Thanks to all of our attendees, speakers, and partners for joining ODSC Europe. We look forward to seeing you again in 2024!
Training and Workshops
Hybrid Sessions
Speakers
Hours of Content
In-Person and Virtual Attendees
Upcoming ODSC Events
Missed ODSC APAC? Join us at our next conference, ODSC East in Boston, April 23rd – 25th. Can’t make it In-Person? Join us virtually!
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Sign up for updates on ODSC conferences, webinars, events, in-person meetups, and more. You’ll also get complimentary access to one of our on-demand courses on Ai+ Training for 2 weeks.
ODSC Europe is returning to one of the most dynamic and creative venues in London, the Tobacco Dock. Rich with history, the recently restored Tobacco Dock has become an icon of East London and an important part of its thriving enterprise zone.
EXPLORE PAST TRACKS
2 Day Training Conference
Build job-ready skills and stay up-to-date with the latest advances in machine learning, NLP, LLMs, data analytics, responsible AI, and more with ODSC East’s expert-led, immersive, training sessions. With 300 hours of content, the conference features a wide range of sessions for data scientists at every level, from beginner to expert
Virtual Experience
A unique hybrid experience.
At ODSC, we pride ourselves in providing two distinct programs, with almost no overlap, of our in-person and virtual conferences.
On the virtual platform, you’ll find completely different training sessions, workshops, and talks. What’s more, post-conference, on-demand access to virtual sessions means you can get access to two conferences with one pass when you get an in-person Europe Pass, all of which include access to our virtual content.
Registration & Discount Resources
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WHAT TO EXPECT
Please visit our What to Expect page here.
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Please submit your request to receive a Purchase Order HERE.
Diversity and Inclusion Scholarship
The data science and tech communities should strive to be more inclusive and offer opportunities for everyone to learn and grow their career in data science. To help achieve this goal ODSC offers diversity and inclusion scholarship passes to minorities, underrepresented groups, graduate students, and others who express interest in the content ODSC offers. The application is open here.
Group Discounts
If you have a group of 3 to 13 or more, please email us at info@odsc.com to enquire about additional discounts. Please mention the size of your group and the types of passes required.
Volunteer
In exchange for a free pass, ODSC is seeking volunteers to help with our program and event planning. The application is open – apply here!
Donate to our Fundraise
For this year’s event, ODSC will double donations and fundraising to Support of Ukraine. Please support Ukraine, its refugees and help those who stayed fighting for their country. All donations would be sent to Come Back Alive Foundation.
Please donate what you can via our registration. No purchase is necessary to donate and 100% of funds raised are donated.
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Past Speakers and Instructors
Henk Boelman
Henk is a Cloud Advocate specializing in Artificial intelligence and Azure with a background in application development. He is currently part of the AI cloud advocate team and based in the Netherlands. Before joining Microsoft, he was a Microsoft AI MVP and worked as a software developer and architect building lots of AI powered platforms on Azure.
He loves to share his knowledge about topics such as DevOps, Azure and Artificial Intelligence by providing training courses and he is a regular speaker at user groups and international conferences.
Build and Deploy PyTorch models with Azure Machine Learning (Keynote)
Luc De Raedt, PhD
Prof. Dr. Luc De Raedt is currently Director of Leuven.AI, the KU Leuven Institute for AI, full professor of Computer Science at KU Leuven, and guestprofessor at Örebro University (Sweden) at the Center for Applied Autonomous Sensor Systems in the Wallenberg AI, Autonomous Systems and Software Program.
Luc De Raedt obtained his PhD in Computer Science from the KU Leuven (1991), was post-doctoral researcher of the Fund for Scientific Research, Flanders (FWO) (1991-99) and part-time assistant/associate professor (1993-1999) KU Leuven; full professor (C4) and Chair of the Machine Learning and Natural Language Processing Lab at the Albert-Ludwigs-University Freiburg, Germany (1999-2006); head of the Lab for Declarative Languages and Artificial intelligence at KU Leuven from (2015-2019).
Luc De Raedt’s research interests are in Artificial Intelligence, Machine Learning and Data Mining, as well as their applications. He is well known for his contributions in the areas of learning and reasoning, in particular, for his contributions to statistical relational learning, probabilistic and inductive programming. Today he is working on the next generation of programming languages, which can automatically learn from data, on combining probabilistic and logical reasoning and learning, on the automation of (data) science, and on verifying learning artificial intelligence systems and robotics. He is also now also focusing on integrating the probabilistic logics with neural networks and wants to apply these to reinforcement learning as well as program induction.
Marta Kwiatkowska, PhD
Marta Kwiatkowska is Professor of Computing Systems and Fellow of Trinity College, University of Oxford. She is known for fundamental contributions to the theory and practice of model checking for probabilistic systems, and is currently focusing on safety, robustness and fairness of automated decision making in Artificial Intelligence. She led the development of the PRISM model checker (www.prismmodelchecker.org), which has been adopted in diverse fields, including wireless networks, security, robotics, healthcare and DNA computing, with genuine flaws found and corrected in real-world protocols. Her research has been supported by two ERC Advanced Grants, VERIWARE and FUN2MODEL, EPSRC Programme Grant on Mobile Autonomy and EPSRC Prosperity Partnership FAIR. Kwiatkowska won the Royal Society Milner Award, the BCS Lovelace Medal and the Van Wijngaarden Award, and received an honorary doctorate from KTH Royal Institute of Technology in Stockholm. She is a Fellow of the Royal Society, Fellow of ACM and Member of Academia Europea.
Safety and Robustness for Deep Learning with Provable Guarantees(Talk)
Thomas Wiecki, PhD
Thomas Wiecki is co-creator of PyMC, the industry-standard tool for statistical data science in Python. To help businesses solve advanced analytical problems he founded PyMC Labs (www.pymc-labs.io) consisting of world-class experts in Bayesian modeling.
Bayesian Marketing Science: Solving Marketing’s 3 Biggest Problems(Track Keynote)
Elisa Fromont
Elisa Fromont is a full professor at Université de Rennes France, since 2017 and a Junior member of the Institut Universitaire de France (IUF). She works at IRISA research institute in the INRIA LACODAM (“Large Scale Collaborative Data Mining”) team. From 2008 until 2017, she was associate professor at Université Jean Monnet in Saint-Etienne, France. She worked at the Hubert Curien research institute in the Data Intelligence team. Elisa received her Research Habilitation (HDR) in December 2015 from the University of Saint-Etienne. Her research interests lie in (explainable) machine learning, data mining and, in particular, time series analysis.
Explainable Time Series Classification (Tutorial)
Brent Mittelstadt, PhD
Professor Brent Mittelstadt is an Associate Professor, Senior Research Fellow, and Director of Research at the Oxford Internet Institute, University of Oxford. He leads the Governance of Emerging Technologies (GET) research programme which works across ethics, law, and emerging information technologies. He is a prominent data ethicist and philosopher specializing in AI ethics, algorithmic fairness and explainability, and technology law and policy. Prof. Mittelstadt is the author of foundational works addressing the ethics of algorithms, AI, and Big Data; fairness, accountability, and transparency in machine learning; data protection and non-discrimination law; group privacy; ethical auditing of automated systems; and digital epidemiology and public health ethics. His contributions in these areas are widely cited and have been implemented by researchers, policy-makers, and companies internationally, featuring in policy proposals and guidelines from the UK government, Information Commissioner’s Office, and European Commission, as well as products from Google, Amazon, and Microsoft.
The Unfairness of Fair Machine Learning: Levelling Down and Strict Egalitarianism by Default(Talk)
Dr Paul A. Bilokon
Bio Coming Soon!
Iterated and Exponentially Weighted Moving Principal Component Analysis(Talk)
Dr. Gözde Gül Şahin
Dr. Gözde Gül Şahin is an Assistant Prof. at Koç University and a KUIS AI Fellow since February 2022. Previously, she was a postdoctoral researcher in the Ubiquitous Knowledge Processing (UKP) Lab at the Technical University of Darmstadt, Germany. Her research spans the fields of linguistics and machine learning, in particular semantics, multilingual representations and large language models. She completed her PhD studies in Istanbul Technical University (İTÜ) Computer Engineering department in 2018. She was a visiting researcher at the Institute for Language, Cognition and Computation (ILCC) of the University of Edinburgh in 2017. Before her Ph.D., she received her Masters and Bachelor degrees from Sabancı University in 2011 and İTÜ in 2009, respectively. She regularly serves as a PC member for *ACL conferences and is a co-organizer for the Workshop on Multilingual Representation Learning (MRL). Her research on NLP has been funded by Tübitak 2232, and 2236 grant programs that are granted to outstanding young principal investigators.
Semantic Analysis and Procedural Language Understanding in the Era of Large Language Models(Talk)
Dr. Yves J. Hilpisch
Dr. Yves J. Hilpisch is founder and CEO of The Python Quants (http://tpq.io), a group focusing on the use of open source technologies for financial data science, artificial intelligence, algorithmic trading, and computational finance. He is also founder and CEO of The AI Machine (http://aimachine.io), a company focused on AI-powered algorithmic trading based on a proprietary strategy execution platform.
Yves has a Diploma in Business Administration, a Ph.D. in Mathematical Finance and is Adjunct Professor for Computational Finance at Miami Herbert Business School.
Sandra Wachter, PhD
Professor Sandra Wachter is Professor of Technology and Regulation at the Oxford Internet Institute at the University of Oxford where she researches the legal and ethical implications of AI, Big Data, and robotics as well as Internet and platform regulation. At the OII, Professor Sandra Wachter leads and 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 an affiliate and member at numerous institutions, such as the Berkman Klein Center for Internet & Society at Harvard University, World Economic Forum’s Global Futures Council on Values, Ethics and Innovation, the European Commission’s Expert Group on Autonomous Cars, the Law Committee of the IEEE, the World Bank’s Task Force on Access to Justice and Technology, the United Kingdom Police Ethics Guidance Group, the British Standards Institution, the Bonavero Institute of Human Rights at Oxford’s Law Faculty and the Oxford Martin School. Professor Wachter also serves as a policy advisor for governments, companies, and NGO’s around the world on regulatory and ethical questions concerning emerging technologies.
Isaac Reyes
Isaac Reyes is a TEDx speaker, data scientist and international keynote presenter in data analytics, data visualization and data presentation. In 2018, his “Art of Data Storytelling” speaking tour visited 23 cities across 5 continents, impacting over 15,000 people with Data Storytelling skills. He is the Co-founder of StoryIQ, a data visualization training company with full-time speakers in New York City, Manila and Singapore. In previous roles, he was the Head of Data Science at Altis Consulting and lectured in statistical theory at the Australian National University. A participant experience focused trainer, he was a keynote speaker at the 2019 Open Data Science Conference in Brazil.
Alan Rutter
Alan Rutter is the founder of consultancy Fire Plus Algebra, and is a specialist in communicating complex subjects through data visualisation, writing and design. He has worked as a journalist, product owner and trainer for brands and organisations including Guardian Masterclasses, WIRED, Riskified,the Home Office, the Biotechnology and Biological Sciences Research Council and Liverpool School of Tropical Medicine.
Guglielmo Iozzia
Guglielmo is a Biomedical Engineer with an extensive background in Software Engineering and Data Science applied to different contexts, such as Biotech Manufacturing, Healthcare and DevOps, just to mention the latest, and a lifelong learner. As part of the Manufacturing IT Advanced Mathematics and Modelling Data Science Team he is currently busy unlocking business value through Deep Learning projects, mostly in Computer Vision (not restricted to this field by the way). He 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.
He is also an international speaker and author of the following book: Hands-on Deep Learning with Apache Spark @Packt https://www.packtpub.com/big-data-and-business-intelligence/hands-deep-learning-apache-spark
Sara Khalid
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.
Me, my Health, and AI: Applications in Medical Diagnostics and Prognostics(Talk)
Piotr Mirowski, PhD
Dr. Piotr Mirowski is a Staff Research Scientist at DeepMind. His research on artificial intelligence covers the subjects of reinforcement learning, navigation, weather and climate forecasting, as well as a socio-technical systems approach to human-machine collaboration and to computational creativity. He is the author of over 60 papers that have been published in Nature, Genome Biology, Clinical Neurophysiology or at ICLR, AAAI and NeurIPS. Piotr studied computer science in France at ENSEEIHT Toulouse and obtained his PhD in computer science in 2011 at New York University, with a thesis supervised by Prof. Yann LeCun (Outstanding Dissertation Award, 2011). A trained actor himself, Piotr founded and directs Improbotics, a theatre company where human actors and robots improvise live comedy performances and investigate the use of AI for artistic human and machine-based co-creation. https://piotrmirowski.com
Oliver Zeigermann
Oliver Zeigermann has been developing software with different approaches and programming languages for more than 3 decades. In the past decade, he has been focusing on Machine Learning and its interactions with humans.
MLOps: Monitoring and Managing Drift(Training)
Matthias Seeger, PhD
Matthias W. Seeger is a principal applied scientist at Amazon. He received a Ph.D. from the School of Informatics, Edinburgh university, UK, in 2003 (advisor Christopher Williams). He was a research fellow with Michael Jordan and Peter Bartlett, University of California at Berkeley, from 2003, and with Bernhard Schoelkopf, Max Planck Institute for Intelligent Systems, Tuebingen, Germany, from 2005. He led a research group at the University of Saarbruecken, Germany, from 2008, and was assistant professor at the Ecole Polytechnique Federale de Lausanne from fall 2010. He joined Amazon as machine learning scientist in 2014. He received the ICML Test of Time Award in 2020.
His interests center around Bayesian learning and decision making with probabilistic models, from gaining understanding to making it work in large scale practice. He has been working on theory and practice of Gaussian processes and Bayesian optimization, scalable variational approximate inference algorithms, Bayesian compressed sensing, and active learning for medical imaging. More recently, he worked on demand forecasting, hyperparameter tuning (Bayesian optimization) applied to deep learning (NLP), and AutoML.
Distributed Hyperparameter Tuning: Finding the Right Model can be Fast and Fun(Tutorial)
Stefanie Molin
Stefanie Molin is a software engineer and data scientist at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science, as well as a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.
Heiko Hotz
Heiko Hotz is a Senior Solutions Architect for AI & Machine Learning at AWS with a special focus on Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI. He is also the founder of the NLP London Meetup group, bringing together NLP enthusiasts and industry experts.
Implementing Generative AI in Organisations: Challenges and Opportunities(Tutorial)
Sofie Van Landeghem, PhD
Sofie is a machine learning and NLP engineer who firmly believes in the power of data to transform decision making in industry. She has a Master in Computer Science (software engineering) and a PhD in Sciences (Bioinformatics), and more than 16 years of experience in Natural Language Processing and Machine Learning, including in the pharmaceutical industry and the food industry. In 2019, she joined Explosion to work on the open-source NLP library spaCy. She is currently leading the open-source team developing and maintaining spaCy, as well as various other open-source developer tools for data scientists.
spaCy: a customizable NLP toolkit designed for developers(Talk)
Leonardo De Marchi
Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks. He now works in Thomson Reuters as VP of Labs, and also provides consultancy and training for small and large companies. His previous experience includes being Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through acquisition and an IPO.
Generative AI(Training)
Leonidas Souliotis, PhD
Leonidas (Leo) is a Senior Data Scientist at Astrazeneca. His work is focused around machine learning in oncology, including clinical and non clinical applications. He is also enthusiastic about NLP applications in oncology and how this can be used to leverage patient treatment. He is also a workshop facilitator in the European Leadership University (ELU), NL and has also been a data science educator at DataCamp. He holds a PhD from the University of Warwick, UK. in bioinformatics and ML, an MSc in statistics from Imperial College London, UK and a BSc in Statistics and Insurance Science from the University of Piraeus, GR.
Introduction to Python for Data Analysis(Bootcamp)
Danushka Bollegala, PhD
Danushka Bollegala is a Professor in the Department of Computer Science, University of Liverpool, UK. He obtained his PhD from the University of Tokyo in 2009 and worked as an Assistant Professor before moving to the UK. He has worked on various problems related to Natural Language Processing and Machine Learning. He has received numerous awards for his research excellence such as the IEEE Young Author Award, best paper awards at GECCO and PRICAI. His research has been supported by various research council and industrial grants such as EU, DSTL, Innovate UK, JSPS, Google and MSRA. He is an Amazon Scholar.
Towards Socially Unbiased Generative Artificial Intelligence(Talk)
Laura Skylaki, PhD
Laura Skylaki is a Manager of Applied Research in Thomson Reuters Labs, where she leads advanced machine learning projects in the domain of Legal and Tax AI.With a career spanning more than a decade at the intersection of research and practical application, she has contributed technical expertise in diverse fields such as bioinformatics and stem cell biology, image processing and natural language processing. She holds a doctorate in stem cell bioinformatics from the University of Edinburgh, UK, and has been publishing on machine learning applications in leading academic journals since 2012.
NLP Fundamentals(Training)
Daniel Voigt Godoy
Daniel has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, for more than three years, helping more than 150 students advance their careers. He writes regularly for Towards Data Science. His blog post “Understanding PyTorch with an example: a step-by-step tutorial” reached more than 220,000 views since it was published. The positive feedback from the readers motivated him to write the book Deep Learning with PyTorch Step-by-Step, which covers a broader range of topics. Daniel is also the main contributor of two python packages: HandySpark and DeepReplay. His professional background includes 20 years of experience working for companies in several industries: banking, government, fintech, retail and mobility.
Diffusion Models 101(Workshop)
Data Science Training Conference
Accelerate your data science knowledge, training, and network. All in one event.
ODSC Europe 2023 was one of the largest applied data science conferences. Our speakers include core contributors to many open-source libraries and languages. Attend the next ODSC Europe 2024 and learn the latest AI & data science topics, tools, and languages from some of the best and brightest minds in the field.
Tracks
Mini-Bootcamp
Open Data Science
Deep Learning
Machine Learning
Data Science Research
AI Business Track
Data Science Kick-start
Hands-on Training
Data Visualization
Quant Finance
AI for Good
Topics
Recommendation Systems
Transfer Learning
Machine Vision
Autonomous Machines
Conversational AI
Artificial Intelligence
Speech Recognition
Unsupervised Learning
Image Classification
Machine Translation
100+ Speakers over 3 days
Tools
Tensorflow, Keras, PyTorch, Caffe, MXNet
Scikit-learn, Theano, Shogun, Pylearn2
Python, Jupyter Notebooks
R programming, Julia, Scala, Stan
Apache Spark, MLlib, Streaming
Azure ML, Amazon ML,H20.ai, Cloud ML
Neo4J, D3.js, R-Shiny
Hadoop, Apache Storm, Apache Flink, Kafka, Druid
SAS Viya
At one of London’s most unique and exciting venues
Bringing Together the Best and Brightest
Awards | Networking Opportunities | Prizes | Virtual Career Expo | Ai Expo Hall & Demo Talks |
Companies Represented at ODSC
We’re Proud to Have Their Best and Brightest in Attendance
Past ODSC Europe Partners
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Partner with ODSC
Last year, ODSC welcomed nearly 20,000 attendees to an unparalleled range of events, from large conferences to hackathons and small community gatherings.
Participate at ODSC Europe 2024
As part of the global data science community we value inclusivity, diversity, and fairness in the pursuit of knowledge and learning. We seek to deliver a conference agenda, speaker program, and attendee participation that moves the global data science community forward with these shared goals. Learn more on our code of conduct, speaker submissions, or speaker committee pages.
ODSC Newsletter
Stay current with the latest news and updates in open source data science. In addition, we’ll inform you about our many upcoming Virtual and in person events in Boston, NYC, Sao Paulo, San Francisco, and London. And keep a lookout for special discount codes, only available to our newsletter subscribers!