IEEE CIS Distinguished Lecturers and Available Talks

 

M Tanveer36M. Tanveer (2024 – 2026) (abstract)
Indian Institute of Technology Indore
606, POD 1A, Indian Institute of Technology Indore
Simrol, Indore 453552, India
Email: mtanveer@iiti.ac.in

Lecture Topic 1: Ensemble deep random vector functional link for Alzheimer's disease diagnosis

Lecture Topic 2: Large scale support vector machine algorithms and applications

Lecture Topic 3: Graph embedded based randomized algorithms for class imbalance learning


Narayan Srinivasa 01 lrg webNarayan Srinivasa, IEEE Fellow (2024 – 2026) (abstract)
Intel Corporation
2200 Mission College Boulevard, Santa Clara, CA 95054
Email: narayan.srinivasa@intel.com  

Lecture Topic 1: On Solving Hard Optimization Problems in an Energy Efficient Way

Lecture Topic 2: Opportunities, Challenges, and Threats Using Synthetic Data for AI

Lecture Topic 3: Machine Learning & Neuromorphic Computing



 XiaodongLiXiaodong Li, IEEE Fellow (2024 – 2026) (abstract)

School of Computing Technologies, RMIT University
Melbourne, VIC 3000, Australia
Email: xiaodong.li@rmit.edu.au
Homepage: https://titan.csit.rmit.edu.au/~e46507/

 

Lecture Topic 1: Large-Scale Optimization and Learning

Lecture Topic 2: Decision Making in Evolutionary Optimization and Beyond

Lecture Topic 3: Adaptive Solution Prediction via Machine Learning for Large-Scale Combinatorial Optimization

Lecture Topic 4: Niching Methods for Multimodal Optimization

Lecture Topic 5: From Nature-inspired Computation to Machine Learning 


OngYewSoon70Ong Yew Soon, IEEE Fellow (2024 – 2026) (abstract)

School of Computer Science and Engineering, 
Nanyang Technological University
Block N4, 2a-28, Nanyang Avenue, Singapore 639798

Center for Frontier AI Research
Agency for Science, Technology and Research
1 Fusionopolis Way, #16-16, Connexis North Tower, Singapore 138632
Email: asysong@ntu.edu.sg, Ong_Yew_Soon@hq.a-star.edu.sg

Lecture Topic 1: Artificial Intelligence for Global Good

Lecture Topic 2: Towards General Optimization Intelligence

Lecture Topic 3: Insights on Multifactorial Evolution: Towards Multitasking Optimization

Lecture Topic 4:Jack and Masters of all Trades: One-Pass Learning Sets of Model Sets from Large Pre-

Lecture Topic 5: From Gradient-Based to Gradient-Free Deep Learning of Physics


Dipankar Dasgupta

Dipankar Dasgupta, IEEE Fellow (2022-2024) (abstract)

William Hill Professor of Computer Science

Director, Center for Information Assurance (CfIA)
The University of Memphis

Homepage: 
www.cs.memphis.edu/~dasgupta
IA center: 
cfia.memphis.edue-mail: dasgupta@memphis.edu 

Topic 1: Computational Intelligence in Cybersecurity (abstract)

Topic 2: Adversarial Machine Learning and Defense Strategies (abstract)

Topic 3: Adaptive Multi-Factor Authentication & Cyber Identity (abstract)

Topic 4: Advances in Immunological Computation (abstract)

Topic 5: AI vs AI: Viewpoints (abstract)

Topic 6: Generative AI from Historical Perspectives  (abstract)


Carlos CoelloCarlos A. Coello Coello (2022-2024)
CINVESTAV-IPN, Department of Computer Science, 
Mexico City, MEXICO
email: 
carlos.coellocoello@cinvestav.mx

 

Lecture Topic 1: An Overview of Evolutionary Multi-Objective Optimization (abstract)

Lecture Topic 2: Recent Research Topics in Evolutionary Multiobjective Optimization: A Personal Perspective (abstract)

Lecture Topic 3: Where is the research on evolutionary multi-objective optimization heading to? (abstract)


JiePhotoWebsiteJie Lu (2022-2024) 
University of Technology Sydney 
email: jie.lu@uts.edu.au

 

 

Lecture Topic 1: Fuzzy Transfer Learning (abstract)

Lecture Topic 2: Concept Drift Detection and Adaptation (abstract)

Lecture Topic 3: Recommender Systems using Computational Intelligence: Methodologies and Applications (for undergraduate students and industries) (abstract)

Lecture Topic 4: Data - Learning - Decision: Innovation and Impact (for undergraduate students and industries)  (abstract)


Nikhil PalNikhil Ranjan Pal (2022-2024) 
Electronics and Communication Sciences Unit
Head, Center for Machine learning and Artificial Intelligence
Indian Statistical Institute
Calcutta 700108, India
email: nrpal59@gmail.com

 Lecture Topic 1: Neural networks with some applications for beginners (For undergraduate students) (abstract)

Lecture Topic 2: Fuzzy systems for dimensionality reduction and manifold learning (abstract)

Lecture Topic 3: Artificial Intelligence Everywhere – how ready are we for it? (abstract)

Lecture Topic 4:  How to make my neural and neuro-fuzzy systems parsimonious?(abstract)


Tingwen Huang

Tingwen Huang (2022 - 2024)
Texas A&M University at Qatar,
Doha, Qatar
email:tingwen.huang@qatar.tamu.edu

 

 Lecture Topic 1: Efficient Computational Approaches and Their Applications (abstract)

Lecture Topic 2: Distributed Consensus of Multi-Agent Systems with or without Attacks (abstract)

Lecture Topic 3: Dynamics of Neural Networks and Their Applications (abstract)


HHussein Abbass (2022 - 2024)
School of Engineering and Information Technology
University of New South Wales,
Canberra, Australia
email: h.abbass@unsw.edu.au
 

 
Lecture Topic 1: Artificial Intelligence: Past, Present and Future (abstract)

Lecture Topic 2: A Proposed Presentation to Undergraduate Students and Industry Analysts Machine Learning for Data Science: An Introduction (abstract)

Lecture Topic 3: Shepherding: Biologically inspired Distributed AI-Enabled Human Swarm Teaming (abstract)

Lecture Topic 4: From Machine Learning to Machine Education (abstract)


UM3

Ujjwal Maulik (2022 - 2024)
Department of Computer Science and Engineering
JADAVPUR UNIVERSITY 
email: ujjwal.maulik@jadavpuruniversity.in, ujjwal_maulik@yahoo.com

 

 
Lecture Topic 1: Artificial Intelligence for Healthcare (abstract)

Lecture Topic 2: Machine Learning and Data Science: Fundamental and Challenges(abstract)

Lecture Topic 3: Computational Intelligence for Computational Biology(abstract)

Lecture Topic 4: Single and Multi-objective Data Clustering (abstract)


Alice Smith Foto

Alice E.Smith, IEEE  Fellow (2023-2025) (abstracts)

Joe W. Forehand/Accenture Distinguished Professor

Industrial and Systems Engineering Department

Auburn University

Homepage: https://www.eng.auburn.edu/~aesmith/

e-mail: smithae@auburn.edu

Lecture Topic 1: Decision Science Inspired by Nature

Lecture Topic 2: Evolutionary Strategies for Design Of Engineered Systems

Lecture Topic 3: Nature Guided Design Optimization in Continuous Space

Lecture Topic 4: Bi-Objective Evolutionary Strategies for Design Optimization

Lecture Topic 5: Facility Design of Order Picking Warehouses Using Evolutionary Strategies

Lecture Topic 6: Drones For Last Mile Logistics with A Medical Humanitarian Application

Lecture Topic 7: Understanding The Journal Paper Process and Writing Papers That Will Be Chosen For Publication (designed for PHD students and newer faculty members, not a research presentation)

Lecture Topic 8: Women in Computational Intelligence – Current Research

Lecture Topic 9: Women Led Research in Computational Intelligence (abstract)



MandicDanilo P. Mandic, IEEE Fellow (2023-2025) (abstracts)

Professor of Signal Processing

Department of Electrical and Electronics Engineering

Imperial College

Homepage https://www.imperial.ac.uk/people/d.mandic

e-mail: d.mandic@imperial.ac.uk

Lecture Topic 1: Machine Intelligence on Graphs

Lecture Topic 2: Machine Intelligence for eHealth: Hearables for 24/7 Doctorless Hospitals?

Lecture Topic 3: Tensor Decompositions for Big Data Applications: Low-complexity Deep Neural Networks

Lecture Topic 4: Interpretable Convolutional NNs and Graph CNNs: Role of Domain Knowledge


 andersonDerek Anderson, IEEE Senior Member (2023-2025) (abtracts)

Electrical Engineering and Computer Science department

The University of Missouri-Columbia

Homepage: http://www.derektanderson.com/cv.pdf.

e-mail: andersondt@missouri.edu

Lecture Topic 1: What is a Good Information Fusion Explanation?

Lecture Topic 2: Generalized Fuzzy Extension Principle and Its Application to Information Fusion

Lecture Topic 3: Explainable Fuzzy Fusion Networks

Lecture Topic 4: Game Engines for Training and Understanding Computational Intelligence Algorithms (this topic might be suitable for beginners/ undergraduate students/industries)



venayagamoorthy

G. Kumar Venayagamoorthy, IEEE Fellow (2023-2025) (abstracts)

Duke Energy Distinguished Professor of Power Engineering 

Professor of Electrical and Computer Engineering

Department of Electrical and Computer Engineering

Clemson University

Homepage: https://www.clemson.edu/cecas/departments/ece/faculty_staff/faculty/kvenaya.html

e-mail: gvenaya@clemson.edu

Lecture Topic 1: Distributed Artificial Intelligence in the Future of Smart Grids

Lecture Topic 2: Swarm Intelligence and Applications in Power and Energy Systems

Lecture Topic 3: Computational Intelligence (CI) with Emphasis on Undergraduate Education