• 1

    ML Ops and Data Engineering

    • Cloud Platforms for AI - Why You Should Care About DevOps, Containers and Kubernetes by Steven Huels

    • Data Science Best Practices: Continuous Delivery for Machine Learning by Christoph Windheuser, PhD, David Johnston, PhD and Eric Nagler

    • Snakes on a Plane: Interactive Data Exploration with PyFlink and Zeppelin Notebooks by Marta Paes

    • Building a Production-level Data Pipeline Using Kedro by Kiyohito Kunii

    • Model Governance: A checklist for getting AI safely to production by David Talby, PhD

    • ML Inference on Edge with ONNX Runtime by Wolfgang Pauli, PhD and Manash Goswami

    • Needles in a Haystack: Big Data and Bigger Promises? by Khurshid Ahmad, PhD

    • Model Training with GPUs and Live Metrics Tracking with Tensorboard on Kubeflow by Kimonas Sotirchos and Konstantinos Andriopoulos

    • GitOps and Multi-Tenancy Combined for an Enterprise Data Science Experience on Kubeflow by Yannis Zarkadas and Stefano Fioravanzo

    • MLOps: ML Engineering Best Practices from the Trenches by Sourav Dey, PhD and Alex Ng

    • ModelOps in Action with SAS Viya by Hans-Joachim Edert and Matteo Landrò

    • Learn How to Train and Deploy Machine Learning Models in Julia by Dhairya Gandhi

    • At Last, a Good Night’s Sleep! Operationalizing your Models the Correct Way by Thodoris Petropoulos

    • Implementing an Automated X-Ray Images Data Pipeline, the Cloud-Native Way by Guillaume Moutier

  • 2


    • R for Python Programmers by Dr. Colin Gillespie

  • 3

    Deep Learning

    • Can Your Model Survive the Crisis: Monitoring, Diagnosis and Mitigation by Jiahang Zhong, PhD

    • Practical, Rigorous Explainability in AI by Tsvi Lev

    • PyTorch 101: Building A Model Step-by-Step by Daniel Voigt Godoy

    • Training a Machine to See What’s Beautiful (esp. for Hotel Photos) by Dat Tran

    • Variational Auto-Encoders for Customer Insight by Yaniv Ben-Ami, PhD

    • Deep Learning Building Blocks by Nathaniel Tucker

    • Deep Learning on Mobile by Anirudh Koul

    • Deep Learning for Anomaly Detection by Nisha Muktewar

    • Forecasting the Economy with Fifty Shades of Emotions by Sonja Tilly, CFA

    • Image Detection as a Service: How we Use APIs and Deep Learning to Support our Products by Laura Mitchell

    • Beyond OCR: Using Deep Learning to Understand Documents by Eitan Anzenberg, PhD

    • Your Future, Today. Using NLP to Advance Your Career by Gabrielle Fournet, PhD

    • Machine Learning Operations: Latent Conditions and Active Failures by Flavio Clesio

    • State of the art AI Methods with TensorFlow: Transfer Learning, RL and GANs by Daniel Whitenack, PhD

    • Modern and Old Reinforcement Learning Part 1 by Leonardo De Marchi

    • Active Learning with a Sprinkle of PyTorch by Olga Petrova, PhD

    • A Deep Dive into Convolutional Neural Networks by Susana Zoghbi, PhD

    • Predicting Future Decisions with Deep learning for Financial Trading by Ning Wang, PhD and Yuting Fu

    • AI Assisting in Traffic Relief by Piotr Kaczyński and Wioletta Stobieniecka

    • Modern and Old Reinforcement Learning Part 2 by Leonardo De Marchi

  • 4

    Machine Learning

    • Explainable ML: Application of Different Approaches by Violeta Misheva, PhD

    • Provenance: a Fundamental Data Governance Tool ⎯ a Case Study for Data Science Pipelines and Their Explanations by Luc Moreau, PhD

    • Rule Induction and Reasoning in Knowledge Graphs by Daria Stepanova, PhD

    • Practical Methods to Optimise Model Stability: A Case Study Using Customer-Lifetime Value at Farfetch by Davide Sarra and Kishan Manani, PhD

    • Sprinting Pandas by Ian Ozsvald

    • Knowledge Graphs for the Greater Good by Bojan Božić, PhD

    • Missing Data in Supervised Machine Learning by Andras Zsom, PhD

    • Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples by Dr. Kirk Borne

    • Atypical Applications of Typical Machine Learning Algorithms by Dr. Kirk Borne

    • Machine Learning in R Part I: Featuring Penalized Regression and Boosted Trees by Jared Lander

    • Good, Fast, Cheap: How to do Data Science with Missing Data by Matt Brems

    • Introduction To Face Processing With Computer Vision by Gabriel Bianconi

    • Interpreting and Explaining XGBoost Models by Brian Lucena, PhD

    • Making Happy Modelers: Build and Maintain Your Data Warehouse with AWS Redshift and Airflow by Stephanie Kirmer

    • Tracking Coal and Solar Power with Machine Learning and Satellites by Laurence Watson

    • Ensuring Ethical Practice in AI by Sray Agarwal

    • Sustainable Retail Through Open Source, Scraping and NLP by Joanneke Meijer

    • Building Personalized Scores for Customers: How to Combine Different Data Types and Learn in the Process by Svetlana Vinogradova, PhD

    • Have I Got (Financial) News for You by Alun Biffin, PhD

    • Multivariate (Flight) Anomalies Detection by Marta Markiewicz

    • CRESST: Complete Rare Event Specification Using Stochastic Treatment by Debanjana Banerjee

    • Which is the Tallest Building in Europe? — Representing and Reasoning About Knowledge by Ian Horrocks, PhD

    • Automated Insights in Finance Using Machine Learning & AI by Dr. Arun Verma

    • Hands-on Machine Learning Engineer with scikit-learn by Olivier Grisel

    • Hands-on Reinforcement Learning with Ray RLlib by Dean Wampler, PhD

    • Federated Learning: AI for the Privacism Movement by Michael Huth, PhD

    • Removing Unfair Bias in Machine Learning by Margriet Groenendijk, PhD

    • Explain Machine Learning Models by Margriet Groenendijk, PhD

    • VerticaPy: Demystifying Machine Learning Complexity with Python at Scale by Badr Ouali

    • Responsible Data Science Using Bias-Dashboards by Ramon van den Akker, PhD and Daan Knoope and Joris Krijger

    • Data Annotation at Scale: Active and Semi-Supervised Learning in Python by Gokhan Ciflikli, PhD

    • Virtual Data Science Learnathon by Paolo Tamagnini

    • Introduction to Time Series Analysis with KNIME by Maarit Widmann and Corey Weisinger

    • Algorithmic Confounding in Recommendation Systems by Allison Chaney, PhD

    • From Longitudinal Patient Observational Data to Individualized Treatments Effects Using Causal Inference by Ioana Bica

    • Building Fair and Explainable AI Pipelines by Margriet Groenendijk, PhD

    • Knowledge Graph Extraction for the Enterprise by Dr. Paul Buitelaar and Dr. John McCrae

    • Machine Learning in R Part II: Featuring Penalized Regression and Boosted Trees

    • The Evolution of Data Labeling by Soo Yang

    • Algorithms with Predictions by Michael Mitzenmacher, PhD

    • On the Automation of Data Science by Luc De Raedt, PhD

  • 5

    Data Visualization

    • Building a Better Data Visualization Culture by Alan Rutter

    • From Numbers to Narrative: Turning Raw Data into Compelling Visual Stories with Impact by Bill Shander

    • Animating Data: From matplotlib plots to GIFs by Max Humber

    • Dare to Start Simple by Dr. Katharina Glass

    • What Do I See in This Data? Visual Tools to Enhance Data Understanding by Max Novelli

  • 6

    Data for Good

    • Mapping the Waters of The United States by Alfredo Kalaitzis, PhD

  • 7

    AI in Business

    • Industrial Artificial Intelligence – From automated Process to Cognitive Analytics by Diego Galar, PhD

    • Democratizing Data for the Enterprise by Sherard Griffin

    • Integrating Small Data, Synthetic Data in AI and Data Strategy for Fashion Retail by Andrey Golub, PhD

  • 8


    • Building an Industry Classifier With The Latest Scraping, NLP and Deployment Tools by Ido Shlomo

    • A Gentle Intro to Transformer Neural Networks by Jay Alammar

    • Spark NLP for Healthcare: Lessons Learned Building Real-World Healthcare AI Systems by Veysel Kocaman, PhD

    • State-of-the-art NLP Made Easy with AdaptNLP by Brian Sacash and Andrew Chang

    • Advanced NLP: From Essentials to Deep Transfer Learning by Dipanjan (DJ) Sarkar and Anuj Gupta

    • Transformer Knows More than Meets the Eye by Michał Chromiak, PhD

    • Natural Language Processing: Feature Engineering in the Context of Stock Investing by Frank Zhao

  • 9


    • Programming with Data: Python and Pandas by Daniel Gerlanc

    • SQL for Data Science by Mona Khalil

    • Introduction to Linear Algebra for Data Science and Machine Learning With Python by Hadrien Jean, PhD

  • 10

    ML for Programmers

    • Bayesian Data Science: Probabilistic Programming by Hugo Bowne-Anderson, PhD

    • Pomegranate: Fast and Flexible Probabilistic Modeling in Python by Jacob Schreiber

  • 11

    Quant Finance

    • How to Build and Test a Trading Strategy Using ML by Stefan Jansen

  • 12

    Research Frontiers

    • Learning and Mining Large-Scale Spatiotemporal Data by Rose Yu, PhD

  • 13


    • Data Excellence: Better Data for Better AI by Dr. Lora Aroyo

    • Data Science Change Is Inevitable, Growth Is Optional by Dr. Iain Brown

    • Machine Learning for Exoplanet Discovery by Dr. David Armstrong

  • 14

    Demo Talks

    • First Aid Kit for Data Science: Keeping Machine Learning Alive by Véronique Van Vlasselaer, PhD

    • eXplainable Predictive c: Combine ML and Decision Management to Promote Trust on Automated Decision Making by Matteo Mortari and Daniele Zonca

    • Sports Analytics - Leveraging Open Source Technology to Improve Athlete Performance by Christopher Connelly

    • Is Infrastructure Holding Back Adoption of AI at Scale? by Nick Patience

    • Learn How to Seamlessly Use Julia for Your Machine Learning Tasks by Dr. Matt Bauman

    • Build and Deploy Custom AI Predictive Models by Yamini Rao

    • A Quick, Practical Overview of KNIME Analytics Platform by Paolo Tamagnini

    • An Overview of Algorithmia: the Industry Leading Machine Learning Operations and Management Platform by Kristopher Overholt

    • Best Practices: Partnerships between ML/AI and Data Labeling Companies by Soo Yang

    • Leverage Data Lineage to Maximize the Benefits of Big Data by Ernie Ostic

    • Revision Control for Structured Data by Gavin Mendel-Gleason

    • Creating Efficiency and Trust with MLOps by Jan van der Vegt

    • Build Your Own Cloud Native Covid-19 Data Analytics with Kubernetes and OpenShift by Dr. Mo Haghighi

    • VerticaPy Demo : Building a Prediction Churn Model Using Random Forest & Logistic Regression by Badr Ouali

    • Annotating Data with AI-assisted Labelling by Eric Landau

  • 15

    Career Mentor Talks

    • Demystifying data science roles and responsibilities by Eva-Marie Muller-Stuler, PhD

    • The Data Engineering Path by Daniela Petruzalek

    • Who is a Data Scientist? by Behrooz Afghahi

    • Changing career paths: be a Data Scientist! by Bea Hernández

    • Navigating Data Science Interviews by Shrilata Murthy

  • 16


    • Women in Data Science Ignite

    • Learning from Failure

ODSC WEST (October 27th - 30th)