Thank you to all our 4,200 Virtual Conference Attendees

A sincere thanks to our attendees, speakers, and partners for making our ODSC Europe Virtual Conference a resounding success.  Some useful links. 

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ODSC West 2020 Virtual Conference
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ODSC APAC 2020 Virtual Conference
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I can’t possibly explain how much I enjoyed the ODSC East Virtual conference. Many of the talks and workshops exceeded my expectations. Thank you very much and well done!

Batool A. | Research Fellow | Liverpool, UK

ODSC East 2020 is the 3rd ODSC event that I have attended. The staff and speakers did a tremendous job approaching the challenge of moving the conference online. The speakers were engaging, the staff attentive, and my team had a wonderful experience this week. Well done! I look forward to attending my next event.

Leslie Walcott | Data Scientist | Chicago

It’s just wonderful to attend this virtual event with so many people over here. Wonderful Initiative by ODSC. Brilliant Content.

Harshit P. | Software Engineering | Accenture

I just want to say a huge thanks to the organizers! I was skeptical about the remote format at first. However, it is an introvert’s dream! It is so much easier to ask questions in chat/slack than it is to raise my hand in a crowded lecture hall

Andras Z.| Lead Data Scientist | Brown University.

ODSC is the best community data science events on the planet. There are other events that cover special topics, or industries, etc., but ODSC is comprehensive and totally community-focused: it’s the conference to engage, to build, to develop, and to learn from the whole data science community.

Kirk Borne | Principal Data Scientist and Executive Advisor at Booz Allen Hamilton

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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.

Training Sessions
Workshops
Speakers
Hours of Content
Attendees

Data Science Virtual Training Conference

Accelerate your data science knowledge, training, and network.  All in one event.

ODSC Europe Virtual Conference 2020 is one of the largest applied data science conferences. Our speakers include core contributors to many open source libraries and languages. Attend ODSC Europe Virtual Conference 2020 and learn the latest AI & data science topics, tools, and languages from some of the best and brightest minds in the field.

Focus Areas

  • 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

  • 200+ Speakers over 4 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

ODSC Europe Main Keynotes

Dr. David Armstrong
Dr. David Armstrong

Researcher, Ernest Rutherford Fellow working on exoplanet detection,

University of Warwick

Dr. Lora Aroyo
Dr. Lora Aroyo

Research Scientist

Google Research, NY

Dr. Iain Brown
Dr. Iain Brown

Head of Data Science, Adjunct Professor, Operational Research Society Member, Author of a book on credit risk modeling

SAS UK | University of Southampton

ODSC Europe Track Keynotes

John Shawe-Taylor, PhD
John Shawe-Taylor, PhD

Director at CSML, Scientific Coordinator of PASCAL: A Framework VI Network of Excellence, Author on Support Vector Machines and Kernel Methods

University College London

Luc De Raedt, PhD
Luc De Raedt, PhD

Director of Leuven.AI, Francqui Chair of AI at the University of Antwerp, EurAI Fellow, AAAI Fellow, IJCAI Trustee and Laureate of the Royal Belgian Academy of Sciences

KU Leuven

Luc Moreau, PhD
Luc Moreau, PhD

Computer Science Professor, Head of Informatics, Editorial Board Member for PeerJ Computer Science, Former Co-chair of the W3C Provenance Working Group

King's College

Steven Huels
Steven Huels

Senior Director of Engineering

Red Hat

Sherard Griffin
Sherard Griffin

Director in AI Center of Excellence

Red Hat

Europe 2020 Presenters

Research ScientistBosch Center for AI

Daria Stepanova, PhD

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.

Rule Induction and Reasoning in Knowledge Graphs(Tutorial)

Head of AIAxel Springer SE

Dat Tran

Dat is heading Axel Springer AI, the artificial intelligence unit of Axel Springer SE which is the largest digital publishing house in Europe. His goal is to make AI more accessible within Axel Springer and hence drive innovation within the group. His ultimate plan is to turn Axel Springer into an AI first company.

Dat’s interests are diverse from traditional machine learning, deep learning, AI in general to computer vision and NLP. He is a regular speaker and has presented at several renowned conferences. He also blogs about his work on Medium.

Training a Machine to See What’s Beautiful (esp. for Hotel Photos)(Talk)

Data ScientistABN AMRO Bank N.V.

Violeta Misheva, PhD

Violeta has been working as a data scientist in the Data Innovation and Analytics department in ABN AMRO bank located in Amsterdam, the Netherlands. In her daily job, she works on projects with different business lines applying the latest machine learning and advanced analytics technologies and algorithms. Before that, she worked for about 1.5 years as a data science consultant in Accenture, the Netherlands. Violeta enjoyed helping clients solve their problems with the use of data and data science but wanted to be able to develop more sophisticated tools, therefore the switch. Before her position at Accenture, she worked on her PhD, which she obtained from Erasmus University, Rotterdam in the area of Applied Microeconometrics. In her research, she used data to investigate the causal effect of negative experiences on human capital, education, problematic behavior and crime commitment.

Explainable ML: Application of Different Approaches(Talk)

Principal Data Scientist, Co-founderPyData London

Ian Ozsvald

Ian is a Chief Data Scientist and has worked in AI and Data Science building teams and high-value IP since 1999. He’s published the 2nd edition of his High-Performance Python book with O’Reilly, speaks and gives keynote talks internationally and co-founded the 11,000 member PyDataLondon community which has delivered 7 years of volunteer-run meetups and conferences to the community

Sprinting Pandas(Talk)

Assistant LecturerTU Dublin

Bojan Božić, PhD

Dr. Bojan Božić is an Assistant Lecturer in Computer Science at TU Dublin and has worked on Semantic Web, Machine Learning, and Data Science technologies since 2010. He was a Software Development Engineer in Speech Recognition at Philips Speech Recognition Systems in Vienna, a Scientist at the Austrian Institute of Technology, an External Lecturer at the University of Applied Sciences Burgenland and University of Vienna, and a Research Fellow at Trinity College Dublin and the Centre for Applied Data Analytics Research at Dublin Institute of Technology. At AIT’s Safety and Security department, he worked in leading roles in European research projects such as SANY (Sensor Web Enablement), TaToo (Tagging Tools for Semantic Discovery), Europeana Creative (Cultural Inheritage), PELAGIOS (Linked Data), and C2-SENSE (Sensor Web and Interoperability). At Trinity College he worked on the H2020 project ALIGNED, modelling data and software engineering processes through ontologies and annotations for the Dacura platform. He joined CeADAR and the Dublin Institute of Technology to commence research in Data Science and approach innovation and collaboration opportunities in new projects and is now lecturing, performing leading research and colaborating on several academic and industrial research projects. His current research interests are Semantic Web, Machine Learning, and Natural Language Processing.

Knowledge Graphs for the Greater Good(Talk)

Data Science Technical LeadJournera

Stephanie Kirmer

Stephanie Kirmer is a Data Science Technical Lead at Journera, an early stage startup that helps companies in the travel industry use data efficiently and securely to create better travel experiences for customers. Previously she worked as a Senior Data Scientist at Uptake, where she developed tools for analyzing diesel engine fuel efficiency, and constructed predictive models for diagnosing and preventing mechanical failure. Before joining Uptake, she worked on data science for social policy research at the University of Chicago and taught sociology and health policy at DePaul University.

Making Happy Modelers: Build and Maintain Your Data Warehouse with AWS Redshift and Airflow(Talk)

Lecturer and Vice-Director of the Data Science InstituteNUI Galway

Dr. Paul Buitelaar

Paul Buitelaar is Senior Lecturer and vice-director of the Data Science Institute (DSI) at NUI Galway where he leads a team in Natural Language Processing. Paul is also co-PI of the Insight Centre for Data Analytics where he lead the Smart Enterprise demonstrator program and co-lead the Multi-modal Data Analysis research program, which brings together interdisciplinary research from across the Insight Centre in close collaboration with industry partners. Paul’s main research interests are in the development and use of Natural Language Processing methods for knowledge extraction and semantic-based information access. He has been involved in a large number of national and international funded projects in this area.

Knowledge Graph Extraction for the Enterprise(Talk)

Assistant ProfessorUniversity of California San Diego

Rose Yu, PhD

Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at the University of Southern California in 2017. She was subsequently a Postdoctoral Fellow at the California Institute of Technology. She was an assistant professor at Northeastern University prior to her appointment at UCSD. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first-principles with data-driven models. Among her awards, she has won Google Faculty Research Award, Adobe Data Science Research Award, NSF CRII Award, Best Dissertation Award in USC, and was nominated as one of the ’MIT Rising Stars in EECS’.

Learning and Mining Large-Scale Spatiotemporal Data(Tutorial)

Co-Founder and CTOXAIN AG

Michael Huth, PhD

Professor Michael Huth (Ph.D.) is Co-Founder and CTO of the technology company XAIN and teaches at Imperial College London. His research focuses on Cybersecurity, Cryptography, Mathematical Modeling, as well as security and privacy in Machine Learning. He served as the technical lead of the Harnessing Economic Value theme at PETRAS IoT Cybersecurity Research Hub in the UK. In 2017, he founded XAIN AG together with Leif-Nissen Lundbæk and Felix Hahmann. The Berlin-based company aims to solve the challenge of combining AI with privacy with an emphasis on Federated Learning. XAIN won the first Porsche Innovation Contest and has already worked successfully with Porsche AG, Daimler AG, Deutsche Bahn, and Siemens.

Professor Huth studied Mathematics at TU Darmstadt and obtained his Ph.D. at Tulane University, New Orleans. He worked at TU Darmstadt, Kansas State University and spent a research sabbatical at The University of Oxford. Huth has authored several scientific publications and is an experienced speaker on international stages.

Federated Learning: AI for the Privacism Movement(Workshop)

Principal Software Engineer, AuthorDomino Data Lab

Dean Wampler, PhD

Dean Wampler is an expert in data engineering for scalable streaming data systems and applications of machine learning and artificial intelligence (ML/AI). He is a Principal Software Engineer at Domino Data Lab. Previously he worked at Anyscale and Lightbend, where he worked on scalable ML with Ray and distributed streaming data systems with Apache Spark, Apache Kafka, Kubernetes, and other tools. Dean is the author of “Programming Scala”, “What Is Ray?”, “Fast Data Architectures for Streaming Applications”, “Functional Programming for Java Developers”, and the coauthor of “Programming Hive”, all from O’Reilly. He is a contributor to several open source projects, a frequent conference speaker. He also co-organizes several conferences around the world and several user groups in Chicago. Dean has a Ph.D. in Physics from the University of Washington. Find Dean on Twitter: @deanwampler.

Hands-on Reinforcement Learning with Ray RLlib(Half-Day Training)

Kirk borne
Principal Data ScientistBooz Allen Hamilton

Dr. Kirk Borne

Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.

Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples(Training)

Atypical Applications of Typical Machine Learning Algorithms(Training)

Phd CandidateUniversity College London

Sonja Tilly, CFA

Sonja Tilly is a phd candidate at UCL. Her research focuses on forecasting macro-economic variables and stock market movements using narrative and emotions from global newspapers.
Sonja has over a decade experience working in asset management, most recently at Quoniam Asset Management, where she contributed to the development of trading strategies using media sentiment. Prior to that, she was a Quantitative Analyst at Hiscox. Sonja is a CFA Charterholder.

Forecasting the Economy with Fifty Shades of Emotions(Talk)

Professor of Computer ScienceHarvard University

Michael Mitzenmacher, PhD

Michael Mitzenmacher is a Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. Michael
has authored or co-authored over 200 conference and journal publications on a variety of topics, including algorithms for the Internet, efficient hash-based data structures, erasure and error-correcting codes, power laws, and compression. He is interested in both algorithms for AI applications, and how predictors from AI systems can yield better algorithms with rigorous performance bounds. He is an ACM Fellow and an IEEE Fellow. He has a widely used textbook on randomized algorithms and probabilistic techniques in computer science published by Cambridge University Press.

Michael Mitzenmacher graduated summa cum laude with a B.A. in mathematics and computer science from Harvard in 1991. After studying
mathematics for a year in Cambridge, England, on the Churchill Scholarship, he obtained his Ph. D. in computer science at U.C. Berkeley in 1996. He then worked at Digital Systems Research Center until joining the Harvard faculty in 1999. He served as the chair for computer science from 2010 to 2013 and co-chair in the 2018-2019 academic year.

Algorithms with Predictions(Tutorial)

Professor of Computer ScienceTrinity College Dublin

Khurshid Ahmad, PhD

Khurshid is the Professor of Computer Science in the School of Statistics and Computer Science, Trinity College Dublin, University of Dublin, Ireland since 2005. He was formerly Professor of Artificial Intelligence at the University of Surrey, UK (1999-2005). He is currently working on spinning out his research in emergency management and decision making as a commercial venture: the product will be a social media monitor for use in emergencies including natural disasters like floods, and health pandemics. Khurshid’s research areas include artificial intelligence, machine learning, social media and networks, fuzzy logic and behavioural finance. He was trained as a nuclear physicist and worked in high-performance computing covering areas such as forecasting, computer-assisted learning, engineering design, and information extraction from continuous information streams comprising texts, images and numbers. His work seeks to maximise the potential of computing systems by enabling these systems to deal with different modalities of human communications, language, vision, and numerical information exchange. Khurshid’s current focus is on the automatic analysis facial expressions and hand gestures of politicians, civil protection officials, and market traders during emergencies. His collaborators have been diverse ranging from National Gallery of Ireland, UK and Irish Police Forces, Bundeskommnado Leipzig, Protezione Civile Veneto, Italy, Siemens AG, Mercedes Benz, AXA Insurance, international banks, and universities across the EU, the USA, and Australia. He has recently completed an EU Framework Programme 7 – Security Area on the use and abuse of social media in disaster emergency (Project Slandail). A book on Social Computing and the Law (Cambridge, 2018), focussing on emergency management, has just been published. In the past by the UK Research Councils (including EPSRC, ESRC, and AHRC) and EU Information Societies Programme have funded his projects. His work on commercialisation of his research on sentiment analysis and on social computing during disasters were funded by Atlantic Bridge Venture Finds and Enterprise Ireland respectively (2018019). He was the founding Head and Professor of Artificial Intelligence of the Department of Computing, University of Surrey, UK (1999-2005); Visiting Professor at the Copenhagen Business School (1998, 2013) and University of Surrey (2005-2008). He has extensive international contacts and has been invited to speak at major conferences across the world. He is currently working on an information system that can find trends in the reporting and blogging about infectious disease. He has published over 250 papers including 6 books; his most recent book is on the topic of Social Computing and the Law (Cambridge) and a five years before he published Affective Computing and Sentiment Analysis: Metaphor, Ontology and Terminology (Springer). He has supervised 45 PhD, 1 MPhil, and 21 MSc students successfully, and is currently supervising 2 PhD students and 6 MSc students. He is an elected Fellow of the British Computer Society and of Trinity College Dublin, and a Chartered Engineer of the UK Engineering Council.

Needles in a Haystack: Big Data and Bigger Promises?(Talk)


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8 Focus Areas. 3 Days. One Data Science Conference.

The largest applied data science conference is now 3 days, including mini-bootcamp days, 3 full training days, and 2 talks/workshops days. You get even more talks, trainings, and workshops spread over 8 focus areas. Accelerate your data science knowledge, training, and network all in one event. Start learning now with our pre-bootcamp live and on-demand training and hackathon.

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Conference Focus Areas

– 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 50 training sessions and workshops over 4 days, ODSC Europe offers an unprecedented breadth and depth of instruction. 

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EVENT SUMMARY

Pre-conference Training & Hackathon

– A real immersive learning experience –

Start learning now with live and on-demand pre-bootcamp sessions.  

3 Days of Data Science Training 

– Taught by World-Class Data Scientists –

Learn the latest data science concepts, tools, and techniques from the best.  

2 Days of Keynotes, Talks, and Workshops 

– Presented by the best in the field of data science –                             

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ODSC Virtual Conference – How It Works

  • Virtual conference experience includes networking lounge area, speaker auditorium, expo halls, and prizes

  • Access multiple livestream tracks on Tuesday, Wednesday, Thursday, or Friday

  • Switch between sessions or tracks as your interests dictate

  • Multiple focus areas including deep learning, machine learning, NLP, research frontiers, AI X for business, and more

  • Sessions you missed can be viewed on demand at your leisure

  • Engage virtually with fellow attendees, speakers, and Expo partners

  • Participate in Q&A sessions with your speaker over live chat

  • Directly download slides and other session materials

  • (Training only) Access training and workshops prerequisites, notebooks, and other materials prior to training session starting

  • (Training only) Access hands-on training and workshops with instructor-led code labs and notebooks.

Superior Content, Training & Networking

Accelerate your data science knowledge with the latest topics, tools, and trends presented by the best practitioners in the field

  • Gain in-demand skills to enhance your data science career or simply become a better data scientist

  • Many topics and formats to choose from, including talks, workshops, and training sessions, all in one single place

  • Learn from the best and brightest in AI and data science to accelerate your learning and insights

  • Learn from the core contributors behind hugely popular tools such as scikit-learn and Caffe

  • Connect with 5,000 fellow data scientists, innovators, practitioners, and influencers

  • Get hired by innovative companies ranging from Fortune 500s to startups at our Data Science Career Fair

  • Meet executives from multiple industries using AI and data science to transform their industries

  • Attend networking events, including Meet the Experts, Demo Talks, Career Lab Talks, Women Ignite Session and more

Bringing Together the Best and Brightest

Awards | Networking Opportunities | Prizes | Virtual Career Expo & Lab | Virtual Expo Hall |

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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!

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