Systematic Review, Policy Evaluation, Data Science
Systematic Review, Policy Evaluation, Data Science
We specialize in transforming complex data into effective strategies, evidenced by our work in renowned scientific journals and sought-after training programs. Trusted by government and private sectors, we excel in systematic review, policy evaluation, and data science.
We conduct comprehensive systematic reviews and meta-analyses of scientific literature to address clients’ unique needs. Our approach ensures thorough, relevant, publication-quality reviews suitable for various applications, including academic research and clinical evaluation.
Our expertise covers an extensive array of quantitative data analysis, such as data visualization, regression analysis, network analysis, and microsimulation. We provide detailed, robust analysis across multiple sectors, offering valuable insights and data-driven solutions.
Specializing in data-driven program and policy evaluation, we apply methods such as cost-benefit analysis, cost-effectiveness analysis, and causal inference modeling. We empower decision-makers with actionable insights for effective policy and program development.
We offer robust AI and data science solutions, developing machine learning and deep neural network models to address real-world challenges. Our expertise includes predictive analytics, time-series forecasting, computer vision, natural language processing, and generative AI.
Dr. Ruopeng An is a tenured professor at Washington University in St. Louis, having previously held tenure at the University of Illinois at Urbana-Champaign. With over 200 peer-reviewed journal publications, he is acknowledged as one of Elsevier’s top 2% most cited scientists worldwide. He holds the distinction of being a Fellow of the American College of Epidemiology and the American Academy of Health Behavior.
Professor An's research has garnered attention from major media outlets, such as TIME, the New York Times, the Los Angeles Times, the Washington Post, Reuters, USA Today, Bloomberg, Forbes, the Atlantic, the Guardian, FOX, NPR, and CNN.
Professor An actively shapes health policy and research, contributing to research grants and expert panels for government agencies such as the NIH, CDC, NSF, HHS, USDA, and the French National Research Agency. His consulting expertise extends to numerous public and private organizations, including the WHO, CDC, HHS, RTI International, OpenAI, Abbott Laboratories, Amgen Foundation, Discovery, American Egg Board, National Cattlemen's Beef Association, National Pork Board, and the University of Michigan.
His teaching portfolio includes applied artificial intelligence and data science, systematic review and meta-analysis, biostatistics, and health data analysis.
His commitment to education is reflected in his consistently high student evaluations, placing him in the top 10% of university faculty for teaching excellence.
At Washington University in St. Louis, he founded and chairs the Artificial Intelligence and Big Data Analytics for Public Health Certificate program and the Artificial Intelligence Applications for Health Data Advanced Learning Certificate program.
He established the AI Interest Group, a dynamic collaboration of master's and doctoral students, colleagues, and researchers from various universities and institutions. This group convenes bi-weekly to present and discuss the latest developments and applications in AI technology, with a steadfast focus on practicality and implementation. To foster a culture of openness and knowledge-sharing, we make all presentation recordings, slides, and programming codes freely available.
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