Call for IEEE CIS Distinguished Lecturers Nomination

The committee shall evaluate the relevant values and merits of each nominee using both qualitative and quantitative evidence. The quantitative measures indicated in section III.1 are part of the input to be considered by the committee. Other criteria include, but are not limited to, qualitative assessments of the nominees work, geographical lecturer distribution requirements, or the need to include lecture topics that would not be, otherwise, well represented. The committee may consider exceptions to these criteria on a case-by-case basis.

DEADLINE: 30 August 2022

For more information please visit the Nominating and Appointing Distinguished Lecturers page.

Call for IEEE CIS Organizing Distinguished Lectures

The IEEE CIS Distinguished Lecturer Program (DLP) aims at serving the Computational Intelligence community by providing stimulating lectures given by distinguished IEEE CIS professionals and scholars. It also aims at helping young researchers (masters and doctoral students) providing suggestions/guidance on their research problems as well as promoting computational intelligence tools and techniques among undergraduate students to make them interested in CIS fields of interest. Distinguished Lecturers present novel basic and/or applied research results in their CIS sub-field. Their lectures offer insights into the trends and challenges of their CIS sub-field and their vision for the given sub-field. In any given year, selected IEEE CIS Distinguished Lecturers (DLs) have a balanced representation of CIS sub-fields and include representatives from academia, industry and government while spanning different IEEE Regions. The program supports local CIS Chapters by enabling a major Chapter event which can considerably improve a Chapter’s visibility to their existing IEEE CIS members, other IEEE Society members and the wider CIS community in their area. It is expected that DLP events actively seek to recruit new CIS professionals to both the CIS field and to IEEE CIS.

It should be noted that this program is NOT intended as a means to finance speakers for conferences/workshops or symposia.

For more information visit the Organizing Distinguished Lectures page.

CI & AI-FML Machine Learning Competition @ IEEE WCCI 2022

CI & AI-FML Machine Learning Competition for Human and Smart Machine Co-Learning on Real-World Applications

Date: 23 July 2022

Time: 8:45-10:05 and 10:20-11:20

Organizers:

Chang-Shing Lee, Yusuke Nojima, Giovanni Acampora Marek Reformat, Naoyuki Kubota, Ryosuke Saga, Jose Manuel Soto Hidalgo, and Shen-Chien Chen

Supported by Task Forces on Competitions of IEEE CIS Fuzzy Systems Technical Committee

Posted by Chang-Shing Lee ([email protected])

Competition Website @ IEEE WCCI 2022

https://wcci2022.org/accepted-competitions/
http://oase.nutn.edu.tw/wcci2022-fmlcompetition/overview/

I. Scope and Topics

Computational Intelligence (CI), which includes fuzzy logic (FL), neural network (NN), and evolutionary computation (EC), is an imperative branch of artificial intelligence (AI). As a core technology of AI, it plays a vital role in developing intelligent systems, such as games and game engines, neural-based systems including a variety of deep network models, evolutionary-based optimization methods, and advanced cognitive techniques. The main contents of this competition proposal focused on the CI & AI-FML machine learning for human and smart machine co-learning on AIoT applications or real-world applications to encourage elementary-school students, high-school students, or undergraduate students to join the competition. This competition is supported by 2021 IEEE CIS High School Outreach Subcommittee, Task Forces on Competitions of IEEE CIS Fuzzy Systems Technical Committee, Task Forces on Web Intelligence of IEEE CIS Fuzzy Systems Technical Committee, Task Forces on Fuzzy System Software of IEEE CIS Fuzzy Systems Technical Committee, KWS research center of National University of Tainan, Taiwan, and CcS research center of Tokyo Metropolitan University, Japan.

Submissions must be received before 10 July 2022, 23:59 (GMT) (Extended)

IEEE CIS Student and Early Career Mentoring Program

The IEEE CIS mentoring program is back in 2022! The program was rated exceptionally high, with around 90% of participants rating the programs Very Useful or Useful and about 9 out of 10 confirming they would recommend participation to a friend. WCCI2022 will offer an expanded IEEE CIS mentoring program compared to 2020, keeping the best parts of the 2020 edition, while adding new activities. It includes: 

• Activity I: One-to-One publication feedback and community point-of-contact
• Activity II: Paper Development Workshop (PDW)  
• Activity III: Networking Event

More details about the program are available here.

Call for Competition on Evolutionary Computation in the Energy Domain: Risk-based Energy Scheduling

Following the success of the previous editions, another challenging edition of the smart grid problems competition in the field of computational intelligence is out there. This edition of the competition proposes one track in Risk-based optimization of aggregators’ day-ahead energy resource management considering uncertainty associated with the high penetration of distributed energy resources. In addition, IEEE CIS is sponsoring the competition offering a prize of US$500 for the best-placed team.

Please visit the Smart Grid Problems Competitions page.

Meet a CIS Member 

Meet: Donald C. Wunsch II, have had many CIS volunteer roles, examples below.

What is your title, and place of work? (or Technical Field of Research)?

I am the Mary K. Finley Missouri Distinguished Professor and Director of the Applied Computational Intelligence Laboratory at Missouri University of Science and Technology (Missouri S&T). From 2021 to present, I have been on Interagency Personnel Assignment from Missouri S&T to the National Science Foundation as a Program Director in the Energy, Power, Controls and Networks program of the Electrical, Communications and Cyber Systems Division. On 1 December 2022, I will return to Missouri S&T to become the inaugural Director of the Kummer Institute Center for Artificial Intelligence and Autonomous Systems. My technical field of research is artificial intelligence, especially neural networks.

How long have you been a member of CIS and what was the reason you chose to join IEEE CIS?

I joined before it was called CIS. I served on the IEEE Neural Networks Council Adcom, joining not long after it was formed in 1989. It began with 12 Society representatives and I joined when some other societies were added, in my case, the Social Implications of Technology Society. I chose to join because of a fascination with neural networks after reading a paper on optical neural networks. I instantly knew neural nets would be a lifelong passion. It was like flipping a switch. I had worked in optics and dabbled in AI and saw then that neural networks were (and are) the most promising path forward.

What Computational Intelligence society committee do you serve?

I stepped away from Society roles to focus on my responsibilities with the National Science Foundation. Before then, I served on various CIS technical committees over the years, also the Fellows selection committee, served on the Adcom from 1989 to around 1994 or 1995, Chaired the History Committee, was in various leadership roles of IJCNN including General Chair, was an Associate Editor and co-Guest-Editor, and other various service roles.

What have you learned from your experience and how has it helped you professionally?

I’ve learned most of what I know about Computational Intelligence, Artificial Intelligence and Neural Networks, as a result of the IEEE CIS along with the International Neural Networks Society (INNS), our closest collaborating society. I have also learned a tremendous amount about effective collaboration. It is an interesting challenge to make decisions with so many brilliant people whose opinions can differ significantly, but I enjoy the process. I have been very fortunate to get started at a time when most of the field’s giants were still active. Some of them have passed on, such as Robert Hecht-Nielsen (founder of the field’s first unicorn start-up), Lotfi Zadeh, and several more. But we are fortunate to have many of the field’s founders still with us. I know most of them and count several among my close friends. Imagine if you were a physicist and had personally known Galileo, Newton, Faraday, Maxwell, Curie, Einstein, Heisenberg, Feynman, Hawking, etc… That would be impossible in physics but it’s what our field is like now! That’s why one of my favourite roles was as member and eventually Chair of the History Committee. We’re living history right now and need to remember it!

What has been the most fun/rewarding thing about being a volunteer for the IEEE Computational Intelligence Society? What have you enjoyed the most?

The most rewarding thing has been interviewing people for the IEEE CIS History committee. You can find these interviews online. The thing I’ve enjoyed the most has been in-person conferences. That’s how most of our professional friendships get formed.

Tell us something about you that we don't know.

I’m an Eagle Scout and so is my son, and I’ve been on two-week backpacking treks to Philmont Scout Ranch, as a kid, and a few years ago with him. We love the outdoors! Also, My wife, Hong, son, Donald, dad, Donald, and brother, Tom are also all engineers.

2022 IEEE CIS Hackathon on Computational Intelligence in Biomedicine and Healthcare

With the development of the computational intelligence (CI) methods, novel applications including smart medicine, smart healthcare and smart hospital attract more and more attentions in the academia and industry communities. CI/AI technology has played a significant role to assist medical professionals and their patients. This hackathon looks forward to an CI/AI-driven tribute to the smart medicine. We welcome that participants present CI/AI solutions for core issues in smart medicine, smart healthcare and smart hospital including but not limited to personalized, predictive, preventive and participatory medicine and healthcare solutions.

Submission due: 31 August 2022

Preliminary contest: 15 October 2022

Final contest: 4-5 December 2022

Each team may consist of up to six eligible students and up to one mentor. More information coming soon. 

Developing Minds Lecture Series

Title: From Reinforcement Learning to Embodied Learning

Speaker: Sergey Levine, UC Berkely, USA
Web: https://people.eecs.berkeley.edu/~svlevine/

Sign up here

Abstract

Reinforcement learning provides one of the most widely studied abstractions for learning-based control. However, while the RL formalism is elegant and concise, real-world embodied learning problems (e.g., in robotics) deviate substantially from the most widely studied RL problem settings. The need to exist in a real physical environment challenges RL methods in terms of generalization, robustness, and capacity for lifelong learning -- all aspects of the RL problem that are often neglected in commonly studied benchmark problems. In this talk, I will discuss how we can devise a framework for learning-based control that is at its core focused on generalization, robustness, and continual adaptation. I will argue that effective utilization of previously collected experience, in combination with multi-task learning, represents one of the most promising paths for tackling these challenges, and present recent research in RL and robotics that studies this perspective.

Bio

Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as computer vision and graphics. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more. 

• IEEE CIM Special Issue on  Machine Learning Assisted Evolutionary Multi-objective Optimization (1 September  2022)
• IEEE TNNLS Special Issue on Explainable and Generalizable Deep Learning for Medical Imaging (1 September  2022)
• IEEE TNNLS Special Issue on Explainable Representation Learning-based Intelligent Inspection and Maintenance of Complex Systems (1 September 2022)
• IEEE TCDS Special Issue on Cognitive Learning of Multi-Agent Systems (30 September 2022)
• IEEE TNNLS Special Issue on Information Theoretic Methods for the Generalization, Robustness and Interpretability of Machine Learning (1 October  2022)
• IEEE TNNLS Special Issue on Deep Learning for Intelligent Media Computing and Applications (30 October  2022)
• IEEE TETCI Special Issue on Resource Sustainable Computational and Artificial Intelligence (1 February 2023)