Changing the Narrative: The Importance of Responsible AI and Human-AI Collaboration
Lama Nachman | Intel Fellow, Director of Human & AI Systems Research Lab | Intel
In this keynote, we will discuss Intel's research in different applications as well as robust and ethical perception utilizing multi-modal sensing and sensemaking, and probabilistic computing.
How Can You Trust Machine Learning?
Carlos Guestrin, PhD | Professor, Computer Science | Stanford University
In this talk, Carlos presents a framework, anchored on three pillars: Clarity, Competence, and Alignment. For each, he describes algorithmic and human processes that can help drive towards more effective, impactful, and trustworthy AIs.
Increasing Accuracy with Human Labeling and Weak Learning
Elliot Branson | Director of AI and Engineering | Scale AI
In this talk, Elliot Branson explains how efficient forms (without SGD, for example) of weak learning can effectively enable unsupervised learning. He’ll compare different approaches to weak labeling and feature engineering with Snorkel, FlyingSquid, and open source packages like scikit-learn and XGBoost.
Unlocking Climate-Related Data Through Open Source and Data Mesh Architecture
Vincent Caldeira | Chief Technologist (FSI) | SAS
Erik Erlandson | Senior Principal Software Engineer | SAS
In this talk, the presenters will discuss how they are implementing the OS-Climate data commons platform based on a data mesh architecture to make data accessible, available, discoverable, and interoperable across various development streams, while supporting strict compliance requirements around data access and regulatory disclosures
Tutorial on Uplift Modeling: How to Optimize using Uplift Predictive Models and Uplift Prescriptive Analytics
Victor Lo, PhD | Head of Data Science & Artificial Intelligence |Fidelity Investments
In this tutorial, Victor will provide an introduction to the concept of Uplift, compare it with traditional response modeling, and review various approaches to Uplift Modeling. The discussion will include approaches to handling a more general situation where only non-experimental (or observational) data are available, integrating causal inference techniques with uplift modeling.
Personalized Machine Learning
Julian McAuley, PhD | Professor | Artificial Intelligence Group, UCSD
In this talk, we'll introduce a common set of principles and methods that underpin the design of personalized predictive models. We'll begin by revising "traditional" forms of personalized learning, such as recommender systems. Later, we'll see how similar ideas apply to domains such as natural language processing and computer vision.
ML Ops: Doing the Things to Preserve Tomorrow’s Machine Learning Sanity Today
Seth Juarez | Principal Cloud Advocate | Microsoft
Going from a well-used notebook to production-ready software that delivers customer value in a repeatable way is hard. In this session, we will look at a couple of concepts that, if employed correctly, will save a ton of grief down the road. Some of these concepts include choosing appropriate frameworks, understanding experimentation progression, continuous integration, safe deployment, and maybe a touch of monitoring.
Applications of Modern Survival Modeling with Python
Brian Kent, PhD | Data Scientist | Founder | The Crosstab Kite
Our goal in this talk is to help the audience add survival modeling to their working data science tool belt. We’ll first introduce basic concepts of survival analysis like censored data, duration matrices, and survival curves. We’ll then show when to consider using survival models instead of other methods, how to use popular Python survival analysis tools Lifelines, Scikit-survival, and Convoys, and how to interpret model results for either prediction or decision-making.
How to Prepare for the Future of Data Science
Daliana Liu | Senior Data Scientist | Amazon
Allie Miller | Global Head of ML BD, Startups and VC | AWS
In this session, you’ll gain a primer of what to expect from the future of data science careers. Topics include the different types of data scientists, core skills you’ll need, and what skills in particular will set you apart from others.
A Framework for Identifying Host-Based Artifacts in Dark Web Investigations |
Arica Kulm | Lead Digital Forensic Analyst | DigForCE Lab, Dakota State University
This session will discuss a framework for identifying host-based artifacts during digital forensic investigations involving suspected dark web use. This framework is reusable, comprehensive, and easy to follow and will assist investigators in finding artifacts that are designed to be hidden or otherwise hard to find. Attendees can expect to learn steps for determining if a system contains host-based artifacts for either Windows-based artifacts or macOS-based artifacts.
A ModelOps Approach to Address Ethical Concerns in AI Systems
May Masoud | Data Science Advisor | SAS
This session takes the philosophical question of ethical responsibility to the pragmatic realm and provides a business framework to address ethical concerns in AI systems. It provides the foundation required to build and implement business AI that is responsible and future-proof.
