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
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.