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We invite you to submit new journal proposal here, and contribute to academic development! In addition, if your institution (university, institute, college, national key laboratory, society, etc.) intends to establish a journal, you can also submit a draft to MDPI on its behalf.

MDPI journals frequently grant travel awards to empower junior researchers to showcase their latest research at academic conferences, thereby amplifying their impact within their research fields.

We extend our heartfelt congratulations to the 103 recipients of MDPI's 2023 Travel Awards from different countries and territories for their exceptional presentations. These outstanding individuals were selected by the journal editors based on the strength of their research proposals and the anticipated impact of their presentations at academic conferences. We commend their exceptional contributions and wish them continued success in their academic endeavors.

MDPI will continue to provide support and recognition to the academic community. To learn more about the list of awardees, please visit the following pages:

• Biology and Life Sciences;
• Chemistry and Materials Sciences;
• Engineering;
• Environmental and Earth Sciences;
• Medicine and Pharmacology;
• Public Health and Healthcare;
• Others.

About MDPI Awards:

To reward the academic community, especially young researchers, and enhance communication among scientists, MDPI journals regularly offer various awards to researchers in specific fields. These awards, serving as a source of inspiration and recognition, help raise the influence of talented individuals who have been credited with outstanding achievements and are making a significant contribution to the advancement of their fields.

To explore more MDPI awards, please click here.

The rapid advancements in artificial intelligence, particularly within the past few decades, have significantly impacted lives, businesses, and industries. ChatGPT and other large language models stand out as being particularly influential among these advancements. This webinar aims to explore these cutting-edge developments in AI while placing a specific focus on deep learning methodologies.

The webinar features three distinguished researchers who have made substantial contributions to the field. Their presentations will delve into the following critical topics:

• Optimization of deep learning;
• Reinforcement learning;
• Generative adversarial networks (GANs).

Join us to gain insights into the latest advancements in computer science and to discuss how these innovative technologies are shaping our world today.

Date: 26 September 2024 EST | 27 September 2024 CST
Time: 08:40–10:40 p.m. (EST) | 09:40–11:40 a.m. (CST)
More Information: https://sciforum.net/event/Mathematics-9

This is a free webinar. After registration, you will receive a confirmation email containing information on how to join the webinar. Registrations with academic institutional email addresses will be prioritized.

Unable to attend? Register anyway and we will let you know when the recording is available for viewing.

Register for free:

Program:

Related Special Issues:

“Intelligent Perception Computing and Graph Neural Networks: Algorithms, Applications, and New Challenges”
Guest Editors: Huakun Huang, Chunhua Su and Zhuotao Lian
Deadline: 1 March 2025

“Deep Neural Networks: Theory, Algorithms and Applications”
Guest Editors: Osslan Osiris Vergara Villegas, Vianey Guadalupe Cruz Sánchez and Vicente García
Deadline: 31 March 2025

“Neural Networks and Their Applications”
Guest Editor: Mario Muñoz Organero
Deadline: 30 November 2024

“Advances in Fuzzy Logic and Artificial Neural Networks”
Guest Editor: Francisco Rodrigues Lima Junior
Deadline: 30 November 2024

We wish to congratulate Prof. Dr. Álvaro Figueira and Dr. Bruno Vaz for winning the Mathematics 2022 Best Paper Award.

Name: Prof. Dr. Álvaro Figueira
Affiliation: CRACS-INESCTEC and Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
Research interests: Computer Science; Generative Adversarial Networks; Synthetic Data Generation; Disinformation Detection; Large Language Models.

Name: Dr. Bruno Vaz
Affiliation: Faculty of Sciences, University of Porto, Rua do Campo Alegre, s/n, 4169-007 Porto, Portugal; DareData Engineering, 1050-117 Lisbon, Portugal
Research interests: Large Language Models; Agentic Systems; Generative Adversarial Networks

The following is from an interview with Prof. Dr. Álvaro Figueira and Dr. Bruno Vaz:

1. Could you give a brief introduction of yourself to the readers? Could you introduce your current research direction and provide an update on your progress?

BV: I was a student at the Faculty of Sciences, University of Porto. I am currently an AI engineer working at a company from the private sector that builds custom AI-powered solutions. I work mainly with generative AI systems, such as chatbots.

AF: I graduated in 1995 with a degree in applied mathematics and computer science from the Faculty of Sciences, University of Porto. I earned an M.Sc. in advanced information technology from Imperial College London in 1997 and a Ph.D. in computer science from the University of Porto in 2004. Currently, I am a tenured Assistant Professor at the Faculty of Sciences, University of Porto. My research focuses on disinformation detection, social media analysis, and data visualization. As a researcher at the CRACS/INESCTEC unit, I have led international projects with institutions such as the University of Texas at Austin, University of Coimbra, and University of Aveiro, specializing in information extraction from social networks using machine learning and AI techniques.

2. Could you please briefly introduce the main content of the winning paper?

AF+BV: We wanted to provide an extensive review of synthetic data generation techniques, with a focus on Generative Adversarial Networks (GANs). The paper explores the need for synthetic data, especially in situations where real data is scarce, of poor quality, or involves privacy concerns, such as medical or financial data. We present various methods of generating synthetic data, both traditional and deep learning-based, with GANs being the primary focus due to their state-of-the-art performance in generating realistic data samples.

3. Could you describe the difficulties and breakthrough innovations in this research field?

AF+BV: The field of synthetic data generation and GANs faces several key challenges, including training instability, mode collapse, and difficulty generating diverse, high-quality data, especially for tabular datasets. Evaluating the quality of synthetic data remains difficult, as different applications require varying metrics, and hyperparameter tuning can be complex and resource-intensive. Moreover, privacy concerns arise when synthetic data generation risks revealing sensitive information from the original dataset.

Despite these challenges, breakthrough innovations have improved the performance and stability of GANs. Techniques like Wasserstein GAN (WGAN) and Progressive Growing GAN (ProGAN) have enhanced training stability and image resolution, while architectures like TGAN, CTGAN, and TabFairGAN have adapted GANs to generate high-quality tabular data. These innovations have significantly expanded the applicability and robustness of synthetic data generation.

4. What appealed to you about the journal that made you want to submit your paper? How was your experience submitting to Mathematics?

AF+BV: We were both drawn to Mathematics because of its reputation for efficiency and a well-structured review process. The journal ensures that reviewers are promptly engaged after submission, with a clear and expedited timeline—typically ranging from one week to a month, even during busy periods. This approach minimizes delays and keeps the process moving smoothly. We also appreciated the user-friendly submission system, which made navigating the various stages straightforward. The blind review process was particularly noteworthy for its clear and effective communication, allowing us to quickly understand and address the reviewers' feedback. Furthermore, the journal's strong impact factor and the consistently high quality of papers, especially in the Computer Science section, reinforced our decision to submit our work to Mathematics.

5. Which research topics do you think will be of particular interest to the research community in the coming years?

BV: In the last few months, LLMs have gained a lot of traction and have become more and more proficient, not only at natural language tasks but also in vision and audio. Even though the advances are astonishing, such models still suffer from hallucinations—they return content that, even though well-written, might not be factual. I believe this will be a clear topic of research in the coming years. Moreover, as AI systems become more autonomous, questions about ethical decision-making, accountability, and transparency will intensify. So, I believe research into AI fairness, bias mitigation, and explainability is expected to grow.

AF: I would also emphasize that the future likely holds a trend toward the specialization of LLMs in specific domains or for individual users, often referred to as personalized LLMs. This specialization could significantly enhance the relevance and effectiveness of AI interactions. Additionally, as LLMs become more tailored to individual needs, we can expect a growth in interactions between different LLMs, opening up new possibilities for making these exchanges mutually beneficial for both the LLMs and their users. This evolution could lead to more dynamic, personalized, and productive AI-driven conversations in the future.

6. Do you have any advice for aspiring young researchers looking to make a meaningful impact in their respective fields?

BV: More and more often I hear about FOMO (fear of missing out). With so many advances in generative AI, it is easy to feel like you are missing out. This has happened to me personally, so the advice that I would give would be to have JOMO (joy of missing out). If one can focus on a specific thing and do it well, I believe they would go a long way.

AF: In today’s fast-paced world of scientific advancement, it can be challenging to keep track of the latest developments and where the cutting-edge lies. To navigate this complex landscape, we rely on an enormous quantity of scientific publications that together with current advanced large language models help us map out the current state of knowledge. However, pushing beyond these boundaries still demands human effort, creativity, sensitivity, and insight. While these tools can guide us, it is ultimately our unique human qualities that drive innovation and meaningful progress.

7. As the winner of this award, is there something you want to express or someone you wish to thank most?

BV: Like Newton once wrote, "If I have seen further, it is by standing on the shoulders of Giants". If I was able to write this paper, it is because of the giants in my life. So, I would like to thank my parents, Albino and Natércia, and girlfriend, Rita, for being my pillars in life. Also, a special thanks to Professor Álvaro Figueira, who has guided me through the whole writing of this paper—without his help, I would still be floating on uncharted waters.

AF: The pursuit of methods to identify fake news led me to the creation of synthetic data (for balancing the datasets). While this was my initial focus, working with Bruno greatly expanded the scope of the research beyond my original goals. I am deeply grateful for his dedication and invaluable contributions to this project, and I want to express my heartfelt thanks for his commitment.

8. What is your opinion of the open access model of publishing?

AF+BV: Open access publishing can remove paywalls, allowing anyone with Internet access to read the published articles. This is particularly beneficial for researchers, students, and professionals in low-income or under-resourced institutions who cannot afford expensive journal subscriptions. Moreover, open access platforms often provide a faster route to publication and allow for the immediate dissemination of research, which is crucial in fast-moving fields like medicine, climate science, and technology.

MDPI’s Best PhD Thesis Awards are granted to promising young scholars whose PhD theses are deemed exceptional within their respective research fields. These awards aim to encourage young scholars to continue their outstanding accomplishments and further contribute to their field.

We extend our heartfelt congratulations to the 54 winners of the 2023 Best PhD Thesis Awards and wish them success with their future research endeavors.

MDPI will continue to provide support and recognition to the academic community. To learn more about all the awardees and their research projects in your field of study, please visit the following pages:

About MDPI Awards:

To reward the academic community, especially young researchers, and enhance communication among scientists, MDPI journals regularly offer various awards to researchers in specific fields. These awards, serving as a source of inspiration and recognition, help raise the influence of talented individuals who have been credited with outstanding achievements and are making a significant contribution to the advancement of their fields.

To explore more MDPI awards, please click here.

MDPI will be attending the 18th European Conference on Computer Vision (ECCV 2024) in MiCo Milano, Italy, which will take place from 29 September to 4 October 2024. The European Conference on Computer Vision (ECCV) is a premier biennial research conference in Computer Vision and Machine Learning, managed by the European Computer Vision Association (ECVA). It is held on even years and gathers the scientific and industrial communities within these areas. The first ECCV was held in 1990 in Antibes, France, and the conference has subsequently been organized all over Europe.

The following MDPI journals will be represented:

• Applied Sciences;
• Electronics;
• AI;
• Future Internet;
• Inventions;
• Journal of Imaging;
• Machine Learning and Knowledge Extraction;
• Mathematics;
• Remote Sensing;
• Technologies.

If you are attending the conference, please visit our booth. Our delegates look forward to meeting you in person and answering any questions that you may have. For more information about the conference, please visit the following link: https://eccv.ecva.net/.

Conference: 2024 IEEE Information Theory Workshop
Organization: Tsinghua Shenzhen International Graduate School and IEEE Information Theory Society
Date: 24–28 November 2024
Place: Shenzhen, China

We are pleased to announce that MDPI will be attending the 2024 IEEE Information Theory Workshop as an exhibitor. This meeting will be held in Shenzhen, China, from 24 to 28 November 2024.

This conference consists of a daily plenary followed by two parallel technical sessions throughout the day. IEEE-ITW’24 welcomes original contributions on the frontiers of information theory, statistics, coding theory, and their applications, as well as interactions between information theory and other fields such as data science, biology, and signal processing.

The following MDPI journals will be represented:

• Entropy;
• Information;
• Data;
• Future Internet;
• Cryptography;
• Network;
• Digital;
• JCP;
• Mathematics;
• Electronics.

If you are attending this conference, please feel free to engage with us while you are there. Our delegates look forward to meeting you in person and answering any questions you may have. For more information about the event, please visit the following website: https://www.ieee-itw2024.org/.

MDPI would like to invite all experts recognized in their fields to attend a webinar organized by our journal Mathematics (ISSN: 2227-7390) entitled—The Utilization of Stochastic Reconstruction in Estimating, Diagnosing and Extending Hawkes Process Models. The aim of the webinar is to focus on the Hawkes process model, a point process model widely used in both natural and social sciences. Its ability to examine clustering effects and positive interactions among events makes it a powerful tool for many researchers. During this talk, the ways in which stochastic reconstruction can be applied to estimate, diagnose, and enhance the Hawkes process model will be explored. This webinar will be valuable and inspiring for anyone working with these models. 

We are looking forward to seeing you at the webinar.

Webinar: The Utilization of Stochastic Reconstruction in Estimating, Diagnosing and Extending Hawkes Process Models
Date: 12 September 2024
Time: 9:00–9:50 (CEST) | 16:00–16:50 (JST)
More information: https://sciforum.net/event/Mathematics-8

This is a free webinar. After registration, you will receive a confirmation email containing information on how to join. Registrations with academic institutional email addresses will be prioritized.

Unable to attend? Register anyway and we will let you know when the recording is available for viewing.

Register for free:

Program:

Relevant Special Issue:
“Stochastic Processes and Its Applications”
Edited by Dr. Elvira Di Nardo and Prof. Dr. Luis Alberiko Gil-Alana
Submission Deadline: 31 May 2025

We wish to congratulate Prof. Dr. Masahiro Yamamoto for winning the Mathematics 2022 Best Paper Award.

Name: Masahiro Yamamoto
Affiliation: Graduate School of Mathematical Sciences, The University of Tokyo, Komaba, Meguro, Tokyo 153-8914, Japan
Interests: inverse problems in mathematical sciences; parameters in evolution equations; determination of shapes of domains from overdetermining data

Masahiro Yamamoto has been a full professor in the Graduate School of Mathematical Sciences of The University of Tokyo, Japan, since 2010, and he is now a Project Professor and Emeritus Professor at the University of Tokyo and a professor at Zonguldak Bulent Ecevit University in Turkey. In 1983, he obtained the degree of Master of Science from the University of Tokyo. He received a Ph.D. from the University of Tokyo for a thesis entitled “Inverse Spectral Problem for Systems of Ordinary Differential Equations” in 1988. His research interests include inverse problems by Carleman estimate; inverse boundary value problems; determination of shapes; fractional partial differential equations and optimal control.

The following is from an interview with Prof. Dr. Masahiro Yamamoto:

1. Could you give a brief introduction of yourself to the readers? Could you introduce your current research direction and provide an update on your progress?
I have been working on control theory, inverse problems for partial differential equations and time fractional partial differential equations. In particular, I have started mathematical research for fractional differential equations, motivated by the analysis of the anomalous diffusion of radioactive nuclide, which was seriously emitted by the Fukushima nuclear disaster in 2011. In order to maintain reasonable shapes of my own theoretical research, I, a mathematician, am confident that it is so important to maintain contact with more applied aspects. Moreover, through such contact, one can find significant topics from the real-world which are awaiting mathematical studies. In other words, in this field, mathematicians must always pursue not only theories but also possible practice of the developed theories.
Holding such views, I have recently been working on the uniqueness and stability of various inverse problems for time-fractional differential equations, as well as for transport, parabolic, hyperbolic, Schrodinger equations.

2. Could you please briefly introduce the main content of the winning paper?
There have been many important works on time-fractional differential equations such as the unique existence of solutions to initial value problems and initial boundary value problems. However, aiming at more consistent convenient applications to inverse problems, I proposed a framework based on the operator theory. This approach is not purely new, but I have integrated existing results and expertise such as the operator theory and the theory of evolution equations, and I have demonstrated the effectiveness of the framework in various problems for time-fractional differential equations.
In the winning paper, I have tried to establish foundations of the theory of time-fractional differential equations, which is directly applicable to inverse problems.

3. Could you describe the difficulties and breakthrough innovations in this research field?
Fractional calculus is similar to classical calculus treating derivatives of natural number orders, but not the same. For example, formulae of integration by parts for fractional derivatives do not allow us to use energy estimates and Carleman estimates, which are strong tools for inverse problems. Such a difference is a main difficulty. Thus, it is definitely a breakthrough that we can completely clarify which properties can still hold and which are essentially different among the properties of parabolic equations and hyperbolic equations. Through such complete clarification, we can also understand the essence of anomalous diffusion.

4. What appealed to you about the journal that made you want to submit your paper? How was your experience submitting to Mathematics?
The wide range of audience is appealing, and the referee procedure was excellent.

5. Which research topics do you think will be of particular interest to the research community in the coming years?

• The inverse problems for fractional partial differential equations;
• The establishment of the theory of time-fractional differential equations which may generalize the classical theory of partial differential equations.

6. Do you have any advice for aspiring young researchers looking to make a meaningful impact in their respective fields?

• Do not aim too much at currently popular research fields. Also, we should keep in mind that we try to find rough gemstones which will open up rich and important research fields;
• It may take a long time until your achievements are well recognized and evaluated; please be patient and continue to work;
• In order to maintain your mathematical research, please grasp wide scopes not only involving mathematics but also real-world problems. Remember that mathematics looks transcendental and far away from reality, but it can solve real world problems and potentially even save the world.

7. As the winner of this award, is there something you want to express or someone you wish to thank most?
The late Professor Dr. Rudolf Gorenflo. He introduced me to the world of fractional calculus and has supported my research. It is a pity that now I cannot directly convey my gratitude to him, and I am surely indebted to him.

8. What is your opinion of the open access model of publishing?
Open access is fine, especially for mathematics if used relevantly, because mathematical achievements are fundamental and so are more similar to common properties, especially among natural sciences.