Theodore Trafalis | Industrial and Systems Engineering | Best Researcher Award |

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Prof. Dr. Theodore Trafalis | Industrial and Systems Engineering | Best Researcher Award 

Professor | Industrial and Systems Engineering at The university of Oklahoma | United States

Prof. Dr. Theodore B. Trafalis is a globally recognized expert in industrial engineering and artificial intelligence, known for his pioneering work in machine learning, optimization, and data analytics. As a long-standing faculty member at the University of Oklahoma, he has made significant contributions to academic research, innovation, and applied science. His expertise spans across weather prediction, financial forecasting, manufacturing systems, and intelligent decision-making, with a strong focus on integrating theory with practical impact.

Professional Profile

Scopus | Orcid | Google scholar |

🎓 Education

Dr. Trafalis holds a Ph.D. in Industrial Engineering (1989), an MSIE (1987), and an M.S. in Mathematics (1984) from Purdue University, USA. His academic journey began with a B.S. in Mathematics from the University of Athens, Greece (1982). This strong educational foundation set the stage for his distinguished interdisciplinary research career.

💼 Experience

With over three decades of academic and research experience, Dr. Trafalis currently serves as Professor in the School of Industrial Engineering and Adjunct Professor in the School of Meteorology at the University of Oklahoma. He began his academic career as an instructor at Purdue and has since held numerous international visiting research positions in countries such as Greece, Japan, France, the Netherlands, and Turkey. These roles have strengthened his global academic perspective and collaborative reach.

🔬 Research Interests

His research is centered on machine learning, robust optimization, support vector machines, and data-driven decision-making systems. He has applied these methods to areas like weather forecasting, including tornado prediction, financial modeling, and intelligent systems in manufacturing. His cross-disciplinary approach has enabled impactful advancements in both engineering and environmental sciences, supported by multiple grants from the NSF and NOAA.

🏆 Honors

Dr. Trafalis has received numerous prestigious awards, including the Regents Award for Superior Accomplishment in Research and Creative Activity (University of Oklahoma), and multiple Best Paper Awards at international conferences such as the International Conference on Artificial Neural Networks in Engineering. He has also held fellowships and lectureships including the David Ross Fellowship from Purdue and the Obermann Faculty Fellowship at the University of Iowa.

📚 Top Noted Publications

Linear Discriminant Analysis
Published In: Robust Data Mining, Pages 27–33
Citations: 653
Year of Publication: 2012

Support Vector Machine for Regression and Applications to Financial Forecasting
Published In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks
Citations: 498
Year of Publication: 2000

Robust Data Mining
Published By: Springer Science & Business Media
Citations: 215
Year of Publication: 2012

A Hybrid Model for Exchange Rate Prediction
Published In: Decision Support Systems, Volume 42, Issue 2, Pages 1054–1062
Citations: 209
Year of Publication: 2006

Robust Weighted Kernel Logistic Regression in Imbalanced and Rare Events Data
Published In: Computational Statistics & Data Analysis, Volume 55, Issue 1, Pages 168–183
Citations: 173
Year of Publication: 2011

Conclusion

Prof. Dr. Theodore B. Trafalis exemplifies the qualities of a transformative academic leader whose work bridges disciplines and borders. Through his teaching, research, and international collaborations, he continues to push the boundaries of intelligent systems and optimization science. His dedication to impactful research and educational excellence makes him a standout figure in global academia.

Dr M sangeetha | Operations research | Excellence in Research Award |

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Dr. M sangeetha | Operations research | Excellence in Research Award | 

Professor, at Dr.N.G.P.Arts and Science college , India.

Dr. M. Sangeetha is a Professor of Mathematics with over 22 years of teaching experience and 7 years of dedicated research in Fuzzy Operations Research. She has a strong academic foundation, with expertise in mathematical modeling, optimization techniques, and decision-making processes. Her research contributions, publications, and mentorship of Ph.D. scholars demonstrate her commitment to advancing mathematical sciences.

Professional Profile

Scopus

Education 🎓

Dr. Sangeetha holds a Ph.D. in Mathematics from Chikkanna Government Arts College, Tirupur (2018). She completed her M.Phil. (2009) and M.Sc. (2001) in Mathematics from Government Arts College, Coimbatore. Additionally, she earned a B.Ed. in Mathematics (2010) from St. Marks B.Ed. College and a Postgraduate Diploma in Operations Research (PGDOR, 2015) from Nirmala College for Women, Bharathiar University. She has also completed online certifications in Graph Theory (NPTEL, 2020) and Basics of Python (Infosys Springboard, 2022), showcasing her commitment to continuous learning.

Professional Experience 💼

With 22+ years of college teaching experience, Dr. Sangeetha has been instrumental in shaping the careers of numerous students. She has also mentored 6 Ph.D. scholars (2 submitted synopsis, 6 ongoing), 1 M.Phil. student, and multiple M.Sc. research projects. Her role as an educator extends beyond teaching, fostering a research-driven academic environment.

Research Interests 🌍

Dr. Sangeetha specializes in Fuzzy Operations Research, focusing on fuzzy transportation problems, multi-objective optimization, and intuitionistic fuzzy systems. Her work contributes to decision-making models, logistics, and applied mathematics, addressing real-world challenges through advanced computational techniques and mathematical frameworks.

Awards & Honors 🏆

Dr. Sangeetha has been recognized for her contributions to research and academics. Her work has been published in Scopus-indexed journals, reflecting her commitment to high-quality research. She has also played a significant role in mentoring Ph.D. scholars and guiding research projects, contributing to the academic growth of her institution.

Top Noted Publications 📚

Dr. Sangeetha has authored several research papers in Scopus-indexed journals, focusing on fuzzy logic, transportation problems, and optimization techniques. Some of her notable publications include:

Fuzzy Largest Cost Entry Method of Transportation Problem Using Heptagonal Fuzzy Numbers (Nonlinear Studies, Scopus-indexed)

Multi-Objective Fuzzy Fully Linear Programming Transportation Problem (Mathematical Sciences International Research Journal, Scopus-indexed)

Similarity Measure Model in Intuitionistic Fuzzy Transportation Problem (IJPAM, Scopus-indexed)

Conclusion

Dr. Sangeetha has a strong foundation in research, publications, and mentorship, making her a deserving candidate for the Excellence in Research Award. Strengthening international collaborations, increasing high-impact publications, and securing research grants will further enhance her profile.

Dr Yan Wang | Risks | Best Researcher Award |

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Dr. Yan Wang | Risks | Best Researcher Award

Applied Scientist, at Kennesaw State University, United States

Dr. Yan Wang is a distinguished researcher in data science, analytics, and machine learning, specializing in credit risk modeling, statistical analysis, and investment decision-making. With an exceptional academic background and hands-on industry experience, Dr. Wang has made significant contributions to predictive modeling, fraud detection, and financial risk assessment. Her work integrates advanced statistical techniques, machine learning algorithms, and big data analytics, impacting both academia and industry.

Professional Profile

Scopus

Education 🎓

Dr. Wang holds a Ph.D. in Analytics and Data Science from Kennesaw State University (GPA: 4.00), where she pioneered research in data-driven investment decision-making in peer-to-peer lending. She also earned an M.S. in Statistics from the University of Georgia (GPA: 4.00), further strengthening her expertise in mathematical modeling and predictive analytics. Her foundational education includes an M.S. in Pharmacokinetics (GPA: 3.92) and a B.S. in Pharmacy (GPA: 3.81) from China Pharmaceutical University, providing a unique interdisciplinary perspective in data science applications within finance, healthcare, and pharmaceuticals.

Experience 💼

Currently, Dr. Wang is a Statistician at Credigy Solutions, where she applies advanced analytics and machine learning to credit risk modeling and investment strategies. Her expertise in data visualization, predictive analytics, and risk assessment has led to a 10% reduction in model errors and improved financial forecasting. Previously, she interned at Hexaware Technologies, where she developed fraud detection models for Starbucks, leveraging cost-sensitive learning methods and ensemble techniques.

Research Interest 🔬

Dr. Wang’s research revolves around machine learning applications in financial analytics, statistical modeling, and credit risk assessment. She has developed novel models for predicting loan defaults, fraud detection, and investment risk. Her work integrates time-series analysis, ensemble learning, deep learning, and feature selection techniques to enhance model accuracy and efficiency. She has also contributed to text mining and natural language processing (NLP), applying these techniques to analyze National Science Foundation (NSF) funding trends.

Award 🏅

Dr. Wang has been recognized for her research excellence with multiple accolades, including the Best Poster Award at ACMSE 2019 for her groundbreaking work in fraud detection. Her proposed machine learning models have significantly improved industry-standard risk assessments, and her patent-pending innovation in predictive modeling showcases her contributions to data-driven financial decision-making.

Top Noted Publication 📑

Title: A Survey of Machine Learning Methodologies for Loan Evaluation in Peer-to-Peer (P2P) Lending

Authors: Yan Wang, Xuelei (Sherry) Ni

This book chapter provides a comprehensive overview of machine learning techniques applied to loan evaluation in P2P lending, exploring methodologies such as supervised learning, ensemble models, and deep learning to enhance credit risk assessment and investment decision-making

Conclusion

Yan Wang is an exceptional candidate for the Best Researcher Award, with an impeccable academic record, innovative research, and real-world industry contributions in data science, finance, and machine learning. Strengthening publication output and expanding interdisciplinary collaborations will further enhance their research impact.