Mary Lynn Reed Headshot

Mary Lynn Reed

Professor

School of Mathematics and Statistics
College of Science

585-475-2163
Office Hours
Monday: 2:00-2:45pm [on ZOOM]; Tuesday: 3:30-4:15pm [In-person]; Wednesday: 3:00-3:45pm [on ZOOM]; Thursday: 11:30am-12:30pm [In-person]
Office Location

Mary Lynn Reed

Professor

School of Mathematics and Statistics
College of Science

Education

BS, Georgia Institute of Technology; MFA, University of Maryland; MS, PhD, University of Illinois

Bio

Dr. Mary Lynn Reed holds a Ph.D. in Mathematics from the University of Illinois, a B.S. in Applied Mathematics from the Georgia Institute of Technology, and an M.F.A. in Creative Writing from the University of Maryland. Dr. Reed's research background and interests include applications of algebraic structures, statistical modeling, network science, and computing. She is currently focused on economic questions related to cybersecurity.

Dr. Reed is an affiliate faculty member with RIT’s ESL Global Cybersecurity Institute. Dr. Reed also serves on the Board of Trustees for the Institute for Defense Analyses (IDA). IDA is a nonprofit corporation that operates three federally funded research and development centers in the public interest. Before arriving at RIT, Dr. Reed served as the chief of mathematics research at the National Security Agency.

Dr. Reed has received a variety of honors and awards throughout her career, including: the Presidential Rank Award of Meritorious Senior Professional, the NSA Director’s Distinguished Service Medal, the NSA Louis W. Tordella Award, the NSA Director's Team Excellence Award, the NSA Gold Bug Team Award for excellence in cryptanalysis, the University of Illinois Department of Mathematics Alumni Award for Outstanding Professional Achievement, the University of Illinois College of Liberal Arts and Sciences Alumni Achievement Award, and the University of Illinois Mathematics Department Outstanding Teaching Award. As an early career faculty member, she was a Mathematical Association of America Project NExT (New Experiences in Teaching) Fellow. Dr. Reed is also a past-President and Distinguished Member of the Crypto-Mathematics Institute, NSA's oldest professional society.

585-475-2163

Areas of Expertise

Currently Teaching

MATH-251
3 Credits
This course introduces sample spaces and events, axioms of probability, counting techniques, conditional probability and independence, distributions of discrete and continuous random variables, joint distributions (discrete and continuous), the central limit theorem, descriptive statistics, interval estimation, and applications of probability and statistics to real-world problems. A statistical package such as Minitab or R is used for data analysis and statistical applications.
MATH-301
3 Credits
This course is an introduction to computer simulation, simulation languages, model building and computer implementation, mathematical analyses of simulation models and their results using techniques from probability and statistics.
MATH-367H
3 Credits
The course introduces students to basic techniques of classical and modern cryptography, and learn about the significant impact of codes and ciphers on historical events. Topics will include the Vignère cipher, affine ciphers, Hill ciphers, one-time pad encryption, Enigma, public key encryption schemes (RSA, Diffie-Hellman, elliptic curves), and cryptographic hash functions. The course will include an introduction to algebraic structures and number theoretic tools used in cryptography. Students in this honors course will also study and explore historical source documents to get first-hand exposure to critical aspects of cryptanalysis from the early to mid-20th century.
MATH-495
1 - 3 Credits
This course is a faculty-directed project that could be considered original in nature. The level of work is appropriate for students in their final two years of undergraduate study.
MATH-498
1 - 3 Credits
This course is a faculty-guided investigation into appropriate topics that are not part of the curriculum.
MATH-799
1 - 3 Credits
Independent Study
STAT-335
3 Credits
This course is a study of the modeling and forecasting of time series. Topics include ARMA and ARIMA models, autocorrelation function, partial autocorrelation function, detrending, residual analysis, graphical methods, and diagnostics. A statistical software package is used for data analysis.

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