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 and Manchester United, and with large social networks, like Justgiving. His previous experience includes Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through an acquisition and an IPO. He is also the lead instructor at ideai.io, a company specialized in Reinforcement Learning, Deep Learning and Machine Learning training. He is also a contractor for several companies and for the European Commission, as an expert in AI and Machine Learning. As an author he wrote “Hands On Deep Learning” and he authored an online training course for O’Reilly, Introduction to Reinforcement Learning. In the academic world, he also helped set up the PhD center on Interactive Artificial Intelligence and will take part in the Inner Assessment Board to assign funding to Irish research in AI.
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.
Luc Moreau is a Professor of Computer Science and Head of the department of Informatics, at King’s College London. Before joining King’s, Luc was Head of the Web and Internet Science, in the department of Electronics and Computer Science, at the University of Southampton.
Luc was co-chair of the W3C Provenance Working Group, which resulted in four W3C Recommendations and nine W3C Notes, specifying PROV, a conceptual data model for provenance the Web, and its serializations in various Web languages. Previously, he initiated the successful Provenance Challenge series, which saw the involvement of over 20 institutions investigating provenance inter-operability in 3 successive challenges, and which resulted in the specification of the community Open Provenance Model (OPM). Before that, he led the development of provenance technology in the FP6 Provenance project and the Provenance Aware Service Oriented Architecture (PASOA) project.
He is on the editorial board of “PeerJ Computer Science” and previously he was editor-in-chief of the journal “Concurrency and Computation: Practice and Experience” and on the editorial board of “ACM Transactions on Internet Technology”.
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.
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.
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.
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.
Tuana is a Developer Advocate at deepset. She works on improving the developer experience and adoption of deepset’s Open Source NLP framework: Haystack. Originally from Istanbul, she moved to the UK in 2014 where she obtained a Master’s degree in Computer Science from the University of Bristol (in 2018). She initially started her career as a Software Engineer but then decided to become more involved with open source communities and educating people. This led her to developer relations. She worked as a developer advocate at Cumul.io before moving to deepset in 2022.
Dipanjan (DJ) Sarkar is a data science consultant and published author, and was recognized as a Google Developer Expert in Machine Learning by Google in 2019. He currently works as a lead data science consultant at Schaffhausen Institute of Technology Academy, Zurich. Dipanjan has led advanced analytics initiatives working with Fortune 500 companies like Intel, Applied Materials, Red Hat / IBM. He works on leveraging data science, machine learning and deep learning to build large- scale intelligent systems. Dipanjan also works as an independent consultant, mentor and AI advisor in his spare time collaborating with multiple universities, organizations and startups across the globe. His passion includes solving challenging data problems as well as educating and helping people upskill in all things data. Dipanjan has also been recognized as one of the top ten Data Scientists in India in 2020, 40 under 40 Data Scientists, 2021 and Top 50 AI Thought Leaders by Global AI Hub, Switzerland. In his spare time he loves reading, gaming, watching interesting documentaries, football. He is also a strong supporter of open-source and publishes his code and analyses from his books, articles and experience on GitHub at https://github.com/dipanjanS and LinkedIn at https://www.linkedin.com/in/dipanzan
Hadrien Jean is a machine learning scientist working at My Medical Assistent where he is developing deep learning models in the medical domain. He wrote the book Essential Math for Data Science (https://www.essentialmathfordatascience.com/) aimed at helping people to get the math needed in data science from a coding perspective. He previously worked at Ava on speech diarization. He also worked on a bird detection project using deep learning. He completed his Ph.D. in cognitive science at the École Normale Supérieure (Paris, France) on the topic of auditory perceptual learning with a behavioral and electrophysiological approach. He has published a series of blog articles aiming at building intuition on mathematics through code and visualization (https://hadrienj.github.io/posts/).
Seyed Saeid Masoumzadeh is a senior data scientist at Lyst, a world largest fashion search platform. He has extensive experience in researching and developing Machine Learning, Deep Learning and NLP, and delivering them into production. Saeid is also the Co-founder of Cyra, a smart AI-based recruiting assistant, backed by “Entrepreneur First”, an international Talent Investor. Saeid has received his master degree in artificial intelligence and his PhD in computer science from the University of Vienna. He has published several peer-reviewed papers in reputed international journals and conferences.
Brian Lucena is Principal at Numeristical, where he advises companies of all sizes on how to apply modern machine learning techniques to solve real-world problems with data. He is the creator of three Python packages: StructureBoost, ML-Insights, and SplineCalib. In previous roles he has served as Principal Data Scientist at Clover Health, Senior VP of Analytics at PCCI, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.
Ryan Dawson is a technologist passionate about data. Ryan works with clients on large-scale data and AI initiatives, helping organizations get more value from data. His work includes strategies to productionize machine learning, organizing the way data is captured and shared, selecting the right data technologies and optimal team structures, as well as writing the code to make it happen. He has over 15 years of experience and, as well as many widely read articles about MLOps, software design, and delivery. is author of the Thoughtworks Guide to Evaluating MLOps Platforms.
Dr Colin Gillespie is the Co-Founder and CTO of Jumping Rivers. A data science consultancy that specialises in all things R and Python. He is also a Senior Statistics lecturer at Newcastle University, has published over eighty peer-reviewed papers, and co-authored the O’Reilly book, Efficient R programming.
Ce is an Assistant Professor in Computer Science at ETH Zurich. The mission of his research is to make machine learning techniques widely accessible---while being cost-efficient and trustworthy---to everyone who wants to use them to make our world a better place. He believes in a system approach to enabling this goal, and his current research focuses on building next-generation machine learning platforms and systems that are data-centric, human-centric, and declaratively scalable. Before joining ETH, Ce finished his PhD at the University of Wisconsin-Madison and spent another year as a postdoctoral researcher at Stanford, both advised by Christopher Ré. His work has received recognitions such as the SIGMOD Best Paper Award, SIGMOD Research Highlight Award, Google Focused Research Award, an ERC Starting Grant, and has been featured and reported by Science, Nature, the Communications of the ACM, and a various media outlets such as Atlantic, WIRED, Quanta Magazine, etc.
Several of the best minds and biggest names in data science will be presenting
Network with attendees from leading data science companies to learn how others are tackling similar problems
Gain quality training in the hottest data science topics, tools, and languages
Learn the latest in data science from industry leaders without having to make room in the budget — tickets are surprisingly inexpensive
Some of the world’s best data science speakers
The brains and authors behind today’s most popular open data science tools, topics, and languages
Hundreds of attendees focused on data science
Chief Data Scientists
Thought leaders working in data science
Data Scientists and Analysts
CEOs, CTOs, CIOs
Data Visualization professionals
Venture Capitalists and Investors
Startup Founders and Executives
Attendees from Healthcare, Finance, Education, Business, Intelligence, and other industries
Big data and data science innovators