Dingming Wu | Computer Science | Best Researcher Award

Posted on by sciencefather

Dr. Dingming Wu | Computer Science | Best Researcher Award

 

Profile

  • scopus

Education

He holds a Ph.D. in Computer Science and Technology from Harbin Institute of Technology, where he studied under the supervision of Professor Xiaolong Wang from March 2018 to December 2022. Prior to that, he earned a Master’s degree in Probability Theory and Mathematical Statistics from Shandong University of Science and Technology in collaboration with the University of Chinese Academy of Sciences, completing his studies under the guidance of Professor Tiande Guo between September 2014 and July 2017. His academic journey began with a Bachelor’s degree in Information and Computational Science from Shandong University of Science and Technology, which he completed between September 2006 and July 2010.

Work experience

He is currently a Postdoctoral Fellow at the University of Electronic Science and Technology of China, Chengdu, a position he has held since December 2022 and will continue until December 2024. His research focuses on EEG signal processing and algorithm feature extraction, specifically addressing the challenges posed by the complexity and individual variations of EEG signals. Given the limitations of traditional classification methods, his work aims to enhance recognition accuracy through advanced deep learning models, improving the decoding of intricate EEG signals and optimizing control accuracy. Additionally, he integrates artificial intelligence technologies to predict user intentions and provide proactive responses, ultimately enhancing the interactive experience. His system is designed for long-term stability and adaptability, leveraging self-learning mechanisms based on user feedback.

Previously, he worked as a Data Analyst at Qingdao Sanlujiu International Trade Co., Ltd., Shanghai, from September 2010 to July 2014. In this role, he was responsible for conducting statistical analysis of trade flow data.

Publication

  • [1] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Jointly modeling transfer learning of
    industrial chain information and deep learning for stock prediction[J]. Expert Systems with
    Applications, 2022, 191(7):116257.
    [2] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu.A hybrid framework based on extreme
    learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock
    prediction[J]. Expert Systems with Applications, 2022, 207(24):118006.
    [3] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Construction of stock portfolio based on
    k-means clustering of continuous trend features[J]. Knowledge-Based Systems, 2022,
    252(18):109358.
    [4] Dingming Wu, Xiaolong Wang∗, Jingyong Su, Buzhou Tang, and Shaocong Wu. A labeling
    method for financial time series prediction based on trends[J]. Entropy, 2020, 22(10):1162.
    [5] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. A hybrid method based on extreme
    learning machine and wavelet transform denoising for stock prediction[J]. Entropy, 2021,
    23(4):440.
    Papers to be published:
    [6] Wavelet transform in conjunction with temporal convolutional networks for time series
    prediction. Journal: PATTERN RECOGNITION; Status: under review; Position: Sole
    Author.
    [7] A Multidimensional Adaptive Transformer Network for Fatigue Detection. Journal: Cognitive
    Neurodynamics; Status: accept; Position: First Author.
    [8] A Multi-branch Feature Fusion Deep Learning Model for EEG-Based Cross-Subject Motor
    Imagery Classification. Journal: ENGINEERING APPLICATIONS OF ARTIFICIAL
    INTELLIGENCE; Status: under review; Position: First Author.
    [9] A Coupling of Common-Private Topological Patterns Learning Approach for Mitigating Interindividual Variability in EEG-based Emotion Recognition. Journal: Biomedical Signal
    Processing and Control; Status: Revise; Position: First Corresponding Author.
    [10] A Function-Structure Adaptive Decoupled Learning Framework for Multi-Cognitive Tasks
    EEG Decoding. Journal: IEEE Transactions on Neural Networks and Learning Systems;
    Status: under review; Position: Co-First Author.
    [11] Decoding Topology-Implicit EEG Representations Under Manifold-Euclidean Hybrid Space.
    Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI);
    Status: under review; Position: Second Corresponding Author.
    [12] Style Transfer Mapping for EEG-Based Neuropsychiatric Diseases Recognition. Journal:
    EXPERT SYSTEMS WITH APPLICATIONS; Status: under review; Position: Second
    Corresponding Author.
    [13] An Adaptive Ascending Learning Strategy Based on Graph Optional Interaction for EEG
    Decoding. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: Second Corresponding Author.
    [14] A Transfer Optimization Methodology of Graph Representation Incorporating CommonPrivate Feature Decomposition for EEG Emotion Recognition. Computer conference:
    International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review;
    Position: Second Corresponding Author.
    [15] An Interpretable Neural Network Incorporating Rule-Based Constraints for EEG Emotion
    Recognition. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: First Author.

Rakesh Meena | Applied Mathematics | Best Researcher Award

Posted on by sciencefather

Mr. Rakesh Meena | Applied Mathematics | Best Researcher Award

Research Scholar at Sardar Vallabhbhai National Institute of Technology, India

Mr. Rakesh Meena is a promising researcher and Ph.D. candidate at the Department of Mathematics, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India. His academic journey is characterized by a focus on advanced mathematical modeling, fractional calculus, and differential equations. With a blend of theoretical and computational expertise, Mr. Meena is dedicated to contributing to innovative solutions in applied mathematics, particularly in areas like epidemic modeling and dynamic systems. He is driven by the desire to combine research with teaching to foster academic growth and knowledge sharing. Throughout his career, he has earned recognition through prestigious scholarships and fellowships, such as the Junior Research Fellowship (JRF) and Senior Research Fellowship (SRF) from CSIR-UGC. His research contributions, including numerous journal publications and conference presentations, reflect his deep commitment to advancing mathematical sciences. Mr. Meena’s aspirations align with the goal of bringing meaningful change to both the academic community and society through his research and teaching.

Profile

Scopus

Google Scholar

Orcid

 

Education 🎓

Mr. Rakesh Meena’s educational background forms a solid foundation for his research career. He began his academic journey at Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, where he completed his Five-Year Integrated M.Sc. in Mathematics with first division in 2020. Following this, he embarked on his Ph.D. in Mathematics, with a focus on linear and nonlinear fractional differential equations. Under the guidance of Dr. Sushil Kumar, he has made notable progress in mathematical modeling, particularly through the semi-analytical approach. His cumulative performance during his Ph.D. coursework reflects dedication, maintaining a CGPA of 7.25. Throughout his education, Mr. Meena has demonstrated a continuous pursuit of knowledge, aiming to contribute to the vast field of mathematical sciences. His educational path has not only provided him with strong analytical skills but also a deep understanding of both theoretical and computational methods. This educational experience, combined with his passion for research, serves as a solid launchpad for his future contributions to the scientific community.

Work Experience 💼

Mr. Rakesh Meena’s professional experience includes extensive academic research at the Department of Mathematics, SVNIT, Surat. Currently pursuing his Ph.D., Mr. Meena has contributed to a range of mathematical research, particularly in fractional calculus, epidemic modeling, and nonlinear differential equations. His expertise in using semi-analytical methods, such as the Residual Power Series (RPS) method and Homotopy Analysis Method, allows him to solve complex mathematical equations, which are pivotal in the fields of mathematical modeling and computational mathematics. As a junior and senior research fellow (JRF/SRF), he has been involved in multiple research projects that align with his goal of applying mathematical theory to real-world problems. Additionally, Mr. Meena has shared his research findings through several journal articles and conference papers, expanding his influence in academic circles. Beyond research, his role in mentoring and teaching aligns with his long-term goal of working in an institution where teaching and research go hand-in-hand. His participation in both national and international conferences further strengthens his professional experience, offering him a platform to engage with global research communities.

Awards and Honors

Mr. Rakesh Meena has been the recipient of several prestigious awards and fellowships, recognizing his academic excellence and research potential. In 2020, he was awarded the Junior Research Fellowship (JRF) by CSIR-UGC, which was followed by the Senior Research Fellowship (SRF) in 2022. These fellowships are granted to outstanding researchers in the field of mathematical sciences and are a testament to his proficiency and dedication to research. Additionally, Mr. Meena qualified for GATE (Graduate Aptitude Test in Engineering) in both 2022 and 2023, further cementing his academic credentials. His work, particularly in mathematical modeling and fractional calculus, has earned him recognition in the academic community. His achievements also include being a recipient of certification from CSIR-HRDG, highlighting his commitment to continuous learning and development. These awards and honors reflect Mr. Meena’s dedication to pushing the boundaries of mathematical research, and they serve as a foundation for his continued contributions to the scientific community.

Research Interests

Mr. Rakesh Meena’s primary research interests lie in mathematical modeling, fractional differential equations, and dynamic systems. His doctoral research specifically focuses on linear and nonlinear fractional differential equations, employing semi-analytical methods for their solutions. He aims to explore these equations’ applications in real-world phenomena, such as epidemic modeling, fluid dynamics, and wave propagation. His work in fractional calculus offers new insights into the mathematical descriptions of complex systems, which are often difficult to model using traditional integer-order differential equations. Through his research, Mr. Meena is particularly interested in understanding the behavior of systems with memory and hereditary properties, common in biological and physical systems. In addition to his work on differential equations, he is exploring the application of the Residual Power Series (RPS) method and other numerical techniques, such as the Euler and Runge-Kutta methods, to obtain approximate solutions to these complex models. His interdisciplinary approach to mathematical modeling promises to contribute to both the advancement of mathematical theory and its practical applications in fields like epidemiology, physics, and engineering.

Research Skills

Mr. Rakesh Meena’s research skills are diverse, encompassing both theoretical and computational techniques. His proficiency in mathematical modeling, especially in the context of fractional differential equations, stands out as a major strength. He is well-versed in various semi-analytical methods, notably the Residual Power Series (RPS) and Homotopy Analysis Method, to solve complex differential equations. These techniques are especially useful in capturing the dynamics of systems governed by fractional order equations, which are prevalent in many natural and social systems. Mr. Meena also possesses strong numerical skills, applying methods like the Euler method, Runge-Kutta method, and finite difference methods for computational analysis. He is skilled in using computational tools, including MATLAB, Maple, Mathematica, and LaTeX, to model, analyze, and visualize mathematical problems. His ability to integrate both analytical and numerical methods enables him to approach research challenges from a comprehensive perspective. Moreover, his academic rigor and attention to detail contribute to his systematic approach to research, making his work both reliable and impactful.

📚 Publications

Conclusion 

Mr. Rakesh Meena is a strong contender for the Best Researcher Award due to his excellent academic record, innovative research in fractional differential equations, and contribution to mathematical modeling. His expertise in semi-analytical and numerical methods provides significant value to his field. With a broader impact focus and increased public engagement, he has the potential to make transformative contributions to both academia and society. This will further cement his position as a leader in his field. 🌟