ODSC Europe | June 14-15th, 2023 | In-person and Virtual

Machine Learning & Deep Learning Track

Learn the latest models, advancements, and trends from the top practitioners behind two of data science’s hottest topics
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Machine Learning & Deep Learning

The Machine Learning & Deep Learning Conference track as part of ODSC Europe 2022 is where experts in the rapidly expanding fields of Deep Learning and Machine Learning gather to discuss the latest advances, trends, and models in this exciting field.

Attend talks, tutorials, and workshops and hear from the creators and top practitioners as they demonstrate and teach the latest developments in Machine Learning and Deep Learning that solve problems in business and society today.

What You'll Learn

Topics

  • Machine Learning

  • Deep Learning

  • Artificial Intelligence

  • Neural Networks

  • Natural Language Processing

  • Computer Vision

  • Pattern Recognition

Tools & Languages

  • Python and R

  • Python SciPy, Pandas, etc

  • Scikit-learn

  • Tensorflow

  • Spark

  • Kubernetes and Kubflow

  • and more….

Frameworks

  • Tensorflow & PyTorch

  • Keras and Pylearn2

  • Theano

  • Caffe

  • Torch

  • Azure Machine Learning API

  • and more..

Some of our Featured Machine Learning and Deep Learning Speakers

Research Director | Director, Scikit-learnINRIA

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)

 

CEOPyMC Labs

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

Bayesian Modeling in Industry(Talk)

ProfessorWeizmann Institute of Science

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.

Professor of Computational Statistics and Machine LearningUCL Centre for Artificial Intelligence

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)

Lead Artificial Intelligence AdvocateGoogle

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)

Associate Director - ML/AI, Computer VisionMerck & Co., Inc.

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)

Lead Research ScientistBosch Center for AI

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)

 

Global Artificial Intelligence Lead | AI-Emtech Lead in Labs | Partner in Analytics InsightsPwC

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

Digital Twins: Not All Digital Twins are Identical(Talk)

Professor of Computer Science and Head of the Department of InformaticsKing's College

Luc Moreau, PhD

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”.

Explainability by Design: a Methodology to Support Explanations in Decision-making Systems(Talk)

Co-founder, CTOPeg

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)

 

Head of Artificial IntelligenceOPEN KNOWLEDGE GmbH

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)

 

Data Scientist and Author of Deep Learning with PyTorch Step-by-Step

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.

PyTorch 101: Building a Model Step-by-step(Workshop)

GANs N’ Roses: Understanding Generative Models(Workshop)

Senior Research Associate in Biomedical Data Science and University Research LecturerUniversity of Oxford

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.

Ethnicity, Equity, and AI(Talk)

Director of Machine LearningVara

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)

Data ScientistWhyLabs

Felipe de Pontes

Felipe is a Data Scientist in WhyLabs. He is a core contributor to whylogs, an open-source data logging library, and focuses on writing technical content and expanding the whylogs library in order to make AI more accessible, robust, and responsible. Previously, Felipe was an AI Researcher at WEG, where he researched and deployed Natural Language Processing approaches to extract knowledge from textual information about electric machinery. He is also a Master in Electronic Systems Engineering from UFSC (Universidade Federal de Santa Catarina), with research focused on developing and deploying fault detection strategies based on machine learning for unmanned underwater vehicles. Felipe has published a series of blog articles about MLOps, Monitoring, and Natural Language Processing in publications such as Towards Data Science, Analytics Vidhya, and Google Cloud Community.

Visually Inspecting Data Profiles for Data Distribution Shifts(Workshop)

Lead Data Scientist / Google Developer Expert - MLSIT Academy, Zurich

Dipanjan (DJ) Sarkar

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

Advanced NLP: Deep Learning and Transfer Learning for Natural Language Processing(Workshop)

 

Data and Machine Learning Scientist

Hadrien Jean, PhD

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/).

Introduction to Linear Algebra for Data Science and Machine Learning With Python(Bootcamp)

 

Data Scientist | Bloomberg | Author of Hands-On Data Analysis with Pandas

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.

Beyond the Basics: Data Visualization in Python(Training)

FounderFire Plus Algebra

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.

Communicating Data Insights with Impact(Talk)

VP of LabsThomson Reuters

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.

Head of Data Science Evangelism and MarketingOuterbounds

Hugo Bowne-Anderson, PhD

Hugo Bowne-Anderson is a data scientist, writer, educator & podcaster. His interests include promoting data & AI literacy/fluency, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. He does many of these at DataCamp, a data science training company educating over 3 million learners worldwide through interactive courses on the use of Python, R, SQL, Git, Bash and Spreadsheets in a data science context. He has spearheaded the development of over 25 courses in DataCamp’s Python curriculum, impacting over 170,000 learners worldwide through my own courses. He hosts and produce the data science podcast DataFramed, in which he uses long-format interviews with working data scientists to delve into what actually happens in the space and what impact it can and does have. He earned PhD in Mathematics from the University of New South Wales, Australia and has conducted biomedical research at the Max Planck Institute in Germany and Yale University, New Haven.

Full-stack Machine Learning for Data Scientists(Tutorial)

 

Data Science InstructorMetis

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.

Head of Data Scienceloveholidays

Ben Auffarth, PhD

Ben Auffarth is the head of data science at loveholidays. With a background and Ph.D. in computational and cognitive neuroscience, he has designed and conducted wet lab experiments on cell cultures, analysed experiments with terabytes of data, run brain models on IBM supercomputers with up to 64k cores. More recently, he’s built production systems processing hundreds of thousands of transactions per day, and trained neural networks on millions of text documents. He’s authored two books on machine learning. When he’s not at work, you might find him on a playground with his young son in West London. He co-founded and is the former president of Data Science Speakers, London.

Time-Series in Python – Preprocessing and Machine Learning(Workshop)

Assistant Professor; Director, Center for Computational HealthYale University

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.

Healthcare Predictive Modeling with Graph Networks(Talk)

Senior Software EngineerNVIDIA

Jacob Tomlinson

Jacob Tomlinson is a senior Python software engineer at NVIDIA with a focus on deployment tooling for distributed systems. His work involves maintaining open source projects including RAPIDS and Dask. RAPIDS is a suite of GPU accelerated open source Python tools which mimic APIs from the PyData stack including those of Numpy, Pandas and SciKit-Learn. Dask provides advanced parallelism for analytics with out-of-core computation, lazy evaluation and distributed execution of the PyData stack. He also tinkers with the open source chatbot automation framework Opsdroid in his spare time. Jacob volunteers with the local tech community group Tech Exeter and lives in Exeter, UK.

GPU Development with Python 101(Workshop)

Data Science and Technology Leadimpactvise

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)

Senior Principal Data ScientistRed Hat

Tuhin Sharma

Tuhin Sharma is Senior Principal Data Scientist at Redhat in the Corporate Development and Strategy group. Prior that he worked at Hpersonix as AI Architect. He also co-founded and has been CEO of Binaize, a website conversion intelligence product for e-commerce SMBs. He received master’s degree from Indian Institute of Technology Roorkee in Computer Science with specialization in Data Mining. He received bachelor’s degree from Indian Institute of Engineering Science and Technology Shibpur in Computer Science. He loves to code and collaborate on open source and research projects. He has 4 research papers and 5 patents in the field of AI and NLP. He is reviewer of IEEE MASS conference in the AI track. He writes deep learning articles for O’reilly with the collaboration with AWS MXNET team. He loves to play TT and Guitar in his leisure time. His favorite quote is “Life is Beautiful”.

Eagleeye: Data Pipeline for Anomaly Detection in Cyber Security(Talk)

Research EngineerSeldon

Alex Athorne

Alex Athorne is a Research Engineer at Seldon, where he works on open-source libraries for explainability and drift detection. He studied mathematics at Warwick and went on to do a PhD at Imperial College London in dynamical systems. He’s passionate about open-source development and writing about his experiences in ML.

Open Source Explainability – Understanding Model Decisions Using Alibi(Talk)

Senior Data ScientistAstraZeneca

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)

Machine Learning EngineerHugging Face

Adrin Jalali, PhD

He is a computer scientist / bioinformatician who has turned to be a core developer of `scikit-learn` and `fairlearn`, and work as a Machine Learning Engineer at Hugging Face. He is also an organizer of PyData Berlin.

These days he mostly focus on aspects of machine learning and tools which help with creating more ethical and fair decision making systems. This trend has influenced him to work on `fairlearn`, and to work on aspects of `scikit-learn` which would help tools such as `fairlearn` to work more fluently with the package; and at Hugging Face, his focus is to enable the community of these libraries to be able to share their models more easily and be more open about their work.

How to Write a Scikit-learn Compatible Estimator(Tutorial)

CPO and Head of Customer SuccessMona

Itai Bar-Sinai

With over 10 years of experience (Google, AI-focused startups) with big data and as the CPO and head of customer success at Mona, the leading AI monitoring intelligence company, Itai has a unique view of the AI industry. Working closely with data science and ML teams applying dozens of solutions in over 10 industries, Itai encounters a wide variety of business use-cases, organizational structures and cultures, and technologies used in today’s AI world.

Utilizing Advanced Monitoring Capabilities to Promote Product-oriented Data Science(Tutorial)

Data ScientistFiverr

Gal Naamani

Gal Naamani has been working as a data scientist for 4 years, with the past 3 years being at Fiverr. As the Senior Data Scientist, Gal works closely with developers, analysts, product managers, and business owners on growth opportunities and new ideas, from research to production. Gal currently has leading roles in projects that are focused around search engine ranking, promoted ads, online bidding optimization, exploration-exploitation problems, monitoring, and more.

Utilizing Advanced Monitoring Capabilities to Promote Product-oriented Data Science(Tutorial)

Founder | Author of “Hands-on Image Generation with TensorFlow”Sooner.ai

Soon Yau Cheong, PhD

Soon-Yau Cheong is the founder of Sooner.ai, an AI consulting and training company specialises in image/video generation and manipulation. Past projects include face swapping, portrait cartoonisation, shoes virtual try-on etc. He is well-versed in generative AI techniques which include GANs, autoregressive transformer and diffusion models. He authored the book “Hands-on Image Generation with TensorFlow” which is well-received for its hands-on approach in making difficult mathematical theories easy to understand. Soon-Yau is also currently doing PhD in AI digital media creation at University of Surrey.

Diffusion Models for Text-to-Image Generation(Workshop)

Predictive Analytics and Machine Learning ConsultantIndependent

Keith McCormick

Keith McCormick serves as CloudFactory’s Chief Data Science Advisor. He’s also an author, LinkedIn Learning contributor, university instructor, and conference speaker. Keith has been building predictive analytics models since the late 90s. More recently his focus has shifted to helping organizations build and manage their data science teams.

What Analytics Leaders need to know about EXplainable AI (XAI)(Talk)

Senior Developer Advocate for Tensorflow.jsGoogle

Jason Mayes

Jason is the public face of TensorFlow.js, helping web engineers globally take their first steps with machine learning in JavaScript. He also combines his knowledge of the technical and creative worlds to develop innovative prototypes for Google’s largest customers and internal teams with over 15 years experience working within web engineering and investigating emerging technologies.

He holds an MEng in Computer Science, is a member of the British Computing Society, and is a certified information privacy technologist. Jason loves sharing knowledge online which has attracted a global following. In his spare time he can be found walking the wings of flying aircraft being one of the few people in the world who has been trained in the art of wing walking.

Next Generation Web Apps: Create a Machine Learning Powered Smart Cam in the Browser with TensorFlow.js(Workshop)

Data ScientistSAS Institute

Kyriakos Fistos

Kyriakos is a Data Scientist and works across a broad set of industries contributing to large scale digital transformation projects for the world’s largest organizations. He specializes in data visualization, predictive modelling and model management.

With his academic background in Mathematics and Operational Research, Applied Statistics and Financial Risk, his day-to-day includes helping clients make better business decisions through Data Management, Business Intelligence, Machine Learning and Artificial Intelligence solutions.

Optimizing Your Analytics Life Cycle with Machine Learning and Open Source(Demo Talk)

Senior EconomistResearch Division of Sveriges Riksbank

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)

Director, AI / ML PracticeCognizant

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)

Data ScientistKNIME

Corey Weisinger

Corey studied Mathematics at Michigan State University and works as a Data Scientist with KNIME where he focuses on Time Series Analysis, Forecasting, and Signal Analytics. He is the creator and instructor of the KNIME Time Series Analysis course, author of the e-book: Alteryx to KNIME, creator of the KNIME Time Series Analysis components, and Co-Author of the upcoming Codeless Time Series Analysis Book with Packt.

Moving into the Frequency Domain with the Fourier Transform(Tutorial)

Senior Research EngineerLightning AI

Kaushik Bokka

Kaushik Bokka is a Senior Research Engineer at Lightning AI and one of the core maintainers of the PyTorch Lightning library. He has prior experience in building production scale Machine Learning and Computer Vision systems for several products ranging from Video Analytics to Fashion AI workflows. He has also been a contributor to a few other open-source projects and aims to empower the way people and organizations build AI applications.

Introduction to the PyTorch Lightning Ecosystem(Workshop)

Senior Data ScientistLyst

Seyed Saeid Masoumzadeh, PhD

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.

Building a Fashion Recommender System from Learned Embeddings(Workshop)

ML Product ResearcherSeMI Technologies

Laura Ham

Laura is a ML Product Researcher at SeMI Technologies, the company behind the open-source vector search engine Weaviate. She researches new machine learning features for Weaviate and works on everything UX/DX related to Weaviate. For example, she is responsible for the GraphQL API design. She is in close contact with our open source community. Additionally, she likes to solve custom use cases with Weaviate, and introduces Weaviate to other people by means of Meetups, talks and presentations.

Vector Search – A Gentle Introduction(Talk)

Senior Solutions Consultant Cloudfactory

Matt Beale

Matt Beale is one of CloudFactorys Senior Solutions Consultants. He helps clients around the world overcome their data challenges with AI and ML projects across Autonomous Vehicles, Green Energy and FinTech. Matt joined CloudFactory due his interest in the ethical issue that impact AI and CloudFactorys mission to create meaningful work in the developing world. Away from work Matt has a passion for photography, travelling and unusual cars. In fact his passion for unusual cars bought him to import a Nissan Stagea from Japan to the UK.

Ethics aren’t just for AI algorithms(Talk)

Data ScientistImperva

Adam Reichenthal, PhD

Adam is an experienced Data Scientist at Imperva’s threat research group where he works on creating machine learning algorithms to help protect Imperva’s customers against database attacks. Before joining Imperva, he obtained a PHD in Neuroscience from Ben-Gurion University of the Negev.

ML with Humans: Integrating Experts into the Learning Process(Workshop)

Partner Technical AdvisorMicrosoft

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)

Principal Engineer, Threat ResearchImperva

Ori Nakar

Ori Nakar is a principal cyber-security researcher, a data engineer and a data scientist at Imperva Threat Research group. Ori has a many years experience as a software engineer and engineering manager, focused on cloud technologies and big data infrastructure. At the Threat Research group Ori is responsible for the data infrastructure and involved in analytics projects, machine learning and innovation projects.

How to Tune a Model using Feature Contribution and Simple Analytics(Workshop)

Head of Data Science at Scouts Consulting Group LLC, Z by HP Global Data Science Ambassador

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)

Senior ML EngineerThoughtworks

Meissane Chami

Meissane Chami serves ThoughtWorks, Inc. as a Senior ML Engineer, advising and developing innovative data science and machine learning solutions from proof of concept to production. She has gained expertise setting up innovation frameworks and conducting fast cycle proof of concepts. Her primary areas of expertise are in Natural Language processing, MLOps, DevOps, cloud computing, containerisation and Python. She holds a MSc degree in Machine Learning and Data Science form University College London School of Engineering.

Data Science in the Industry: Continuous Delivery for Machine Learning(Training)

VP of Research and DevelopmentcausaLens

Dr. Andre Franca

Andre joined causaLens from Goldman Sachs, where he was an executive director in the Model Risk Management group in Hong Kong and Frankfurt. Today he is working with industry leading, global organisations to apply cutting edge Causal AI research in production level solutions that empower individuals and teams to make better decisions. Andre received his PhD in theoretical physics from the University of Munich, where he studied the interplay between quantum mechanics and general relativity in black-holes.

From Correlation to Causality in AI(Tutorial)

Assistant Professor, Computer Science | DS3 LabETH Zurich

Ce Zhang, PhD

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.

Building Machine Learning Systems for the Era of Data-centric Ai (Talk)

Professor School of Computer Science and EngineeringHebrew University of Jerusalem

Yair Weiss, PhD

Yair Weiss is a Professor of Computer Science at the Hebrew University and the former Dean of the School of Computer Science and Engineering. His research interests include Machine Learning, Computer Vision and Neural Computation. He served as the program chair of the Neural Information Processing Systems conference (2004) and the European Conference on Computer Vision (2018). From 2004-2019 He was a Senior Fellow of the Canadian Institute for Advanced Research and he is currently a Fellow of the European Laboratory for Learning and Intelligent Systems. With his students and colleagues he has received best paper awards at UAI, NIPS, CVPR and ECCV.

Scientific Discovery and Unsupervised Disentanglement(Talk)

Co-Founder, CTOJumping Rivers

Dr. Colin Gillespie

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.

Managing your Data Science Git Repositories(Training)

Research ScientistSeMI Technologies

Connor Shorten, PhD

Connor Shorten is a Research Scientist at Weaviate, an Open-Source Vector Search Database. Connor has had a role in the development of Ref2Vec, Hybrid Search, Generative Search, Weaviate’s Pipe API, and Re-Ranking. Connor has also hosted 34 episodes of the Weaviate podcast featuring guests from OpenAI, Cohere, You.com, MosaicML, Jina AI, Deepset, Neural Magic and many others! Connor also co-hosts Weaviate meetups in Boston and New York City! Prior to Weaviate, Connor has earned a Ph.D. in Computer Science from Florida Atlantic University. Connor’s Ph.D. was primarily focusing on Data Augmentation in Deep Learning and Applications of Deep Learning for COVID-19. Connor’s publication “A survey on image data augmentation in deep learning” has achieved over 5,000 citations.

Building Recommendation Systems(Workshop)

Data Scientist, Senior ConsultantThoughtworks

Andy Symonds, PhD

Andy Symonds is a technologist passionate about using data science in new and interesting ways. With a background in academia before moving into consulting, he loves problem solving and experimentation. Andy works with clients to help them gain insights and drive business value, by developing proof of concepts and moving these solutions into production.

Data Science in the Industry: Continuous Delivery for Machine Learning(Training)

Data Science ManagerJaguar Land Rover

Alice Grout-Smith

Alice Grout-Smith is a Data Science Manager at Jaguar Land Rover (JLR). Over the past 5 years at JLR she has enjoyed being part of the data science team that has enabled £300m+ of value to the business. After presenting at the Open Data Science Conference back in 2019 on Hierarchical Bayesian Models, Alice and the team have been busy pursuing the exciting applications of causal inference. They have observed that data science in a business context often involves making interventions and taking actions, requiring techniques beyond traditional machine learning. Prior to joining JLR, Alice graduated from the University of Oxford with a degree in Chemistry and a Masters specialising in Quantum Mechanics, which was later published.

Causal Inference for Price Elasticity Modelling(Workshop)

Senior Data ScientistJaguar Land Rover

Jamie Hilton

Jamie Hilton is a Senior Data Scientist at Jaguar Land Rover (JLR) with over 5 years of experience realising business value through data insights. At JLR his work focuses on driving digital transformation with data, helping the business to make the right decisions at the right time. Previously, he led advanced analytics initiatives as Head of Customer Science at Manchester-based e-commerce business THG. He holds a MA in Mathematics from the University of Cambridge.

Jamie is particularly passionate about the application of data science to the automotive and motorsport industries. In 2021, he worked with leading Formula 2 team Virtuosi Racing to deliver a competitive advantage by leveraging their data, having studied Advanced Motorsport Engineering at Cranfield University.

Causal Inference for Price Elasticity Modelling(Workshop)

Data ScientistAdevinta Spain

Didac Fortuny Almiñana, PhD

Didac, PhD in Physics and expert Data Scientist, is part of the Machine Learning team of InfoJobs (the job portal of Adevinta Spain). His work is focused on delivering product solutions based on Artificial Intelligence algorithms, most of them related with NLP. He has also driven several initiatives in Barcelona to leverage the power of data and Machine Learning to social initiatives, for example being a board member of DataForGoodBCN and organising data competitions to tackle problems with a clear social background.

End-to-end CV Parsing Solution Using Transformers(Talk)


Why Attend

Immerse yourself in talks, tutorials, and workshops on Machine Learning and Deep Learning tools, topics, models, and advanced trends

Expand your network and connect with like- minded attendees to discover how Machine Learning and Deep Learning knowledge can transform not only your data models but also your business and career

Meet and connect with the core contributors and top practitioners in the expanding and exciting fields of Machine Learning and Deep Learning

Learn how the rapid rise of intelligent machines is revolutionizing how we make sense of data in the real world and impacting the domains of business, society, healthcare, finance, manufacturing, and more

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Who should attend

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ODSC EUROPE Hybrid Conference 2023 | June 14-15th

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