Special Sessions

Special session proposals should include the title, aim and scope (including a list of main topics), and the names of the organizers of the special session, together with a short biography of all organizers. A list of potential contributors will be helpful.

Special session proposals will be evaluated based on the timeliness, uniqueness of the topic and qualifications of the proposers. The proposers are expected to have a PhD degree and have a good publication track record in the proposed area. After review, a decision on whether the proposal will be accepted will be sent to the proposers within two weeks after receipt of the proposals. Accepted special sessions will be listed on the website. However, it is likely that an accepted proposal will be combined with another one to avoid multiple special sessions covering a similar topic.

Please send us the proposal via email: fsdm@fsdmconf.org

1. Special Session on "Applied Mathematics and Intelligent Algorithms for Modern Industry (AMIAMI)"
1. Aims of the Session
The session on "Applied Mathematics and Intelligent Algorithms for Modern Industry" aims to establish a dynamic forum for the dissemination of the latest research in applied mathematics and intelligent algorithms, specifically within the industrial context. It seeks to encourage interdisciplinary collaboration, connecting researchers, industry experts, and academics to explore and discuss both the challenges and emerging opportunities in the application of sophisticated mathematics and algorithmic solutions to real-world industrial problems. The session is dedicated to not only highlighting innovative research but also to fostering a deeper understanding of how these advanced methodologies can drive progress in various industrial sectors.

2. Targets and Contributors
The targets and contributors include the following:
  • Researchers and academicians specializing in applied mathematics, computer science, engineering, and related fields.
  • Industry professionals and practitioners who are implementing intelligent algorithms in various industrial sectors.
  • Postgraduate students and early-career researchers who are engaged in relevant research.
  • Policy makers and educators interested in the latest developments in applied mathematics and its industrial applications.
3. Main Topics
Topics in this special session will include, but not limited to:
  • Development and application of intelligent algorithms in industries such as manufacturing, logistics, healthcare, finance, energy, insurance, and telecommunications.
  • Case studies showcasing successful implementation of mathematics models and algorithms in solving real-world industrial problems.
  • Advances in computational methods, machine learning, and artificial intelligence that contribute to industrial applications.
  • Theoretical and practical challenges in applying mathematics and algorithmic solutions in the industry.
  • Future trends and emerging technologies in the field of applied mathematics and intelligent algorithms for industry.
  • Ethical, legal, and societal implications of deploying algorithmic solutions in an industrial context.
Submission Deadline for Full Papers: September 1, 2024
Submission Deadline for Abstracts (without full paper publication in conference proceeding or related journals): October 10, 2024

This special session is now open for submission and registration. Please submit your abstract/full paper via the Submission System and choose the option of Special Session on " Applied Mathematics and Intelligent Algorithms for Modern Industry (AMIAMI)". After that, you can make the conference registration directly via the Registration Page or the conference secretary will contact you for further information.

Session Chair and Committee Profiles
Session Chair: Assoc. Prof. Sayan Kaennakham (Ph.D.), Institute of Science, Suranaree University of Technology, Thailand.

Dr. Sayan Kaennakham, an Associate Professor at Suranaree University of Technology, Thailand, specializes in Computational Fluid Dynamics, a field he delved into during his Ph.D. at Coventry University, UK. He holds a Senior Fellowship with the UKPSF, underlining his academic prowess. His work is focused on areas like data science, neural networks, machine learning, and AI-driven innovation. He's authored notable books including ‘Mathematics in Daily Life’ and ‘An Introduction to a Collocation Meshless Method for DEs’. As a Graduate Program Coordinator, he leads modules in Applied Machine Learning and Scientific Data Analysis. Involved in various cutting-edge research projects, Dr. Kaennakham contributes significantly to the fields of medical imaging diagnosis and smart healthcare solutions through AI. His commitment to interdisciplinary research is evident in his roles in the Multidisciplinary Innovation Research Centre and the Applied and Computational Mathematics Research Group.

Organizing Committee
Assoc.Prof. Dr.Konstantin Ryabinin, Heidelberg University, Germany.

Dr. Konstantin Ryabinin is a research worker at the Astronomisches Rechen-Institut (ARI), Centre for Astronomy of Heidelberg University, and an associate professor at Perm State University (Computer Science Department). He currently resides in Mannheim, Germany, and works at ARI as a researcher and software engineer, developing a parallel direct solver for systems of astrometric equations within the Japan Astrometry Satellite Mission for INfrared Exploration (JASMINE). He graduated from the Mechanics and Mathematics faculty of Perm State University in 2011 and defended his Ph.D. in Computer Science in 2015. Since 2011, he has conducted research in the fields of scientific visualization, visual analytics, human-computer interaction, computational geometry, computer graphics, multimedia, ontology engineering, semantic data mining, multiplatform portability, ubiquitous computing, and the Internet of Things. He is the leading developer of the SciVi visual analytics platform and the NChart3D data visualization library. He has published more than 70 papers in scientific journals and proceedings of international conferences in the area of his research expertise.

Assoc.Prof. Dr.Geanette Polanco, The Arctic University of Norway, Norway.

Dr. Geanette Polanco is a renowned mechanical engineer specializing in fluid mechanics, with a Ph.D. from Coventry University, UK. She has an extensive academic background, notably as an Associate Professor at UiT The Arctic University of Norway and previously as a Professor at Simon Bolivar University in Venezuela. Dr. Polanco has led various research projects and is recognized for her contributions in energy, environment, mechanical engineering, and engineering education. Fluent in Spanish and English, and with intermediate Norwegian skills, she combines her professional expertise with a passion for culture and nature. Her career is distinguished by a commitment to advancing mechanical engineering through innovative research and education to address actual problems.

Asst.Prof. Dr.Sungsu Kim, University of Wisconsin-Green Bay, USA.

Dr. Sungsu Kim is an Assistant Professor of Statistics in the Resch School of Engineering at the University of Wisconsin-Green Bay, where he has been a faculty member since 2022. He completed his Ph.D. in Applied Statistics at the University of California, Riverside, and his undergraduate studies at the University of California, San Diego. His main research interest lies in the area of Directional Statistics, particularly in Circular Statistics. He has published more than 25 papers in peer-reviewed domestic and international journals and has given talks at more than 20 domestic and international conferences and seminars. He has taught various undergraduate and graduate Statistics courses at several universities, and he enjoys teaching more than ever.

Asst.Prof. Dr. Nara Samattapapong, Suranaree University of Technology, Thailand.

Dr. Nara Samattapapong is a distinguished Assistant Professor in the Industrial Engineering Department at Suranaree University of Technology, Thailand. His academic journey includes earning a Doctorate in Mechanical Electronic Engineering from the Asian Institute of Technology in 2016, preceded by a Master's degree in the same field from the same institution in 2005, and a Bachelor's degree in Industrial Engineering from Suranaree University of Technology in 2000. Dr. Samattapapong's professional career is marked by his role as the Head of the Industrial Engineering Department at Suranaree University from 2017 to 2021, following his position as a Lecturer in the same department since 2009. His expertise spans across various domains including industrial automation, robotics, sensors, simulation, artificial intelligence, and neural network optimization. Dr. Samattapapong has also made significant contributions to the field through his international and national publications, focusing on areas such as metaheuristic approaches for vaccine cold chain networks and enterprise resource planning for Thai agricultural cooperatives. His work demonstrates a profound commitment to advancing industrial engineering through innovative research and practical applications.

Asst.Prof. Dr.Pornthip Pongchalee, Rajamangala University of Technology Isan, Thailand.

Dr. Pornthip Pongchalee, a distinguished figure in the field of Applied Mathematics, has made significant contributions to academia and research. After obtaining her Ph.D. in Applied Mathematics from Suranaree University of Technology in 2007, she further honed her expertise at Chiang Mai University and Khon Kaen University in Thailand. Dr. Pongchalee has been an influential Assistant Professor at the Department of Applied Mathematics and Statistics, Faculty of Science and Liberal Arts, Rajamangala University of Technology Isan, Nakhon Ratchasima, Thailand, since June 2017. Her work experience also includes previous faculty positions within the same university. She is an author of academic books, notably 'Calculus 3 for Engineers' (2017), and her research interests span radial basis functions, multiquadric neural networks, numerical methods, differential equations, and optimization techniques. Dr. Pongchalee's impressive array of selected publications showcases her deep engagement with computational and experimental simulations in engineering, solidifying her as a leading mind in her field.

Asst.Prof. Dr.Krittidej Chanthawara, Ubon Ratchathani Rajabhat University, Thailand.

Krittidej Chanthawara, Ph.D., is an Assistant Professor in the Program of Mathematics at the Faculty of Science, Ubon Ratchathani Rajabhat University, Thailand. His academic journey began at Khon Kaen University, Thailand, where he earned a BSc in Mathematics in 2001, an MSc in Mathematics in 2005, and a Ph.D. in Applied Mathematics in 2016. Dr. Chanthawara has been a faculty member at Ubon Ratchathani Rajabhat University since June 2006. His areas of interest include numerical methods, boundary element methods, meshless/meshfree methods, radial basis functions, partial differential equations, and data interpolation and analysis. He is also the author of a book on Calculus and Analytical Geometry and has contributed to numerous publications and research projects in his field.

Asst.Prof. Chantana Simtrakankun, Loei Rajabhat University, Thailand.

Chantana Simtrakankul, an accomplished academic in the field of mathematics, currently serves as an Assistant Professor at the Division of Mathematics, Department of Science, Faculty of Science and Technology, Loei Rajabhat University in Thailand. She has been in this role since October 2015, after a tenure as a faculty member at the same institution and earlier at Rajamangala University of Technology Isan. Chantana completed her Master of Science in Mathematics at Khon Kaen University, Thailand, in 2006. Her areas of interest include numerical methods and analysis, differential equations, and fuzzy C mean. Chantana has contributed significantly to her field, as evident in her publications on topics like adaptive particle swarm optimization in conjunction with support vector machine and the application of wavelet convolution neural networks for breast cancer detection. Her work demonstrates a keen focus on integrating mathematical principles with practical applications in technology and health sciences.

Dr.Wiwat Nuansing, Suranaree University of Technology, Thailand.

Dr. Wiwat Nuansing, a highly accomplished physicist, specializes in the field of nanotechnology and advanced materials. His journey began with a B.Sc. and M.Sc. in Physics from Khon Kaen University, Thailand, followed by a M.Sc. in Nanoscience and a Ph.D. in Physics of Nanostructures and Advanced Materials from the University of the Basque Country, Spain. Currently, he serves at the School of Physics, Institute of Science, Suranaree University of Technology, Thailand, and is also the leader of the G5 Materials Enterprise and Industry Group at the Center of Excellent of Advanced Functional Materials (CoE-AFM). His prior experiences include significant research roles at CIC nanoGUNE, San-Sebastian, Spain, and the Walter Schottky Institute, Munich, Germany. Dr. Nuansing's research interests span a wide range, including 3D printing technologies, nanofibers, electrospinning techniques, and the application of nanotechnology in biomedical and industrial fields. His work has led to numerous patents and publications, underscoring his contributions to the field and his commitment to advancing the application of nanotechnology.

Dr.Tanakorn Sritarapipat, Suranaree University of Technology, Thailand.

Tanakorn Sritarapiwat, Ph.D., is a distinguished Lecturer in Geoinformatics at Suranaree University of Technology. He holds a Doctorate in Civil Engineering from The University of Tokyo and Master’s and Bachelor’s degrees in Electrical Engineering from Kasetsart University. His expertise in remote sensing, GIS, machine learning, neural networks, and deep learning is reflected in his teaching of Master's and Ph.D. courses in modern Geoinformatics. Dr. Sritarapiwat has developed various computer applications based on image processing and machine learning, including human harm detection using UAV video and land cover classification using LANDSAT8. His research experience spans roles at the Geo-Informatics and Space Technology Development Agency and as a Software Developer at Kasetsart University. Additionally, he possesses advanced skills in programming languages like Python, C, and MATLAB, and is fluent in Thai, English, and basic Japanese.

Mr. Sathitthep Sangthong, Rajamangala University of Technology Rattanakosin, Thailand.

Mr. Sathitthep Sangthong, an experienced industrial engineer and teacher, possesses a Bachelor's degree in Industrial Engineering from Narasuan University and a Master's from Chulalongkorn University in the same field. He is presently working towards a Ph.D. in Industrial Engineering at Suranaree University of Technology and anticipates finishing the program in the current academic year. Since 2013, he has been a lecturer at Rajamangala University of Technology Rattanakosin, specializing in Industrial and Logistics Engineering. His professional career includes roles as an independent consultant for various industries and government departments since 2015, and he has provided consultancy to numerous organizations including Thai Rom Klao Co. Ltd, Dairy Thai Co. Ltd, and Phramongkutklao Hospital. Earlier in his career, he worked as a Design Engineer at Makino Asia PTE Ltd in Singapore and as a Planning Engineer at Thai Bridgestone Co. Ltd. Recognized as a Senior Professional Engineer by the Council of Engineers Thailand in 2021, Mr. Sangthong's expertise spans computer simulation, production planning, maintenance engineering, industrial plant layout, and logistics management.

Mr. Apilak Waengwan, Suranaree University of Technology, Thailand.

Mr. Apilak Waengwan, a prominent figure in the field of Industrial Engineering, currently serves as the Managing Director at Mastersoft Solution Co. Ltd. He boasts a diverse educational background, including a Bachelor's degree in Civil Engineering from King Mongkut's University of Technology (North Bangkok), a Master's degree in Computer Science from the National Institute of Development Administration (NIDA), and is now a Ph.D. candidate in Industrial and Environmental Engineering at Suranaree University of Technology, expecting to complete his degree in this academic year. With substantial experience in Healthcare IT Solution Logistics & Supply Chain Engineering, Mr. Waengwan has held multiple influential roles, including Senior Counselor at Life Plus EAP, General Manager at IE Thai Software Co. Ltd., and has been a consultant for various prestigious organizations. His expertise and leadership in the industry are evident in his numerous successful projects and contributions to the field.

Mr.Pirapong Inthapong, Suranaree University of Technology, Thailand.

Mr. Pirapong Inthapong, currently the Head of the Mathematics Department at Surawiwat School, Suranaree University of Technology, is a multi-talented academic with a rich background in mathematics and computer science. He holds a Bachelor’s degree in Mathematics from Khon Kaen University, a Master’s degree in Applied Mathematics and Computational Sciences from Chulalongkorn University, and another Master’s in Mathematical and Physical Sciences from Kanazawa University, Japan. Additionally, Mr. Inthapong is now a Ph.D. candidate in the Applied Machine Learning and Scientific Data Analysis module at the Institute of Science, Suranaree University of Technology, and is expected to complete his degree within this academic year. His professional journey includes roles as a Mathematics teacher, an Information Efficiency Development Division officer at the Civil Aviation Authority of Thailand, and significant contributions to the fields of optimization techniques, numerical methods, machine learning, data science, and web application development.

2. Special Session on "Application of Generative AI"
Aims
The goal of this special session is to explore the latest advances in generative AI and their applications in a variety of fields. This special session will provide a valuable opportunity for attendees to learn about the latest trends in generative AI and to network with leading experts in the field.

Here are some specific benefits that attendees can expect from this special session:
  • Gain an understanding of the latest advances in generative AI
  • Learn about the applications of generative AI in a variety of fields
  • Learn about the modern models and development tools of generative AI
  • Network with leading experts in the field
  • Get inspired by the latest research in generative AI
Target and Contributors
The target and contributors include the following:
  • Leading experts from both academia and industry to share their insights on generative AI applications at industrial level.
  • Management and technicians from industrial to learn about the applications of Generative AI on how it is being used to solve real-world problems, and what are their advantages and limitations.
  • Graduate school students to learn about the generative AI trends and development techniques.
Main Topics
Topics in this special session will include, but not limited to:
  • Industrial applications of generative AI in healthcare, finance, manufacturing, and other industries
  • Techniques and applications of Image and Video generation
  • Ethical and legal considerations for generative AI
  • Tutorial for Generative AI models, development tools, such as prompt engineering, LangChain, etc.
  • Generative AI application evaluation
  • MLOps for Generative AI
  • Fine tuning and in-context learning of Large Language Model (LLM)
  • Mitigation of LLM hallucinations
Submission Deadline for Full Papers: September 1, 2024
Submission Deadline for Abstracts (without full paper publication in conference proceeding or related journals): October 10, 2024

This special session is now open for submission and registration. Please submit your abstract/full paper via the Submission System and choose the option of Special Session on "Application of Generative AI". After that, you can make the conference registration directly via the Registration Page or the conference secretary will contact you for further information.

Session Chair
Yingwei Yu received his Ph.D. in Computer Science from Texas A&M University. He is currently a Senior Applied Scientist at Amazon Web Services (AWS) in the Generative AI Innovation Center (GAIIC). His research interests focus on applying machine learning, deep neural networks, and generative AI technologies to industrial-level applications.

Yingwei has experience working with several organizations across industries on various machine learning applications. He has publications in professional conferences and journals, and has been granted a number of patents in the industrial applications of artificial intelligence.

Organizing Committee of this Special Session (order by last name)
Aman Chadha is an Applied Science Manager at the AWS Generative AI Innovation Center (GAIIC). Previously, he led AI for Speaker Understanding and Personalization at Amazon Alexa. Prior to Amazon, he was part of the Machine Intelligence Neural Design (MIND) team at Apple, where he led teams that developed on-device multimodal AI models for a wide range of applications including Natural Language Processing, Speech Recognition, and Computer Vision. He was also one of the early architects of Apple's M1 chip, developing ML models for estimating performance of future Macs and iPads years in advance. Before Apple, he was at Qualcomm and Nvidia working on ML accelerators and AI workloads for GPUs respectively. He pursued his graduate studies in Artificial Intelligence from Stanford University, specializing in Multimodal AI.

Ziqing Hu received his Ph.D. in Applied Mathematics and Statistics from the Department of Applied Computational Mathematics and Statistics at University of Notre Dame. He is currently an Applied Scientist at Amazon Web Services (AWS) in the Generative AI Innovation Center (GAIIC). His research interests focus on the generative modeling, AI for physics, graph neural network and State-Of-The-Art machine learning techniques. 

Xinghua Liang is a Senior Applied Scientist at the Amazon People Science Solution Lab (PSSL). He leads numerous projects in collaboration with customers across finance, healthcare, manufacturing, retail, and automotive industries. In these partnerships, Xinghua helps customers accelerate their adoption of machine learning, artificial intelligence, causal analysis and cloud computing capabilities. Xinghua obtained his Ph.D. in Mechanical Engineering from Carnegie Mellon University.

Diego Socolinsky is a Senior Applied Science Manager at the AWS Generative AI Innovation Center, where he leads the delivery teams for the Eastern US and Latin America regions. During his nearly 25 years career, he has led development and deployment of numerous machine learning and computer vision systems for commercial, industrial and government customers. This includes pioneering work in face biometrics, high-speed object tracking, low-power video processing and augmented reality, among others. He holds a PhD degree in mathematics from The Johns Hopkins University.