Christine Zarges, Aberystwyth University, UK
Theresa Schmiedel, University of Applied Sciences and Arts Northwestern Switzerland, Basel, Switzerland
Ke Feng, Singapore-ETH Centre, The National University of Singapore, Singapore
Stefka Fidanova, Bulgarian Academy of Sciences, Bulgaria
Diego Oliva, Universidad de Guadalajara, Mexico
Sebastian Ventura, University of Cordoba, Spain
João Pedrosa, INESCTEC, Portugal
Nuno Bettencourt , Instituto Superior de Engenharia do Porto, Portugal
Aboul Ella Hassanien, Cairo University, Egypt
Milan Tuba, Singidunum University, Serbia
Dalia Kriksciuniene, Vilnius University / Kaunas University of Applied Sciences, Lithuania
Kusum Deep, Indian Institute of Technology Roorkee, India

Speaker 1:Christine Zarges, Aberystwyth University, UK

Title: Mathematical foundations of randomised optimisation algorithms.

Abstract:Randomised Optimisation Algorithms such as evolutionary algorithms, simulated annealing or estimation of distribution algorithms implement a general idea of how to search for solutions for (hard) optimisation problems. They iteratively sample candidate solutions from a search space and assess the quality of a solution using an objective function. They provide a powerful and flexible way of tackling different complex problems where classical optimisation methods fail. While the general idea is to apply such algorithms 'right out of the box', in practice it is almost always necessary to adjust them to the problem at hand by modifying the overall search strategy to achieve acceptable performance. It is thus highly desirable to obtain a clear understanding of the working principles of different operators and strategies. Mathematical analysis can provide such an understanding, including properties of problems and operators, parameterisation, and limitations of different approaches, and can inspire the design of better algorithms. Over the last few decades significant progress on mathematical foundations of Randomised Optimisation Algorithms has been made. This talk will provide an overview of the main lines of research in the area with a focus on runtime and anytime analysis in combinatorial optimisation. It will highlight example results and illustrate how these results can be used for the modification and development of algorithms in relevant applications. I will also point out future research directions with the aim to initiate a dialogue between researchers interested in theory and applications.

Biography:Christine Zarges is currently a Senior Lecturer (Associate Professor) in the Department of Computer Science at Aberystwyth University which she joined as a Lecturer in 2016. Before, she held a postdoctoral research position at the University of  Warwick, UK, and a Birmingham Fellowship at the University of Birmingham, UK. She obtained her PhD from TU Dortmund, Germany, in 2011. Christine's research focuses on heuristic search in the context of optimisation. She is interested in the theoretical analysis of all kinds of randomised search heuristics such as evolutionary algorithms and artificial immune systems with the aim to understand their working principles and guide their design and application. She is also interested in applications in combinatorial optimisation as well as computational and theoretical aspects of natural processes and systems. She has given tutorials on these topics at various conferences and contributed to the organisation of such conferences in different capacities, most importantly as track, programme, and workshop chair at GECCO, PPSN, FOGA and EvoCop as well as local chair of EvoStar 2024. She is member of the editorial board of Evolutionary Computation (MIT Press) and Associate Editor of Engineering Applications of Artificial Intelligence (Elsevier). She is a member of the Executive Board of SPECIES, the Society for the Promotion of EC In Europe and Surroundings and a Manage Committee member for the UK in European research networks concerned with Randomised Optimisation Algorithms (COST actions CA15140 and CA22137).


Speaker 2:Theresa Schmiedel, University of Applied Sciences and Arts Northwestern Switzerland, Basel, Switzerland

Title:Value-sensitive design of socially intelligent agents

Abstract: With the rise of large language models that can generate human-like conversations, physical and virtual intelligent agents are all of a sudden able to communicate with humans in very smooth way. While such conversations can leave a very positive impression, we can increasingly identify concerns that interactions with intelligent agents can become harmful, for example, through manipulation. Value-sensitive design (VSD) is an approach that calls for the consideration of human values in the design of technology. In the context of intelligent agents, VSD provides a relevant perspective to reflect on the way we would like intelligent agents to be designed so they interact in a socially appropriate way. This talk uses VSD as a lense to discuss the notion of “socially” intelligent agents.

Biography: Theresa Schmiedel is a professor at the Institute of Information Systems at FHNW. After her studies in economics at the University of Hohenheim, she did her PhD and habilitated at the University of Liechtenstein. Her research interests focus on social phenomena in the information systems field. Particularly, her interests include social robots, culture, values, human-centered design, intelligent agents. She heads the Competence Center Technology, Organization, and People at FHNW.

Speaker 3: Ke Feng, Singapore-ETH Centre, The National University of Singapore, Singapore

Title: Digital twin-driven health management and remaining useful life prediction of the gearbox transmission system.

Abstract: The gearbox transmission system plays a vital role in advanced manufacturing, aerospace, renewable energy, vehicle, and mining system. Its degradation and failure would cause unexpected economic loss and even serious accidents. For example, the degradation and failure of the gearbox will impair the performance of the machine tool, affecting the production quality and quantity significantly and resulting in enormous economic loss. Therefore, monitoring the health condition of the gearbox transmission system is of great significance. However, the gearbox transmission system usually operates in harsh working environments, and it is difficult to conduct regular manual inspections and maintenance. Thus, the use of advanced online algorithms to monitor the degradation status of the gearbox transmission system and predict its remaining useful life (RUL) can bring significant benefits to industry practices. Digital twin (DT) is a virtual representation (mirror) of a physical structure or a system in real space along its lifecycles. Through real-time interaction between the virtual model and physical structure, the degradation status of the system and its RUL can be reflected and evaluated effectively. Thanks to its unique specialty, DT has recently received considerable attention from the research community. However, due to the complex structures and harsh operation conditions, research on DT-based gearbox transmission system RUL prediction is limited. Moreover, existing conceptual approaches have limitations in indicating the specific contact status and providing insights into the degradation stages of gearbox transmission systems, which greatly benefit RUL prediction. To this end, this work presents a systematic and practical digital-twin technology for gearbox transmission systems RUL prediction, including the development of the realistic virtual model, real-time interaction between the virtual model and physical structures, and ‘transfer learning’ for a wider mechanical transmission system RUL prediction. This work can significantly benefit the health management of the gearbox transmission system and bring significant benefits to various industrial applications, including advanced manufacturing equipment/machinery, industrial machinery, aerospace applications, and wind turbines.

Biography: Dr. Ke Feng is a Marie Curie Fellow affiliated with Imperial College London and Brunel University London. He earned his Ph.D. from the University of New South Wales, Australia, in 2021. Following his doctoral studies, Dr. Feng held positions at the University of British Columbia and the National University of Singapore in 2022 and 2023, respectively. Dr. Feng's research focuses on digital-twin-based Remaining Useful Life (RUL) prediction, vibration analysis, structural health monitoring, dynamics, tribology, signal processing, and machine learning. Recognized as a Vebleo Fellow and an Emerging Leader by Measurement Science and Technology, Dr. Feng actively contributes to the academic community. He serves as an editor and guest editor for esteemed journals, including Mechanical Systems and Signal Processing, IEEE Transactions on Industrial Cyber-Physical Systems, Engineering Applications of Artificial Intelligence, IEEE Transactions on Instrumentation and Measurement, Measurement, Measurement Science and Technology, Computer Systems Science and Engineering, and Digital Engineering and Digital Twin. In addition to his editorial roles, Dr. Feng has played a pivotal role in organizing the International Conference on Aerospace Structural Dynamics (ICASD). He has also served as a section chair for renowned conferences such as ICSMD 2022, SRSE 2022, QR2MSE 2023, and IECON 2023. Furthermore, he has been invited as a speaker at the 2nd Digital Twin International Conference and the 6th International Conference on Dynamics, Vibration, and Control.


Speaker 4:Stefka Fidanova, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Bulgaria

Title: How Ants Can Solve Engineering Problems

Abstract: We can learn a lot by observing nature. There is no waste with it. Everything is done in the most economical, optimal way. Particularly impressive is the collective intelligence of a group of individuals working together. Bees, ant colonies, bird flocks, fish passages, etc. can be given as examples of group intelligence. Animals that do not have a high level of individual intelligence deal with difficult problems using a collective approach. This gave scientists the idea to create algorithms inspired by nature, mimicking the collective intelligence of some animals. These are the so-called metaheuristic methods. There are complex optimization problems coming from real life and industry that require large computing resources to find close to the optimal solution. For the most part, these are combinatorial optimization problems. Exact methods and traditional numerical methods are not suitable for this type of problems. In this case, the only possibility is metaheuristic methods. Their advantage is quickly finding a good solution. Their disadvantage is that their accuracy is not guaranteed. In cases where the accuracy of the solution can be compromised and it is more important to find it quickly, metaheuristic methods are preferable. The unique behavior of ants in nature and their ability to always find the shortest path between the nest and the food source, gives the idea for the creation of the ants’ method. The original idea has been expanded and modified by different researchers to apply to a wider class of tasks.

Biography: Stefka Fidanova is Professor of Computer Science at Institute of Information and Communication Technologies, Bulgarian Academy of Sciences. Her research interests include theory, methods, applications of combinatorial optimization and parallel algorithms. She heads the research group of Parallel Algorithms and Machine Learning. She has authored over 200 refereed journal, proceedings and collection papers, edited 13 proceedings, collections and special issues and written a 2 monograph. She belongs to the editorial boards of several international journals. She has received Career Award 2018 of Marie Curie Alumni Association of EU.


Speaker 5: Diego Oliva, Universidad de Guadalajara, Mexico

Title: Metaheuristic Algorithms: Open Challenges in Engineering

Abstract: Engineering is changing and the use of intelligent algorithms to solve different challenges is more common nowadays. For example, in logistics, different problems are solved by using optimization algorithms as metaheuristics. In the same way, images are widely used in different engineering domains, and the use of metaheuristics in combination with other intelligent approaches permits to perform the proper analysis of the scenes. However, the problems in engineering are still growing and it is important to have powerful methods that permit improve the processes in an effective way. In this talk, the principles of optimization and the basic concepts of metaheuristics are explained. Their classification and importance are also discussed. In this context, they have also analyzed some important points related to multiobjective optimization. Finally, some challenges in different domains of engineering are discussed.

Biography: Diego Oliva received the B.S. degree in Electronics and Computer Engineering from the Industrial Technical Education Center (CETI) of Guadalajara, Mexico, in 2007, the M.Sc. degree in Electronic Engineering and Computer Sciences from the University of Guadalajara, Mexico in 2010. He obtained a Ph. D. in Informatics in 2015 from the Universidad Complutense de Madrid. Currently, he is an Associate Professor at the University of Guadalajara in Mexico. Since 2020 he has been a visiting professor at the Tomsk Polytechnic University in Russia. He has the distinction of National Researcher Rank 2 by the Mexican Council of Science and Technology. Since 2017 he has been a member of the IEEE. Diego Oliva is co-author of more than 100 papers in international journals and different books. He is part of the editorial board of IEEE Access, Plos One, Mathematical Problems in Engineering, IEEE Latin America Transactions, and Engineering Applications of Artificial Intelligence. His research interest include Evolutionary and swarm algorithms, hybridization of evolutionary and swarm algorithms, and Computational intelligence.


Speaker 6: Sebastian Ventura, University of Cordoba, Spain

Title: Advance machine learning to improve predictive maintenance

Abstract: Maintenance costs constitute a significant portion of the overall operational costs in any manufacturing or production facility. The proportion can vary widely, ranging from 15% to 60% of the production costs, depending on the industry. Recent studies on the efficiency of maintenance management reveal that about a third of these costs are squandered due to unnecessary or incorrectly executed maintenance tasks. Therefore, it is evident that the role of maintenance activities has a significant influence on overall productivity. Today's manufacturing setups employ a massive number of sensors that collect data at rates ranging from hundreds to thousands of samples every second. Predictive maintenance leverages this vast data pool to forecast system malfunctions or failures, enabling the scheduling of maintenance activities right before issues arise. Predictive maintenance has seen significant advancements through the incorporation of machine learning methodologies. Nevertheless, the field continues to be a work in progress with ample room for improvement. This presentation aims to shed light on recent innovations that focus on enhancing the quality, resilience, and dependability of predictive maintenance algorithms. We will explore cutting-edge approaches that promise to push the boundaries of what predictive maintenance systems can achieve.

Biography: Sebastián Ventura has been full professor of Computer Science and Artificial Intelligence at the Universidad de Córdoba since April 2016 where leads the KDIS research group since its creation at 2009. He also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA). In the last five years, Prof. Ventura has published 50+ high-impact papers, many in collaboration with global institutions, and has a total of over 150 articles in prestigious journals. His interdisciplinary work, particularly in medicine and industry, has earned him 24,000+ citations and an h-index of 59. He has also contributed to around 200 books and conferences, authored and edited multiple books, and his four most notable papers each have over 1,500 citations. Additionally, he has led 10 national/international projects requiring interdisciplinary collaboration and has advised 5 doctoral theses in the past year, bringing his career total to 22. Dr. Ventura has held leadership roles in several international conferences, including serving as the general chair for EDM in 2009, ISDA in 2011 and 2012, and the IEEE CBMS Symposium in 2019. He is also a member of the program committees for a variety of international conferences. In addition to reviewing for multiple prestigious publications since 2006, he serves as an associate editor for journals like Engineering Applications of Artificial Intelligence, Information Fusion, IEEE Trans. on Cybernetics, and is the editor-in-chief of Progress in Artificial Intelligence journal.


Speaker 7: João Pedrosa, INESCTEC, Portugal

Title: AI in Medical Imaging: Growing Pains and How to Dig Deeper

Abstract:The challenging and time-consuming nature of medical image interpretation makes it an extremely attractive field for the application of artificial intelligence (AI) and the advances made in the last few years have allowed to achieve near-human performance in several imaging modalities. Chest radiography is a particularly interesting example as it is an almost ubiquitous medical imaging modality and this high image throughput has allowed for the creation of large annotated datasets. These have in turn been used to train deep learning systems with excellent performance but are these systems ready for the clinic? In this presentation, the main challenges in the development and application of deep learning systems in high stakes situations such as medical imaging - and in particular chest radiography - will be presented with a focus on interpretability and a case study on the COVID-19 pandemic.

Biography:João Pedrosa was born in Figueira da Foz, Portugal, in 1990. He received the M.Sc. degree in biomedical engineering from the University of Porto, Porto, Portugal, in 2013 and the Ph.D. degree in biomedical sciences with KU Leuven, Leuven, Belgium, in 2018 where he focused on the development of a framework for segmentation of the left ventricle in 3D echocardiography. He joined INESC TEC (Porto, Portugal) in 2018 as a postdoctoral researcher and is an invited assistant professor at the Faculty of Engineering of the University of Porto since 2020. His research interests include medical imaging acquisition and processing, machine/deep learning always with a focus on applying research for improved patient care.


Speaker 8: Nuno Bettencourt , Instituto Superior de Engenharia do Porto, Portugal

Title:Blockchain and DLT: Where Does It Stand


Speaker 9: Aboul Ella Hassanien, Cairo University, Egypt

Title:Innovations for intelligent systems: basics, trends and open problems.

Abstract: Intelligent systems (IC) is being rapidly integrated into many areas of computer engineering by exploiting new developments in machine learning areas such as drones, IoT, image processing, biomedical engineering, bioinformatics, and chemoinformatics greatly benefits from recent advances in deep learning. AI and big data are work together to achieve more. This talk will discuss the basics of intelligent systems and their connection with big data. It explores the applications in different areas and highlights the current research. Discuss recent research problems in different applications, including digital twining in heritage, medical imaging, drone-based applications, chemo-informatics, agriculture, and energy.

Biography: Aboul Ella Hassanien is the Founder and Head of the Egyptian Scientific Research Group (SRGE) and a Professor of Information Technology at the Faculty of Computer and AI, Cairo University. Professor Hassanien has more than 1500 scientific research papers published in prestigious international journals and over 60 books covering such diverse topics as data mining, medical images, intelligent systems, social networks, and smart environment. His other research areas include computational intelligence, medical image analysis, security, animal identification, space sciences, telemetry mining, and multimedia data mining.

Speaker 10:Milan Tuba, Singidunum University, Serbia

Title:Application of Bio-inspired Optimization Algorithms to Problems in Artificial Intelligence

Abstract:  Nowadays many optimization problems can be solved relatively easily by deterministic mathematical methods. However, there is a large group of optimization problems of great practical importance that even though they can appear as simple problems with a clear solution they cannot be solved in a reasonable time. These problems can be combinatorial problems or continuous optimization problems with a large number of local optima. For these problems, guided random search by imitating the principles and behaviors observed in natural systems, i.e. bio-inspired optimization algorithms achieved remarkable results. One of the domains where these algorithms have been successfully used is artificial intelligence (AI). For example, digital image classification problems are the core of many applications in computer vision and related fields such as medical diagnostic systems, autonomous vehicles, and security systems. Some of the most important steps in classification are feature extraction and selection. Feature selection problem is a combinatorial problem and bio-inspired optimization algorithms have been widely adjusted and adapted for solving it. On the other hand, the feature extraction step was automatized in recent years by using convolutional neural networks (CNNs) which brought revolutionary improvements in digital image classification. Using almost any CNN architecture for classification will outperform previous classification algorithms but fine-tuning the large number of hyperparameters that define architecture and learning model could further improve results. Due to the large number of hyperparameters that should be considered, this is a hard optimization problem and bio-inspired algorithms have shown good results in tackling this problem.

Biography: Milan Tuba, Professor of Computer Science, Mathematics and Electrical Engineering, Head of the Artificial Intelligence Project at Singidunum University and Vice-Rector of Research at Sinergia University, is included in both versions of the Stanford University list of 2% of the most influential scientists in the world in all disciplines, one for contribution during the entire career and other for contribution in the previous year (for years 2020, 2021, 2022 and 2023). He was Vice Rector for International Relations at Singidunum University, Head of the Department for Mathematical Sciences at State University of Novi Pazar and Dean of the Graduate School of Computer Science at John Naisbitt University. Prof. Tuba is the author or coauthor of around 300 scientific papers (cited around 7,000 times, h-index 49) and editor, coeditor or member of the editorial board or scientific committee of number of scientific journals, Springer books, congresses and international conferences. He was invited and delivered more than 90 keynote and inaugural lectures at international conferences. He received B. S. in Mathematics, M. S. in Mathematics, M. S. in Computer Science, M. Ph. in Computer Science, Ph.D. in Computer Science from University of Belgrade and New York University. From 1983 to 1994 he was in the U.S.A. at Vanderbilt University in Nashville and Courant Institute of Mathematical Sciences, New York University and later as Assistant Professor of Electrical Engineering at Cooper Union School of Engineering, New York. During that time, he was the founder and director of Microprocessor Lab and VLSI Lab, leader of the NSF scientific projects and theses supervisor. He was the mentor of dozens of doctoral and master's dissertations at the Faculty of Mathematics University of Belgrade, Singidunum University, University of Sarajevo, State University of Novi Pazar, John Nesbitt University and University of East Sarajevo. He was teaching more than 20 graduate and undergraduate courses, from VLSI Design and Computer Architecture to Computer Networks, Operating Systems, Artificial Intelligence, Image Processing, Calculus, Probability, Mathematical Statistics and Queuing Theory at numerous universities in Europe and the USA. Prof. Tuba is a member of the National Agency for Accreditation of Universities of the Republic of Serbia. His research interest includes Artificial Intelligence, Deep Learning, Neural Networks, Nature-inspired Optimization Algorithms, Image Processing, Computer Networks. Senior Member IEEE, ACM, AMS, SIAM, IFNA, Executive Board of IASEI.


Speaker 11: Dalia Kriksciuniene, Vilnius University / Kaunas University of Applied Sciences, Lithuania

Title:Application of artificial intelligence methods in the neurology healthcare domain

Abstract: The role of technology in healthcare became pervasive and raise expectations for assisting medical professionals and treatment efficiency in broad medical problem areas. However, its application has not yet reached its full potential, as the most suitable data sources and the methods for their processing and analysis are still in their development and evaluation stage. The research discussion focusses to comparative evaluation of scientific literature and analysis of experimental research results in the interdisciplinary domains of artificial intelligence, its application in neurology and emerging healthcare approach of person-centred care.
The neurology domain of healthcare has reached high level of urgency in many countries worldwide. It has attracted attention of research due to severity of outcomes of neurological disorders, factors hindering accuracy of diagnosis, high risk of repeated cases (about 40% for stroke), and slow and inefficient rehabilitation period. The neurological disorders (such as stroke) often reduce capability of persons to take care of themselves during rehabilitation period, and require to involve family care givers and community efforts to fulfil the treatment and rehabilitation programs. The complexity of the domain arises need of various data sources and their extraction models, such as health measures and expert evaluation data. Although these data can potentially be scattered, unstructured and hard to uncover, the AI methods bring potential for their inclusion to efficient healthcare. The experimental research of neurological patient enabled to explore and highlight the challenges and implication of applying AI methods for the domain.

Biography: Dalia Kriksciuniene is a professor of Vilnius University and Kaunas University of Applied Sciences in Lithuania. Her applied research area is Marketing Information Systems. Her expertise lies in the field of data analytics and marketing technology solutions, with a particular focus on computational intelligence algorithms, artificial intelligence in electronic marketing, digital marketing, e-commerce, and social network research. D. Kriksciuniene has made significant contributions to the academic community through her publications in ISI WOS journals, including Neurocomputing, Transformations in Business and Economics, Advances in Intelligent Systems and Computing, and Information Technology and Control. Additionally, she serves as an Associate Editor for "Electronic Commerce Research and Applications (ECRA)" and actively participates as a PC member in various international conferences. Furthermore, she contributes her expertise as a reviewer for several prestigious journals. ORCID: 0000-0002-0730-3763

Speaker 12: Kusum Deep, Indian Institute of Technology Roorkee, India

Title: Use of nature inspired optimization techniques to solve real life problems.

Abstract: Optimization is the art of selecting "the best" alternative among a given set of options. Optimization problems arise in almost all fields of science, engineering, business, finance and Industry – in fact, in all walks of human activity in which the problem may be mathematically modeled. The traditional optimization techniques are unable to tackle the complexities of real world optimization problems. Recently, a number of nature inspired optimization techniques (NIOT) are being developed and proposed in literature. They are gaining popularity and are considered efficient due to their ability to find a reasonably acceptable solution within a fair amount of computational time. Some of the methods in this category are: Genetic Algorithms, Particle Swarm Optimization, Artificial Bee Colony, Biogeography Based Optimization, Grey Wolf Optimization, Sine Cosine Algorithm, Ant Lion Optimization, etc. This talk will focus on the state-of-the-art of Nature Inspired optimization Techniques. Then the talk will demonstrate the use of these techniques in many real life application problems in various areas of Engineering, particularly in Computer Games, Self-Driving cars, Defence, Medicine, Pattern Recognition, Electrical Engineering, Forecasting of Avalanches, Earthquake Engineering, etc.

Biography:Dr. Kusum Deep, is a full Professor (HAG), with the Department of Mathematics as well as Joint Faculty at the Mehta Family School of Data Science and Artificial Intelligence at the Indian Institute of Technology Roorkee, India. Also, she is a Visiting Professor, Liverpool Hope University, UK, University of Technology Sydney, Australia and University of Wollongong, Australia. With B.Sc Hons & M.Sc Hons. School from Centre for Advanced Studies, Panjab University, Chandigarh, she is an M.Phil Gold Medalist. She earned her PhD from UOR (now IIT Roorkee) in 1988. She has been a national scholarship holder and a Post-Doctoral from Loughborough University, UK assisted by International Bursary funded by Commission of European Communities, Brussels. She has won numerous awards like Khosla Research Award, UGC Career Award, Starred Performer of IITR Faculty, best paper awards by Railway Bulletin of Indian Railways, special facilitation in memory of late Prof. M. C. Puri, AIAP Excellence Award. She is one of the four women from IIT Roorkee to feature in the ebook “Women in STEM-2021” celebrating the contributions made by 50 Indian women in STEM published by Confederation of Indian Industries. According to Stanford University, she falls within top 2 % of the scientists in the world for 2019 and 2020. In 2021 she bagged the prestigious POWER grant awarded by DST, Govt. of India. In 2022 she is leading a collaborative consultancy project with Deloitte. On September 5, 2022, she was awarded Uttarakhand State Level “Excellence in Research of the Year 2022 Award, jointly organized in collaboration with DIVYA HIMGIRI (Premier Weekly News Magazine of Uttarakhand), VMSB Uttarakhand Technical University, Uttarakhand State Council for Science & Technology (UCOST) and Society for Research & Development in Science, Technology and Agriculture (SRADSTA). She has authored two books, supervised 20 PhDs, and published 125 research papers. She is a Senior Member of ORSI, CSI, IMS and ISIM. She is the Executive Editor of International Journal of Swarm Intelligence, Inderscience. She is Associate Editor of Swarm and Evolutionary Algorithms, Elsevier and is on the editorial board of many reputed journals. She is the Founder President of Soft Computing Research Society, India. She is the General Chair of series of International Conference on Soft Computing for Problems Solving (SocProS). She has a vast teaching experience in Mathematics, Operations Research, Numerical and Analytical Optimization, Parallel Computing, Computer Programming, Numerical Methods, etc. Her research interests are nature inspired optimization techniques, particularly Evolutionary Algorithms, and Swarm Intelligence Techniques and their applications to solve real life problems.