Prof. Jiantao Shi | Information Technology | Best Researcher Award

Prof. Jiantao Shi | Information Technology | Best Researcher Award

Prof. Jiantao Shi, Njing Tech University, China

Prof. Jiantao Shi is a distinguished researcher in control science and information technology, currently serving as a Professor at Nanjing Tech University. He holds a Ph.D. in Control Science and Engineering from Tsinghua University and has extensive experience in multi-robot cooperative control, fault diagnosis, and UAV learning control. His research has been published in leading IEEE journals, and he has significantly contributed to distributed system reliability. With a strong academic background and practical research experience, he has advanced intelligent control methodologies for autonomous systems. His contributions have positioned him as a leader in modern automation and robotics.

🌍 Professional Profile:

ORCID

🏆 Suitability for Best Researcher Award 

Prof. Jiantao Shi is an outstanding candidate for the Best Researcher Award due to his pioneering contributions to intelligent control systems, multi-robot cooperation, and UAV learning control. His work integrates cutting-edge AI techniques with control science, enabling the development of robust and fault-tolerant autonomous systems. With over 60 high-impact journal and conference papers in prestigious IEEE and SCI-indexed publications, he has made fundamental advances in the field. His leadership in both academic and applied research underscores his influence on the next generation of intelligent automation technologies. His innovative solutions make him highly deserving of this recognition.

🎓 Education

Prof. Jiantao Shi obtained his Bachelor’s degree in Electrical Engineering and Automation from Beijing Institute of Technology in 2011. He then pursued a Ph.D. in Control Science and Engineering at Tsinghua University, earning his doctorate in 2016. His academic journey at these top institutions equipped him with expertise in control systems, automation, and intelligent sensing technologies. His doctoral research focused on advanced fault diagnosis and cooperative control of multi-agent systems. This solid educational foundation has propelled him to the forefront of intelligent control and automation, enabling him to address complex challenges in distributed autonomous systems.

💼 Work Experience

Prof. Jiantao Shi has an extensive research career spanning academia and industry. From 2016 to 2018, he worked as an Associate Research Fellow at the Nanjing Research Institute of Electronic Technology, specializing in intelligent sensing. He was promoted to Research Fellow in 2019, leading projects in autonomous systems and fault-tolerant control. Since 2021, he has been a Professor at Nanjing Tech University, where he mentors students and advances research in AI-driven control methodologies. His experience in both applied research and academia allows him to bridge theoretical advancements with real-world applications in robotics, UAVs, and industrial automation.

🏅 Awards & Honors

Prof. Jiantao Shi has received several prestigious awards recognizing his contributions to control science and automation. His research has been featured in top-tier IEEE Transactions journals, demonstrating its high impact. He has been honored with multiple best paper awards at international conferences. Additionally, his work on UAV control and multi-robot systems has been acknowledged with research grants and government funding for innovation in automation. As a key contributor to cutting-edge intelligent control systems, he continues to earn accolades for his groundbreaking contributions, positioning himself as a leading researcher in distributed autonomous system control.

🔬 Research Focus

Prof. Jiantao Shi’s research centers on advanced control methodologies for intelligent automation. His key areas of expertise include cooperative control of multi-robot systems, fault diagnosis and fault-tolerant control of distributed systems, and learning-based control of UAVs. His work integrates AI and machine learning with traditional control science to enhance system resilience and autonomy. By developing robust, intelligent algorithms, he aims to improve automation reliability in real-world applications. His research has profound implications for robotics, autonomous vehicles, and industrial automation, paving the way for next-generation intelligent systems with enhanced adaptability, efficiency, and fault resilience.

📖 Publication Top Notes 

  1. A Parallel Weighted ADTC-Transformer Framework with FUnet Fusion and KAN for Improved Lithium-Ion Battery SOH Prediction
    • Publication Year: 2025
  2. Bipartite Fault-Tolerant Consensus Control for Multi-Agent Systems with a Leader of Unknown Input Under a Signed Digraph
    • Publication Year: 2025
  3. Iterative Learning-Based Fault Estimation for Stochastic Systems with Variable Pass Lengths and Data Dropouts
    • Publication Year: 2025
  1. A Two-Stage Fault Diagnosis Method with Rough and Fine Classifiers for Phased Array Radar Transceivers
    • Publication Year: 2024
  2. An Intuitively-Derived Decoupling and Calibration Model to the Multi-Axis Force Sensor Using Polynomials Basis
    • Publication Year: 2024
  3. Event-Based Adaptive Fault Tolerant Control and Collision Avoidance of Wheel Mobile Robots with Communication Limits
    • Publication Year: 2024

 

Mr. Mohammad Mahdi Badami | Data Analysis | Young Scientist Award

Mr. Mohammad Mahdi Badami | Data Analysis | Young Scientist Award

Mr. Mohammad Mahdi Badami | University of Southern California | United States

Mehdi Badami is a dedicated Ph.D. candidate in Environmental Engineering at the University of Southern California (USC) under Prof. Constantinos Sioutas. His expertise lies in air quality improvement, with hands-on experience in air pollution monitoring using advanced instrumentation such as SMPS-CPC, OPS, and Aethalometer 51. He specializes in data-driven environmental assessments, employing Python for pollution source apportionment and emission trend analysis. His research contributes to community-centric environmental policies and sustainable air quality solutions. Passionate about environmental justice, he aims to bridge scientific research with real-world policy implementation. 🌱🔬

Professional Profile:

Google Scholar

Suitability for the Young Scientist Award

Mehdi Badami is a strong candidate for the Young Scientist Award due to his significant contributions to environmental engineering, particularly in air quality improvement. As a Ph.D. candidate at the University of Southern California (USC), his research focuses on air pollution monitoring and data-driven environmental assessments. His expertise in advanced instrumentation (e.g., SMPS-CPC, OPS, Aethalometer 51) and Python-based pollution source apportionment makes him a valuable asset to the field.

Education & Experience 🏢🎓

  • Ph.D. Candidate in Environmental Engineering (2022-Present) – USC, Los Angeles, USA 🇺🇸

    • GPA: 3.95/4
    • Advisor: Prof. Constantinos Sioutas
  • M.Sc. in Mechanical Engineering (Fluid Mechanics) (2017-2020) – University of Tehran, Iran 🇮🇷

    • GPA: 3.77/4
    • Supervisors: Dr. Alireza Riasi, Prof. Kayvan Sadeghy
  • B.Sc. in Mechanical Engineering (2012-2016) – K. N. Toosi University of Technology, Iran 🇮🇷

  • Research Assistant – USC Aerosol Lab (2023–Present) 🏭🌫️

    • Conducted air pollution measurements using state-of-the-art monitoring systems
    • Developed Python programs for data automation and pollution trend analysis
    • Led collaborations with institutions like Harvard, UCLA, and Dresden University
    • Mentored Ph.D. students on environmental research projects
  • Research Assistant – Hydro-kinetic Energy Lab, University of Tehran (2017–2022) 🔬💧

    • Investigated fluid mechanics phenomena related to blood hammer effects in arteries
  • Teaching Assistant – USC & University of Tehran (2018–2024) 📚👨‍🏫

    • Assisted in courses on climate change, air quality, fluid mechanics, and thermodynamics

Professional Development 🚀

Mehdi Badami has actively contributed to the field of environmental engineering through cutting-edge research on air pollution, sustainability, and emission control. His extensive knowledge of aerosol science, atmospheric chemistry, and data analysis allows him to assess air quality trends with precision. He has developed innovative models for pollution source apportionment, worked on real-time monitoring systems, and collaborated with leading institutions to improve urban air quality. His passion for environmental justice drives his work towards creating actionable solutions that ensure healthier air for communities. His dedication extends beyond academia, as he actively engages in outreach and policy-driven initiatives. 🌿📊

Research Focus 🔍

Mehdi’s research centers on air pollution control, environmental monitoring, and sustainable urban development. His work involves identifying and mitigating pollution sources through field measurements and computational models. He specializes in:

  • Air Quality Assessment 🌫️📊 – Studying PM2.5 and ultrafine particle pollution in urban environments
  • Pollution Source Apportionment 🏭⚖️ – Analyzing emissions from vehicles, industries, and natural sources
  • Aerosol Science 🌪️💨 – Investigating the toxicity and health impacts of airborne particles
  • Machine Learning in Environmental Studies 🤖📉 – Utilizing data science to model pollution trends
  • Climate and Environmental Justice 🌎⚖️ – Advocating for equitable air quality policies in urban communities

Awards & Honors 🏆

  • Outstanding Research Assistant Award – USC, Sonny Astani Department of Civil and Environmental Engineering (2024) 🏅
  • Fellowship Award – USC (2022-2023) 🎓💰 (Recognized for academic excellence in Environmental Engineering)
  • National Fellowship for Master’s Studies – University of Tehran (2017) 📖🏆
  • Top 0.15% Rank in National Entrance Exam – Iran (Competitive ranking in Mechanical Engineering)

Publication Top Notes:

📄 Design, optimization, and evaluation of a wet electrostatic precipitator (ESP) for aerosol collectionAtmospheric Environment (2023) – 📑 Cited by: 11
📄 Size-segregated source identification of water-soluble and water-insoluble metals and trace elements of coarse and fine PM in central Los AngelesAtmospheric Environment (2023) – 📑 Cited by: 7
📄 Numerical study of blood hammer phenomenon considering blood viscoelastic effectsEuropean Journal of Mechanics-B/Fluids (2022) – 📑 Cited by: 7
📄 Development and performance evaluation of online monitors for near real-time measurement of total and water-soluble organic carbon in fine and coarse ambient PMAtmospheric Environment (2024) – 📑 Cited by: 4
📄 Numerical analysis of laminar viscoelastic fluid hammer phenomenon in an axisymmetric pipeJournal of the Brazilian Society of Mechanical Sciences and Engineering (2021) – 📑 Cited by: 3
📄 Urban emissions of fine and ultrafine particulate matter in Los Angeles: Sources and variations in lung-deposited surface areaEnvironmental Pollution (2025) – 📑 Cited by: 1

 

 

 

Assoc. Prof. Dr. Kincső Decsi | Data in Brief | Best Researcher Award

Assoc. Prof. Dr. Kincső Decsi | Data in Brief | Best Researcher Award

Assoc. Prof. Dr. Kincső Decsi, Hungarian University of Agricultural and Life Sciences, Institute of Agronomy, Hungary

Assoc. Prof. Dr. Kincső Decsi is a renowned academic in the field of plant physiology and plant ecology, currently serving as an associate professor at the Hungarian University of Agriculture and Life Sciences, Georgikon Campus. She has an extensive academic career, having previously held assistant professor roles at the same institution and at Pannon University. Dr. Decsi earned her Ph.D. in Agricultural and Horticultural Sciences in 2005, summa cum laude, with a dissertation on abiotic stress effects in maize. Her research and teaching focus on plant biotic and abiotic stress physiology, plant growth, and development. She has taught a wide range of courses at the BSc, MSc, and PhD levels, in both Hungarian and English. Dr. Decsi’s research contributions, particularly in plant genetics, bioinformatics, and environmental stress physiology, have significantly advanced our understanding of plant resilience and adaptation. 🌱📚🌍

Professional Profile

Orcid

Suitability for Award

Assoc. Prof. Dr. Kincső Decsi is an exceptional candidate for the Research for Best Researcher Award due to her significant contributions to plant physiology, environmental stress, and plant genetics. Her extensive teaching experience at both undergraduate and postgraduate levels, coupled with her research on plant adaptation to biotic and abiotic stresses, has earned her recognition in academia. Dr. Decsi’s work in bioinformatics and transcriptomics has enhanced the understanding of plant responses to environmental challenges, which is vital for sustainable agriculture. Her leadership in the scientific community, especially in plant physiology and molecular biology, makes her a suitable candidate for this prestigious award. Dr. Decsi’s ability to bridge research and teaching, coupled with her impact on both local and international scientific communities, reflects her dedication to advancing agricultural sciences. 🌾🔬🏅

Education

Assoc. Prof. Dr. Kincső Decsi has an extensive academic background in agricultural sciences. She earned her Ph.D. in Agricultural and Horticultural Sciences from the Hungarian University of Agriculture and Life Sciences in 2005, with summa cum laude honors. Her doctoral research focused on examining the effects of various abiotic stresses on maize. Dr. Decsi’s educational journey began with a Certified Agricultural Engineer qualification from the University of Veszprém, where she also studied plant genetics and plant breeding. Additionally, she completed a Certified Chemistry Teacher qualification at Pannon University in 2023. Dr. Decsi’s early academic experiences were enriched by scholarships such as the Martonvásár and Pioneer Hi-Bred Rt. scholarships, which allowed her to deepen her expertise in plant science. Her education has laid the foundation for her ongoing research and teaching in plant physiology, molecular biology, and bioinformatics. 🎓🌾📖

Experience 

Assoc. Prof. Dr. Kincső Decsi has over two decades of experience in both research and teaching. She currently holds the position of associate professor at the Hungarian University of Agriculture and Life Sciences, Georgikon Campus, where she has taught various plant physiology and molecular biology courses at the BSc, MSc, and PhD levels. Her research experience spans from genetic mapping of potato blight resistance genes to the study of abiotic stress effects in plants. Dr. Decsi has also been involved in bioinformatics research, particularly in transcriptomic studies, enhancing her expertise in plant adaptation and resilience. Her role as a scientific associate at the Festetics György Bioinnovation Research Center further strengthened her research portfolio, contributing to projects on plant genetic mapping and resistance genes. Dr. Decsi’s experience is a blend of practical research, teaching, and leadership in the scientific community. 🌿💼🔬

Awards and Honors

Assoc. Prof. Dr. Kincső Decsi has been recognized for her academic excellence through various scholarships and awards. She received the Pioneer Hi-Bred Rt. Scientific Scholarship and the Martonvásár Scientific Scholarship in the late 1990s and early 2000s, which supported her early academic development. Dr. Decsi was also honored with the Georgikon Outstanding Scholarship for her exceptional performance during her studies. Additionally, she was awarded the Lászlóffy Woldemár Diploma Thesis Application special fee in recognition of her outstanding academic achievements. Her participation in international language courses, such as the Sommerakademie in Neubrandenburg and Wiener Internationale Hochschulkurse, further enriched her academic journey. These awards and honors reflect Dr. Decsi’s dedication to her field and her commitment to advancing plant science research. 🏆🎓🌍

Research Focus 

Assoc. Prof. Dr. Kincső Decsi’s research focuses on plant physiology, particularly the effects of biotic and abiotic stresses on plant growth and development. Her work explores how plants respond to environmental challenges such as drought, salinity, and pathogen attacks, which are critical for improving agricultural resilience. Dr. Decsi has contributed significantly to the field of plant genetics, including the genetic mapping of resistance genes for potato blight and PVY virus resistance. Her research also delves into bioinformatics, particularly in transcriptomic studies, to understand gene expression under stress conditions. Dr. Decsi’s work aims to enhance the sustainability of agricultural practices by improving plant stress tolerance, which is essential for food security in the face of climate change. Her contributions to molecular plant biology, biotechnology, and environmental stress physiology are pivotal in advancing our understanding of plant adaptation mechanisms. 🌱🔬🌿

Publication Top Notes

  • Title: RNA-seq Datasets of Field Rapeseed (Brassica napus) Cultures Conditioned by Elice16Indures (R) Biostimulator
    • Year: 2022
  • Title: RNA-seq Datasets of Field Soybean Cultures Conditioned by Elice16Indures (R) Biostimulator
    • Year: 2022
  • Title: Time-course Gene Expression Profiling Data of Triticum Aestivum Treated by Supercritical CO2 Garlic Extract Encapsulated in Nanoscale Liposomes
    • Year: 2022
  • Title: Transcriptome Datasets of Beta-Aminobutyric Acid (BABA)-Primed Mono- and Dicotyledonous Plants, Hordeum Vulgare and Arabidopsis Thaliana
    • Year: 2022
  • Title: Transcriptome Profiling Dataset of Different Developmental Stage Flowers of Soybean (Glycine Max)
    • Year: 2022

 

 

Mohammadreza Mahmoudi | Data Science | Best Researcher Award

Dr. Mohammadreza Mahmoudi | Data Science | Best Researcher Award

Professor, Fasa University, Iran 

Dr. Mohammadreza Mahmoudi is an esteemed researcher with a robust background in mathematical statistics and applied probability. His contributions span several impactful projects, including advanced statistical methods and applications in diverse fields. His research excellence and distinguished academic career make him a strong candidate for the Best Researcher Award.

Professional Profile:

Scopus

Summary of Suitability for the Research for Best Researcher Award: 

Dr. Mohammadreza Mahmoudi stands out as a prime candidate for the Best Researcher Award due to his exceptional contributions to mathematical statistics and applied probability. His extensive research on periodograms, statistical properties of simple processes, and advanced non-parametric methodologies demonstrates a deep expertise in his field. Dr. Mahmoudi’s accolades, including being a top student at all levels of his education and his role as an Advisory Board Member of ScienceVier Canada, underscore his recognition and influence in statistical research. His robust teaching experience and impactful projects further solidify his suitability for this prestigious award, highlighting his dedication to advancing statistical science and education.

🎓Education:

Dr. Mahmoudi completed his Ph.D. in Mathematical Statistics (Applied Probability) from Shiraz University in 2016, following a Master of Science in Mathematical Statistics and a Bachelor of Science in Statistics from the same institution. His educational journey reflects a profound commitment to advancing statistical science.

🏢Work Experience:

Dr. Mahmoudi has served as a researcher and educator in statistical methodologies, specializing in areas such as time series analysis, stochastic processes, and nonparametric methodologies. He has been actively involved in teaching a broad range of statistical courses at Shiraz University and has contributed to several high-impact research projects.

🏆Awards and Grants:

Dr. Mahmoudi has been recognized as a top student during his Ph.D., M.Sc., and B.Sc. periods at Shiraz University. He has also been elected as an Advisory Board Member of ScienceVier Canada, showcasing his expertise and influence in the field of statistics.

Publication Top Notes:

  1. “Machine learning models for predicting interactions between air pollutants in Tehran Megacity, Iran”
    • Year: 2024
    • Journal: Alexandria Engineering Journal
  2. “Solving optimal control problems governed by nonlinear PDEs using a multilevel method based on an artificial neural network”
    • Year: 2024
    • Journal: Computational and Applied Mathematics
  3. “The removal of methylene blue from aqueous solutions by polyethylene microplastics: Modeling batch adsorption using random forest regression”
    • Year: 2024
    • Journal: Alexandria Engineering Journal
  4. “Meteorological Drought Prediction Based on Evaluating the Efficacy of Several Prediction Models”
    • Year: 2024
    • Journal: Water Resources Management
  5. “Spatial and temporal assessment and forecasting vulnerability to meteorological drought”
    • Year: 2024
    • Journal: Environment, Development and Sustainability
  6. “Assessment of Continuity Changes in Spatial and Temporal Trend of Rainfall and Drought”
    • Year: 2023
    • Journal: Pure and Applied Geophysics
  7. “Using the multiple linear regression based on the relative importance metric and data visualization models for assessing the ability of drought indices”
    • Year: 2023
    • Journal: Journal of Water and Climate Change
  8. “Dryland farming wheat yield prediction using the Lasso regression model and meteorological variables in dry and semi-dry region”
    • Year: 2023
    • Journal: Stochastic Environmental Research and Risk Assessment
  9. “Cyclic clustering approach to impute missing values for cyclostationary hydrological time series”
    • Year: 2023
    • Journal: Quality and Quantity
  10. “Statistical and Mathematical Modeling for Predicting Caffeine Removal from Aqueous Media by Rice Husk-Derived Activated Carbon”
    • Year: 2023
    • Journal: Sustainability (Switzerland)