Ammar Khaleel l Reinforcement Learning | Research Excellence Award

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Mr. Ammar Khaleel l Reinforcement Learning | Research Excellence Award

Széchenyi István Egyetem | Hungary

Mr. Ammar Khaleel PhD-level researcher in computer science with ongoing doctoral training, focusing on reinforcement learning–based decision making for autonomous vehicles. Experience includes designing, training, and evaluating deep reinforcement learning and control algorithms for autonomous driving, particularly lane-changing, within large-scale traffic simulations using SUMO and the TraCI Python API. Research interests span reinforcement learning, deep learning, model predictive control, intelligent transportation systems, and traffic modeling. Technical expertise covers Python, C/C++, simulation frameworks, and reproducible research workflows. Academic contributions emphasize simulation-driven experimentation and algorithmic innovation; no formal awards are listed. Overall, the work aims to advance safe, efficient, and intelligent mobility systems.

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Featured Publications


A Multi-Levels RNG Permutation

– Indonesian Journal of Electrical Engineering and Computer Science, 2019

A New Permutation Method for Sequence of Order 28

– Journal of Theoretical and Applied Information Technology, 2019

N/A and Signature Analysis for Malwares Detection and Removal

– Indian Journal of Science and Technology, 2019

Ali Raza Shaikh | Mechanical Engineering | Research Excellence Award

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Mr. Ali Raza Shaikh | Mechanical Engineering | Research Excellence Award

Technical University of Darmstadt | Germany

Ali Raza Shaikh is a dedicated mechanical engineer and researcher specializing in fluid mechanics, heat transfer, ice adhesion, aerodynamics, and surface-interface science. He holds a B.Eng. in Mechanical Engineering, an M.Eng. in Power Engineering and Engineering Thermophysics, and is currently pursuing a Dr.-Ing. in Mechanical Engineering. With extensive research and practical experience, he has developed experimental setups, conducted thermal-fluid and droplet impact studies, and contributed to advancements in superhydrophobic and anti-icing surfaces. He has served as a research assistant in the MSCA-ITN SURFICE project, conducted industrial research internships, and held graduate research positions, collaborating on international projects and presenting findings at leading conferences. His technical expertise spans experimental design, surface characterization, high-speed imaging, and numerical simulations, complemented by proficiency in MATLAB, Python, LabView, CAD, and LaTeX. His research interests focus on ice adhesion dynamics, surface wetting, thermal-fluid interactions, and functional surface design. Ali has actively participated in workshops and training schools worldwide, contributing to knowledge exchange and innovation in engineering applications. His dedication to advancing research and practical solutions in fluid mechanics and surface science underscores his commitment to scientific excellence and technological development.

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Featured Publications


Transient testing of tensile ice adhesion

– Cold Regions Science and Technology, 2025

Ice adhesion dynamics in the tensile mode

– Smart Surface Design for Efficient Ice Protection and Control, 2025

The physics of icing

– Smart Surface Design for Efficient Ice Protection and Control, 2025

Understanding the physics of ice adhesion on complex substrates

– International Conference on Icing of Aircraft, Engines, and Structure, 2023

Xiaochen Xie | Quantitative Research | Research Excellence Award

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Dr. Xiaochen Xie | Quantitative Research | Research Excellence Award

Renmin University of China | China

Dr. Xiaochen Xie is an Assistant Professor at the School of Finance, Renmin University of China, with concurrent research associate positions at the China Financial Policy Research Center, the Institute of Public Finance and Taxation, and the Institute of Digital Economics and Taxation. He holds a Ph.D. in Economics from Pennsylvania State University, an M.S. in Economics from the University of Wisconsin–Madison, and dual B.E. in Finance and B.S. in Statistics from Peking University. His research spans international trade, industrial organization, public economics, and urban economics, with a focus on firm entry, pricing strategies, and the economic impacts of policy interventions. Dr. Xie has published in leading journals, including the Journal of Economic Geography, and has multiple working papers under review in top-tier journals. He has received awards such as Outstanding Young Scholar at Renmin University and recognition for outstanding papers at the NCER-CCER Chinese Economy Conference. Dr. Xie has extensive teaching experience in undergraduate and graduate courses on public finance, quantitative empirical economics, and structural methods. His research integrates empirical and structural approaches to inform trade, fiscal, and industrial policies. Dr. Xie’s work contributes to understanding the intersection of market structure, policy design, and economic welfare, influencing both academic research and practical policy-making.

Profiles : Orcid | Google Scholar

Featured Publications

Chen, Y., Huang, T., & Xie, X. (2025). “Place-based policies: First-mover advantage and persistence.” Journal of Economic Geography, lbaf001.

Lu, W., & Xie, X. (2024). “Trade, markups, and consumer welfare: Evidence from the global smartphone industry.” Available at SSRN 4804811.

Xie, X. (2021). Two essays on firm entry and pricing. The Pennsylvania State University.

Arun Kumar | Mathematical Modeling | Best Researcher Award

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Dr. Arun Kumar | Mathematical Modeling | Best Researcher Award

IIT Mandi | India

Dr. Arun Kumar is a Research Associate (Postdoctoral) at the Indian Institute of Technology Mandi, specializing in mathematical and computational modeling of complex biological, ecological, and epidemiological systems. He earned his Ph.D. in Mathematics from Banaras Hindu University in 2023, following an M.Sc. in Mathematics from the same institution and a B.Sc. from CCS University, Meerut. His research focuses on nonlinear dynamics, bifurcation theory, delay differential equations, reaction–diffusion systems, Turing patterns, and the integration of deep learning and physics-informed neural networks (PINNs) for solving partial differential equations. Dr. Kumar has published extensively in high-impact journals on topics including SIR/SIRS epidemic models, cross-diffusion models, predator–prey dynamics, and pattern formation in spatial ecological systems. His work bridges theoretical mathematics and practical applications in disease modeling and ecology, offering insights into complex population interactions, control strategies, and spatio-temporal dynamics. Currently, he is developing deep learning algorithms to solve PDEs with applications in ecological and epidemiological systems. His ongoing research explores predator-prey interactions, learning in ecological models, and the forecasting of infectious diseases such as monkeypox. Dr. Kumar’s contributions have advanced the understanding of nonlinear systems, providing both analytical and computational tools for studying complex biological and ecological phenomena.

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Featured Publications

Gupta, R. P., & Kumar, A. (2022). “Endemic bubble and multiple cusps generated by saturated treatment of an SIR model through Hopf and Bogdanov–Takens bifurcations.” Mathematics and Computers in Simulation, 197, 1–21.

Gupta, R. P., Kumar, A., & Yadav, D. K. (2024). “The complex dynamical study of a UAI epidemic model in non-spatial and spatial environments.” The European Physical Journal Plus, 139(2), 117.

Yadav, D. K., Gupta, R. P., & Kumar, A. (2022). “Nonlinear dynamics of a three species prey-predator system incorporating fear effect and harvesting.” Journal of Mathematical Control Science and Applications.

Kumar, A., Gupta, R. P., & Tiwari, S. (2022). “Influences of nonlinear cross-diffusion on a reduced SI epidemic model with saturated treatment.”

Kumar, A., Kumari, N., Mandal, S., & Tiwari, P. K. (2025). “Autonomous and non-autonomous dynamics of an SIRS model with convex incidence rate.” Journal of the Franklin Institute, 108236.