Seidenberg School of CSIS
Computer Science NY
- @New York City
163 William Street 223
New York City
Mon, Wed 9:00am-10:00am
Dr. Parisi is a clinical professor of computer science and the data science program director. His expertise includes statistical learning, computational statistics, probability theory, and extreme value methods, with applications in finance and climatology. He joined Pace University after a long career in credit and risk management.
PhD , Southern California University for Professional Studies , Santa Ana, CA , 2003
Management of Engineering & Technology
MS , Colorado State University , Ft. Collins, CO , 1998
BA , Brooklyn College, City University of New York , Brooklyn, NY , 1977
Parisi, F. (2020, October (4th Quarter/Autumn)). But This Time IS Different—COVID Recession. The Journal of Sturctured Finance. Vol 26 (Issue 3) , pages 8.
Ceman, E. & Parisi, F. (2018). Extreme Market Value Declines: How Well Do Rating Agency Assumptions Hold?. The Journal of Structured Finance. Vol 24 (Issue 3) , pages 79-88.
Karvetski, C., Lund, R. & Parisi, F. (2009). A statistical study of extreme Nor'easter snowstorms. Involve. Vol 2 (Issue 3) , pages 341-350.
Parisi, F. & Lund, R. (2008). Return periods of Continental U.S. hurricanes. Journal of Climate. Vol 21 , pages 403-410.
Khadem, V. & Parisi, F. (2007). Residential Mortgage-Backed Securities. Arnaud de Servigny and Nobert Jobst (Eds.), The Handbook of Structured Finance. New York, NY, US: McGraw-Hill. , pages 543-592.
Parisi, F. (2006, August). Choosing The Right Quantitative Model For The Job.
Parisi, F. (2006, August). Fitting Time Into Models Of Default Recovery Rates. S&P RatingsDirect.
Parisi, F. (2006, August). The Differing Role Of Quantitative Analytics In Credit And Equity Ratings. S&P RatingsDirect.
Parisi, F. (2004). Extreme Value Modeling with S-PLUS and S+FinMetrics in Standard & Poor's Ratings.
Raiter, F. & Parisi, F. (2004). Mortgage credit and the evolution of risk-based pricing. (Issue BABC 04-23)
Raiter, F. & Parisi, F. (2004). Risk-based pricing in the non-conforming market. Mortgage Banking. Vol 64 (Issue 7) , pages 56-63.
Parisi, F. (2004, February). Loss Correlations Among U.S. Consumer Assets.
Parisi, F. (2001). U.S. Consumer Debt: Volume Heads North as Economy Heads South. S&P RatingsDirect.
Parisi, F. & Lund, R. (2000). Seasonality and return periods of landfalling Atlantic basin hurricanes. Australian & New Zealand Journal of Statistics. Vol 42 , pages 271-282.
Parisi, F. (2000, October (4th Quarter/Autumn)). Extreme Value Theory and Standard & Poor's Ratings. S&P RatingsDirect.
Extreme value theory, Markov decision processes, time series analysis, statistical climatology, credit default modeling, financial risk modeling
Data Science Association
The Data Science Association is a non-profit professional association of data scientists that serves our members, improving the data science profession, eliminating bias and enhancing diversity, and advancing ethical data science throughout the world. The DSA is committed to supporting the data science profession with practical resources for data professionals while improving the practice of data science, accrediting schools, and establishing model ethical codes. Membership is open to data scientists, scientists, students, academics, and others interested in science and the data science profession.
The Academic Data Science Alliance
The Academic Data Science Alliance builds communities of academic data science leaders, practitioners, and educators, and academic-adjacent colleagues, to thoughtfully integrate data science best practices in higher education. Our members connect and share their data-intensive approaches and responsible applications.
Mathematical Association of America
American Statistical Association
The American Statistical Association is the world's largest community of statisticians. It is the second-oldest, continuously operating professional association in the country. Since it was founded in 1839, the ASA has supported excellence in the development, application, and dissemination of statistical science through meetings, publications, membership services, education, accreditation, and advocacy.
NYC Faculty Council Curriculum Committee (Co-Chair)
Desc: Co-Chair starting Fall 2020
NYC Faculty Council Curriculum Committee