Visual Analytics for Explainable AI and Automated Machine Learning

Speaker:        Professor Huamin QU
                Department of Computer Science and Engineering
                Hong Kong University of Science and Technology

Title:          "Visual Analytics for Explainable AI and Automated
                 Machine Learning"

Date:           Monday, 30 Sept 2019

Time:           4:00pm - 5:00pm

Venue:          Lecture Theater F (near lift no. 25/26), HKUST

Abstract:

With the recent advances in machine learning, especially deep learning,
we have witnessed increasing applications of AI in various domains. The
past few years have seen a growing interest towards explainable AI (XAI)
and automated machine learning (AutoML). At the VisLab of Hong Kong
University of Science and Technology, we focus on human-centered
approaches to make AI explainable, interactive, and trustworthy. In
critical domains such as finance, security, and healthcare, explainability
allows users to make more reliable decisions powered by the collaboration
of machine- and human-intelligence. Interactive machine learning includes
human inputs in the model training process to improve the performance of
models. With transparency and intuitive feedback to users, it is easier to
build trustworthy AI solutions.  In this talk, I will first outline the
research issues in visual analytics for explainable AI and automated
machine learning.  After that I will introduce some representative works
from the VisLab including 1)  DeepTracker, a visual analytics system to
facilitate the exploration of the rich dynamics of CNN training processes
and to identify the unusual patterns that are hidden behind the huge
amount of training log;  2) RNNVis, a visual analytics tool for
understanding and comparing recurrent neural networks (RNNs)
for text-based applications; 3) RuleMatrix, an interactive visualization
technique that helps users with little expertise in machine learning to
understand, explore and validate classification models using rule-based
explanations; 4) ATMSeer, an interactive visualization tool that supports
machine learning experts in analyzing the results of AutoML and enables
them to monitor the AutoML process, analyze the searched models, and
refine the search space in real time.


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Biography:

Huamin Qu is a professor in the Department of Computer Science and
Engineering (CSE) at the Hong Kong University of Science and Technology.
His main research interests are in visualization and human-computer
interaction, with focuses on urban informatics, social network analysis,
e-learning, text visualization, and explainable artificial
intelligence. His research has been recognized by 9 best paper/honorable
mention awards, 2009 IBM Faculty Award, 2014 Higher Education Scientific
and Technological Progress Award (Second Class) from the Ministry of
Education of China, 2015 HKICT Best Innovation (Innovative Technology)
Silver Award from the Hong Kong Institution of Engineers, 2015 APICTA
Merit Award in E-Learning from the Asia Pacific ICT Alliance, 2016
Distinguished Collaborator Award from Huawei Noah's Ark Lab, and 2018 Yelp
Dataset Challenge Round 10 Grand Prize Award. He is currently an associate
editor of Computer Graphics Forum (CGF), and was an associate editor of
IEEE TVCG, a paper co-chair for IEEE VIS'14, VIS'15 and VIS'18. He
obtained a BS in Mathematics from Xi'an Jiaotong University, China, an MS
and a PhD (2004) in Computer Science from the Stony Brook University.