural network? Not really – they are still inspiring and often usefull. It’s just fun to try and recreate everything. There is only one thing that I probably like even more and that’s making music. I’ve been playing piano and guitar since the age of 7 and it is a big part of who I am. Later on in life I’ve gotten more interested in the electronic world of music too and now have arrived at a point where I can combine my two favorite things. Create instruments using AI.
Alex Honchar is a tech entrepreneur and educator. Currently, he is co-founder and ML director at Neurons Lab – a consulting firm specializing in healthcare, finance, and IoT. Also, he writes a popular blog on Medium about machine learning applications and leadership. Previously he worked as an independent consultant with SMBs and startups on rapid go-to-market ML solutions and taught machine learning courses at the University of Verona and Ukrainian Catholic University.
Karol Przystalski obtained a PhD degree in Computer Science in 2015 at the Jagiellonian University in Cracow. He is the CTO and founder of Codete where he’s leading and mentoring teams as they work with Fortune 500 companies on data science projects. He also built a research lab for machine learning methods and big data solutions at Codete. Karol gives speeches and trainings in data science with a focus on applied machine learning in German, Polish, and English.