Department Directory

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Postdoctoral Research Scientist

Poly Hanna Da Silva

Postdoctoral Research Scientist

Email: phd2120@columbia.edu
Program(s): Postdocs, Statistics Department
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Professor of Professional Practice in the Faculty of Professional Studies

Siddhartha R. Dalal

Professor of Professional Practice in the Faculty of Professional Studies

Email: sd2803@columbia.edu
Program(s): Affiliated Faculty, Statistics Department
Siddhartha (Sid) Dalal is a Professor of Professional Practice at Columbia University. Prior to Columbia he was Chief Data Scientist and Sr. VP at AIG in charge of R&D that included creation and application of AI, Statistics and CS to Computer Vision, Natural Language Processing and Sensors/IOT for managing risks. He came to AIG from RAND Corporation where was the CTO. Sid also was VP of Research at Xerox overseeing their worldwide imaging and software services research, and Bell Labs and Bellcore/SAIC as Chief Scientist and Executive Director. Sid has an MBA and a Ph.D. from the University of Rochester with over 100 peer reviewed publications, patents and monographs covering the areas of risk analysis, medical informatics Bayesian statistics and economics, image processing and sensor networks. At Rand he was responsible for the creation of technology and spinning off of Praedicat, Inc., a casualty insurance analytics company. Sid is a member of US Army Science Board, an advisory board of 20 scientists appointed by Secretary of Defense to advise US Army on technology. He has received several awards including from IEEE, ASA and ASQ.

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Richard A. Davis

Howard Levene Professor

Office: Room 1026 SSW
Phone: 212-851-2137
Email: rdavis@stat.columbia.edu
Program(s): Full-Time Faculty, Senior Faculty, Statistics Department
Website: http://stat.columbia.edu/~rdavis/

Research Interests: Time series analysis, financial time series modeling, extreme value theory, spatial-temporal modeling with environmental applications, applied probability, and stochastic processes.


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Victor de la Pena

Professor

Office: Room 1027 SSW
Phone: 212-851-2144
Email: vp@stat.columbia.edu
Program(s): Full-Time Faculty, Senior Faculty, Statistics Department
Website: http://stat.columbia.edu/~vp/
 
Professor Victor de la Pena's paper ``From Decoupling and Self-Normalization to Machine Learning'' was invited and has been published in the ``Notices of the American Society'' 
which is distributed to over 30,000 members and member institutions worldwide.
 

Research Interests: Inequalities in Probability and Statistics, Sequential Analysis, Boundary Crossing and Change Point Problems, U-Statistics and Processes, Self-Normalized Processes, General Dependence Structures Including Martingales, Decoupling, Copulas. Managing Risk in Complex Systems, Banking, Actuarial Sciences, Earth Sciences, Climatology Global Warming.


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Victor Hugo De Souza Daniel

Ph.D. Student

Email: vdd2111@columbia.edu
Program(s): Ph.D. Students, Statistics Department
Year Entered Ph.D. Program: Fall 2022
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Postdoctoral Research Scientist

Khanh Dinh

Postdoctoral Research Scientist

Email: knd2127@columbia.edu
Program(s): Postdocs, Statistics Department
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Regina Dolgoarshinnykh

Adjunct Assistant Professor

Office: Room 929 SSW
Email: regina@stat.columbia.edu
Program(s): Adjunct Faculty, Statistics Department
Website: http://www.stat.columbia.edu/~regina/
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Adjunct Assistant Professor

Anthony Donoghue

Adjunct Assistant Professor

Office: Room 901A SSW
Email: ad2798@columbia.edu
Program(s): Adjunct Faculty, Statistics Department
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Adjunct Associate Professor

Praijat Dube

Adjunct Associate Professor

Email: pd2637@columbia.edu
Program(s): Adjunct Faculty, Statistics Department
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Assistant Professor of Statistics and Herbert Florence Irving Assistant Professor of Cancer Data Research

Bianca Dumitrascu

Assistant Professor of Statistics and Herbert Florence Irving Assistant Professor of Cancer Data Research

Email: bmd2151@columbia.edu
Program(s): Junior Faculty, Tenure Track, Statistics Department
Website: https://computational-morphogenomics-group.github.io/

Research interests: statistical machine learning for genomics: single-cell genomics, spatial transcriptomics, live-imaging

 


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Administrative Staff

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