Advances and Frontiers in Auto AI & Machine Learning


Automated Machine Learning (Auto ML) refers broadly to technologies that automate the process of generating, evaluating, and selecting an ML pipeline optimized for a specific dataset. Techniques tackle both traditional ML pipelines with data pre-processing, feature engineering & selection, algorithm selection and hyper parameter optimization, and neural architecture search (NAS) for deep learning models. While current capabilities in Auto ML enable users to complete these steps in a few mouse clicks or lines of code, it still automates only a small portion of the data scientist and ML engineer’s workloads. In this talk, I will focus on recent advances that will have a dramatic impact on driving automation across the entire AI/ML lifecycle, from data discovery and curation; to advanced model building with business and fairness constraints; to automation to monitor models in deployment, recognizing deficiencies and recommending corrections. Automation for this end-to-end AI/ML lifecycle is sometimes referred to as AutoAI. I will also describe frontiers of Auto ML/AI, including research into automating knowledge augmentation for AI/ML models, specifically by mining and incorporating domain knowledge from open data sources, enterprise structured knowledge, Jupyter notebooks and text documents. Another example targets expanding the scope of Auto ML/AI to novel and more powerful tasks. Current AutoML focuses on supervised learning for classification, prediction, and forecasting tasks. I will describe research into automation for tackling novel tasks such as unsupervised learning and decision optimization. This session will be of interest to practicing data scientists and AI/ML engineers, as well as AI/ML researchers.


Dr. Lisa Amini is the Director of IBM Research Cambridge, which is also home to the MIT-IBM Watson AI Lab, and of IBM's AI Horizons Network. Lisa was previously Director of Knowledge & Reasoning Research in the Cognitive Computing group at IBM’s TJ Watson Research Center in New York, and she is also an IBM Distinguished Engineer. Lisa was the founding Director of IBM Research Ireland, and the first woman Lab Director for an IBM Research Global (i.e., non-US) Lab (2010-2013). In this role she developed the strategy and led researchers in advancing science and technology for intelligent urban and environmental systems (Smarter Cities), with a focus on creating analytics, optimizations, and systems for sustainable energy, constrained resources (e.g., urban water management), transportation, and the linked open data systems that assimilate and share data and models for these domains. She earned her PhD degree in Computer Science from Columbia University.