
ODSC Europe 2023 registration opens very soon. The Conference will be held on June 14-15th, 2023
Training and Workshops
Hybrid Sessions
Speakers
Hours of Content
In-Person and Virtual Attendees
RECONNECT @ ODSC Europe
You’re In Good Company
ODSC Europe will be as inclusive as we have demonstrated before — from in-person sessions to digital experiences available to everyone, from anywhere. Get ready for the hybrid ODSC Europe Conference. Combining small immersive in-person sessions and hands-on training with innovative and insightful virtual ones — it’s going to be one fantastic event to reconnect, all done with your safety in mind.
ODSC Europe 2022 Registration
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Diversity and Inclusion Scholarship
The data science and the 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 would be open soon.
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.
Participate at ODSC Europe 2023
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.
EVENT SUMMARY
Pre-conference Training: Start learning now with live and on-demand bootcamp warmup sessions.
3 Days of Data Science Training
– Taught by World-Class Data Scientists –
2 Days of Keynotes, Talks, and Workshops
– Presented by the best in the field of data science –
Europe Featured Past Speakers

Michael Bronstein, PhD
Michael Bronstein is the DeepMind Professor of AI at the University of Oxford and Head of Graph Learning Research at Twitter. He was previously a professor at Imperial College London and held visiting appointments at Stanford, MIT, and Harvard, and has also been affiliated with three Institutes for Advanced Study (at TUM as a Rudolf Diesel Fellow (2017-2019), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton as a short-time scholar (2020)). Michael received his PhD from the Technion in 2007. He is the recipient of the Royal Society Wolfson Research Merit Award, Royal Academy of Engineering Silver Medal, five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, BCS, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019).
Physics-inspired Learning on Graph(Keynote)

Luis Vargas, PhD
Luis Vargas is a Partner Technical Advisor to the CTO of Microsoft. Responsible for Microsoft’s AI at Scale initiative coordinating efforts across infrastructure, systems software, models, and products. He bootstrapped the productization of Automated ML and Reinforcement Learning in the Azure AI Platform, worked on the launch of Azure Database Services, and lead the high-availability area for SQL Server. Luis has a PhD in Computer Science from Cambridge University.
The Big Wave of AI at Scale(Keynote)

Ken Jee
As the Head of Data Science at Scouts Consulting Group, Ken spends his workdays improving the performance of athletes and teams by analyzing the data collected on them. He also dabbles in entrepreneurship and content creation, best known for his YouTube channel where he helps over 80,000 people navigate the data science landscape. More recently, Ken is focused on project-based learning through Kaggle. He hopes to share the processes that data scientists take when approaching Kaggle competitions and new datasets. He started the #66DaysOfData challenge to help people create the habit of learning and working on projects every day.
Bridging the Gap Between Data Scientists and Decision Makers(Keynote)

Gaël Varoquaux, PhD
Gaël Varoquaux is a research director working on data science and health at Inria (French Computer Science National research). His research focuses on using data and machine learning for scientific inference, with applications to health and social science, as well as developing tools that make it easier for non-specialists to use machine learning. He has long applied it to brain-imaging data to understand cognition. Years before the NSA, he was hoping to make bleeding-edge data processing available across new fields, and he has been working on a mastermind plan building easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, Mayavi and nilearn, a nominated member of the PSF, and often teaches scientific computing with Python using the scipy lecture notes.
Prediction with Missing Values(Tutorial)

Yonina Eldar
Yonina C. Eldar is a Professor in the Department of Math and Computer Science at the Weizmann Institute of Science, Rehovot, Israel, where she heads the center for Biomedical Engineering and Signal Processing. She is also a Visiting Professor at MIT and at the Broad Institute and an Adjunct Professor at Duke University, and was a Visiting Professor at Stanford University. She is a member of the Israel Academy of Sciences and Humanities, an IEEE Fellow and a EURASIP Fellow. She has received many awards for excellence in research and teaching, including the IEEE Signal Processing Society Technical Achievement Award, the IEEE/AESS Fred Nathanson Memorial Radar Award, the IEEE Kiyo Tomiyasu Award, the Michael Bruno Memorial Award from the Rothschild Foundation, the Weizmann Prize for Exact Sciences, and the Wolf Foundation Krill Prize for Excellence in Scientific Research. She is the Editor in Chief of Foundations and Trends in Signal Processing, and serves the IEEE on several technical and award committees. She heads the Committee for Promoting Gender Fairness in Higher Education Institutions in Israel.

John Shawe-Taylor, PhD
John Shawe-Taylor is professor of Computational Statistics and Machine Learning at University College London and Director of the International Research Centre on Artificial Intelligence (IRCAI) under the auspices of UNESCO at the Jozef Stefan Institute in Slovenia. He has helped to drive a fundamental rebirth in the field of machine learning, with applications in novel domains including computer vision, document classification, and applications in biology and medicine focussed on brain scan, immunity and proteome analysis. He has published over 300 papers and two books that have attracted over 84000 citations.
He has assembled a series of influential European Networks of Excellence. The scientific coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing. More recently he coordinated the X5gon (x5gon.org) European project developing infrastructure and portals for AI enhanced delivery of open educational materials.
Towards Human-Centric Education with Artificial Intelligence(Talk)

Duygu Altinok, PhD
Duygu Altinok is a senior NLP engineer with 12 years of experience in almost all areas of NLP including search engine technology, speech recognition, text analytics and conversational AI. She authored several publications in NLP area at conferences such as LREC and CLNLP. She also enjoys working for open-source projects and a contributor of spaCy library.
Duygu earned her undergraduate degree in Computer Engineering from METU, Ankara in 2010 and later earned her Master’s degree in Mathematics from Bilkent University, Ankara in 2012. She spent 2 years at University of Bonn for her PhD studies. She is currently a senior engineer at Deepgram with a focus on conversational AI and speech technology.
Originally from Istanbul, Duygu currently resides in Berlin, DE with her cute dog Adele.
Sentiment Analysis Tricks with Keras, spaCy and Transformers(Tutorial)

Thomas Wiecki, PhD
Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled some of the best Bayesian modelers out there and founded PyMC Labs — the Bayesian consultancy. He did his PhD at Brown University. Website link: https://www.pymc-labs.io

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.

Jay Alammar
Jay Alammar, Through his popular machine learning blog, Jay has helped millions of engineers visually understand machine learning tools and concepts from the basic (ending up in NumPy, pandas docs) to the cutting-edge (The Illustrated Transformer, BERT, GPT-3).
Large Language Models for Real-World Applications – A Gentle Intro(Talk)

Daria Stepanova, PhD
Daria Stepanova is a lead research scientist at Bosch Center for Artificial Intelligence. Her research interests include knowledge representation and reasoning, machine learning and neuro-symbolic AI. 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 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.
Rule Induction and Reasoning in Knowledge Graphs(Tutorial)

Laurence Moroney
Laurence Moroney leads AI Advocacy at Google, working with the Google AI Research and product development teams. He’s the best-selling author of ‘AI and Machine Learning for Coders,’ as well as the instructor on the Fundamentals of TinyML course at HarvardX, and the popular TensorFlow specializations with deeplearning.ai and Coursera. He’s passionate about empowering software developers to succeed in Machine Learning, democratizing AI as a result. Laurence is based on Washington State in the USA.
A Hands-on Guide to Machine Learning with TensorFlow(Tutorial)

Isaiah Hull, PhD
Isaiah Hull is a senior economist in the research division of Sweden’s Central Bank (Sveriges Riksbank). He holds a PhD in economics from Boston College and conducts research on computational economics, machine learning, and quantum computing. He is also the instructor for DataCamp’s “Introduction to TensorFlow in Python” course and the author of “Machine Learning for Economics in Finance in TensorFlow 2.”
Machine Learning for Economics and Finance in TensorFlow 2(Tutorial)

Nicole Koenigstein
Nicole is a Data Scientist & Quant and Data Engineer currently working at impactvise as Data Science and Technology Lead and at quantmate as Quant. She has over 8 years of experience leading technology projects. She additionally reviews machine learning books and online courses for Manning Publications. Her research interests include time series prediction and natural language processing. She is dedicated to showing others how to succeed in machine learning and is committed to making STEM more attractive to women.
Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment(Talk)

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.
Social Biases in Text Representations and their Mitigation(Talk)

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.

Wade Schulz, MD, PhD
Dr. Schulz is a physician scientist with a background in computational healthcare, molecular biology, and virology. Dr. Schulz has over 20 years’ experience in software development with a focus on enterprise system architecture and has a research interests in the management of large, biomedical data sets and the use of real-world data for predictive modeling. At Yale School of Medicine, he has led the deployment of the organization’s data science infrastructure which consists of a composable computing infrastructure to support the development of biomedical AI applications. Dr. Schulz is also a co-founder of Refactor Health, a digital health startup focused on the development of AI-driven digital signatures and automated healthcare DataOps.

Dieuwke Hupkes, PhD
Dieuwke Hupkes is a research scientist at Facebook AI Research in FAIR. Her work centers around the evaluation of models of natural language processing (NLP), with a specific focus on how such models can show more human-like behaviour, where they fail and what are areas where they should still improved. In the recent past, she has focussed specifically on large language models (LLMs) and neural machine translation (NMT) models.
Evaluating Generalisation in Natural Language Processing Models(Talk)

Carl Osipov
Carl implemented his first neural net in 2000. He is a senior director of the AI / ML practice at Cognizant, focusing on communications, technology, and media customers. Previously he worked on deep learning and machine learning at Google and IBM. Carl is an author of over 20 articles in professional, trade, and academic journals, an inventor with 6 patents at USPTO, and holds 3 corporate awards from IBM for his innovative work. His machine learning book, “MLOps Engineering at Scale” continues to receive reader acclaim. You can find out more about Carl from his blog www.cloudswithcarl.com
Revealing the Inner Self: Automatic Differentiation (Autodiff) Clearly Explained(Workshop)

Dr. Anand Srinivasa Rao
Dr. Anand S. Rao is the Global Artificial Intelligence Leader for PwC. He is also the leader of PwC’s AI and Emerging Technology practice. With over 35 years of industry and consulting experience, Anand leads a team of practitioners who advise C-level executives and implement advanced analytics and AI-based solutions on a variety of strategic, operational, and ethical use cases. With his PhD and research career in Artificial Intelligence and his subsequent experience in management consulting he brings business domain knowledge, software engineer expertise, and statistical expertise to generate unique insights into the practice of ‘data science’.
Prior to joining management consulting, Anand was the Chief Research Scientist at the Australian Artificial Intelligence Institute. He received his PhD from University of Sydney (with a University Postgraduate Research Award-UPRA) in 1988 and an MBA (with Award of Distinction) from Melbourne Business School in 1997. Anand has also co-edited four books on Intelligent Agents and has published over fifty papers in Computer Science and Artificial Intelligence in major journals, conferences, and workshops.
He has received widespread recognition for his extraordinary contributions in the field of consulting and Artificial Intelligence Research. He has received the Most Influential Paper Award for the Decade in 2007 from the Autonomous Agents & Multi-Agent Systems organization for his contribution on the Belief-Desire-Intention Architecture; MBA Award of Distinction from Melbourne Business School, 1997 and University Postgraduate Research Award (UPRA) from University of Sydney, 1985; Distinguished Alumnus Award from Birla Institute of Technology and Science, Pilani, India; He was recognized as one of Top 50 Data & Analytics professionals in USA and Canada by Corinium; one of Top 50 professionals in InsureTech; one of Top 25 Technology Leaders in Consulting; and has won a number of awards for his academic and business papers. Anand is an Adjunct Professor in BITS Pilani’s APPCAIR AI Center. He also serves on the Advisory Board of Oxford University’s Institute for Ethics in AI, World Economic Forum’s Global AI Council, OECD’s Network of Experts on AI (ONE), OECD’s AI Compute initiative, Advisory Board of Northwestern’s MBAi program, Responsible AI Institute, Nordic AI Institute, and International Congress for the Governance of AI. Anand Rao can be contacted on any of the following channels: Linkedin: https://www.linkedin.com/in/anandsrao/ Twitter:@AnandSRao Medium: https://anandsrao.medium.com/ Semantic Scholar: https://www.semanticscholar.org/author/Anand-Srinivasa-Rao/145946928

Julia Ive, PhD
Julia Ive is a Lecturer in Natural Language Processing at Queen Mary University of London, UK. She is the author of many mono- and multimodal text generation approaches in Machine Translation and Summarisation. Currently, she is working on the theoretical aspects of style preservation and privacy-safety in artificial text generation.
Controlled Text Generation with Transformer-based Language Models(Workshop)

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.

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.

Christian Leibig, PhD
Christian Leibig is Director of Machine Learning at Vara, leading the development of methods from research to production. He obtained a Ph.D. in Neural Information Processing from the International Max Planck Research School in Tübingen and a diploma in physics from the University of Konstanz. Before joining Vara, he worked as a Postdoctoral Researcher at the University Clinics in Tübingen on the applicability of Bayesian Deep Learning and machine learning applications for the healthcare space for ZEISS and held research and internship positions with Max Planck, LMU Munich and the Natural and Medical Sciences Institute in Reutlingen. The method and software of his PhD work, an unsupervised solution for neural spike sorting from HDCMOS-MEA data is distributed by Multichannel Systems (Harvard Bioscience). His work on applying and assessing uncertainty methods to large scale medical imaging was among the first in the field and awarded with key note speaker invitations. He enjoys all of theory, software engineering, and people management, in particular for applications that have a meaningful impact, such as diagnosing cancer early.
Towards a Scalable Deployment of AI Models via Uncertainty Quantification(Workshop)

Oliver Zeigermann
Oliver is a software developer from Hamburg Germany and has been a practitioner for more than 3 decades. He specializes in frontend development and machine learning. He is the author of many video courses and textbooks.
Image Recognition with OpenCV and TensorFlow(Training)

Alex Peattie
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.
Hearing is Believing: Generating Realistic Speech with Deep Learning(Workshop)

Julia Lintern
Julia Lintern currently works as an instructor for the Metis Data Science Flex Program. Previously, she worked as a Data Scientist for the New York Times. Julia began her career as a structures engineer designing repairs for damaged aircraft. Julia holds an MA in applied math from Hunter College, where she focused on visualizations of various numerical methods and discovered a deep appreciation for the combination of mathematics and visualizations. During certain seasons of her career, she has also worked on creative side projects such as Lia Lintern, her own fashion label.
Introduction to Machine Learning(Bootcamp)

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. 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
Not Just Deep Fakes: Applications of Visual Generative Models in Pharma Manufacturing(Tutorial)

Alan Rutter
Alan Rutter is the founder of consultancy Fire Plus Algebra and is a specialist in communicating complex subjects through data visualization, writing, and design. He has worked as a journalist, product owner, and trainer for brands and organizations including Guardian Masterclasses, WIRED, Time Out, the Home Office, the Biotechnology and Biological Sciences Research Council, and Liverpool School of Tropical Medicine.

Shawn Kyzer
Shawn is passionate about harnessing the power of data strategy, engineering and analytics in order to help businesses uncover new opportunities. As an innovative technologist with over 13 years experience, Shawn removes technology as a barrier, and broadens the art of the possible for business and product leaders. His holistic view of technology and emphasis on developing and motivating strong engineering talent, with a focus on delivering outcomes whilst minimising outputs, is one of the characteristics which sets him apart from the crowd.
Shawn’s deep technical knowledge includes distributed computing, cloud architecture, data science, machine learning and engineering analytics platforms. He has years of experience working as a consultant practitioner for a variety of prestigious clients ranging from secret clearance level government organizations to Fortune 500 companies.
– Taught by World-Class Data Science Instructors –
In our full-day immersive training sessions and workshops, get hands-on with the latest data science platforms, tools, models, and techniques. Forge a connection with these rock stars from industry and academia who are passionate about teaching data science skills to those who will build the future of the industry.
With over 20 training sessions and workshops over 3 days, ODSC Europe offers unprecedented breadth and depth of instruction.
Hands-On Hybrid Training & Workshops
Data Science Training Conference
Accelerate your data science knowledge, training, and network. All in one event.
ODSC Europe 2023 is one of the largest applied data science conferences. Our speakers include core contributors to many open source libraries and languages. Attend ODSC Europe 2023 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 & Lab | Ai Expo Hall & Demo Talks |
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.
ODSC Newsletter
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