ODSC Europe | June 15 - 16, 2022 | 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
Register and save 30%

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 Current 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

Thomas Wiecki is the Chief Executive Officer at PyMC Labs (www.pymc-labs.io), the world’s first Bayesian consultancy. Prior to that Thomas was the VP of Data Science at Quantopian, where he used probabilistic programming and machine learning to help build the world’s first crowdsourced hedge fund. He is an author of the popular PyMC3 package — a probabilistic programming framework written in Python. He holds a PhD from Brown University.

The Bayesian Revolution in Online Marketing(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. 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 250 papers and two books that have attracted over 70000 citations.


He has also been instrumental in assembling 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. He was appointed UNESCO Chair of Artificial Intelligence in November 2018 and is the leading trustee of the UK Charity, Knowledge 4 All Foundation, promoting open education and helping to establish a network of AI researchers and practitioners in sub-Saharan Africa.

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 – Business Tech Analysis, ML/AIMSD

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)

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)

 

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)

 

ConsultantSelf-employed

Oliver Zeigermann

Oliver is a software developer and architect from Hamburg, Germany. He has been developing software with different approaches and programming languages for more than 3 decades. Lately, he has been focusing on Machine Learning and its interactions with humans.

How to Teach Our World Knowledge to a Neural Network?(Workshop)

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(Training)

 

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, Software Engineer, Author of Hands-On Data Analysis with PandasBloomberg

Stefanie Molin

Stefanie Molin is a data scientist and software engineer at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around anomaly detection, 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. She is currently pursuing 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)

Director of AIideai.io

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 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.

NLP Fundamentals(Training)

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.

Introduction to Machine Learning (Bootcamp)

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

Bio Coming Soon!

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)

Chief Data Science AdvisorCloudFactory

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.

Computer Perception Challenges in Drone Applications Using Quality Data Annotation(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 EngineerGrid AI | PyTorch Lightning

Kaushik Bokka

Kaushik Bokka is a Senior Research Engineer at Grid.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 for few other open source projects and aims to empower the way people and organizations build AI applications.

Distributed Training with PyTorch Lightning(Workshop)

Senior Data ScientistLyst

Seyed Saeid Masoumzadeh, PhD

Bio Coming Soon!

A Novel Neural Embedding Approach for Fashion Recommender Systems(Workshop)

Data ScientistSeMI 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.

What is Vector Search?(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

Bio Coming Soon!

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


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

More Reasons To Attend?

Download the why attend guide

Who should attend

  • Top speakers and practitioners in Machine Learning and Deep Learning

  • Data Scientists and Data Analysts

  • Decision makers

  • Industry leaders

  • Data Science Enthusiasts

  • Software Developers focused on Machine Learning and Deep Learning

  • Data Science Innovators

  • CEOs, CTOs, CIOs

  • Core contributors in the fields of Machine Learning and Deep Learning

ODSC EUROPE Hybrid Conference 2022 | June 15 - 16th

Register and save 30%

ODSC Newsletter

Stay current with the latest news and updates in open source data science. In addition, we’ll inform you about our many upcoming Virtual and in person events in Boston, NYC, Sao Paulo, San Francisco, and London. And keep a lookout for special discount codes, only available to our newsletter subscribers!


Open Data Science

 

 

 

Open Data Science
One Broadway
Cambridge, MA 02142
info@odsc.com

Menu
Conferences
Extras
Copyright ODSC 2022. All Rights Reserved
Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Youtube
Consent to display content from Youtube
Vimeo
Consent to display content from Vimeo
Google Maps
Consent to display content from Google
Save