Guo Tian | Machine Learning and Statistics | Best Researcher Award

Assoc Prof. Dr. Guo Tian | Machine Learning and Statistics | Best Researcher Award

Tsinghua University | China

Assoc Prof. Dr. Guo Tian is an accomplished young chemical engineer whose research lies at the frontier of sustainable catalysis and CO₂/CO conversion. He earned his Bachelor’s degree in Chemical Engineering under Prof. Xuezhi Duan at the East China University of Science and Technology and pursued his doctoral studies in Chemical Engineering at Tsinghua University under the guidance of Prof. Fei Wei. Following his doctoral training, he joined Southwest Jiaotong University as an Associate Professor and Principal Investigator. At only twenty-five years of age, Guo has led pioneering work on high-pressure thermo-catalytic systems, including the design of a reactor capable of stable operation at up to 60 bar integrated with surface-enhanced infrared absorption spectroscopy (SEIRAS) for in-situ monitoring of reaction intermediates. His studies have revealed critical mechanistic pathways in CO/CO₂ conversion using bifunctional catalysts, identifying oxygenate intermediates as key to improving the traditional methanol-to-hydrocarbons (MTH) mechanism. Drawing inspiration from biological systems, he has advanced the concept of bio-inspired multifunctional catalysts and introduced the innovative idea of “catalytic shunt” strategies to enhance selectivity and efficiency. Combining experimental research with density-functional theory (DFT) and micromodel simulations, his work bridges molecular-level understanding with reactor-scale engineering. Dr. Tian has authored numerous influential publications in high-impact journals such as Nature Sustainability, Nature Communications, ACS Catalysis, and the Journal of the American Chemical Society. Notable among these are “Efficient syngas conversion via catalytic shunt” (Nature Sustainability), and “Upgrading CO₂ to sustainable aromatics via perovskite-mediated tandem catalysis” (Nature Communications). According to his Scopus profile, he has authored 14 documents, accumulated around 297 citations, and holds an h-index of 9, reflecting a strong and growing impact in the field. His expertise includes thermochemical measurement and data analysis, catalytic materials design, reactor and reaction-system development, in-situ spectroscopy (SEM, XRD, XPS, XAS), and DFT-based theoretical modeling. Integrating theory, advanced characterization, and engineering innovation, Guo Tian’s vision focuses on transforming CO₂ and CO into high-value sustainable fuels such as aviation fuel components, contributing to global carbon-neutral energy goals. Through his scientific rigor, leadership, and creativity, he has rapidly emerged as a rising star in heterogeneous catalysis and sustainable chemical engineering.

Profiles: Scopus Google Scholar Orcid

Featured Publications

M. Zhao, Q. Wu, X. Chen, H. Xiong, G. Tian, L. Yan, F. Xiao, & F. Wei. (2025). Entropy-governed zeolite intergrowth. Journal of the American Chemical Society.

Z. Wang, X. Liu, G. Tian, Z. Wang, L. Li, F. Lu, Y. Yu, Z. Li, F. Wei, & C. Zhang. (2025). Research advances in coal-based syngas to aromatics technology. Clean Energy, 9(5), 136–152.

J. He, G. Tian, D. Liao, Z. Li, Y. Cui, F. Wei, C. Zeng, & C. Zhang. (2025). Mechanistic insights into methanol conversion and methanol-mediated tandem catalysis toward hydrocarbons. Journal of Energy Chemistry.

H. Xiong, Y. C. Wang, X. Liang, M. Zhao, G. Tian, G. Wang, L. Gu, & X. Chen. (2025). In situ quantitative imaging of nonuniformly distributed molecules in zeolites. Journal of the American Chemical Society, 147(32), 28965–28972.

Z. Li, J. Chen, G. Xu, Z. Tang, X. Liang, G. Tian, F. Lu, Y. Yu, Y. Wen, & J. Yang. (2025). Constructing three-dimensional covalent organic framework with aea topology and flattened spherical cages. Chemistry of Materials, 37(5), 1942–1948.

Kuruba Chandrakala | Machine Learning and Statistics | Best Researcher Award

Dr. Kuruba Chandrakala | Machine Learning and Statistics | Best Researcher Award

Siddhartha Academy of Higher Education | India

Dr. Kuruba Chandrakala is an emerging researcher in the domains of computer vision, deep learning, and medical image processing, currently serving as Assistant Professor (Selection Grade) in the CSE department at Siddhartha Academy of Higher Education, Vijayawada. She earned her Ph.D. from NIT Tiruchirappalli, preceded by M.Tech in Computer Science and Engineering with distinction from JNTU Kakinada and B.Tech in the same discipline from JNTU Anantapur. She has qualified both NET and APSET examinations. Her professional trajectory includes roles as Head of Department (CSE-AIML) at Vignan’s Nirula Institute of Technology & Science for Women and previous teaching appointments at VNITSW and SITAM, along with industry experience as a System Engineer with Tata Consultancy Services. Her publication record comprises five Scopus indexed papers, four of which are in SCIE journals, two IEEE conference papers, and one book chapter; she also holds one patent. Her Scopus metrics include an h-index of 4, 10 documents, and 150 citations. Her research has addressed areas such as diabetic retinopathy segmentation, robust blood vessel detection, and image enhancement through deep learning architectures. She teaches courses including Deep Learning, Machine Learning, Big Data Analytics, Cloud Computing, and programming in C, C++, Java, and Python. She has earned numerous certifications from NPTEL, Coursera, Microsoft, IBM, and Wipro and received awards such as the NPTEL Discipline Star and Wipro Project Excellence Award. Her leadership and mentoring roles include serving as a mentor for Wipro TalentNext, nodal officer for Microsoft Upskilling and APSCHE virtual internship programs, and coordinator for various hackathons. She is a life member of professional bodies such as CSI, ISTE, IAENG, and IET, and has delivered several invited and guest lectures, contributing significantly to academic excellence and research advancement.

Profiles: Scopus Google Scholar Orcid

Featured Publications

Chandrakala, K., & Gopalan, N. P. (2025). 3DECNN: A novel method for segmentation of diabetic retinopathy in retinal fundus images using 3D-edge CNN. Neural Computing and Applications.

Kuruba, C., Sharmila, S. K., Mounika, V., Aswini, D., & Poojitha, G. (2023). Three layered security model to prevent credit card fraud using LBPH and CNN-ResNet architecture. International Conference on Hybrid Intelligent Systems, 422–428.

Dharmaiah, K., Mebarek-Oudina, F., Sreenivasa Kumar, M., & Chandra Kala. (2023). Nuclear reactor application on Jeffrey fluid flow with Falkner-Skan factor, Brownian and thermophoresis, non-linear thermal radiation impacts past a wedge. Journal of the Indian Chemical Society, 100(2), 117.

Kuruba, C., & Gopalan, N. P. (2023). Robust blood vessel detection with image enhancement using relative intensity order transformation and deep learning. Biomedical Signal Processing and Control, 86, 105195.

Kuruba, C., Pushpalatha, N., Ramu, G., Suneetha, I., Kumar, M. R., & Harish, P. (2023). Data mining and deep learning-based hybrid health care application. Applied Nanoscience, 13(3), 2431–2437.