• 1

    ODSC Talks

    • A Decade of Machine Learning Accelerators_ Lessons Learned and Carbon Footprint by David Patterson, PhD

    • AI in a Minefield_Learning from Poisoned Data by Johnathan Roy Azaria

    • Denoising Diffusion-based Generative Modeling by Stefano Ermon, PhD

    • Toward Robust, Knowledge-Rich Natural Language Processing by Hannaneh Hajishirzi, PhD

    • Operationalizing Organizational Knowledge with Data-Centric AI by Alex Ratner, PhD

    • Inclusive Search and Recommendations by Nadia Fawaz, PhD

    • How the Changing MLOps Landscape is Reinventing DataOps by Rajsekhar (Raj) Aikat

    • The Next Thousand Languages by Steven Bird, PhD

    • An Intuition-Based Approach to Reinforcement Learning by Oswald Campesato

    • Introduction to Generative Art with Stable Diffusion, presented by HP Inc by Hunter Kempf

    • Real-time Data Science Made Easy by Chip Kent

    • Emerging Approaches to AI Governance_Tech-Led vs Policy-Led by Ilana Golbin

    • Look, Listen, Read:Unified AI with TorchMultimodal by Suraj Subramanian and Evan Smothers

    • Continual Learning of Natural Language Processing Tasks by Bing Liu, PhD

    • Why you can’t Apply Common Software Best Practices Directly to Data Workflows, and What you can do About it by Anna Filippova

    • Data Science Without Data Collection Using FedScale by Mosharaf Chowdhury, PhD

    • A Tale of Adversarial Attacks & Out-of-Distribution Detection Stories in the Activation Space by Celia Cintas, PhD

    • Open-source Data Curation and Governance for Large and Growing Data Lakes by Roger Dev

    • Rethinking ML Development - A Data-Centric Approach by Jimmy Whitaker

    • Impact of Data Science on Social Media Data by Jyotika Singh

    • Graph Data Science:The Secret Ingredient for Relationship-Driven AI by Katie Roberts, PhD

    • Four Reasons the Data Science Development Experience Sucks by Greg Michaelson, PhD

    • Data Analytics at Scale:A Four-legged Stool by Michael Stonebraker, PhD

    • Unified and Efficient Multimodal Pretraining Across Vision and Language by Mohit Bansal, PhD

    • Interpretable AI or How I Learned to Stop Worrying and Trust AI by Ajay Thampi, PhD

    • Causal AI by Robert Osazuwa Ness, PhD

    • AI TCO (Total Cost of Ownership) Considerations from Pilot to Production Scale by Justin Emerson

    • Search and Discovery in News and Research by Dr. Anju Kambadur

    • DS-AI for Incident Response & Threat Hunting with CHRYSALIS & DAISY by Jess Garcia

    • Robust and Equitable Uncertainty Estimation by Aaron Roth, PhD

    • AI-driven Healthcare Navigation by Kira Radinsky, PhD and Guy Elad

    • Orchestrating Data Assets instead of Tasks, with Dagster by Sandy Ryza

    • Tackling Climate Change with Machine Learning by Priya Donti, PhD

    • Riding the Tailwind of NLP Explosion by Rongyao Huang

    • Continual Learning: Build Sustainable AI Models in Production by Ke Ji

    • Vector Search - A gentle introduction by Zain Hasan

    • Human Factors of Explainable AI by Meg Kurdziolek, PhD

    • CI:CD for Machine Learning by Alex Kim

    • Cybersecurity and Policing in the Metaverse by Jack McCauley

    • Archetypal Analysis: Maintaining Contrastive Categories in Cluster Analysis by Jacob Nelson

    • Achieving Techquity_Digital Health Equity by Tushar Mehrotra and Michael Thompson

    • Cloud Directions, MLOps and Production Data Science by Joe Hellerstein, PhD
  • 2

    ODSC Workshops & Trainings

    • Any Way You Want It_ Integrating Complex Business Requirements into ML Forecasting Systems by David Koll

    • Making Data-driven Decisions with Azure Machine Learning & Responsible AI Dashboard by Manesh Raveendran Pillai

    • Big Data Analytics and Visualization with R by Ysis Wilson-Tarter

    • Transforming Enterprise Data Science with Transformers by Rajiv Shah, Ph

    • Perspectives on Hyperparameter Scheduling in Deep Learning by Cameron Wolfe

    • Lightning AI - Introduction to the PyTorch Lightning Ecosystem by Kaushik Bokka

    • Domino Data Lab - Large Scale Deep Learning using the High-Performance Computing Library OpenMPI and DeepSpeed by Jennifer Dawn Davis

    • Data Drift Identification for NLP Models in the Context of AI Governance for Enterprises by Sourav Mazumder

    • A Hands-on Introduction to Transfer Learning by Tamoghna Ghosh

    • NLP Fundamentals by Leonardo De Marchi and Laura Skylaki, PhD

    • Advanced Gradient Boosting: Probabilistic Regression and Categorical Structure by Brian Lucena

    • Deep Learning with Python and Keras (Tensorflow 2) by Amita Kapoor, PhD

    • Statistics for Data Science by Andrew Zirm, PhD

    • Introduction to Python for Data Analysis by Leonidas Souliotis, PhD

    • Introduction to Machine Learning by Julia Lintern
  • 3

    ODSC Tutorials

    • Practical Tutorial on Uncertainty and Out-of-distribution Robustness in Deep Learning by Balaji Lakshminarayanan, PhD

    • Getting Started With Quantum Bayesian Networks in Python and Qiskit by Frank Zickert, PhD

    • AI4Cyber_ An Overview of the Field and an Open-Source Virtual Machine for Research and Education by Sagar Samtani, PhD

    • Colossal-AI_A Unified Deep Learning System For Large-Scale Parallel Training by James Demmel, PhD and Yang You, PhD

    • Practicing Trustworthy Machine Learning: A Tutorial by Subho Majumdar, PhD, Matthew McAteer, Yada Pruksachatkunv

    • Not Just Deep Fakes_Applications of Visual Generative Models in Pharma Manufacturing by Guglielmo Iozzia

    • Unifying ML With One Line of Code by Daniel Lenton, PhD

    • Full-stack Machine Learning for Data Scientists by Hugo Bowne-Anderson, PhD and Eddie Mattia

    • Foundations of Deep Reinforcement Learning by Pieter Abbeel, PhD