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New Editor-in-Chief for the IEEE Transactions on Artificial Intelligence
It is with great pleasure to announce the new Editor-in-Chief (EiC) for the IEEE Transactions on Artificial Intelligence (IEEE TAI), Prof. Jun Wang.
Prof. Wang will commence his term as EiC for IEEE TAI from the 1 January 2025 till the 31 December 2026. The memorandum of understanding governing the operations of TAI allows the term for the EiC to be renewed once. Although Prof. Wang is very well-known to our community, for our younger generations, a bio for Prof. Wang can be found here.
Prof. Kay Chen Tan
IEEE CIS VP Publications
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IEEE CIS Election Coming in November
CIS will launch the Annual Election on 4 November 2024 to elect the following positions:
ADCOM Member-at-Large (2025-2027) - 5 positions
The CIS Nominations Committee has followed the process outlined in our governing documents and has selected candidates for your consideration. Their biography and position statements are available for you to review in preparation for the election by visiting the IEEE CIS Election page.
Jim Keller
Chair, Nominations Committee
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Call for Nominations for the IEEE Frank Rosenblatt Award
The IEEE Frank Rosenblatt Award Committee invites nominations for the 2026 award. The deadline for nominations is 15 January 2025.
To help nominators to benchmark potential nominees, examples of previous recipients include Lawrence J Fogel, John J Hopfield, Vladimir Vapnick, Geoffrey Hinton, and Marco Dorigo. The 2025 award went to Yaochu Jin, MAE , FIEEE.
Hussein Abbass
Chair (2024-2025)
IEEE Frank Rosenblatt Award Committee
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Upcoming Live CIS Webinars
Diverse Solutions for Complex Problems: Exploring Multimodal Optimization
 Speaker: Dr Ali Ahrari, University of New South Wales (UNSW), Canberra, Australia
Friday, 25 October 2024 6:00 AM - 7:00 AM (UTC-04:00) Eastern Time (US & Canada)
Join us to discover how multimodal optimization empowers decision-makers to navigate complexities and discover innovative and practical solutions.
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Scaling Challenges in LLM Training 
Speaker: Lalit Chourey, Meta Platforms INC, Seattle, USA
Monday, 11 November 2024 12:00 PM - 1:00 PM (UTC-04:00) Eastern Time (US & Canada)
The session will cover the challenges associated with scaling the training of large language models (LLMs).
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CIS Welcomes New Chapters
CIS would like to congratulate and welcome the new Chapters that were formed in the third quarter of 2024!
- IEEE Beijing Section Computational Intelligence Society Chapter
- Mohan Babu University-Tirupati Computational Intelligence Society Student Branch Chapter, Hyderabad Section
- KIET Group Of Institutions Computational Intelligence Society Student Branch Chapter, Uttar Pradesh Section
- Universidad Simon Bolivar Computational Intelligence Society Student Branch Chapter, Colombian Caribbean Section
- Manipal University Jaipur Computational Intelligence Society Student Branch Chapter, Delhi Section
- Universidad de Santiago Computational Intelligence Society Student Branch Chapter, Chile Section
- Basaveshwar Engineering College Computational Intelligence Society Student Branch Chapter, Bangalore Section
- Malnad College of Engineering - Hassan Computational Intelligence Society Student Branch Chapter, Bangalore Section
- Cummins College of Engineering for Women-Pune Computational Intelligence Society Student Branch Chapter, Pune Section
- Hamdard University Islamabad Computational Intelligence Society Student Branch Chapter, Islamabad Section
- Lakireddy Bali Reddy College of Engineering Computational Intelligence Society Student Branch Chapter, Hyderabad Section
Sansanee Auephanwiriyakul
Chapters Subcommittee Chair
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Congratulations to FLAME Technical Challenge 2024 Teams Moving to the Next Level
Thank you to everyone who submitted Expressions of Interest to the IEEE CIS FLAME Technical Challenge 2024. Congratulations to the team leaders and teams who were selected to move on to the next, intermediate step, with an 14 October submission deadline.
For the complete list of team leaders and teams moving on to the intermediate step please click here.
Follow our YouTube channel for more information on the submissions.
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IEEE ICDL 2025
 16-19 September 2025, Czech Technical University (CTU) in Prague
The IEEE International Conference on Development and Learning (ICDL) conference is a unique meeting gathering researchers from computer science, robotics, psychology, neuroscience, and other disciplines to share and discuss research on how humans and other animals learn and develop and how this can inform and be informed by robotics and machine learning systems.
Visit IEEE ICDL 2025 Important Dates to contribute and register.
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IEEE 2025 CoG
The annual IEEE Conference on Games (IEEE CoG) aims to be a leading venue for researchers and practitioners to exchange ideas and novel approaches to bring innovation in and through games. Games are a great domain to study and develop novel ideas in design, artificial intelligence, human-computer interaction, psychology, education, sociology, and creativity, as well as their applications in real-world problems.
Mark your calendars! IEEE 2025 CoG will take place 26 - 29 August 2025 in Lisboa, Portugal.
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IEEE CIS Conference Participation and Travel Grants
IEEE CIS is pleased to offer travel grants to support our Society members to attend and present in selected CIS-sponsored conferences. The travel grant for the following conferences are opening for application:
For more information visit IEEE CIS Conference Participation and Travel Grants.
Yi Mei
Travel Grant Subcommittee Chair
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IEEE CIS sponsors and co-sponsors a number of conferences across the globe.
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IEEE International Symposium on Emerging Metaverse Registration is Open
The IEEE International Symposium on Emerging Metaverse (ISEMV 2024) extends a warm invitation to join us on 21 October 2024 in Bellevue, the Greater Seattle area, WA, USA.
This year’s theme is "Navigating the Virtual Frontier." ISEMV 2024 will be a full-day in-person event on Monday, October 21, co-located with the 23rd IEEE International Symposium on Mixed and Augmented Reality (ISMAR).
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IEEE CIS Pioneer Award Ceremony at IEEE WCCI 2024
IEEE CIS President Yaochu Jin and Vice President, Technical Activities Christian Wagner introduce and present the 2024 IEEE CIS Fuzzy Systems Pioneer Award to Qiang Shen during the 2024 IEEE CIS World Congress on Computational Intelligence (IEEE WCCI 2024) in Yokohama, Japan "for developing data-driven approximate knowledge-based decision support systems and their applications."
IEEE CIS President Yaochu Jin, 2024-2025, (left) presenting Qiang Shen (right) with the 2024 Fuzzy Systems Pioneer Award at IEEE WCCI 2024
Johan Suykens (right), receiving the 2024 Neural Network Pioneer Award presented by Yaochu Jin (left) at IEEE WCCI 2024.
Johan Suykens was presented with the 2024 Neural Network Pioneer Award "for contributions to least squares support vector machines" at IEEE WCCI 2024.
Please visit IEEE CIS Awards for more information.
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2025 Call For Summer School Proposals
You are encouraged to submit a proposal to hold a CIS summer school in Computational Intelligence from April to December 2025. If the proposal is approved, and upon request, CIS will provide a financial contribution to support the initiative. The amount of the financial support from CIS depends on the available budget, the number of financed proposals and the soundness of the school budget. Organizers can take advantage of other initiatives, e.g., the CIS Distinguished Lecture Program to further support the school (related regulations apply).
Important Dates:
- Eligible period: 1 April 2025 to 31 December 2025
- Deadline for submitting the proposal: 15 January 2025 (late submissions may also be considered, but subject to the availability of the budget balance)
- Notification of the outcome of the review process: 15 February 2025
For more information visit the IEEE CIS Summer Schools webpage.
Chun-Rong Huang
Summer School Subcommittee Chair
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Call for Associate Editors for the IEEE Transactions on Artificial Intelligence
A limited number of Associate Editors will be invited to join the Editorial Board for the IEEE Transactions on Artificial Intelligence (IEEE TAI) in 2025. Those interested to join the editorial board, please fill out the application form at this link.
Applications close 2 December 2024. Successful applicants will be notified by early 2025.
Hussein Abbass
Editor-in-Chief, IEEE TAI
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Call for Associate Editors for the IEEE Transactions on Evolutionary Computation
 Please note that a few new positions will become available for joining the Editorial Board of the IEEE Transactions on Evolutionary Computation (TEVC).
The deadline for applying is 16 October 2024.
For information on submitting your application please visit the Call for Associate Editors for the IEEE Transactions on Evolutionary Computation.
Prof. Carlos A. Coello Coello
Editor-in-Chief, IEEE TEVC
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Rearrange Anatomy Inputs Like LEGO Bricks: Applying InSSS-P and a Mobile-Dense Hybrid Network to Distill Vascular Significance From Retina OCT-Angiography
Medical deep neural networks (DNNs) trained upon coarse image inputs are inherently insensible to fine-grained anatomic features. To enhance DNN perception on delicate microvascular structures, we proposed using a straightforward angiographic mobile-dense hybrid network (AMDenseNet) in tandem with a flexible input split, suppression, and swap perturbation (InSSS-P) framework to perform explainable diagnostics for microvascular diseases. Mechanistically, InSSS-P and AMDenseNet conjointly (1) decompose complex anatomy inputs into LEGO-like blocks, then (2) distill plexus-wise vascular block significance from the rearranged anatomy input-output samplings. Read more
IEEE Computational Intelligence Magazine, August 2024
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Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review
Machine learning draws its power from various disciplines, including computer science, cognitive science, and statistics. Although machine learning has achieved great advancements in both theory and practice, its methods have some limitations when dealing with complex situations and highly uncertain environments. Insufficient data, imprecise observations, and ambiguous information/relationships can all confound traditional machine learning systems. To address these problems, researchers have integrated machine learning from different aspects and fuzzy techniques, including fuzzy sets, fuzzy systems, fuzzy logic, fuzzy measures, fuzzy relations, and so on. This article presents a systematic review of fuzzy machine learning, from theory, approach to application, with the overall objective of providing an overview of recent achievements in the field of fuzzy machine learning. Read more
IEEE Transactions on Fuzzy Systems, July 2024
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A Survey on Evolutionary Multiobjective Feature Selection in Classification: Approaches, Applications, and Challenges
Maximizing the classification accuracy and minimizing the number of selected features are two primary objectives in feature selection (FS), which is inherently a multiobjective task. Multiobjective FS (MOFS) enables us to gain various insights from complex data in addition to dimensionality reduction and improved accuracy, which has attracted increasing attention from researchers and practitioners. Over the past two decades, significant advancements in MOFS in classification have been achieved in both the methodologies and applications, but have not been well summarized and discussed. Read more
IEEE Transactions on Evolutionary Computation, August 2024
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Enhancing Reinforcement Learning via Transformer-Based State Predictive Representations
Enhancing state representations can effectively mitigate the issue of low sample efficiency in reinforcement learning (RL) within high-dimensional input environments. Existing methods attempt to improve sample efficiency by learning predictive state representations from sequence data. However, there still remain significant challenges in achieving a comprehensive understanding and learning of information within long sequences. Motivated by this, we introduce a transformer-based state predictive representations (TSPR) auxiliary task that promotes better representation learning through self-supervised goals. Read more
IEEE Transactions on Transactions on Artificial Intelligence, September 2024
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Augmented Intelligence Based COVID-19 Diagnostics and Deep Feature Categorization Based on Federated Learning
The global pandemic of COVID-19 has had profound and devastating effects on human life since its emergence in 2019. This viral infection predominantly impacts the respiratory system, causing a range of severity in alveolar overlapping that results in breathlessness and fatality. A novel methodology was assessed using the primary COVID-19 dataset from Kaggle, employing a federated learning ecosystem with multi-user datasets. This technique involves extracting data logs from various user repositories and datasets within the federated learning framework. Subsequently, a validation process is conducted, followed by computation utilizing a deep feature set categorization technique augmented by artificial intelligence. Read more
IEEE Transactions Emerging Topics in Computational Intelligence, Early Access
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Physics-Informed Explainable Continual Learning on Graphs
Temporal graph learning has attracted great attention with its ability to deal with dynamic graphs. Although current methods are reasonably accurate, most of them are unexplainable due to their black-box nature. It remains a challenge to explain how temporal graph learning models adapt to information evolution. Furthermore, with the increasing application of artificial intelligence in various scientific domains, such as chemistry and biomedicine, the importance of delivering not only precise outcomes but also offering explanations regarding the learning models becomes paramount. Read more
IEEE Transactions on Neural Networks and Learning Systems, September 2024
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RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games
The balance of game content significantly impacts the gaming experience. Unbalanced game content diminishes engagement or increases frustration because of repetitive failure. Although game designers intend to adjust the difficulty of game content, this is a repetitive, labor-intensive, and challenging process, especially for commercial-level games with extensive content. To address this issue, the game research community has explored automated game balancing using artificial intelligence (AI) techniques. However, previous studies have focused on limited game content and did not consider the importance of the generalization ability of play-testing agents when encountering content changes. Read more
IEEE Transactions on Games, September 2024
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The Inadequacy of Reinforcement Learning From Human Feedback—Radicalizing Large Language Models via Semantic Vulnerabilities
This study is an empirical investigation into the semantic vulnerabilities of four popular pretrained commercial large language models (LLMs) to ideological manipulation. Using tactics reminiscent of human semantic conditioning in psychology, we have induced and assessed ideological misalignments and their retention in four commercial pretrained LLMs, in response to 30 controversial questions that spanned a broad ideological and social spectrum, encompassing both extreme left- and right-wing viewpoints. Such semantic vulnerabilities arise due to fundamental limitations in LLMs’ capability to comprehend detailed linguistic variations, making them susceptible to ideological manipulation through targeted semantic exploits. Read more
IEEE Transactions Cognitive and Developmental Systems, August 2024
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