Aizhen Ren | Machine learning | Excellence in Innovation Award

Published on by Engineering Scientist

Prof. Aizhen Ren | Machine learning
| Excellence in Innovation Award

College of Science, Inner Mongolia Agricultural University | China

Prof. Aizhen Ren research work centers on machine learning, deep learning, statistical inference, and their applications in economics, finance, and computational biology. A significant portion of the research contributes to the development and mathematical validation of advanced bootstrap techniques, including the speedy double bootstrap method, which enhances the statistical reliability of phylogenetic tree estimation and provides third-order accurate unbiased p-values. These methods have been applied to evolutionary analyses of horse breeds, supporting biological and genomic investigations with high-precision statistical tools. In the financial domain, the research explores machine-learning-based trend prediction models, such as multiscale bootstrap-corrected random forest voting systems used to forecast stock index movement with improved accuracy and inference reliability. Additional work includes the construction of financial risk early-warning models for listed companies using multiple machine learning approaches, reflecting an interdisciplinary blend of statistics, computing, and economics. Contributions also extend to consumption behavior analysis employing regression-based models, as well as deep learning ensemble frameworks integrating empirical mode decomposition and temporal convolutional networks for time-series prediction tasks. The released R package SDBP operationalizes the novel bootstrap methodology, enabling researchers to compute unbiased p-values efficiently. Overall, the research advances methodological innovation and practical applications across data-intensive scientific domains.

 Profile: Orcid

Featured Publications

Ren, A., Duan, Y., & Liu, J. (2025). Multiscale bootstrap correction for random forest voting: A statistical inference approach to stock index trend prediction. Mathematics, 13(22), 3601. https://doi.org/10.3390/math13223601

Ren, A., Ishida, T., & Akiyama, Y. (2020). Mathematical proof of the third order accuracy of the speedy double bootstrap method. Communications in Statistics – Theory and Methods, 49(16), 3950–3964. https://doi.org/10.1080/03610926.2019.1594295

Ren, A., Ishida, T., & Akiyama, Y. (2013). Assessing statistical reliability of phylogenetic trees via a speedy double bootstrap method. Molecular Phylogenetics and Evolution, 67(2), 429–435. https://doi.org/10.1016/j.ympev.2013.02.011

Jamshaid Ul Rahman | Artificial Intelligence | Editorial Board Member

Published on by Engineering Scientist

Dr. Jamshaid Ul Rahman | Artificial Intelligence
| Editorial Board Member

Abdus Salam School of Mathematical Sciences, GC University Lahore | Pakistan

Dr. Jamshaid Ul Rahman’s research spans artificial intelligence, deep learning, mathematical modeling, and scientific computing, with a strong emphasis on applying advanced neural architectures to complex problems in molecular modeling, epidemiology, biomechanics, and computational physics. His work integrates graph neural networks, wavelet neural networks, periodic neural networks, and deep optimization frameworks to improve prediction accuracy and interpretability in molecular property analysis, virus transmission modeling, and dynamic system simulation. He has contributed significantly to AI-driven analysis of infectious diseases such as Marburg, Ebola, and Hepatitis C through nonlinear modeling, hybrid optimization, and neural simulation techniques. His research also advances computational mechanics, including deep learning-based vibration analysis in tapered beams, micro-electromechanical systems, and nonlinear oscillators. In cheminformatics, he has enhanced molecular property prediction using quantized GNNs, optimized activation functions, and improved training strategies for message-passing networks. His contributions extend to biophysical modeling, where neural networks simulate glycolysis, biochemical oscillators, and biological system dynamics with high fidelity. He has also explored angular softmax variations, Laplacian smoothing, and stochastic gradient methodologies in deep convolutional networks, advancing core theoretical aspects of machine learning. Across more than fifty publications, his work consistently integrates mathematical rigor with cutting-edge AI to address interdisciplinary scientific and engineering challenges.

 Profile:  Google Scholar

Featured Publications

Rahman, J. U., Noureen, I., Mannan, A., & Uwitije, R. (2025). PhyFold: Environment aware physics informed neural network for protein folding dynamics.

Ul Rahamn, J., Iqbal, M. A., Rasool, A., & Uwitije, R. (2025). p-GIN: A graph isomorphism network based on p-Laplacian operator to enhance molecular property prediction. Discover Applied Sciences, 7(11), 1251.

Shoket, N., Ul Rahman, J., & Zafar, N. (2025). Mathematical modeling and computational investigation of the COVID-19 epidemic using wavelet neural networks and coupled optimization. SN Soft Computing, 1–18.

Rahman, J. U., Ali, H., & Rassol, A. (2025). A comprehensive analysis of optimizers in message passing neural networks for molecular property prediction task. Computational Biology and Chemistry, 108556

Chaitanya Kumar Mankala | Machine Learning | Best Computer Engineering Award

Published on by Engineering Scientist

Dr. Chaitanya Kumar Mankala | Machine Learning
| Best Computer Engineering Award

Villanova University | United States

Dr. Chaitanya Kumar Mankala’s research focuses on advancing sustainable, scalable, and intelligent computing through the convergence of artificial intelligence, serverless architectures, and neuroidal network models. His work in real-time natural language processing emphasizes the development of energy-efficient and low-latency AI systems using cloud-native parallel processing on platforms such as AWS, enabling large-scale language models to operate dynamically with minimal environmental impact and operational cost. His contributions to evolutionary artificial neuroidal networks propose next-generation neural architectures capable of adaptively restructuring themselves through evolutionary algorithms to enhance learning efficiency, fault tolerance, and inference accuracy across diverse data environments. By integrating distributed serverless infrastructure with neuromorphic design principles, his research addresses limitations in current AI scalability, offering frameworks that support autonomous decision-making and real-time processing for healthcare, cybersecurity, and industrial automation. His conference work on the Next Generation Artificial Neural Network further explores biologically inspired computational models that bridge cognitive mechanisms with advanced deep learning, paving the way for highly interpretable, resilient, and self-evolving AI systems. Collectively, his research advances the paradigm of intelligent computing by integrating sustainability, scalability, and adaptive learning, contributing to the future of autonomous AI systems deployed on edge and cloud environments for mission-critical applications.

 Profile:  Orcid | Google Scholar 

Featured Publications

Mankala, C. K., & Silva, R. J. (2025). Sustainable real-time NLP with serverless parallel processing on AWS. Information, 16(10). https://doi.org/10.3390/info16100903

Mankala, C. K. (2025). Evolutionary artificial neuroidal network using serverless architecture [Doctoral dissertation].

Mankala, C. K., & Silva, R. (2023, November 16). Next generation artificial neural network. In Proceedings of the ICEHTMC Conference.