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<!DOCTYPE html>
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<li><a href="#abstract">Abstract </a></li>
<li><a href="#outline">Outline </a></li>
<li><a href="#audience">Target Audience</a></li>
<li><a href="#materials">Materials</a></li>
<li><a href="#venue">Venue</a></li>
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<h2><span class="highlight">Towards Autonomous Machine Learning:</span><br> Evolution of AutoML, Roles of Humans, and Related Topics</h2>
<div class="ajcai-bit">
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<h3>Tutorial at AJCAI 2024</h3>
<a href="https://ajcai2024.org/index.html" target="_blank" rel="noreferrer">
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<img src="./index_files/ai_2024_logo.jpg" alt="AJCAI 2024" class="logo-group">
</div>
</a>
</div>
<h4>Monday 25 November 2024, 13h30 - 17h00 AEST<br>Room 03, Level 3, RMIT University City Campus Building 80, Melbourne, Australia</h4>
</div>
</div>
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<canvas width="3020" height="800" style="width: 1510px; height: 400px;"></canvas>
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<main id="main" class="container">
<section id="abstract">
<h2>Abstract</h2>
<p class="justified-text">
Recent years have seen an unprecedented level of technological uptake and engagement by the mainstream. From deepfakes for memes to recommendation systems for
commerce, machine learning (ML) has become a regular fixture in society. This ongoing transition from purely academic confines to the general public is not
smooth as the public does not have the extensive expertise in data science required to fully exploit the capabilities of ML. As automated machine learning
(AutoML) systems continue to progress in both sophistication and performance, it becomes important to understand the 'how' and 'why' of human-computer
interaction (HCI) within these frameworks. This is necessary for optimal system design and leveraging advanced data-processing capabilities to support
decision-making involving humans. It is also key to identifying the opportunities and risks presented by ever-increasing levels of machine autonomy.
<br>
This tutorial provides an expansive perspective on what constitutes an automated/autonomous ML system and how humans interact with such systems. The authors
also focus on the following questions: (i) What does HCI currently look like for state-of-the-art AutoML algorithms? (ii) Do the expectations of HCI within
AutoML frameworks vary for different types of users and stakeholders? (iii) How can HCI be managed so that AutoML solutions acquire human trust and broad
acceptance? (iv) As AutoML systems become more autonomous and capable of learning from complex open-ended environments, will the fundamental nature of HCI
evolve? To consider these questions, the authors project existing literature in HCI into the space of AutoML and review topics such as user-interface design,
human-bias mitigation, and trust in artificial intelligence (AI). Additionally, to rigorously gauge the future of HCI, they contemplate how AutoML may manifest
in effectively open-ended environments. Ultimately, this tutorial serves to identify key research directions aimed at better facilitating the roles and modes of
human interactions with both current and future AutoML systems.
</p>
</section>
<section id="outline">
<h2>Tutorial Outline</h2>
<h3><b style="color: red;">Goals:</b> The main goal of this tutorial is to provide the audiences with an overview of how AutoML systems evolve to become steadily more autonomous over
time, as well as the roles and modes of human interactions with such AutoML/AutonoML systems.</h3>
<ol>
<li><b>Introduction to AutoML, AutonoML, and related topics</b> (35 mins)
<ul>
<li>Existing challenges of data science (5 mins)</li>
<li>ML workflow and the need for AutoML and Autonomous ML systems (10 mins)</li>
<li>Fundamental components of AutoML and AutonoML systems (10 mins)</li>
<li>AutoML Toolboxes (5 mins)</li>
<li>Questions (5 mins)</li>
</ul>
</li>
<li>
<b>Interacting with AutoML Systems: Current Practices</b> (45 mins)
<ul>
<li>Types of stakeholders (5 mins)</li>
<li>Roles and Modes within the machine learning workflow (10 mins)</li>
<li>The user interface: Many modalities (5 mins)</li>
<li>Improving the outcomes of interactions (10 mins)</li>
<li>The user interface: Key requirements (5 mins)</li>
<li>Questions (5 mins)</li>
<li>Break (5 mins)</li>
</ul>
</li>
<li>
<b>Interacting with AutoML Systems: Constrained but Fully Automated</b> (35 mins)
<ul>
<li>An introduction to AutonoML systems and their fundemental components (15 mins)</li>
<li>A solution for building constrained but fully automated systems: (5 mins)</li>
<li>Roles and Modes when human involvement is no longer required (8 mins)</li>
<li>What lies beyond the Constraints (2 mins)</li>
<li>Questions (5 mins)</li>
</ul>
</li>
<li>
<b>Interacting with AutoML Systems: Open-ended Environments</b> (35 mins)
<ul>
<li>The challenges of learning in an open world (5 mins)</li>
<li>One possible future: AutonoML and reasoning (10 mins)</li>
<li>Roles and Modes in relation to autonomous open-world systems (10 mins)</li>
<li>Questions (5 mins)</li>
<li>Break (5 mins)</li>
</ul>
</li>
<li>
<b>Critical Discussion and Future Directions</b> (30 mins)
<ul>
<li>Critical discussions (10 mins)</li>
<li>A case study of AutonoML in an end-to-end digital twin of bioprocesses for monoclonal antibody manufacturing (10 mins)</li>
<li>Potential research directions (5 mins)</li>
<li>Questions (5 mins)</li>
</ul>
</li>
</ol>
</section>
<section id="audience">
<h2>Target Audience</h2>
<p>
This tutorial is aimed at Machine learning researchers and developers who have experience in building AutoML solutions and are interested in exploring the
potential of AutoML in improving their system building process. This tutorial is also appropriate for computer science researchers and specific domain experts
in understanding how they can interact with AutoML systems and leverage them for their works. Recommended prerequisites are:
</p>
<ul>
<li><b>Machine Learning</b>: Understanding of classical and deep machine learning models. Knowledge of AutoML system development will be a plus, but not required.</li>
<li><b>Data Science</b>: Understanding of basic concepts in Data Science and Artificial Intelligence. Basic experience with human-machine interactions will be a plus.</li>
</ul>
</section>
<section id="materials">
<h2>Materials</h2>
<p>All of the slides and other accompanying materials are available on the <a href="https://github.com/UTS-CASLab/autonoml-tutorial" rel="noreferrer">GitHub repo</a>.</p>
<p>The primary content of this tutorial is drawn from our recent comprehensive review papers and monographs as follows:</p>
<ol>
<li>T. T. Khuat, D. J. Kedziora, and B. Gabrys, "<a href="http://dx.doi.org/10.1561/1100000091">The roles and modes of human interactions with
automated machine learning systems: A critical review and perspectives</a>," <em>Foundations and
Trends® in Human-Computer Interaction</em>, vol. 17, no. 3-4, pp. 195-387, 2023.</li>
<li>D. J. Kedziora, K. Musial, and B. Gabrys, "<a href="http://dx.doi.org/10.1561/2200000093">Autonoml: Towards an integrated framework for autonomous
machine learning</a>," <em>Foundations and Trends® in Machine Learning</em>, vol. 17, no. 4, pp. 590-766, 2024.</li>
<li>A. Scriven, D. J. Kedziora, K. Musial, and B. Gabrys, "<a href="http://dx.doi.org/10.1561/2900000030">The technological emergence of automl:
A survey of performant software and applications in the context of industry</a>," <em>Foundations and
Trends® in Information Systems</em>, vol. 7, no. 1-2, pp. 1-252, 2023.</li>
<li>X. Dong, D. J. Kedziora, K. Musial, and B. Gabrys, "<a href="http://dx.doi.org/10.1561/2200000119">Automated deep learning: Neural architecture search is not the end</a>,"
<em>Foundations and Trends® in Machine Learning</em>, vol. 17, no. 5, pp. 767-920, 2024.</li>
<li>T. T. Khuat, R. Bassett, E. Otte, A. Grevis-James, and B. Gabrys, "<a href="https://doi.org/10.1016/j.compchemeng.2024.108585">Applications of machine learning in antibody discovery, process development,
manufacturing and formulation: Current trends, challenges, and opportunities</a>," <em>Computers & Chemical Engineering</em>, vol. 182, pp. 108585, 2024</li>
</ol>
</section>
<section id="venue">
<h2>Venue</h2>
<p>The tutorial will be located in <b>Room 03, Level 3, RMIT University City Campus Building 80</b>, 445 Swanston Street, Melbourne, VIC 3000, Australia.</p>
</section>
<section id="speakers">
<h2>Speakers</h2>
<div class="flex-container flex-align-center profile">
<div class="flex-item"><img src="./index_files/Bogdan_Gabrys.jpg" alt="Prof. Bogdan Gabrys" class="profile-photo">
<div class="profile-details">
<h3>Prof. Bogdan Gabrys</h3>
<h4>Professor, UTS</h4>
<ul class="profile-links">
<li><a href="https://profiles.uts.edu.au/Bogdan.Gabrys" target="_blank" rel="noreferrer">UTS Profile</a></li>
<li><a href="https://scholar.google.com.au/citations?user=E1rkUpQAAAAJ&hl=en" target="_blank" rel="noreferrer">Google Scholar</a></li>
</ul>
</div>
</div>
<div class="flex-item">
<p class="justified-text">
Bogdan Gabrys is a data scientist and strategist with deep expertise in Data Science, Complex Adaptive Systems, Computational Intelligence, Machine Learning
and Predictive Analytics gained from a broad range of application areas and industries. He is currently a Professor of Data Science and a Co-Director of
the Complex Adaptive Systems Laboratory at the University of Technology Sydney, Sydney, Australia. Over the last 30 years, he has been working at various
universities and research and development departments of commercial institutions. His research activities have concentrated on the areas of data science,
complex adaptive systems, computational intelligence, machine learning, predictive analytics, and their diverse applications. He has published over 220
research papers, chaired conferences, workshops, and special sessions, and been on program committees of a large number of international conferences with
the data science, computational intelligence, machine learning, and data mining themes. He is frequently invited to give keynote and plenary talks at
international conferences and lectures at internationally leading research centres and commercial research labs.
</p>
</div>
</div>
<div class="flex-container flex-align-center profile">
<div class="flex-item"><img src="./index_files/Thanh_Tung_Khuat.jpg" alt="Dr. Thanh Tung Khuat" class="profile-photo">
<div class="profile-details">
<h3>Dr. Thanh Tung Khuat</h3>
<h4>Research Fellow, UTS</h4>
<ul class="profile-links">
<li><a href="https://profiles.uts.edu.au/ThanhTung.Khuat" target="_blank" rel="noreferrer">UTS Profile</a></li>
<li><a href="https://scholar.google.com.au/citations?user=2UDpb4cAAAAJ&hl=en" target="_blank" rel="noreferrer">Google Scholar</a></li>
</ul>
</div>
</div>
<div class="flex-item">
<p class="justified-text">
Thanh Tung Khuat is now working as a Postdoctoral Research Fellow at the Complex Adaptive Systems Lab at the University of Technology Sydney (UTS),
focusing on building modern explainable, transparent, and robust machine learning algorithms. He obtained a Ph.D. in Data Science in 2021 from UTS.
He has previously been a Senior software engineer at the FPT Software Danang, Vietnam (Jan-2018 to Jun-2018), has been a project officer at the School
of Computer Science and Engineering, Nanyang Technological University, Singapore (Aug-2016 to Mar-2017).
Dr. Khuat is also an active reviewer for many prestigious journals and international conferences such as IEEE TNNLS, IEEE TIE, IEEE TSMC: Systems,
Soft Computing, AI Review, ASE, IEEE Access, and ICONIP. He is currently a member of the IEEE, IEEE Computational Intelligence Society, and IEEE Computer
Society. Khuat's research interests include machine learning, interpretable machine learning, fuzzy systems, knowledge discovery, evolutionary computation,
intelligent optimization techniques, and applications in biopharmaceuticals, medicine, finance, and agriculture. He has authored and co-authored over 40
peer-reviewed publications in the areas of machine learning and computational intelligence.
</p>
</div>
</div>
</section>
<section id="acknowledgment">
<h2>Acknowledgment</h2>
<div class="flex-container flex-align-center profile">
<div class="flex-item acknowledgment">
<p class="justified-text">This tutorial is partly supported under the Australian Research Council's Industrial Transformation Research Program (ITRP) funding scheme
(project number IH210100051). However, the vast majority of the materials, including four monographs, had been generated and published prior to and independent from the Hub.</p>
<p class="justified-text">The ARC Digital Bioprocess Development Hub is a collaboration between The University of Melbourne, University of Technology Sydney, RMIT University,
CSL Innovation Pty Ltd, Cytiva (Global Life Science Solutions Australia Pty Ltd) and Patheon Biologics Australia Pty Ltd.</p>
<p>For more information on the Centre, please visit the website at <a href="https://digitalbioprocesshub.org.au/" target="_blank" rel="noreferrer">https://digitalbioprocesshub.org.au</a>.</p>
</div>
<div class="flex-item">
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