Training & Workshop Sessions
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Confirmed Sessions & Instructors
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Track Keynote
Data science pipelines are increasingly used to build new services to the general public. Significant concerns about privacy, fairness, bias, and ethics have been raised and are now well documented. Increasingly, service providers and data controllers are under the obligation of providing explanations about the data they process.
Meanwhile, a decade of research on provenance, a standardization of provenance at the World Wide Web Consortium (PROV), and applications, toolkits and services adopting provenance have led to the recognition that provenance is a critical facet of good data governance for businesses, governments, and organizations in general. Provenance, which is defined as a record that describes the people, institutions, entities, and activities involved in producing, influencing, or delivering a piece of data or a thing, turns out to be a solid foundation to construct explanations about data science pipelines...more details
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. [For further details, please see https://nms.kcl.ac.uk/luc.moreau/about.html]
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”.
Half-Day Training | Machine Learning | MLOps & Data Engineering | Intermediate-Advanced
In this half-day training session, we will explore how Active (human-in-the-loop) and Semi-Supervised (ML/AI-assisted) Learning frameworks can be combined to develop in-house solutions for executing rapid data labelling projects. We will consider various sampling strategies, query methods, measures of informativeness, and types of learners. By the end of the session, you will be equipped with a multitude of tools that you can utilise to scale up your data annotation efforts without losing all-important context…more details
Gokhan is a senior data scientist at Attest. He is also a member of the Quanteda Initiative, and a guest lecturer for the LSE summer school course Introduction to Data Science and Machine Learning. As a computational social scientist, his core expertise lies in latent variable analysis, predictive modelling, and causal inference. Prior to industry, he was a postdoctoral researcher in analytic software development at the London School of Economics, where he received his PhD. Previously, he held research positions at UCL and Uppsala University, primarily developing machine learning pipelines and working on large-scale NLP problems.
Half-Day Training | MLOps & Data Engineering | Beginner-Intermediate
In this workshop, we show how to maintain data science productivity as well as collaborate effectively and deliver value continuously and seamlessly. We demonstrate and guide the participants through CI/CD practices for machine learning and a new pattern of working that avoids most of the pitfalls of the typical approach. Participants will learn how to utilize new patterns of repeatable continuous model development to collaborate effectively and deliver value continuously and seamlessly in industrial data science projects using Continuous Integration (CI) and Continuous Delivery (CD) practices…more details
Christoph Windheuser is the Global Head of Artificial Intelligence at ThoughtWorks Inc. Before joining ThoughtWorks, he gained more than 20 years of experience in the industry in several positions at SAP and Capgemini. Prior to that, he completed his Ph.D. in Neural Networks with a focus on Speech Recognition at the University of Bonn, Germany, Carnegie Mellon University in Pittsburgh, USA, Waseda University in Tokyo, Japan, and France Telekom (E.N.S.T.) in Paris, France.
David Johnston is a Principal Data Scientist and founding data scientist of the ThoughtWorks Data Science & Engineering practice. David has over 25 years of experience working with data, data processing pipelines, algorithms, optimization and statistical and machine learning models. David has a Ph.D. in physics and worked previously as a researcher at top universities, NASA and US government labs in the field of cosmology. Since leaving academia he has specialized in helping clients apply these techniques in their business environments with a focus on end-to-end delivery of valuable data-driven products and creating working, maintainable production systems. David is a frequent writer and speaker on data science, artificial intelligence and the importance of applying quality software development best practices toward data science-driven applications.
Eric Nagler serves ThoughtWorks, Inc. as a Lead Data Engineer with eight years of developing innovative batch and real-time data solutions for multiple different clients in multiple different domains. Eric holds a Masters Degree in Computer Science with a focus in Parallel and Distributed Algorithms. Eric’s data interests include Big Data Application Architecture and Design, Parallel Computation, Big Data ETL, NoSQL Data Storage, Natural Language Processing, Geospatial Analytics, Web Services, and Data Visualization.
Half-Day Training | Machine Learning | R-programming | Beginner
Linear regression is the foundation of supervised learning, though it has its limits. During this workshop we extend regression using penalization for automated variable selection and increased flexibility. We then introduce trees, and in particular boosted trees, via xgboost to get incredibly powerful predictions. We will go over some of the theory and also practical considerations such as hyperparameters…more details
Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.
Full-Day Training | Machine Learning | Deep Learning | Advanced
This course introduces the main concepts behind Time Series Analysis, with an emphasis on forecasting applications: data cleaning, missing value imputation, time-based aggregation techniques, creation of a vector/tensor of past values, descriptive analysis, model training (from simple basic models to more complex statistics and machine learning based models), hyperparameter optimization, and model evaluation.
Learn how to implement all these steps using real-world time series datasets. Put what you’ve learnt into practice with the hands-on exercises…more details
Maarit Widmann is a data scientist at KNIME. She started with quantitative sociology and she holds her Bachelors degree in Social Sciences. The University of Konstanz made her drop the “social” as a Master of Science. Her ambition is to communicate concepts behind data science to others in videos and blog posts.
Corey Weisinger is a Data Scientist with KNIME in Austin Texas. He studied Mathematics at Michigan State University focusing on Actuarial Techniques and Functional Analysis. Before coming to work for KNIME he worked as an Analytics Consultant for the Auto Industry in Detroit Michigan. He currently focuses on Signal Processing and Numeric Prediction techniques and is the Author of the Alteryx to KNIME guidebook.
Half-Day Training | Kickstarter | Beginner
Programming is a great way to get practical insights about math theoretical concepts. The goal of this session is to show you that you can start learning the math needed for machine learning and data science using code. You’ll learn about scalars, vectors, matrices, and tensors, and see how to use linear algebra on your data. Don’t worry if you don’t have a math background, we’ll explain the mathematical notations and conventions. At the end of the session, you’ll know how to operate on vectors, matrices, and tensors, use the norm of vectors, and apply the dot product to vectors. You’ll also see more advanced concepts like matrices as linear transformations, linear combinations, basis, and how to use matrices to express systems of equations…more details
Hadrien Jean is a machine learning scientist. He’s currently working on the book “Essential Math for Data Science” with O’Reilly (https://learning.oreilly.com/library/view/essential-math-for/9781098115555/). He previously worked at Ava on speech diarization. He also works 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/).
Full-Day Training | NLP | Deep Learning | Intermediate-Advanced
Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any datascientist in today’s fast-paced world! With a hands-on and interactive approach, we will understand essential concepts in NLP along with extensive hands-on examples to master state-of-the-art tools, techniques and methodologies for actually applying NLP to solve real- world problems. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models…more details
Dipanjan (DJ) Sarkar is a Data Science Lead at Applied Materials, leading advanced analytics efforts around computer vision, natural language processing and deep learning. He is also a Google Developer Expert in Machine Learning. He has consulted and worked with several startups as well as Fortune 500 companies like Intel and Open Source organizations like Red Hat. He primarily works on leveraging data science, machine learning and deep learning to build large- scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. Dipanjan has been an analytics practitioner for several years now, specializing in machine learning, natural language processing, computer vision and deep learning. Having a passion for data science and education, he also acts as an AI Consultant and Mentor at various organizations like Springboard, where he helps people build their skills on areas like Data Science and Machine Learning. Dipanjan is also a published author, having authored several books on R, Python, Machine Learning, Social Media Analytics, Natural Language Processing, and Deep Learning. In his spare time he loves reading, gaming, watching popular sitcoms and football and writing interesting articles on https://medium.com/@dipanzan.sarkar and https://www.linkedin.com/in/dipanzan. He is also a strong supporter of open-source and publishes his code and analyses from his books and articles on GitHub at https://github.com/dipanjanS.
Anuj Gupta is a head the Machine Learning and Data Science teams at Vahan. Prior to this, he was heading ML efforts for Intuit, Huawei Technologies, Freshworks, Chennai and Airwoot, Delhi. He did his masters in theoretical computer science from IIIT Hyderabad and he dropped out of his Phd from IIT Delhi to work with startups.
He is a regular speaker at ML conferences like Pydata, Nvidia forums, Fifth Elephant, Anthill. He has also conducted a bunch of workshop attended by machine learning practitioners. He is also the co-organizer for one of the early Deep Learning meetups in Bangalore. He is also Editor of “”Anthill-2018″” – deep learning focused conference by HasGeek.
Half-Day Training | Open-source | Beginner
This tutorial will introduce you to the wonderful world of Bayesian data science through the lens of probabilistic programming in Python. In the first half of the tutorial, we will introduce the key concepts of probability distributions via hacker statistics, hands-on simulation, and telling stories of the data-generation processes. We will also cover the basics of joint and conditional probability, Bayes’ rule, and Bayesian inference, all through hands-on coding and real-world examples. In the second half of the tutorial, we will use a series of models to build your familiarity with PyMC3, showcasing how to perform the foundational inference tasks of parameter estimation, group comparison (for example, A/B tests and hypothesis testing), and arbitrary curve regression…more details
Hugo Bowne-Anderson is Head of Data Science Evangelism and VP of Marketing at Coiled, a company that makes it simple for organizations to scale their data science and machine learning in Python. He has extensive experience as a data scientist, educator, evangelist, content marketer, and a data strategy consultant at DataCamp, the online education platform for all things data. He also has experience teaching basic to advanced data science topics at institutions such as Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and ODSC and with organizations such as Data Carpentry. He has developed over 30 courses on the DataCamp platform, impacting over 500,000 learners worldwide through his own courses. He also created the weekly data industry podcast DataFramed, which he hosted and produced for 2 years. He is committed to spreading data skills, access to data science tooling, and open-source software, both for individuals and the enterprise.
Full-Day Training | Machine Learning | Beginner-Intermediate
This session is a hands-on introduction to Machine Learning in Python with scikit-learn. You will learn to build and evaluate predictive models on tabular data using the main tools of the Python data-science stack (Jupyter, numpy, pandas, matplotlib and … scikit-learn)…more details
Olivier Grisel is a machine learning engineer at Inria. He is a member of the team of maintainers of the scikit-learn project. Scikit-learn is an Open Source machine learning library written in Python. His work is supported by the Fondation Inria and its partners
Half-Day Training | Data Visualization | | Intermediate
In this session, you’ll learn how to turn any matplotlib plot into an animated gif with just two extra lines of code (well… three, if you count “import gif”)!..more details
Max is a Lead Instructor at General Assembly and an Apress Author. He likes climbing, making pottery, and fantasy sports. This will be his fifth ODSC!
Half-Day Training | R-Programming | Beginner
Should a data scientist use R or should they use Python? The answer to this rather delicate question is, of course, they should know a bit each and use the most appropriate language for the task in hand. In this tutorial, we’ll take participants through the R tidyverse, one of the (many!) areas that R shines. The tidyverse is essential for any data scientist who deals with data on a day-to-day basis. By focusing on small key tasks, the tidyverse suite of packages removes the pain of data manipulation. We’ll cover some of the core features of the tidyverse, such as dplyr (the workhorse of the tidyverse), string manipulation, graphics and the concept of tidy data…more details
Colin Gillespie is the co-founder of Jumping Rivers, a data science & machine learning consultancy. He has been using R since 1999 and Python since 2002.
He’s the author of a number of R packages and has published the book Efficient R Programming with O’Reilly.
Tutorial | NLP | Computer Vision | Advanced
Transformer has been around for a while now, and has proven to be one of the most interesting models of modern deep learning.It turns out that Transformer models have proven to be domain agnostic. It means that, that despite of its initial application for seq2seq NLP tasks with 1D sequences of text, the 1D transformer input can be of any form. Namely, a 2D image unrolled into long 1D sequence of pixels can be understood with notion of its 2D image characteristics involving object appearance, category, or even predicting next image appearance in very long sequences…more details
Dr. Michał Chromiak is a Director at UBS, contributing to text document analytics and leading efforts to democratize AI in financial sector and investigate applications for multiple ML based tasks. Michal is also a member of Department of Intelligent Systems at Maria Curie-Skłodowska University. Past research in integrating big data from distributed and heterogeneous sources, brought him to concentrate on data perception using deep learning. He is interested in improving and understanding ways to generalize the modern deep learning algorithms and finding their best suited AI applications. He is strongly fascinated with understanding how deep learning can be improved to match, and exceed biological forms of intelligence, in terms of performance.
Tutorial | Deep Learning | Intermediate-Advanced
Convolutional Neural Networks (CNNs) are responsible for unprecedented advances in the field of Computer Vision since they have achieved impressive performance in challenging tasks such as image classification, attribute recognition, object detection and segmentation, among others. In this talk, we will take a deep dive of the inner workings of CNNs. We will answer questions like: What are CNNs? What exactly do they compute? Why and when do they work well? How do they learn? We will explore common architectures and we will showcase an application on multi-label attribute recognition...more details
Susana Zoghbi, Co-Founder & CEO, Macty. Susana is a researcher and entrepreneur in a quest to help businesses grow with Artificial Intelligence. She received a PhD in Computer Science and her research focused on cross-modal processing of textual and visual Information. She designed deep neural network architectures and probabilistic graphical models to understand visual and textual content from e-commerce and social media. Her work has been published in top conferences and journals in Artificial Intelligence. She has worked for NASA’s Frontier Development Lab as a Deep Learning Researcher to automatically search for long-period comets that might impact Earth. She has also worked for Microsoft Research in Cambridge, where she focused on machine learning for optimizing environments for large scale software development. Before her PhD, she obtained two Masters degrees, one in Mechanical Engineering from the University of British Columbia, where her research focused on human-robot interaction technologies, and one in Mathematical Physics, where she focused on gravitational fluctuations in Domain Wall Spacetimes. In 2014, she was granted a Google Anita Borg award for her contributions in Computer Science and her community.
Tutorial | Data Visualization | All Levels
Data visualization has become part of everybody’s lives – even before we all turned into armchair epidemiologists during the pandemic. Executives in every organisation understand the value in data and want to glean insights. Workers of all stripes are being guided by visualizations of demand, opportunities, progress and risk. And consumers are habitually using apps to track their finances, fitness and the environment through the lens of data viz. Despite this ubiquity, there are barriers that many data viz practitioners hit in creating maximum value from their work. Requests for insight from bosses and colleagues can be poorly defined, inconsistent and constantly changing. Data literacy can vary hugely between audiences...more details
Alan Rutter is the founder of consultancy Fire Plus Algebra, and is a specialist in communicating complex subjects through data visualisation, writing and design. He has worked as a journalist, product owner and trainer for brands and organisations including Guardian Masterclasses, WIRED, Time Out,the Home Office, the Biotechnology and Biological Sciences Research Council and Liverpool School of Tropical Medicine.
Tutorial | MLOps & Data Engineering | ML for Programmers | Advanced
Kubeflow is the de facto standard for running Machine Learning (ML) workflows on Kubernetes. Its goal is to simplify the day-to-day operations of the data scientists and accelerate the production deployment of models.
Kubeflow comes with all of the tools and technologies that end users are accustomed to like Jupyter Notebooks, Tensorflow, and Tensorboard. It also provides intuitive UIs for managing and consuming the data of the cluster.
In this session you will: 1) learn the basics of Kubeflow, including configuring a Jupyter Notebook on a K8s cluster, 2) upload data from your local machine directly to the cluster using Kubeflow’s UIs, 3) tackle a real world ML problem using Keras and GPUs to train a dog breed identifier, 4) track and visualize training metrics using Tensorboard…more details
Kimonas is a Software Engineer at Arrikto, working on storage solutions on the cloud. He loves Open Source and has been a core contributor to the Kubeflow project for more than a year. Kimonas is the owner of the platform’s Jupyter infrastructure and his main goal is to improve the way users manage the lifecycle of their ML tools, like Notebooks, and data on top of Kubeflow. He is also a mentor at the Kubeflow project at Google Summer of Code 2020 providing guidance for adding seamless support for launching Tensorboard instances.
Konstantinos is an undergraduate student at the National Technical University of Athens in the school of Electrical and Computer Engineering. Currently, he is a member of the Kubeflow organization for which he developed the Tensorboard web-app as a part of his Google Summer of Code proposal. His other interests involve speech signal processing, machine learning and distributed systems.
Workshops | Deep Learning | Beginner-Intermediate
Learn the basics of building a PyTorch model using a structured, incremental and from first principles approach. Find out why PyTorch is the fastest growing Deep Learning framework and how to make use of its capabilities: autograd, dynamic computation graph, model classes, data loaders and more.
The main goal of this session is to show you how PyTorch works: we will start with a simple and familiar example in Numpy and “torch” it! At the end of it, you should be able to understand PyTorch’s key components and how to assemble them together into a working model. We will use Google Colab and work our way together into building a complete model in PyTorch. You should be comfortable using Jupyter notebooks, Numpy and, preferably, object oriented programming…more details
Coming Soon!
Workshop | Machine Learning | Intermediate
Explainable ML and AI (also known as XAI) is not only a booming field of research but is also widely applied across industries, such as healthcare, finance, and insurance, among others. There are a lot of approaches out there that provide a certain level of explainability, as well as different packages and libraries.
In this workshop, we will introduce (some of the more common and promising) approaches for ML explainability. We will also get hands-on experience with the application of different XAI libraries, and get a feeling about their shortcomings and advantages...more details
Violeta has been working as a data scientist in the Data Innovation and Analytics department in ABN AMRO bank located in Amsterdam, the Netherlands. In her daily job, she works on projects with different business lines applying the latest machine learning and advanced analytics technologies and algorithms. Before that, she worked for about 1.5 years as a data science consultant in Accenture, the Netherlands. Violeta enjoyed helping clients solve their problems with the use of data and data science but wanted to be able to develop more sophisticated tools, therefore the switch. Before her position at Accenture, she worked on her PhD, which she obtained from Erasmus University, Rotterdam in the area of Applied Microeconometrics. In her research, she used data to investigate the causal effect of negative experiences on human capital, education, problematic behavior and crime commitment.
Workshop | Machine Learning | Data Visualization | Beginner
This free online learnathon is a mix between a hackathon and a workshop. It’s like a workshop because we’ll learn more about the data science cycle: data access, data blending, data preparation, model training, optimization, testing, and deployment. It’s like a hackathon because we’ll solve together a number of challenges by hacking a workflow-based solution via guided exercises. The tool of choice is the open-source, GUI-driven KNIME Analytics Platform. Because KNIME is open, it offers great integrations with an IDE environment for R, Python; SQL, and Spark…more details
Paolo Tamagnini is a data science evangelist at KNIME and based in Berlin. After graduating with a master’s degree in data science at Sapienza University of Rome, Paolo gathered research experience at New York University in machine learning interpretability and visual analytics tools. Since working at KNIME, Paolo has presented different workshops in the USA and Europe and developed a number of reusable guided analytics applications for automated machine learning and human-in-the-loop analytics.
Workshop | NLP | Research Frontiers | Intermediate-Advanced
The speaker will review case studies from real-world projects that built AI systems using Natural Language Processing (NLP) in healthcare. These case studies cover projects that deployed automated patient risk prediction, automated diagnosis, clinical guidelines, and revenue cycle optimization. He will also cover why and how NLP was used, what deep learning models and libraries were used, and what was achieved. Key takeaways for attendees will include important considerations for NLP projects including how to build domain-specific healthcare models and using NLP as part of larger and scalable machine learning and deep learning pipelines in distributed environment…more details
Veysel Kocaman is a Senior Data Scientist and ML Engineer at John Snow Labs and has a decade long industry experience. He is also pursuing his PhD in CS as well as giving lectures at Leiden University (NL) and holds an MS degree in Operations Research from Penn State University. He is affiliated with Google as a Developer Expert in Machine Learning.
Workshop | Machine Learning | Intermediate
In this workshop you will explore different kinds of explanations suited to different users in the context of a credit approval process enabled by machine learning. The three types of users that we will look at are a data scientist, who evaluates the machine learning model before deployment, a loan officer, who makes the final decision based on the model’s output, and a bank customer, who wants to understand the reasons for their application result…more details
Margriet is the global data science developer advocacy focal at IBM. As a Data Scientist she has a passion for exploring different ways to work with and understand diverse data by using open-source tools. She is active in developer communities through attending and presenting at conferences and organising meetups. She has a background as a climate scientist researching large observational datasets of carbon uptake by forests and the output of global scale weather and climate models.
Tutorial | Deep Learning | Intermediate-Advanced
Most of the recent advances in the deep learning field come at a high price. The costs involved in developing and training these models are two-fold: namely, they can be attributed to computing power and training data. Computational resources are getting increasingly more affordable through the wide spread of cloud computing services. On the other hand, gathering and especially manually labeling data cannot not scale in the same way. A common scenario is that in which unlabeled data comes cheap, but the labeling budget is severely limited. Practice shows that all data is not created equal: the choice of which data is prioritised to be labeled has a profound effect on the final performance of the resulting model. The task of determining which data samples would be most “informative” when labeled, goes under what is known as active learning…more details
Olga is a deep learning R&D engineer at Scaleway, the second largest french cloud provider. She received her PhD in theoretical physics from Johns Hopkins University in 2013, followed by postdoctorate appointments at the Max Planck Institute in Dresden and the École Normale Supérieure in Paris. In the latter, she looked into the possible applications of artificial intelligence to quantum systems, among other things.
Olga’s current interests focus on semi-supervised and active machine learning. On the community side, she enjoys blogging about the latest advancements in AI both in and out of working hours. Some of her writing can be seen on medium.com/@olgapetrova_92798
Workshop | Machine Learning | Advanced
AI, especially machine learning based on neural nets, is seen as a key technology in the digitization of society and its private and public sectors. But the normal workflow of such AI is normally at odds with data privacy, which is becoming more and more important to consumers and regulators alike. This poses a challenge for the acceptability and wider adoption or AI, including its use in edge environments. Federated learning is seen as an approach for collaborative and distributed AI that has the potential to resolve this challenge while also creating AI models with high utility. This session will introduce its concept and discuss one of its instances, the open-source framework XayNet, in greater detail, first in a hands-off and then in a hands-on manner through a demo of XayNet...more details
Professor Michael Huth (Ph.D.) is Co-Founder and CTO of the technology company XAIN and teaches at Imperial College London. His research focuses on Cybersecurity, Cryptography, Mathematical Modeling, as well as security and privacy in Machine Learning. He served as the technical lead of the Harnessing Economic Value theme at PETRAS IoT Cybersecurity Research Hub in the UK. In 2017, he founded XAIN AG together with Leif-Nissen Lundbæk and Felix Hahmann. The Berlin-based company aims to solve the challenge of combining AI with privacy with an emphasis on Federated Learning. XAIN won the first Porsche Innovation Contest and has already worked successfully with Porsche AG, Daimler AG, Deutsche Bahn, and Siemens.
Professor Huth studied Mathematics at TU Darmstadt and obtained his Ph.D. at Tulane University, New Orleans. He worked at TU Darmstadt, Kansas State University, and spent a research sabbatical at The University of Oxford. Huth has authored several scientific publications and is an experienced speaker on international stages.
Workshop | Machine Learning | Ai for Business | Intermediate-Advanced
Recently, academics as well as policy makers have written many papers, on responsible data science / AI. Moreover, many open-source packages for bias dashboards or tools for `fairness’ have been proposed. This session aims to provide attendees a broad overview as well as the specific technical background to use the available ` fairness’ tools. In addition, a governance framework describing the precise responsibilities of data scientists will be discussed…more details
Ramon van den Akker works as a data scientist at the AI Center of Excellence and the Risk Modelling departments of de Volksbank, a Dutch retail bank located in Utrecht. He also works, as an associate professor, at the econometrics group of Tilburg University. His research interests cover various fields in data science, machine learning, econometrics and statistics and his research findings have been published in leading journal in econometrics and statistics. In his work at de Volksbank, Ramon mainly works on data science projects and the exploration of new opportunities, but also on governance aspects like frameworks for responsible AI & data science and the use of privacy-preserving data analytics.
Daan Knoope works as an AI Engineer at de Volksbank, the Dutch parent company of several banks and mortgage providers. He has a background in Computer Science (MSc) and has specialized in Algorithmic Data Analysis. During his studies, he researched the application of Dynamic Bayesian Networks on practical use cases to help further the development of explainable AI. Currently, he is focusing on developing AI-models for the bank as well as providing fellow AI Engineers the tools they need to efficiently explore data and build production-ready models
Joris Krijger (1991) works as an Ethics & AI specialist at the Dutch bank de Volksbank while also holding a PhD position at the Erasmus University Rotterdam on that topic. He has a background in Economic Psychology (MA), Philosophy (MA) and Film and Literary Studies (BA) and studied in Glasgow, Buenos Aires and Leiden, where he was awarded a national thesis prize in 2017 by the Royal Dutch Society of the Sciences for his graduation thesis on technology ethics and the financial crisis of 2008. He co-founded high-tech startup Condi Food (Rabobank Wijffels Innovation Award 2014) and was involved in various biomedical initiatives related to bacteriophages. He presently works on bridging the gap between principle and practice in AI Ethics by studying the operationalization of ethical principles from an academic and practical perspective and is reviewer for the AI Ethics Journal, Subject Matter Expert for CertNexus’ ‘Certified Ethical Emerging Technologist’ and Editorial Board Member for Springer’s AI and Ethics Journal.
Tutorial | MLOps & Data Engineering | Intermediate-Advanced
The workshop is aimed at data scientists and data engineers who are interested in building a production-ready data pipelines.
We will go into:
– The challenges associated with creating ML models that are deployable;
– Software engineering principles that should be applied to ML code to make it easier to deploy in the production environment;
– And, how you can use an open-source Python library, called [Kedro](https://github.com/quantumblacklabs/kedro), to enhance your exploratory data analysis workflow as well as their transition to production-ready code.
Kedro is an open-source development workflow framework that implements software engineering best-practice for data pipelines with an eye towards productionising machine learning models...more details
Kiyo is a software engineer at QuantumBlack, an advanced analytics firm operating at the intersection of strategy, technology and design to improve performance outcomes for organizations. Kiyo is one of the core contributors and maintainers of Kedro, a Python library that implements software engineering best-practice for data and ML pipelines.
Kiyo holds MSc in Computing Science from Imperial College London, and MA in Economics from The University of Edinburgh.
Workshop | MLOps & Data Engineering | Machine Learning | Intermediate
Demystify machine learning buzzwords by learning how to train and use your own neural network in this interactive workshop. We’ll cover the foundational principles that underpin modern machine learning and demonstrate how Julia makes it easy and fast...more details
Dhairya got his Bachelors in Electrical and Electronic Engineering from Birla Institute of Technology, Pilani and is presently a data scientist at Julia Computing Inc. He is responsible for developing and maintaining performant differentiable programming routines and providing core tooling through Flux.jl, Zygote.jl etc. He has been contributing to various parts of the Julia ecosystem for more than 2 years, and helped develop differentiable pipelines for scientific computing and more recently on materials exploration.
Workshop | Machine Learning | Social Good | Intermediate
AI can embed human and societal bias and be then deployed at scale. Many algorithms are now being reexamined due to illegal bias. So how do you remove bias & discrimination in the machine learning pipeline? In this workshop you will learn the debiasing techniques that can be implemented by using the open source toolkit AI Fairness 360.
AI Fairness 360 (AIF360) is an extensible, open source toolkit for measuring, understanding, and removing AI bias. It contains the most widely used bias metrics, bias mitigation algorithms, and metric explainers from the top AI fairness researchers across industry & academia…more details
Margriet is the global data science developer advocacy focal at IBM. As a Data Scientist she has a passion for exploring different ways to work with and understand diverse data by using open-source tools. She is active in developer communities through attending and presenting at conferences and organising meetups. She has a background as a climate scientist researching large observational datasets of carbon uptake by forests and the output of global scale weather and climate models.
Tutorial | MLOps & Data Engineering | Intermediate-Advanced
Kubeflow is a machine learning (ML) platform built on top of Kubernetes. The Kubeflow project is dedicated to making deployments of ML workflows on Kubernetes simple, portable and scalable.
In this session, you will: 1) learn how to apply GitOps in order to deploy and manage a Kubeflow cluster; 2) learn how to enable multiple users to work together on the same cluster in a secure and isolated way, with authentication and authorization best practices; 3) follow a data scientist’s journey to running a hyperparameter tuning optimization workflow; 4) scale up your workloads in a UI driven environment…more details
Yannis is a software engineer at Arrikto, working with Kubeflow, the cloud-native machine learning platform built on Kubernetes. He loves contributing to open source projects and has authored the Cassandra Operator in Rook and the official Scylla Operator, which he is currently maintaining.
Stefano Fioravanzo is a Software Engineer at Arrikto, his background is in Data Science and ML Research. He understands the value of building robust Machine Learning infrastructure and providing Data Scientists with the necessary tools to scale up their workflows. He works as a full-time contributor to Kubeflow and he is the creator of the Kubeflow Kale project which enables Jupyter Notebooks deployments to Kubeflow Pipelines.
Workshop | MLOps & Data Engineering | Intermediate
Without doubt, putting analytics into action is one of the biggest challenges across industries. To generate business value from analytics investments, organizations need to adopt ModelOps practices to streamline the way they validate and deploy analytical models. However, implementing a ModelOps approach is not a simple task as it involves many technical and non-technical (i.e. cultural, organisational) aspects.
In this session we will discuss some of the technical key principles of ModelOps and we will demonstrate a pipeline approach to quickly move from development to test and production using a mix of Python, SAS® Viya and other CI/CD tools...more details
Hans has been supporting SAS customers in Germany, Austria and Switzerland as a Presales consultant and Solution Architect since he joined SAS in 2002. Currently he is working in an international team of architects and developers which takes care for the EMEA region. His work is focused on enterprise architectures, Kubernetes and cloud technologies.
Matteo is a data scientist at SAS. He supports global initiatives and projects that involves advanced analytics and artificial intelligence technologies. He is passionate about helping companies transforming their business processes with analytics. Matteo has a MSc in Management Engineering from Politecnico di Milano, with a specialization in Industrial Management.
Half-Day Training | Quant Finance | Machine Learning | Intermediate
This workshop covers popular ML use cases for the investment industry. In particular, it focuses on how ML fits into the workflow of developing a trading strategy, from the engineering of financial features to the development of an ML model that generates tradable signals, the backtesting of a trading strategy that acts on these signals and the evaluation of its performance. We’ll use common Python data science and ML libraries as well as Zipline, Pyfolio, and Alphalens. The code examples will be presented using jupyter notebooks and are based on the second edition of my book ‘Machine Learning for Algorithmic Trading’…more details
Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. Before his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. He also was a senior executive at a global fintech company with operations in 15 markets.
Earlier, he advised Central Banks in emerging markets, worked for the World Bank, raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He is the author for ‘Machine Learning for Algorithmic Trading’ and has been teaching data science at Datacamp and General Assembly.
Half-Day Training | Deep Learning | Machine Learning | Intermediate-Advanced
Welcome to deep learning building blocks. This is an intermediate tutorial on deep learning that focuses on how to design neural networks for various data types….more details
Nathaniel earned his AB/SM in Computer Science from Harvard. He previously worked as a Quant and Trader at Jane Street and Goldman Sachs before transitioning into the pure tech industry. Nathaniel worked as a Data Scientist at Facebook, a Product Manager at Microsoft and a Software Engineer at Google before joining Vicarious. He is an avid reader and learner. He teaches part time at General Assembly and is developing open source teaching material for data science, machine learning, and web development.
Half-Day Training | Machine Learning | Intermediate
How could a violation of the triangle inequality theorem in mathematics lead to a cure for cancer? How can a mathematical concept from the 18th century be used to estimate the mass density of galaxies across the Universe? How could a marketing segmentation algorithm protect astronauts traveling to Mars from certain death? How does a F1 race from the 1950’s inspire one of the greatest machine learning use cases for the Internet of Things? This workshop will answer these questions, and more, by presenting several examples of typical algorithms that were adopted for specific use cases or application domains, then showing how each one can be adapted to an atypical (often mind-bending) use case, producing significantly surprising results in some other domain…more details
Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.
Half-Day Training | Machine Learning | R-programming | Intermediate
In this workshop we go over two fundamentals machine learning methods: Penalized regression and boosted trees. Learn the theory behind the models and get practical hands-on experience using {glmnet} and {xgboost} in R. You’ll fit fit the models, assess model fit, tune hyperparameters and make predictions…more details
Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.
Half-Day Training | Machine Learning | R-programming | Intermediate
In this workshop we go over two fundamentals machine learning methods: Penalized regression and boosted trees. Learn the theory behind the models and get practical hands-on experience using {glmnet} and {xgboost} in R. You’ll fit fit the models, assess model fit, tune hyperparameters and make predictions...more details
Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.
Half-Day Training | Kick-starter | Open-source | Intermediate
In this training, you will learn how to accelerate your data analyses using the Python language and Pandas, a library specifically designed for tabular data analysis. We start by learning the core Pandas data structures, the Series and DataFrame. From these foundations, we will learn to use the split-apply-combine paradigm for grouped computations, manipulate time series, and perform advanced joins between datasets. Specifically, loading, filtering, grouping, and transforming data. Having completed this workshop, you will understand the fundamentals and advanced features of Pandas, be aware of common pitfalls, and be ready to perform your own analyses…more details
Daniel Gerlanc has worked as a data scientist for more than decade and been writing sofware for nearly 20 years. He frequently teaches live trainings on oreilly.com and is the author of the video course Programming with Data: Python and Pandas. He has coauthored several open source R packages, published in peer-reviewed journals, and is a graduate of Williams College.
Half-Day Training | MLOps & Data Engineering | R-programming | Intermediate
Modern machine learning is mostly brute forcing through a multitude of hyperparameters. We look at new tools for building and tuning models in R, some so new they are only available on GitHub. We use xgboost and glmnet models (learned in Part I) as motivation for learning how to split data and conduct cross-validation with rsample, perform feature engineering with recipes, build model specifications with parsnip, tune over hyperparameters with dials and tune, evaluate performance with yardstick and put it all together with workflows…more details
Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.
Half-Day Training | Deep Learning | Advanced
Although supervised learning has dominated industry machine learning implementations, unsupervised and semi-supervised methods have started to be practically applied to real world problems (outside of playing video games). Generative Adversarial Networks (GANs) are being utilized to augment data and generate dialogue, and Reinforcement Learning (RL) is helping people plan marketing campaigns and control robots. In this training, you will develop a theoretical understanding of these and other related state-of-the-art AI methods along with the hands-on skills needed to train and utilize them. You will implement a variety of models in TensorFlow for tasks including object recognition, image generation and robotics…more details
Daniel Whitenack (aka Data Dan) is a PhD trained data scientist who has been developing artificial intelligence applications in the real world for over 10 years. He knows how to see beyond the hype of AI and machine learning to build systems that create business value, and he has taught these skills to 1000’s of developers, data scientists, and engineers all around the world. Now with the AI Classroom event, Data Dan is bringing this knowledge to an live, online learning environment so that you can level up your career from anywhere!
Half-Day Training | Machine Learning | Beginner
If you’ve never heard of the “good, fast, cheap” dilemma, it goes something like this: You can have something good and fast, but it won’t be cheap. You can have something good and cheap, but it won’t be fast. You can have something fast and cheap, but it won’t be good. In short, you can pick two of the three but you can’t have all three. If you’ve tackled a data science problem before, I can all but guarantee that you’ve run into missing data. How do we handle it? Well, we can avoid, ignore, or try to account for missing data. The problem is, none of these strategies are good, fast, *and* cheap...more details
Matt is currently Managing Partner and Principal Data Scientist at BetaVector. His full-time professional data work spans finance, education, consumer-packaged goods, and politics and he earned General Assembly’s 2019 “Distinguished Faculty Member of the Year” award. Matt earned his Master’s degree in statistics from Ohio State. Matt is passionate about responsibly putting the power of machine learning into the hands of as many people as possible and mentoring folx in data and tech careers. Matt also volunteers with Statistics Without Borders and currently serves on their Executive Committee as the Marketing & Communications Director.
Bootcamp | Kickstarter | Open-source | Beginner
By completing this workshop, you will develop an understanding of relational models of data, how SQL is used to retrieve that data, and how to join tables, aggregate information, and answer data science questions. You will also become familiar with many of the common types of SQL databases, how to access information in a database from the command line, and how to integrate database access from within Python…more details
Mona is a Data Scientist at Greenhouse Software in New York City, where they contribute to data-informed decision making across the company and machine learning solutions to improve the hiring process for Greenhouse customers. They’ve previously worked in government, creating analytics and machine learning solutions to improve the lives of New Yorkers, and continue to be involved in civic projects through a number of volunteer and non-profit organizations. They’ve also been a statistics and data science educator with DataCamp, Emeritus, and in university settings. They hold a graduate degree in Developmental Psychology, and are passionate about contributing to the ethical use of data science methodology in the public and private sector.
Half-Day Training | MLOps & Data Engineering | R-programming | Intermediate-Advanced
After we build various machine learning models we need to make them accessible to others. We use modeldb so we can store models in a database and plumber to expose our model as a REST API that can be hosted in a Docker container…more details
Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.
Half-Day Training | Data Visualization | MLOps & Data Engineering | Beginner
Learn the art and science, the theory and practical hands-on tactics of creating compelling communications experiences based on surveys, scientific research, public or proprietary data sources. In this talk, you will learn about some of the best practices for data storytelling and visualization and walk away with a new way of thinking and some practical tips you can put to work the moment the session is done…more details
Bill is an information designer, helping clients turn their data into compelling visual and often interactive experiences. Project and workshop clients include the World Bank, United Nations, International Monetary Fund, Starbucks, American Express, PricewaterhouseCoopers, Facebook, and the City of Boston. He is the founder of Beehive Media, a Boston-based data visualization and information design consultancy. Bill teaches data storytelling, information design and data visualization on LinkedIn Learning & Lynda.com and in workshops around the world. Bill has a new keynote talk about how you can wield outsider attributes to wield influence beyond your role within your organization. Ask him about it!
Half-Day Training | Machine Learning | Intermediate
Unsupervised learning models (including analysis of correlations, clusters, and associations in data) converge more readily to a useful solution if we start with good model parameterizations. Feature engineering is key, but selection of features often becomes guesswork. Similarly, in supervised machine learning, the choice of features in labeled data to use in training may still seem arbitrary. So, how does model-building start and move towards an optimal solution? This challenge is known as the cold-start problem! The solution to the problem is easy (sort of): We start with a guess, a totally random guess! That sounds so random and so wrong! But there is an orderly and productive way forward from such a start, which we will describe in this workshop…more details
Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.
Tutorial | Deep Learning | Beginner-Intermediate
This workshop explains how to practically bring the power of convolutional neural networks and deep learning to memory and power-constrained devices like smartphones. You will learn various strategies to circumvent obstacles and build mobile-friendly shallow CNN architectures that significantly reduce the memory footprint and therefore make them easier to store on a smartphone; The workshop also dives into how to use a family of model compression techniques to prune the network size for live image processing, enabling you to build a CNN version optimized for inference on mobile devices…more details
Workshop | ML for Programmers | Open-source | Intermediate
Pomegranate is a Python package for probabilistic modeling that emphasizes both ease of use and speed. In keeping with the first emphasis, pomegranate has a convenient modular API for building complex models out of simple components and a simple sklearn-like API for using them. In keeping with the second emphasis, the computationally intensive parts of pomegranate are written in Cython and all models support multithreaded parallelism and out-of-core computations. In this talk I will give an overview of the features in pomegranate, including missing value support and the new data generators, and demonstrate how they can yield more accurate models. I will also demonstrate how one can use existing components to build neural probabilistic models, such as neural HMMs, using your favorite neural network package…more details
Jacob Schreiber is a fifth year Ph.D. student and NSF IGERT big data fellow in the Computer Science and Engineering department at the University of Washington. His primary research focus is on the application of machine larning methods, primarily deep learning ones, to the massive amount of data being generated in the field of genome science. His research projects have involved using convolutional neural networks to predict the three dimensional structure of the genome and using deep tensor factorization to learn a latent representation of the human epigenome. He routinely contributes to the Python open source community, currently as the core developer of the pomegranate package for flexible probabilistic modeling, and in the past as a developer for the scikit-learn project. Future projects include graduating.
Workshop | MLOps & Data Engineering | Machine Learning | Beginner
In this session, we explain some of the ML development best practices we have developed from working in the trenches. The core of our approach comes down to a philosophy of engineering discipline around “being the Navy, and not pirates.” Wherever possible, we explain how to reduce the incidental complexity in ML development by using appropriate tooling and process. We deep dive into certain best practices like using Docker on day one, continuous integration for ML, experiment tracking using MLFlow, and experimenting at scale in the cloud...more details
As CTO for Manifold, Sourav is responsible for the overall delivery of data science and data product services to make clients successful. Before Manifold, Sourav led teams to build data products across the technology stack, from smart thermostats and security cams (Google / Nest) to power grid forecasting (AutoGrid) to wireless communication chips (Qualcomm). He holds patents for his work, has been published in several IEEE journals, and has won numerous awards. He earned his PhD, MS, and BS degrees from MIT in Electrical Engineering and Computer Science.
Alexander Ng is a Senior Data Engineer at Manifold, an artificial intelligence engineering services firm with offices in Boston and Silicon Valley. Prior to Manifold, Alex served as both a Sales Engineering Tech Lead and a DevOps Tech Lead for Kyruus, a startup that built SaaS products for enterprise healthcare organizations. Alex got his start as a Software Systems Engineer at the MITRE Corporation and the Naval Undersea Warfare Center in Newport, RI. His recent projects at the intersection of systems and machine learning continue to combine a deep understanding of the entire development lifecycle with cutting-edge tools and techniques. Alex earned his Bachelor of Science degree in Electrical Engineering from Boston University, and is an AWS Certified Solutions Architect.
Tutorial | Machine Learning | Intermediate-Advanced
Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, YAGO, Wikidata of Google Knowledge Graph. Learning rules from KGs is a crucial task for KG completion, cleaning and curation. This tutorial presents state-of-the-art rule induction methods, recent advances, research opportunities as well as open challenges along this avenue...more details
Daria Stepanova is a research scientist at Bosch Center for Artificial Intelligence. Her research interests include Knowledge Representation and Reasoning with the special focus on 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.
Workshop
Data is becoming the bread and butter of many organizations, or at least something most couldn’t live without. And many applications or solutions, from storage to platforms and middleware, can help support the whole lifecycle of the data throughout its acquisition, transformation, storage, and consumption...more details
Guillaume Moutier is a Sr. Principal Technical Evangelist at Red Hat, focusing his work on data services, AI/ML workloads and data science platforms. Former CTO of Laval University in Canada, he is constantly looking for and promoting new and innovative solutions, but always with a focus on usability and business alignment brought by 20 years of IT management.
Tutorial | Machine Learning | MLOps & Data Engineering | Intermediate
ONNX Runtime is the inference engine for inferencing ML models in different HW environments. Applications of AI are everywhere. This requires ML models that are trained in the cloud to execute on small devices with low power, low compute, and low memory. Such devices are typically used in IoT scenarios. The data captured by these devices are processed before sending the telemetry to the cloud for further actions on the business application. ONNX Runtime has made enhancements to enable execution of ML models in these edge devices to power AI on the edge applications. This session will walk through the workflow to train an image classification model, package in container and deploy to IoT device…more details
Principal Program Manager, Cloud and AI Manash Goswami is a product management leader with a passion to solve problems and build products that meet end-user needs. Grown from development – coding, test & verification, systems design & architecture, to product management and business development. Delivered consumer electronics products from smartwatch to tablets. Deep understanding of the Android, Chrome & Windows eco-system. Demonstrated leadership by managing through influence across a matrixed org structure. Capable of driving new initiative/agenda internally or with customers, enable collaboration across functional teams and drive for financial results. Excel at dealing with ambiguity and managing complex, cross-group interactions. Can successfully tie long-term strategic thinking with near-term execution.
Wolfgang Pauli is an AI developer at Microsoft, with 15+ years of experience with Machine Learning and Artificial Intelligence research. He received his Ph.D. in Computational Neuroscience from the University of Colorado and has published numerous high-profile articles in scientific journals on Computational Neuroscience, Reinforcement Learning, and Neural Networks. Before joining the Microsoft AI Platform team in 2018, he was a research scientist at the California Institute of Technology. He supports the democratization of AI by developing open-source solutions that apply recent breakthroughs to real-world problems.
Tutorial | Machine Learning | Beginner-Intermediate
Datasets are almost never complete and this can introduce various biases to your analysis. Due to these biases, your supervised machine learning model can produce incorrect predictions. The goal of this post is to give you an idea of why some of the most common approaches for dealing with missing values often introduce some type of bias. At ODSC Europe 2020, I will describe the methods and techniques that can help you to arrive at an unbiased conclusion in the face of missing data…more details
Andras Zsom is a Lead Data Scientist in the Center for Computation and Visualization group at Brown University, Providence, RI. He works with high-level academic administrators to tackle predictive modeling problems, he collaborates with faculty members on data-intensive research projects, and he was the instructor of a data science course offered to the data science master students at Brown.
Tutorial | Deep Learning | Computer Vision | Intermediate
Due to the increasing number of cars, frustration accompanying drivers searching for free parking spot is escalating. During our tutorial we would like to present a practical approach to implement a real-time smart vision parking solution targeted to improve parking experience. The solution is based on applying object detection model on real-time video stream captured by city cameras and providing information on the number of available parking spots and their location. Thus the system allows for a significant reduction in time wasted by drivers on wandering around, which generates extra traffic. We lead the audience through the multiple-stage process of solution deployment assuming some level of latency required by this type of application...more details
Wioletta Stobieniecka is a Data Scientist with over 5 years of experience. Open-minded individual with deep passion for knowledge discovery and solving real-world problems using advanced statistical and machine learning tools. Strongly self-motivated and eager to constant development of skills including both technical and social with major in analytics. Deep theoretical and practical knowledge of statistical learning methods. Inquisitive mind searching for optimal solutions and ready to demonstrate own initiative and ambition to gain experience across a variety of industries. Having experience in fraud analytics with strong focus on building predictive models for banking and insurance. Looking for opportunities with exposure to projects involving development of recommender systems and image recognition. Striving to find strong mentorship and inspiring environment with well-rounded people open to knowledge transfer. Having long exposure to both open source and commercial software (R, Python, SAS, Oracle)
Piotr Kaczyński is a Senior Business Solutions Manager in the analytical team. Responsible for business development in the area of analytics and system integration, also at the interface between SAS and open source tools. For almost 8 years, as a researcher, he conducted research on the convergence of linearization algorithms for stochastic processes. Since 2003, he has been associated with analytics through the practical use of predictive modeling for forecasting in the energy sector. He also participated in projects in the field of data mining, BI and forecasting for sea transport. His main interests are artificial intelligence, and in particular its practical use in image processing
Workshop | Deep Learning | Data Visualization
Variational Auto-Encoders (VAEs) are cutting edge deep learning technology. The purpose of a VAE is to decomplexify data. Another name for this task is dimension reduction. The idea is that you have data that has lots of variables in it, but with so many variables it’s hard to understand what’s going on. You want to think about your customers, not as this huge spreadsheet with hundreds or thousands of pieces of information about each customer, but as something simple. You want to think of just a couple of customer types, with the idea that you know people are not very different from those ideal types…more details
Yaniv Ben-Ami loves seeing data with my own eyes. It’s frustrating to him that it’s impossible to observe the geometry of multidimensional data that’s more than 3d. Yaniv first became obsessed with this challenge as a Bioinformatics undergrad at Tel Aviv University. Little did Yaniv knows that my obsession would become an epic journey. As an Economics Master student, Yaniv met the ultimate beast of a data set. This beast is The American Time Use Survey (ATUS), which has 389 dimensions. To Yaniv, it appeared as a 389-head Hydra that nobody would dare to slay. Gazing upon that beast, Yaniv initially fell into despair. Fast forward 15 years, 3 kids and a Ph.D. in Economics from Yale, Yaniv reads Deep Learning in Python by François Chollet. Within this tome of wisdom, Yaniv found a gift from the Gods. A deep learning algorithm by the name of Variational Auto-Encoder. This mythical weapon can map multidimensional data to a one-dimensional range and maintain relative distances. Finally, Yaniv Ben-Ami can see the whole data set. Yaniv will show you how he used this weapon to slay the ATUS monster. Just in case there’s a monster you want to slay.
Tutorial | Machine Learning | Beginner-Intermediate
We introduce algorithms that use predictions from machine learning applied to the input to circumvent worst-case analysis. We aim for algorithms that have near optimal performance when these predictions are good, but recover the prediction-less worst case behavior when the predictions have large errors. We look at examples of recent results in job scheduling, caching, and network measurement to show how predictions can be used effectively while still allowing for theoretical guarantees…more details
Michael Mitzenmacher is a Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. Michael
has authored or co-authored over 200 conference and journal publications on a variety of topics, including algorithms for the Internet, efficient hash-based data structures, erasure and error-correcting codes, power laws, and compression. He is interested in both algorithms for AI applications, and how predictors from AI systems can yield better algorithms with rigorous performance bounds. He is an ACM Fellow and an IEEE Fellow. He has a widely used textbook on randomized algorithms and probabilistic techniques in computer science published by Cambridge University Press.
Michael Mitzenmacher graduated summa cum laude with a B.A. in mathematics and computer science from Harvard in 1991. After studying
mathematics for a year in Cambridge, England, on the Churchill Scholarship, he obtained his Ph. D. in computer science at U.C. Berkeley in 1996. He then worked at Digital Systems Research Center until joining the Harvard faculty in 1999. He served as the chair for computer science from 2010 to 2013 and co-chair in the 2018-2019 academic year.
Tutorial | NLP | MLOps & Data Engineering | Beginner
Advances in Natural Language Processing (NLP) over the last year have been changing the way we work with text-based data. We have seen the release of Google’s BERT, OpenAI’s GPT-2, and Hugging Face’s Transformers library that has helped many use these kinds of models. But it can still be challenging for many users to get started and apply them to their own datasets.
To address this challenge, Novetta has open sourced AdaptNLP, an intuitive framework that lowers the barrier to entry to use these advanced capabilities. This high-level framework enables users to use fine-tune pre-trained language models for text classification, question answering, entity extraction, and part-of-speech tagging...more details
Brian Sacash is a Machine Learning Engineer in Novetta’s Machine Learning Center of Excellence. He helps various organizations discover the best ways to extract value from data. His interests are in the areas of Natural Language Processing, Machine Learning, Big Data, and Statistical Methods. Brian holds a Master of Science in Quantitative Analysis from the University of Cincinnati and a Bachelor of Science in Physics from Ohio Northern University.
Andrew Chang is an Applied Machine Learning Researcher in Novetta’s Machine Learning (ML) Center of Excellence. Andrew is a graduate from Carnegie Mellon University who has a focus on researching state of the art machine learning models and rapid prototyping ML technologies and solutions across the scope of customer problems. He has an interest in open source projects and research in natural language processing, geometric deep learning, reinforcement learning, and computer vision. Andrew is the author and creator of NovettaNLP.
Workshop | Machine Learning | Open-source | Intermediate-Advanced
In this workshop, we will work hands-on using XGBoost with real-world data sets to demonstrate how to approach data sets with the twin goals of prediction and understanding in a manner such that improvements in one area yield improvements in the other. Using modern tooling such as Individual Conditional Expectation (ICE) plots and SHAP, as well as a sense of curiosity, we will extract powerful insights that could not be gained from simpler methods. In particular, attention will be placed on how to approach a data set with the goal of understanding as well as prediction…more details
Brian Lucena is a Principal at Lucena Consulting and a consulting Data Scientist at Agentero. An applied mathematician in every sense, he is passionate about applying modern machine learning techniques to understand the world and act upon it. In previous roles, he has served as SVP of Analytics at PCCI, Principal Data Scientist at Clover Health, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.
Workshops | NLP | Machine Learning | Intermediate
For BlueVine, and indeed for any Fintech company, figuring out the client’s industry is a critical factor in making precise financial decisions. Traditional sources are invariably pricey, inaccurate and unavailable, and as such leave an opening for an ML based solution. We met that challenge building a service that predicts the industry using the business’s publicly available web data. By employing the latest innovations in NLP (BERT) and some of the most powerful scraping and deployment tools available (Scrapy and Amazon SageMaker) we were able to dramatically surpass the performance achieved by any other such tool in the space…more details
Ido Shlomo is the head of BlueVine’s data science team in the US, where he works on applying machine learning and other automation solutions for risk management, fraud detection and marketing purposes. Recent work is focused on implementing complex NLP tasks in production systems, and specifically on dealing with the the challenge of consuming unstructured data. Previously Ido worked in the Economics department at Tel Aviv University as a researcher in structural macroeconomic modeling. Ido holds a dual BA in mathematics and philosophy and an MA in economics, both from Tel Aviv University.
Tutorial | NLP | Machine Learning | Intermediate
This is a hands-on tutorial on applying the latest advances in deep learning and transfer learning for common NLP tasks such as named entity recognition, document classification, spell checking, and sentiment analysis. Learn to building complete text analysis pipelines using the highly accurate, high performant, open-source Spark NLP library in Python.
Spark NLP provides state-of-the-art accuracy, speed, and scalability for natural language processing by delivering production-grade implementations of some of the most recent research in applied deep learning and transfer learning. It is the most widely used NLP library in the enterprise today…more details
David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a Ph.D. in computer science and master’s degrees in both computer science and business administration.
Tutorial | Research Frontiers | Machine Learning | Intermediate
Many real-world phenomena such as epidemics spreading, traffic flow, team sports, and climate variation involve complex spatial patterns evolving through time. Our ability to learn and mine from large-scale spatiotemporal data is critical to real-time decision making in various science and engineering fields. However, existing machine learning methods are still insufficient for large-scale spatiotemporal data due to the presence of non-linear, non-Euclidean, multi-resolution, high-dimensional, and complicated physical characteristics. The goal of this tutorial is to (1) provide an overview of the nature of spatiotemporal data and its relevance to the data mining community (2) survey recent development in machine learning to address the challenges specific to spatiotemporal data, and (3) identify the open problems and future directions…more details
Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at the University of Southern California in 2017. She was subsequently a Postdoctoral Fellow at the California Institute of Technology. She was an assistant professor at Northeastern University prior to her appointment at UCSD. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first-principles with data-driven models. Among her awards, she has won Google Faculty Research Award, Adobe Data Science Research Award, NSF CRII Award, Best Dissertation Award in USC, and was nominated as one of the ’MIT Rising Stars in EECS’.
Tutorial | Machine Learning | Open-source | Beginer-Intermediate
Faces are a fundamental piece of photography, and building applications around them has never been easier with open-source libraries and pre-trained models. In this tutorial, we’ll help you understand some of the computer vision and machine learning techniques behind these applications. Then, we’ll use this knowledge to develop our own prototypes to tackle tasks such as face detection (e.g. digital cameras), recognition (e.g. Facebook Photos), classification (e.g. identifying emotions), manipulation (e.g. Snapchat filters), and more…more details
Gabriel is the founder of Scalar Research, a full-service artificial intelligence & data science consulting firm. Scalar helps companies tackle complex business challenges with data-driven solutions leveraging cutting-edge machine learning and advanced analytics.
Previously, Gabriel was a B.S. & M.S. student in computer science at Stanford, where he conducted research on computer vision, deep learning, and quantum computing. He’s also spent time at Google, Facebook, startups, and investment firms.
Tutorial | Deep Learning | All Levels
Transformer has been around for a while now, and has proven to be one of the most interesting models of modern deep learning.
It turns out that Transformer models have proven to be domain agnostic. It means that, that despite of its initial application for seq2seq NLP tasks with 1D sequences of text, the 1D transformer input can be of any form. Namely, a 2D image unrolled into long 1D sequence of pixels can be understood with notion of its 2D image characteristics involving object appearance, category, or even predicting next image appearance in very long sequences.
Recent research show that transformer originated architectures for computer vision often tends to be simpler and provide performance at worst on pair with modern architectures such as RCNNs used for the computer vision tasks. Presentation is going to discuss recent research in area of transformer applications in new domains..more details
Dr. Michał Chromiak is a Director at UBS, contributing to text document analytics and leading efforts to democratize AI in financial sector and investigate applications for multiple ML based tasks. Michal is also a member of Department of Intelligent Systems at Maria Curie-Skłodowska University. Past research in integrating big data from distributed and heterogeneous sources, brought him to concentrate on data perception using deep learning. He is interested in improving and understanding ways to generalize the modern deep learning algorithms and finding their best suited AI applications. He is strongly fascinated with understanding how deep learning can be improved to match, and exceed biological forms of intelligence, in terms of performance.
Track Keynote
Inspired by recent successes towards automating highly complex jobs like automatic programming and scientific experimentation, I want to automate the task of the data scientist when developing intelligent systems. In this talk, I shall introduce some of the involved challenges and some possible approaches and tools for automating data science.
More specifically, I shall discuss how automated data wrangling approaches can be used for pre-processing and how both predictive and descriptive models can in principle be combined to automatically complete spreadsheets and relational databases. I will argue that autocompleting spreadsheets is a simple yet highly challenging setting for the automation of data science. Special attention will be given towards the induction of constraints in spreadsheets and in an operations research context…more details
Luc De Raedt is full professor, now at KU Leuven, formerly at the Albert-Ludwigs-University Freiburg. He direct the KU Leuven Institute for Artificial intelligence. He is well known for his contributions on learning and reasoning, in particular, for contributions to statistical relational learning, probabilistic and inductive programming. He is a AAAI and a EurAI fellow and he received an ERC Advanced Grant in 2016 on the topic of automating data science. He is action editor for Artificial Intelligence, Machine Learning and the Journal of Machine Learning Research. He was or will be a pc-(co)chair of flagship conferences in machine learning and in artificial intelligence (including ECMLPKDD 2001, ICML 2005, ECAI 2012 and IJCAI 2022). He was coordinator of 7 European projects (most recently FP7-FET ICON en Chist-Era ReGround), and a principle investigator in many further European and other projects. He has co-authored more than 300 publications, including 3 books, 10 papers in Artificial Intelligence, 17 in Machine Learning, and 36 at core AI conferences such as IJCAI, AAAI and ECAI. He has delivered numerous tutorials at major AI and machine learning conferences such as IJCAI, AAAI, ECCAI, ICML, NeurIPS, SIGKDD, …. According to google scholar he is cited more than 18000 times and his h-index is 65. He has (co)advised 27 Phd students and 17 post-docs. Many of his students have won prestigious awards, including 4 ECCAI (&1 ACP) Dissertation awards for the best European thesis in AI (resp. Constraint Programming).
Talk | Machine Learning | Computer Vision | Advanced
Clearly explaining a rationale for a classification decision to an end-user can be as important as the decision itself. Existing approaches for deep visual recognition are generally opaque and do not output any justification text; contemporary vision-language models can describe image content but fail to take into account class-discriminative image properties which justify visual predictions. In this talk, I will present my past and current work on Zero-Shot Learning, Vision and Language for Generative Modeling and Explainable Machine Learning where we show (1) how to generalize image classification models to cases when no visual training data is available, (2) how to generate images and image features using detailed visual descriptions, and (3) how our models focus on discriminating properties of the visible object, jointly predict a class label, explain why/not the predicted label is chosen for the image…more details
Zeynep Akata is a professor of Computer Science within the Cluster of Excellence Machine Learning at the University of Tübingen. After completing her PhD at the INRIA Rhone Alpes with Prof Cordelia Schmid (2014), she worked as a post-doctoral researcher at the Max Planck Institute for Informatics with Prof Bernt Schiele (2014-17) and at University of California Berkeley with Prof Trevor Darrell (2016-17). Before moving to Tübingen in October 2019, she was an assistant professor at the University of Amsterdam with Prof Max Welling (2017-19). She received a Lise-Meitner Award for Excellent Women in Computer Science from Max Planck Society in 2014, a young scientist honor from the Werner-von-Siemens-Ring foundation in 2019 and an ERC-2019 Starting Grant from the European Commission. Her research interests include multimodal learning and explainable AI.