AI Track

ITC has a special program this year with a two-day AI education track on Tuesday and Wednesday. Four sessions offer tutorial-like talks, each dedicated to an AI-related topic, including safe AI, unbiased AI, explainable AI, AI face detection, AI in yield learning, and AI for autonomous vehicles. We hope these talks will facilitate increased dialog about the role of AI in test during this year‘s ITC test week.

Talk day/time Speaker name Email address Title Short Description
Tuesday 13:50 Andre Platzer aplatzer@cs.cmu.edu Safe AI in CPS This talk presents provably safe reinforcement learning that provides the best of both worlds: the exploration and optimization capabilities of learning along with the safety guarantees of formal verification.
Tuesday 14:40 Roy Maxion

 

 

maxion@cs.cmu.edu The Mismeasure of AI This talk will examine sources and consequences of a range of biases, how they influence data, how outcomes based on flawed protocols and hence biased data can be invalid, and how the hazards posed by such invalidities can be mitigated.
Tuesday 16:00 Marios Savvides marioss@andrew.cmu.edu Seeing faces through the eyes of Artificial Intelligence Despite common belief, face detection is not a solved problem especially in tough environments that include crowds and occlusion. Face detection using non-deep learning approaches that meet these challenges will be presented.
Tuesday 16:45 Anupam Datta danupam@cmu.edu Influence-directed explanations for machine learning systems Influence-directed explanations shed light on the inner workings of black-box machine learning systems by identifying components that causally influence system behavior and by providing human-understandable interpretation to the concepts represented by these components. This talk describes instances of this paradigm that are model-agnostic and instances that are specific to deep neural networks.
Wednesday 08:30 David Pan dpan@ece.utexas.edu Machine Learning for Yield Learning and Optimization This talk surveys recent results of using various machine learning/deep learning techniques for performance modeling under uncertainty, lithography modeling with transfer/active learning, lithography hotspot detection, and IC mask optimizations. State-of-the-art methods are explained and challenges/opportunities are discussed.
Wednesday 09:15 Ken Harris ken.harris@pdf.com Practical Applications of Big Data Analytics in Semiconductor Manufacturing, Assembly and Test Traditional data gathering and visualization techniques are becoming less and less useful in today’s manufacturing environment.  In this talk, we review the reasons why, along with practical examples illustrating the importance of integrated, automated analysis and triggered actions in the product ramps of today’s advanced products and manufacturing technologies.
Wednesday14:00 Li-C. Wang licwang@ece.ucsb.edu An Autonomous System View To Apply Machine Learning This talk draws an analogy to the autonomous system view of self-driving car for applying machine learning in a test application. Through such a system view it is more intuitive to see where a particular machine learning approach might be applied and what type of learning problem is to be solved. To give a concrete example, a short demo will be included to illustrate how such an autonomous system can be used for production yield data analytics.
Wednesday 14:45 Qi Zhu qzhu@northwestern.edu Design Automation for Intelligent Automotive Systems This talk will discuss the challenges in designing the next-generation autonomous and connected vehicles, and present promising design automation techniques that tackle these challenges.
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