Mathematical Sciences Programs - UT Dallas 2025 Graduate Catalog - The University of Texas at Dallas

Mathematical Sciences Programs - UT Dallas 2025 Graduate Catalog - The University of Texas at Dallas
UT Dallas 2025 Graduate Catalog
School of Natural Sciences and Mathematics
Graduate Programs in Mathematical Sciences
Objectives
The Mathematical Sciences Department at The University of Texas at Dallas offers seven graduate programs, namely, Doctor of Philosophy and Master of Science in Mathematics; Doctor of Philosophy and Master of Science in Data Science and Statistics; Master of Science in Actuarial Science; Master of Science in Bioinformatics and Computational Biology (jointly with the Department of Biological Sciences); and Graduate Certificate in Data Science. The Master of Science degrees in Mathematics and Data Science and Statistics offer a number of specializations, including Applied Mathematics, Mathematics for Decision and Engineering Sciences, Applied Statistics, and Data Science. Altogether the wide spectrum of our programs prepares students for a variety of careers in mathematics, statistics, data science, actuarial science, bioinformatics, and other mathematically oriented disciplines.
A Master of Science degree may also be pursued by those who plan to teach Mathematics or Statistics above the remedial level at a community college or at a college or university. For such students, the Master of Science degree is a minimum as a doctoral degree is often required.
For information concerning the Master of Arts in Teaching in Mathematics Education, designed for persons who are teaching in grades 6-12, see the
Science and Mathematics Education section
NOTE> Please be advised, the admissions section below feeds in from a separate page. Any changes made to the section below will not be retained. To update Admissions policies please take all changes to Grad Council for initial approval and then send them to Serenity King in the Provost's Office. If you have any questions, please contact catalog@utdallas.edu.
Admission Requirements
The University's general admission requirements are discussed on the
Graduate Admission
page.
Specific additional admission requirements for students in degree programs in the Department of Mathematical Sciences follow. Students lacking undergraduate prerequisites for graduate courses in their area must complete these prerequisites or receive approval from the graduate advisor and the course instructor before registering.
One of the components of a student's academic history which is evaluated when the student is seeking admission to the graduate program is his/her performance on certain standardized tests. Since these tests are designed to indicate only the student's potential for graduate study, they are used in conjunction with other measures of student proficiency, such as GPA (grade point average), etc., in determining the admission status of a potential graduate student. Accordingly, there is no rigid minimum cutoff score for admission to the program. Higher standards prevail for applicants seeking Teaching Assistantships.
Master of Science in Mathematics
36 semester credit hours minimum
Department Faculty
FACG> nsm-mathematics-ms
Professors:
Swati Biswas
@sxb125731
Min Chen
@mxc136030
Pankaj Choudhary
@pkc022000
Baris Coskunuzer
@bxc190014
Mieczyslaw Dabkowski
@mkd034000
Vladimir Dragovic
@vxd123630
Sam Efromovich
@sxe062000
Yulia Gel
@yxg142030
Wieslaw Krawcewicz
@wzk091000
Susan Minkoff
@sem120030
L. Felipe Pereira
@lfp140030
Dmitry Rachinskiy
@dxr124030
Viswanath Ramakrishna
@vish
Janos Turi
@turi
Associate Professors:
Maxim Arnold
@mxa149530
Yan Cao
@yxc069200
Liang Hong
@lxh019000
Oleg Makarenkov
@oxm130230
Tomoki Ohsawa
@txo140730
Anh Tran
@att140830
Assistant Professors:
Carlos Arreche
@cxa171230
Noirrit Chandra
@nxc220031
Ronan Conlon
@rxc200021
Rizwanur Khan
@rxk230072
Qiwei Li
@qxl190009
Stephen McKeown
@sxm190098
Chuan-Fa Tang
@cxt190011
Jiayi Wang
@jxw220029
Nathan Williams
@nxw170830
Nan Wu
@nxw220012
Yunan Wu
@yxw200032
Professors Emeriti:
Larry Ammann
@ammann
Ali Hooshyar
@ali
Patrick Odell
@pxo062000
John Van Ness
@ness
Clinical Professor:
Natalia Humphreys
@nah103020
Clinical Associate Professor:
Mohammad Akbar
@mma110020
Clinical Assistant Professor:
Wenyi Lu
@wxl153330
Professors of Instruction:
Anatoly Eydelzon
@axe031000
Manjula Foley
@mxf091000
Bentley Garrett
@btg032000
Yuly Koshevnik
@yxk055000
Associate Professors of Instruction:
Mohammad Ahsan
@mka120030
Kelly Aman
@kxa143530
Malgorzata Dabkowski
@mxd066000
Rabin Dahal
@rxd153030
Derege Mussa
@dxm146130
My Linh Nguyen
@mln018200
Jigarkumar Patel
@jsp061000
Julie Sutton
@jxs158030
Assistant Professors of Instruction:
Anani Komla Adabrah
@aaa130530
Iris Alvarado
@ila180000
Saikat Biswas
@sxb230137
Hui Ding
@hxd162130
Kemelli Estacio-Hiroms
@kxe160930
Huizhen Guo
@hxg190020
Shengjie Jiang
@sxj220069
Joselle Kehoe
@jxk061000
Runzhou Liu
@rxl210013
Neha Makhijani
@nxm165130
Irina Martynova
@ixm140930
Diarisoa Mihaja Rakotomalala
@dmr150130
Adrian Murza
@acm170730
Ajaya Paudel
@abp062000
Octavious Smiley
@oxs230011
Nasrin Sultana
@nxs190039
Che-Yu Wu
@cxw153530
Admission Requirements
In addition to the University's
general admission requirements
, applicants are required to submit the following materials as part of their application file for holistic admission consideration:
Three letters of recommendation.
A statement of purpose/essay.
A resume or CV.
Deadline:
For first-round consideration for assistantships and scholarships, the application must be complete by December 15 for the upcoming Fall term and August 31 for the upcoming Spring term. Applications may be accepted for later rounds also, see
Graduate Admissions
for additional deadlines.
Degree Requirements
The University's general degree requirements are discussed on the
Graduate Policies and Procedures
page.
Students seeking a Master of Science in Mathematics must complete a total of 12 three-semester credit hour courses. The student may choose a thesis plan or a non-thesis plan. In the thesis plan, the thesis replaces two elective courses with completion of an approved thesis (six semester credit hours). The thesis is directed by a Supervising Professor and must be approved by the Head of the Mathematical Sciences Department. The thesis must be successfully defended before a thesis committee.
Each student must earn a 3.0 minimum GPA in the courses listed for the student's program.
To satisfy the MS degree requirements, we currently offer a choice between four specializations -
Mathematics
Applied Mathematics
Decision and Engineering Sciences
, and
Data Science.
Mathematics Specialization (MS)
MATH 6301
Real Analysis
MATH 6303
Theory of Complex Functions I
MATH 6311
Abstract Algebra I
MATH 6315
Ordinary Differential Equations
Choose four courses from the following:
MATH 6302
Functional Analysis I
MATH 6309
Differential Geometry
MATH 6310
Topology
MATH 6312
Combinatorics and Graph Theory
MATH 6325
Nonlinear Analysis I
MATH 7313
Partial Differential Equations I
MATH 7361
Algebraic Geometry and Non-linear Equations
Plus four guided electives with the approval of the Graduate Advisor for Mathematics.
Applied Mathematics Specialization (MS)
MATH 6313
Numerical Analysis
MATH 6315
Ordinary Differential Equations
MATH 6319
Principles and Techniques in Applied Mathematics I
MATH 6321
Optimization
MATH 5301
Elementary Analysis I and
MATH 5302
Elementary Analysis II
or
MATH 6301
Real Analysis
Choose three courses from the following:
MATH 6303
Theory of Complex Functions I
MATH 6308
Inverse Problems and Applications
MATH 6312
Combinatorics and Graph Theory
MATH 6318
Numerical Analysis of Differential Equations
MATH 6320
Principles and Techniques in Applied Mathematics II
MATH 6324
Applied Dynamical Systems I
MATH 6336
Nonlinear Control Systems
MATH 6340
Numerical Linear Algebra
MATH 6342
Scientific Computing
MATH 7313
Partial Differential Equations I
Plus four guided electives with the approval of the Graduate Advisor for Mathematics.
Mathematics for Decision and Engineering Sciences (MS)
MATH 5301
Elementary Analysis I (or equivalent)
MATH 5302
Elementary Analysis II (or equivalent)
MATH 6305
Mathematics of Signal Processing
MATH 6321
Optimization
MATH 6331
Mathematics of Signals, Systems, and Controls
MATH 7318
or
OPRE 7318
Stochastic Dynamic Programming
STAT 5353
Probability and Statistics for Data Science and Bioinformatics
STAT 6329
Applied Probability and Stochastic Processes
or
MATH 6364
Stochastic Calculus in Finance
STAT 6340
Statistical and Machine Learning
ACTS 6308
Actuarial Financial Mathematics
Plus two guided electives with the approval of the Graduate Advisor for Mathematics.
Data Science Specialization (MS)
CS 5303
Computer Science I
CS 5343
Algorithm Analysis and Data Structures
CS 6307
Introduction to Big Data Management and Analytics for non CS-Majors
CS 6375
Machine Learning
MATH 6312
Combinatorics and Graph Theory
MATH 6321
Optimization
MATH 6340
Numerical Linear Algebra
or
MATH 6319
Principles and Techniques in Applied Mathematics I
MATH 6322
Mathematical Foundations of Data Science
STAT 5353
Probability and Statistics for Data Science and Bioinformatics
STAT 6340
Statistical and Machine Learning
Plus two guided electives with the approval of the Graduate Advisor for Mathematics.
Other Requirements
Electives must be approved by the Graduate Advisor for Mathematics. Typically, electives are 6000- and 7000-level Mathematics courses. Courses from other disciplines may also be used upon approval. Substitutions for required courses may be made if approved by the Graduate Advisor for Mathematics. Instructors may substitute stated prerequisites for students with relevant experience.
1. If a student takes both MATH 5301 (or equivalent) and MATH 5302 (or equivalent), then one of these classes can be counted towards the guided elective requirement. Therefore, such a student will need to take only three guided electives with the approval of the graduate advisor for mathematics.
2. Students who have not taken the CS 5333 Discrete Structures prerequisite for CS 5343 Algorithm Analysis and Data Structures should consult with their Graduate Advisor from the Mathematical Sciences Department to determine eligibility.
Master of Science in Data Science and Statistics
36 semester credit hours minimum
Department Faculty
FACG> nsm-statistics-ms
Professors:
Swati Biswas
@sxb125731
Min Chen
@mxc136030
Pankaj Choudhary
@pkc022000
Baris Coskunuzer
@bxc190014
Mieczyslaw Dabkowski
@mkd034000
Vladimir Dragovic
@vxd123630
Sam Efromovich
@sxe062000
Yulia Gel
@yxg142030
Wieslaw Krawcewicz
@wzk091000
Susan Minkoff
@sem120030
L. Felipe Pereira
@lfp140030
Dmitry Rachinskiy
@dxr124030
Viswanath Ramakrishna
@vish
Janos Turi
@turi
Associate Professors:
Maxim Arnold
@mxa149530
Yan Cao
@yxc069200
Liang Hong
@lxh019000
Oleg Makarenkov
@oxm130230
Tomoki Ohsawa
@txo140730
Anh Tran
@att140830
Assistant Professors:
Carlos Arreche
@cxa171230
Noirrit Chandra
@nxc220031
Ronan Conlon
@rxc200021
Rizwanur Khan
@rxk230072
Qiwei Li
@qxl190009
Stephen McKeown
@sxm190098
Chuan-Fa Tang
@cxt190011
Jiayi Wang
@jxw220029
Nathan Williams
@nxw170830
Nan Wu
@nxw220012
Yunan Wu
@yxw200032
Professors Emeriti:
Larry Ammann
@ammann
Ali Hooshyar
@ali
Patrick Odell
@pxo062000
John Van Ness
@ness
Clinical Professor:
Natalia Humphreys
@nah103020
Clinical Associate Professor:
Mohammad Akbar
@mma110020
Clinical Assistant Professor:
Wenyi Lu
@wxl153330
Professors of Instruction:
Anatoly Eydelzon
@axe031000
Manjula Foley
@mxf091000
Bentley Garrett
@btg032000
Yuly Koshevnik
@yxk055000
Associate Professors of Instruction:
Mohammad Ahsan
@mka120030
Kelly Aman
@kxa143530
Malgorzata Dabkowski
@mxd066000
Rabin Dahal
@rxd153030
Derege Mussa
@dxm146130
My Linh Nguyen
@mln018200
Jigarkumar Patel
@jsp061000
Julie Sutton
@jxs158030
Assistant Professors of Instruction:
Anani Komla Adabrah
@aaa130530
Iris Alvarado
@ila180000
Saikat Biswas
@sxb230137
Hui Ding
@hxd162130
Kemelli Estacio-Hiroms
@kxe160930
Huizhen Guo
@hxg190020
Shengjie Jiang
@sxj220069
Joselle Kehoe
@jxk061000
Runzhou Liu
@rxl210013
Neha Makhijani
@nxm165130
Irina Martynova
@ixm140930
Diarisoa Mihaja Rakotomalala
@dmr150130
Adrian Murza
@acm170730
Ajaya Paudel
@abp062000
Octavious Smiley
@oxs230011
Nasrin Sultana
@nxs190039
Che-Yu Wu
@cxw153530
Program Objective
The curriculum for Master of Science in Data Science and Statistics offers a balanced list of courses in theory, methodology, and application of statistics and data science. During their study, our Master of Science students acquire the necessary skills that make them competitive in the modern job market. Our graduates generally find employment as statisticians, biostatisticians, data scientists, quantitative analysts, and so on, or they continue into doctoral degree programs.
Admission Requirements
In addition to the University's
general admission requirements
, applicants are required to submit the following materials as part of their application file for holistic admission consideration:
Three letters of recommendation.
A statement of purpose/essay.
A resume or CV.
Deadline:
For first-round consideration for assistantships and scholarships, the application must be complete by December 15 for the upcoming Fall term and August 31 for the upcoming Spring term. Applications may be accepted for later rounds also, see
Graduate Admissions
for additional deadlines.
Degree Requirements
The University's general degree requirements are discussed on the
Graduate Policies and Procedures
page.
Students seeking a Master of Science in Data Science and Statistics must complete a total of 12 three-semester credit hour courses. The student may choose a thesis plan or a non-thesis plan. In the thesis plan, the thesis replaces two elective courses with completion of an approved thesis (six semester credit hours). The thesis is directed by a Supervising Professor and must be approved by the Head of the Mathematical Sciences Department. The thesis must be successfully defended before a thesis committee.
Each student must earn a 3.0 minimum GPA in the courses listed for the student's program.
To satisfy the MS degree requirements, we currently offer a choice between three specializations -
Statistics
Applied Statistics
, and
Data Science
Statistics Specialization (MS)
1.
Six Core Courses:
STAT 6331
Statistical Inference I
STAT 6337
Advanced Statistical Methods I
STAT 6338
Advanced Statistical Methods II
STAT 6339
Linear Statistical Models
STAT 6340
Statistical and Machine Learning
STAT 6341
Numerical Linear Algebra and Statistical Computing
2.
Two or more courses from the following list:
STAT 6329
Applied Probability and Stochastic Processes
or
STAT 7345
Advanced Probability and Stochastic Processes
STAT 6348
Applied Multivariate Analysis
or
STAT 7331
Multivariate Analysis
STAT 6347
Applied Time Series Analysis
or
STAT 7338
Time Series Modeling and Filtering
STAT 7330
Bayesian Data Analysis
STAT 7334
Nonparametric and Robust Statistical Methods
3.
The remaining courses are electives and must be approved by the Graduate Advisor for Data Science and Statistics. Up to two of the following 5000-level courses may be counted as electives:
MATH 5301
Elementary Analysis I
MATH 5302
Elementary Analysis II
STAT 5351
Probability and Statistics I
STAT 5352
Probability and Statistics II
Applied Statistics Specialization (MS)
1.
Six core courses:
STAT 5351
Probability and Statistics I
STAT 5352
Probability and Statistics II
STAT 6337
Advanced Statistical Methods I
STAT 6338
Advanced Statistical Methods II
STAT 6340
Statistical and Machine Learning
STAT 6341
Numerical Linear Algebra and Statistical Computing
2.
Two or more courses from the following list:
STAT 6329
Applied Probability and Stochastic Processes
STAT 6347
Applied Time Series Analysis
STAT 6348
Applied Multivariate Analysis
STAT 7330
Bayesian Data Analysis
MATH 5303
Advanced Calculus and Linear Algebra
3.
The remaining courses are electives and must be approved by the Graduate Advisor for Data Science and Statistics. Many students select the electives to build expertise in another subject to enhance their employment opportunities.
Data Science Specialization (MS)
CS 5303
Computer Science I
CS 5343
Algorithm Analysis and Data Structures
CS 6307
Introduction to Big Data Management and Analytics for non CS-Majors
CS 6375
Machine Learning
MATH 6312
Combinatorics and Graph Theory
STAT 5351
Probability and Statistics I
STAT 5352
Probability and Statistics II
STAT 6337
Advanced Statistical Methods I
STAT 6338
Advanced Statistical Methods II
STAT 6348
Applied Multivariate Analysis
STAT 6340
Statistical and Machine Learning
MATH 5303
Advanced Calculus and Linear Algebra
Other Requirements
Electives must be approved by the Graduate Advisor for Data Science and Statistics. Typically, the electives are graduate courses in statistics and mathematics. Courses from other disciplines may also be used upon approval. Substitutions for required courses may be made if approved by the Graduate Advisor for Data Science and Statistics. Instructors may substitute stated prerequisites for students with relevant experience.
1. For students with sufficient background in the subject, this course can be replaced by an elective course approved by the Graduate Advisor for Data Science and Statistics.
2. Students who have not taken the CS 5333 Discrete Structures prerequisite for CS 5343 Algorithm Analysis and Data Structures should consult with their Graduate Advisor from the Mathematical Sciences Department to determine eligibility.
Master of Science in Actuarial Science
36 semester credit hours minimum
Department Faculty
FACG> nsm-actuarial-science-ms
Professors:
Swati Biswas
@sxb125731
Min Chen
@mxc136030
Pankaj Choudhary
@pkc022000
Baris Coskunuzer
@bxc190014
Mieczyslaw Dabkowski
@mkd034000
Vladimir Dragovic
@vxd123630
Sam Efromovich
@sxe062000
Yulia Gel
@yxg142030
Wieslaw Krawcewicz
@wzk091000
Susan Minkoff
@sem120030
L. Felipe Pereira
@lfp140030
Dmitry Rachinskiy
@dxr124030
Viswanath Ramakrishna
@vish
Janos Turi
@turi
Associate Professors:
Maxim Arnold
@mxa149530
Yan Cao
@yxc069200
Liang Hong
@lxh019000
Oleg Makarenkov
@oxm130230
Tomoki Ohsawa
@txo140730
Anh Tran
@att140830
Assistant Professors:
Carlos Arreche
@cxa171230
Noirrit Chandra
@nxc220031
Ronan Conlon
@rxc200021
Rizwanur Khan
@rxk230072
Qiwei Li
@qxl190009
Stephen McKeown
@sxm190098
Chuan-Fa Tang
@cxt190011
Jiayi Wang
@jxw220029
Nathan Williams
@nxw170830
Nan Wu
@nxw220012
Yunan Wu
@yxw200032
Professors Emeriti:
Larry Ammann
@ammann
Ali Hooshyar
@ali
Patrick Odell
@pxo062000
John Van Ness
@ness
Clinical Professor:
Natalia Humphreys
@nah103020
Clinical Associate Professor:
Mohammad Akbar
@mma110020
Clinical Assistant Professor:
Wenyi Lu
@wxl153330
Professors of Instruction:
Anatoly Eydelzon
@axe031000
Manjula Foley
@mxf091000
Bentley Garrett
@btg032000
Yuly Koshevnik
@yxk055000
Associate Professors of Instruction:
Mohammad Ahsan
@mka120030
Kelly Aman
@kxa143530
Malgorzata Dabkowski
@mxd066000
Rabin Dahal
@rxd153030
Derege Mussa
@dxm146130
My Linh Nguyen
@mln018200
Jigarkumar Patel
@jsp061000
Julie Sutton
@jxs158030
Assistant Professors of Instruction:
Anani Komla Adabrah
@aaa130530
Iris Alvarado
@ila180000
Saikat Biswas
@sxb230137
Hui Ding
@hxd162130
Kemelli Estacio-Hiroms
@kxe160930
Huizhen Guo
@hxg190020
Shengjie Jiang
@sxj220069
Joselle Kehoe
@jxk061000
Runzhou Liu
@rxl210013
Neha Makhijani
@nxm165130
Irina Martynova
@ixm140930
Diarisoa Mihaja Rakotomalala
@dmr150130
Adrian Murza
@acm170730
Ajaya Paudel
@abp062000
Octavious Smiley
@oxs230011
Nasrin Sultana
@nxs190039
Che-Yu Wu
@cxw153530
Program Objective
The objective of the program is to educate future leaders of the actuarial industry with training in actuarial theory and methods in a wide spectrum of actuarial applications involving probabilistic and statistical models. All students will be prepared to take seven actuarial preliminary exams and will take two advanced actuarial classes to prepare for professional accreditation. Furthermore, students who did not take classes required for VEE (Validation of Educational Experience) credits in Accounting and Finance, Economics, and Mathematical Statistics will have such opportunity. With this combined knowledge of mathematics particularly of probability, statistics, and decision theory together with knowledge of financial mathematics and insurance, the expected passing of five actuarial exams, and the three required VEE credits, graduates of the program will be able to work as senior actuaries in insurance, consulting, finance, government, and emerging markets.
Admission Requirements
In addition to the University's
general admission requirements
, applicants are required to submit the following materials as part of their application file for holistic admission consideration:
Three letters of recommendation.
A statement of purpose/essay.
A resume or CV.
Deadline:
For first-round consideration for assistantships and scholarships, the application must be complete by December 15 for the upcoming Fall term and August 31 for the upcoming Spring term. Applications may be accepted for later rounds also, see
Graduate Admissions
for additional deadlines.
Course Requirements
The University's general degree requirements are discussed on the
Graduate Policies and Procedures
page.
The minimal total required number of classes for graduation is 36 semester credit hours. Among them, 24 semester credit hours of required courses and 12 semester credit hours of electives.
Required Courses: 24 semester credit hours
STAT 5351
Probability and Statistics I
STAT 5352
Probability and Statistics II
ACTS 6301
Principles of Long Term Actuarial Mathematics I
ACTS 6303
Principles of Long Term Actuarial Mathematics II
ACTS 6304
Principles of Short Term Actuarial Mathematics I
ACTS 6305
Principles of Short Term Actuarial Mathematics II
ACTS 6307
Advanced Statistics for Risk Modeling
ACTS 6310
Advanced Predictive Analytics
Prescribed Elective Courses: 12 semester credit hours
For the prescribed elective courses select four courses from the following:
ACTS 6302
Investment and Financial Markets
ACTS 6306
Theory of Credibility
ACTS 6308
Actuarial Financial Mathematics
ACCT 6301
Financial Accounting
10
ACCT 6305
Accounting for Managers
10
FIN 6301
Financial Management
10
FIN 6308
Regulation of Business and Financial Markets
FIN 6310
Investment Theory and Practice
FIN 6314
Fixed Income Securities
FIN 6360
Derivatives Markets
FIN 6382
Financial Applications and Statistical Methods
MATH 6313
Numerical Analysis
MECO 6303
Business Economics
11
OPRE 6301
Statistics and Data Analysis
12
OPRE 6335
Risk and Decision Analysis
PPPE 6321
Economics for Public Policy
STAT 6329
Applied Probability and Stochastic Processes
STAT 6331
Statistical Inference I
STAT 6337
Advanced Statistical Methods I
13
STAT 6338
Advanced Statistical Methods II
STAT 6347
Applied Time Series Analysis
13
STAT 6348
Applied Multivariate Analysis
STAT 6390
Topics in Statistics - Level 6
STAT 7330
Bayesian Data Analysis
STAT 7334
Nonparametric and Robust Statistical Methods
STAT 7338
Time Series Modeling and Filtering
Preparation for Actuarial Exams
These classes prepare for the three preliminary actuarial examinations jointly administered by the Society of Actuaries (SOA), Casualty Actuarial Society (CAS) and the Canadian Institute of Actuaries (CIA):
Exam 1/P:
STAT 5351
and
STAT 5352
Exam 2/FM:
ACTS 6308
Exam 3L/FAM/ALTAM:
ACTS 6301
ACTS 6303
Exam 3F/ ALTAM/ FAP modules:
ACTS 6302
Exam 4/ FAM/ASTAM:
ACTS 6304
ACTS 6305
ACTS 6306
Exam SRM:
ACTS 6307
Exam PA:
ACTS 6310
Validation by Educational Experience (VEE) Credits
Mathematical Statistics:
STAT 5352
OPRE 6301
Accounting and Finance:
FIN 6301
ACCT 6301
ACCT 6305
Economics:
MECO 6303
3. Exam 1/P
4. Exam 1/P and VEE, Mathematical Statistics
5. Exam 3L/FAM/ALTAM, Part I
6. Exam 4/FAM/ASTAM
7. Exam SRM
8. Exam PA
9. Exam 3F/ALTAM/FAP
10. VEE, Accounting and Finance
11. VEE, Economics
12. VEE, Mathematical Statistics
13. VEE, Applied Statistical Methods
Master of Science in Bioinformatics and Computational Biology
36 semester credit hours minimum
Mathematics Faculty
FACG> nsm-bioinformatics-and-computational-biology-ms-math
Professors:
Juan E. González
@jgonzal
Tae Hoon Kim
@txk142630
Kelli Palmer
@klp120030
Lawrence J. Reitzer
@reitzer
Donal Skinner
@dcs220007
Stephen Spiro
@sxs067400
Li Zhang
@lxz075000
Michael Qiwei Zhang
@mqz091000
Associate Professors:
Joseph Boll
@jxb230044
Nicole De Nisco
@njd160330
Nikki Delk
@nad140230
Tian Hong
@txh240018
Faruck Morcos
@afg150230
Duane D. Winkler
@ddw130330
Zhenyu Xuan
@zxx091000
Assistant Professors:
Nicholas Dillon
@nxd210018
Xintong Dong
@xxd220000
Lin Jia
@lxj200008
Purna Joshi
@pxj210010
Brandon Kim
@dal882867
Erica Sanchez
@exs220022
Darshan Sapkota
@dxs210043
Yuki Shindo
@yxs240025
Jon Sin
@dal112948
Professors Emeriti:
Hans Bremer
@hxb068000
Lee A. Bulla
@bulla
Rockford Draper
@draper
Donald M. Gray
@dongray
Associate Professors Emeriti:
Gail A. M. Breen
@breen
Dennis L. Miller
@dmiller
Clinical Professor:
David Murchison
@dfm100020
Eberhard Voit
@eov230000
Professors of Instruction:
Mehmet Candas
@candas
Elizabeth Pickett
@eaw016100
Uma Srikanth
@ukrish
Associate Professors of Instruction:
Meenakshi Maitra
@mxm172731
Jing Pan
@jxp134330
Eva Sadat
@exs190014
Subha Sarcar
@sns064000
Michelle Wilson
@mxw084000
Zhuoru Wu
@zxw190014
Wen-Ho Yu
@why061000
Assistant Professors of Instruction:
Stephanie Boyd
@sdb074000
Andy Cheshire
@dal515348
Anne Davenport
@axd240011
Matthew Esparza
@dal998419
Amy Jo Gomez
@amh240001
Yi Huang
@yxh220019
Li Liu
@lliu
Simbarashe Mazambani
@sxm170074
Iti Mehta
@ixm121430
Ritu Mishra
@dal831670
Ramesh Padmanabhan
@rxp104120
Narges Salamat
@nxs158730
Research Associate Professor:
Ru-Hung Wang
@rxw012400
Lecturer:
Kathleen McRoy
@dal311023
Mathematics Faculty With Research Interests in Bioinformatics and Computational Biology: Swati Biswas, Yan Cao, and Min Chen
Biology Faculty
FACG> nsm-bioinformatics-and-computational-biology-ms-biol
Professors:
Juan E. González
@jgonzal
Tae Hoon Kim
@txk142630
Kelli Palmer
@klp120030
Lawrence J. Reitzer
@reitzer
Donal Skinner
@dcs220007
Stephen Spiro
@sxs067400
Li Zhang
@lxz075000
Michael Qiwei Zhang
@mqz091000
Associate Professors:
Joseph Boll
@jxb230044
Nicole De Nisco
@njd160330
Nikki Delk
@nad140230
Tian Hong
@txh240018
Faruck Morcos
@afg150230
Duane D. Winkler
@ddw130330
Zhenyu Xuan
@zxx091000
Assistant Professors:
Nicholas Dillon
@nxd210018
Xintong Dong
@xxd220000
Lin Jia
@lxj200008
Purna Joshi
@pxj210010
Brandon Kim
@dal882867
Erica Sanchez
@exs220022
Darshan Sapkota
@dxs210043
Yuki Shindo
@yxs240025
Jon Sin
@dal112948
Professors Emeriti:
Hans Bremer
@hxb068000
Lee A. Bulla
@bulla
Rockford Draper
@draper
Donald M. Gray
@dongray
Associate Professors Emeriti:
Gail A. M. Breen
@breen
Dennis L. Miller
@dmiller
Clinical Professor:
David Murchison
@dfm100020
Eberhard Voit
@eov230000
Professors of Instruction:
Mehmet Candas
@candas
Elizabeth Pickett
@eaw016100
Uma Srikanth
@ukrish
Associate Professors of Instruction:
Meenakshi Maitra
@mxm172731
Jing Pan
@jxp134330
Eva Sadat
@exs190014
Subha Sarcar
@sns064000
Michelle Wilson
@mxw084000
Zhuoru Wu
@zxw190014
Wen-Ho Yu
@why061000
Assistant Professors of Instruction:
Stephanie Boyd
@sdb074000
Andy Cheshire
@dal515348
Anne Davenport
@axd240011
Matthew Esparza
@dal998419
Amy Jo Gomez
@amh240001
Yi Huang
@yxh220019
Li Liu
@lliu
Simbarashe Mazambani
@sxm170074
Iti Mehta
@ixm121430
Ritu Mishra
@dal831670
Ramesh Padmanabhan
@rxp104120
Narges Salamat
@nxs158730
Research Associate Professor:
Ru-Hung Wang
@rxw012400
Lecturer:
Kathleen McRoy
@dal311023
Professor of Insruction Emeritus:
Scott Rippel
@rippel
Biological Sciences Faculty With Research Interests in Bioinformatics and Computational Biology: Faruck Morcos, Zhenyu Xuan, Hyuntae Yoo, and Michael Q. Zhang
Program Objective
The Master of Science program in Bioinformatics and Computational Biology is an interdisciplinary program offered jointly by the Departments of Mathematical Sciences and Biological Sciences, with the former serving as the administrative unit. By combining coursework from the disciplines of Biology, Computer Science, Mathematics, and Statistics, it caters to the growing demand of a new breed of scientists who have expertise in all these disciplines. In addition to coursework, the program also provides opportunities to gain practical experience by getting involved in research with faculty members.
A successful applicant to the program is expected to have a Bachelor's degree in Biology, Mathematics, Statistics, or in another science/engineering discipline, and must have completed at least one semester of Calculus. Additional coursework in one or more of the disciplines of Biology, Computer Science, Mathematics, and Statistics is desirable but is not required.
Admission Requirements
In addition to the University's
general admission requirements
, applicants are required to submit the following materials as part of their application file for holistic admission consideration:
Three letters of recommendation.
A statement of purpose/essay.
A resume or CV.
Deadline:
For first-round consideration for assistantships and scholarships, the application must be complete by December 15 for the upcoming Fall term and August 31 for the upcoming Spring term. Applications may be accepted for later rounds also, see
Graduate Admissions
for additional deadlines.
Degree Requirements
The University's general degree requirements are discussed on the
Graduate Policies and Procedures
page.
The MS program in Bioinformatics and Computational Biology requires completion of at least 36 semester credit hours. The program offers a choice between two tracks. Track 1 is designed for students with a general background in science/engineering, whereas Track 2 is designed for students with a strong background in biology. To build further expertise, both tracks offer a choice of three elective groups, namely, Computer Science oriented, Statistics oriented, and Biology oriented elective groups. Both also offer opportunities for research. Students are expected to choose a track and an elective group based on their backgrounds and interests in consultation with the Graduate Advisor for the program.
Track 1 (MS)
I. Core: 15 semester credit hours
BMEN 6374
Genes, Proteins and Cell Biology for Engineers
BIOL 5385
Computational Molecular Evolution
CS 5303
Computer Science I
MATH 5303
Advanced Calculus and Linear Algebra
STAT 5351
Probability and Statistics I (for Elective Group 2)
or
STAT 5353
Probability and Statistics for Data Science and Bioinformatics (for Elective Groups 1 and 3)
II. Elective Groups (Choose one elective group)
Elective Group 1 (Computer Science Oriented): 15 semester credit hours
CS 5343
Algorithm Analysis and Data Structures
MATH 6312
Combinatorics and Graph Theory
MATH 6341
Bioinformatics
or
BIOL 5376
Applied Bioinformatics
MATH 6346
Medical Image Analysis
AND one of the following:
CS 6307
Introduction to Big Data Management and Analytics for non CS-Majors
CS 6314
Web Programming Languages
CS 6360
Database Design
CS 6375
Machine Learning
Elective Group 2 (Statistics Oriented): 18 semester credit hours
STAT 5352
Probability and Statistics II
STAT 6337
Advanced Statistical Methods I
STAT 6338
Advanced Statistical Methods II
STAT 6340
Statistical and Machine Learning
MATH 6341
Bioinformatics
or
BIOL 5376
Applied Bioinformatics
MATH 6346
Medical Image Analysis
Elective Group 3 (Biology oriented): 15 semester credit hours
MATH 6341
Bioinformatics
or
BIOL 5376
Applied Bioinformatics
MATH 6345
Mathematical Methods in Medicine and Biology
MATH 6346
Medical Image Analysis
AND two of the following:
BIOL 5375
Genes to Genomes
BIOL 5381
Genomics
BIOL 6315
Epigenetics
BIOL 6373
Proteomics
BIOL 6385
Computational Biology
or
BMEN 6389
Computational Biology
or
MATH 6343
Computational Biology
III. Research or Elective(s) or a Combination Thereof
Elective Group 1: 6 semester credit hours
Elective Group 2: 3 semester credit hours
Elective Group 3: 6 semester credit hours
Track 2 (MS)
I. Core: 14 semester credit hours
BIOL 5410
Biochemistry
BIOL 5420
Molecular Biology
STAT 5351
Probability and Statistics I (for Elective Group 2)
or
STAT 5353
Probability and Statistics for Data Science and Bioinformatics (for Elective Groups 1 and 3)
MATH 5303
Advanced Calculus and Linear Algebra
II. Elective Groups (Choose one elective group)
Elective Group 1 (Computer Science oriented): 18 semester credit hours
CS 5303
Computer Science I
CS 5343
Algorithm Analysis and Data Structures
MATH 6312
Combinatorics and Graph Theory
MATH 6341
Bioinformatics
or
BIOL 5376
Applied Bioinformatics
MATH 6346
Medical Image Analysis
AND one of the following:
CS 6307
Introduction to Big Data Management and Analytics for non CS-Majors
CS 6314
Web Programming Languages
CS 6360
Database Design
CS 6375
Machine Learning
Elective Group 2 (Statistics oriented): 18 semester credit hours
STAT 5352
Probability and Statistics II
STAT 6337
Advanced Statistical Methods I
STAT 6338
Advanced Statistical Methods II
STAT 6340
Statistical and Machine Learning
MATH 6341
Bioinformatics
or
BIOL 5376
Applied Bioinformatics
MATH 6346
Medical Image Analysis
Elective Group 3 (Biology oriented): At least 18 semester credit hours
MATH 6341
Bioinformatics
or
BIOL 5376
Applied Bioinformatics
MATH 6346
Medical Image Analysis
MATH 6345
Mathematical Methods in Medicine and Biology
3 semester credit hour Elective Course
AND two of the following:
BIOL 5375
Genes to Genomes
BIOL 5381
Genomics
BIOL 6315
Epigenetics
BIOL 6373
Proteomics
BIOL 6385
Computational Biology
or
BMEN 6389
Computational Biology
or
MATH 6343
Computational Biology
BIOL 5385
Computational Molecular Evolution
BIOL 5312
Programming in the Biological Sciences for Graduate Students
III. Research or Elective(s) or a Combination Thereof
All Elective Groups: 4 semester credit hours
Other Requirements
For a PhD bound student in the Department of Biological Sciences,
BIOL 5440
Cell Biology and
BIOL 5460
Quantitative Biology (or an equivalent) are required. This requirement can be fulfilled by taking these courses as 'electives' in the Bioinformatics and Computational Biology program.
Electives must be approved by the Graduate Advisor of the program.
Substitutions for required courses may be made if approved by the Graduate Advisor of the program and the Head of the Mathematical Sciences Department.
A student may choose to write an MS thesis under the supervision of a faculty member. The thesis project can count for 3 to 6 semester credit hours of electives towards the required 36 hours, in accordance with University policies. The thesis must be approved by the Head of the Mathematical Sciences Department. Once the thesis project is completed, the student must successfully defend it before his/her thesis committee.
1. Students who have not taken the CS 5333 Discrete Structures prerequisite for CS 5343 Algorithm Analysis and Data Structures should consult with their Graduate Advisor from the Mathematical Sciences Department to determine eligibility.
Master of Science in Artificial Intelligence for Biomedical Sciences
30 semester credit hours minimum
Department Faculty
FACG> nsm-mathematics-ms
Professors:
Swati Biswas
@sxb125731
Min Chen
@mxc136030
Pankaj Choudhary
@pkc022000
Baris Coskunuzer
@bxc190014
Mieczyslaw Dabkowski
@mkd034000
Vladimir Dragovic
@vxd123630
Sam Efromovich
@sxe062000
Yulia Gel
@yxg142030
Wieslaw Krawcewicz
@wzk091000
Susan Minkoff
@sem120030
L. Felipe Pereira
@lfp140030
Dmitry Rachinskiy
@dxr124030
Viswanath Ramakrishna
@vish
Janos Turi
@turi
Associate Professors:
Maxim Arnold
@mxa149530
Yan Cao
@yxc069200
Liang Hong
@lxh019000
Oleg Makarenkov
@oxm130230
Tomoki Ohsawa
@txo140730
Anh Tran
@att140830
Assistant Professors:
Carlos Arreche
@cxa171230
Noirrit Chandra
@nxc220031
Ronan Conlon
@rxc200021
Rizwanur Khan
@rxk230072
Qiwei Li
@qxl190009
Stephen McKeown
@sxm190098
Chuan-Fa Tang
@cxt190011
Jiayi Wang
@jxw220029
Nathan Williams
@nxw170830
Nan Wu
@nxw220012
Yunan Wu
@yxw200032
Professors Emeriti:
Larry Ammann
@ammann
Ali Hooshyar
@ali
Patrick Odell
@pxo062000
John Van Ness
@ness
Clinical Professor:
Natalia Humphreys
@nah103020
Clinical Associate Professor:
Mohammad Akbar
@mma110020
Clinical Assistant Professor:
Wenyi Lu
@wxl153330
Professors of Instruction:
Anatoly Eydelzon
@axe031000
Manjula Foley
@mxf091000
Bentley Garrett
@btg032000
Yuly Koshevnik
@yxk055000
Associate Professors of Instruction:
Mohammad Ahsan
@mka120030
Kelly Aman
@kxa143530
Malgorzata Dabkowski
@mxd066000
Rabin Dahal
@rxd153030
Derege Mussa
@dxm146130
My Linh Nguyen
@mln018200
Jigarkumar Patel
@jsp061000
Julie Sutton
@jxs158030
Assistant Professors of Instruction:
Anani Komla Adabrah
@aaa130530
Iris Alvarado
@ila180000
Saikat Biswas
@sxb230137
Hui Ding
@hxd162130
Kemelli Estacio-Hiroms
@kxe160930
Huizhen Guo
@hxg190020
Shengjie Jiang
@sxj220069
Joselle Kehoe
@jxk061000
Runzhou Liu
@rxl210013
Neha Makhijani
@nxm165130
Irina Martynova
@ixm140930
Diarisoa Mihaja Rakotomalala
@dmr150130
Adrian Murza
@acm170730
Ajaya Paudel
@abp062000
Octavious Smiley
@oxs230011
Nasrin Sultana
@nxs190039
Che-Yu Wu
@cxw153530
Admission Requirements
In addition to the University's general admission requirements, applicants are required to submit the following materials as part of their application file for holistic admission consideration:
Three letters of recommendation.
A statement of purpose/essay.
A resume or CV.
Deadline:
For first-round consideration for assistantships and scholarships, the application must be complete by December 15 for the upcoming Fall term and August 31 for the upcoming Spring term. Applications may be accepted for later rounds also, see Graduate Admissions for additional deadlines.
Degree Requirements
The University's general degree requirements are discussed on the
Graduate Policies and Procedures
page.
Course Requirements
Core Courses (18 semester credit hours):
BIMS 5304
Introduction to Human Health Research
or
STAT 5304
Introduction to Human Health Research
STAT 5305
Informatics and Programming for Biomedical Sciences
MATH 5303
Advanced Calculus and Linear Algebra
14
STAT 6305
Biostatistics and Epidemiology
STAT 6306
Artificial Intelligence for Human Health with Lab
STAT 6342
Deep Learning
Elective Courses (12 semester credit hours):
Elective courses can be any relevant graduate level course with the approval of the Graduate Advisor. The students are expected to work with their Graduate Advisor to choose the elective courses based on their backgrounds and interests.
The approved electives include the following courses:
BIOL 5371
Biomedical Case Studies in Artificial Intelligence
or
BIMS 5371
Biomedical Case Studies in Artificial Intelligence
BIOL 5372
Artificial Intelligence Ethics in Scientific Publishing
or
BIMS 5372
Artificial Intelligence Ethics in Scientific Publishing
BIOL 6371
Biomedical Dataset Analysis with Artificial Intelligence
or
BIMS 6371
Biomedical Dataset Analysis with Artificial Intelligence
BIOL 6372
Human-Artificial Intelligence Interactions in Biology
or
BIMS 6372
Human-Artificial Intelligence Interactions in Biology
BIOL 5381
Genomics
BIOL 5385
Computational Molecular Evolution
BIOL 5460
Quantitative Biology
BIOL 6315
Epigenetics
BIOL 6373
Proteomics
BIOL 6385
MATH 6343
BMEN 6389
Computational Biology
BIOL 6V29
Topics in Molecular Biology
or
BIOL 6395
Quantitative Systems Pharmacology
BMEN 6328
Data Science in Digital Health
BMEN 6365
Biomedical Image Processing
BMEN 6368
Cancer Therapy: Design, Development and Imaging
BMEN 6382
Systems Biology
BMEN 6393
Neural Engineering Methods and Applications
BMEN 6394
Medical Imaging Techniques and Image Processing
BMEN 6395
Advanced Topics in Neuroscience for Engineers
BMEN 6396
CRISPR and Genome Editing
CHEM 5310
Introduction to Programming and Machine Learning for Chemistry
CHEM 5342
Nanomedicine Fundamentals and Applications
CHEM 6V39
Special Topics in Organic Chemistry (when the topic is Polymer Chemistry)
CHEM 6V39
Special Topics in Organic Chemistry (when the topic is Introduction to Biomaterials Science)
MATH 6335
Machine Learning and Control Theory
MATH 6346
Medical Image Analysis
PHYS 5V48
Topics in Physics (when the topic is Computational Biophysics)
PHYS 5V48
Topics in Physics (when the topic is Tissue and Biomedical Optics)
Some of the electives have prerequisites that are not part of the general program plan. These prerequisites will need to be completed before taking the electives.
Up to 12 SCH of faculty supervised research on topics relevant to the discipline can be used to satisfy the elective requirements of the degree.
14. Students with sufficient background in the subject, can replace MATH 5303 with an elective approved by the advisor.
Doctor of Philosophy in Mathematics
75 semester credit hours minimum beyond the baccalaureate degree
Department Faculty
FACG> nsm-mathematics-phd
Professors:
Swati Biswas
@sxb125731
Min Chen
@mxc136030
Pankaj Choudhary
@pkc022000
Baris Coskunuzer
@bxc190014
Mieczyslaw Dabkowski
@mkd034000
Vladimir Dragovic
@vxd123630
Sam Efromovich
@sxe062000
Yulia Gel
@yxg142030
Wieslaw Krawcewicz
@wzk091000
Susan Minkoff
@sem120030
L. Felipe Pereira
@lfp140030
Dmitry Rachinskiy
@dxr124030
Viswanath Ramakrishna
@vish
Janos Turi
@turi
Associate Professors:
Maxim Arnold
@mxa149530
Yan Cao
@yxc069200
Liang Hong
@lxh019000
Oleg Makarenkov
@oxm130230
Tomoki Ohsawa
@txo140730
Anh Tran
@att140830
Assistant Professors:
Carlos Arreche
@cxa171230
Noirrit Chandra
@nxc220031
Ronan Conlon
@rxc200021
Rizwanur Khan
@rxk230072
Qiwei Li
@qxl190009
Stephen McKeown
@sxm190098
Chuan-Fa Tang
@cxt190011
Jiayi Wang
@jxw220029
Nathan Williams
@nxw170830
Nan Wu
@nxw220012
Yunan Wu
@yxw200032
Professors Emeriti:
Larry Ammann
@ammann
Ali Hooshyar
@ali
Patrick Odell
@pxo062000
John Van Ness
@ness
Clinical Professor:
Natalia Humphreys
@nah103020
Clinical Associate Professor:
Mohammad Akbar
@mma110020
Clinical Assistant Professor:
Wenyi Lu
@wxl153330
Professors of Instruction:
Anatoly Eydelzon
@axe031000
Manjula Foley
@mxf091000
Bentley Garrett
@btg032000
Yuly Koshevnik
@yxk055000
Associate Professors of Instruction:
Mohammad Ahsan
@mka120030
Kelly Aman
@kxa143530
Malgorzata Dabkowski
@mxd066000
Rabin Dahal
@rxd153030
Derege Mussa
@dxm146130
My Linh Nguyen
@mln018200
Jigarkumar Patel
@jsp061000
Julie Sutton
@jxs158030
Assistant Professors of Instruction:
Anani Komla Adabrah
@aaa130530
Iris Alvarado
@ila180000
Saikat Biswas
@sxb230137
Hui Ding
@hxd162130
Kemelli Estacio-Hiroms
@kxe160930
Huizhen Guo
@hxg190020
Shengjie Jiang
@sxj220069
Joselle Kehoe
@jxk061000
Runzhou Liu
@rxl210013
Neha Makhijani
@nxm165130
Irina Martynova
@ixm140930
Diarisoa Mihaja Rakotomalala
@dmr150130
Adrian Murza
@acm170730
Ajaya Paudel
@abp062000
Octavious Smiley
@oxs230011
Nasrin Sultana
@nxs190039
Che-Yu Wu
@cxw153530
Admission Requirements
In addition to the University's
general admission requirements
, applicants are required to submit the following materials as part of their application file for holistic admission consideration:
Three letters of recommendation.
A statement of purpose/essay.
A resume or CV.
Deadline:
For first-round consideration for assistantships and scholarships, the application must be complete by December 15 for the upcoming Fall term and August 31 for the upcoming Spring term. Applications may be accepted for later rounds also, see
Graduate Admissions
for additional deadlines.
Degree Requirements
The University's general degree requirements are discussed on the
Graduate Policies and Procedures
page.
The student must arrange a course program with the guidance and approval of the Graduate Advisor for Mathematics. A minimum of 75 semester credit hours beyond the bachelor's degree is required.
The following five courses have to be taken by each student:
MATH 6301
Real Analysis
MATH 6302
Functional Analysis I
MATH 6303
Theory of Complex Functions I
MATH 6311
Abstract Algebra I
MATH 6315
Ordinary Differential Equations
Each student should take at least six courses from the following list:
MATH 6309
Differential Geometry
MATH 6310
Topology
MATH 6312
Combinatorics and Graph Theory
MATH 6313
Numerical Analysis
MATH 6316
Differential Equations
MATH 6318
Numerical Analysis of Differential Equations
MATH 6319
Principles and Techniques in Applied Mathematics I
MATH 6320
Principles and Techniques in Applied Mathematics II
MATH 6321
Optimization
MATH 6325
Nonlinear Analysis I
MATH 6340
Numerical Linear Algebra
MATH 6342
Scientific Computing
MATH 7313
Partial Differential Equations I
MATH 7319
Functional Analysis II
MATH 7361
Algebraic Geometry and Non-linear Equations
Electives and Dissertation
At least an additional four courses designed for the student's area of specialization are taken as electives in a degree plan designed by the student and the Graduate Advisor for Mathematics (or the student's PhD advisor). This plan is subject to approval by the Department Head. The student must pass a PhD Qualifying Examination and the oral examination in accordance with departmental policies in order to continue in the PhD program. Finally, a dissertation is required and must be approved by the graduate program.
There must be available a dissertation research advisor or group of dissertation advisors willing to supervise and guide the student. A dissertation Supervising Committee should be formed in accordance with the UT Dallas policy memorandum (
UTDPP1052
).
Doctor of Philosophy in Data Science and Statistics
75 semester credit hours minimum beyond the baccalaureate degree
Department Faculty
FACG> nsm-statistics-phd
Professors:
Swati Biswas
@sxb125731
Min Chen
@mxc136030
Pankaj Choudhary
@pkc022000
Baris Coskunuzer
@bxc190014
Mieczyslaw Dabkowski
@mkd034000
Vladimir Dragovic
@vxd123630
Sam Efromovich
@sxe062000
Yulia Gel
@yxg142030
Wieslaw Krawcewicz
@wzk091000
Susan Minkoff
@sem120030
L. Felipe Pereira
@lfp140030
Dmitry Rachinskiy
@dxr124030
Viswanath Ramakrishna
@vish
Janos Turi
@turi
Associate Professors:
Maxim Arnold
@mxa149530
Yan Cao
@yxc069200
Liang Hong
@lxh019000
Oleg Makarenkov
@oxm130230
Tomoki Ohsawa
@txo140730
Anh Tran
@att140830
Assistant Professors:
Carlos Arreche
@cxa171230
Noirrit Chandra
@nxc220031
Ronan Conlon
@rxc200021
Rizwanur Khan
@rxk230072
Qiwei Li
@qxl190009
Stephen McKeown
@sxm190098
Chuan-Fa Tang
@cxt190011
Jiayi Wang
@jxw220029
Nathan Williams
@nxw170830
Nan Wu
@nxw220012
Yunan Wu
@yxw200032
Professors Emeriti:
Larry Ammann
@ammann
Ali Hooshyar
@ali
Patrick Odell
@pxo062000
John Van Ness
@ness
Clinical Professor:
Natalia Humphreys
@nah103020
Clinical Associate Professor:
Mohammad Akbar
@mma110020
Clinical Assistant Professor:
Wenyi Lu
@wxl153330
Professors of Instruction:
Anatoly Eydelzon
@axe031000
Manjula Foley
@mxf091000
Bentley Garrett
@btg032000
Yuly Koshevnik
@yxk055000
Associate Professors of Instruction:
Mohammad Ahsan
@mka120030
Kelly Aman
@kxa143530
Malgorzata Dabkowski
@mxd066000
Rabin Dahal
@rxd153030
Derege Mussa
@dxm146130
My Linh Nguyen
@mln018200
Jigarkumar Patel
@jsp061000
Julie Sutton
@jxs158030
Assistant Professors of Instruction:
Anani Komla Adabrah
@aaa130530
Iris Alvarado
@ila180000
Saikat Biswas
@sxb230137
Hui Ding
@hxd162130
Kemelli Estacio-Hiroms
@kxe160930
Huizhen Guo
@hxg190020
Shengjie Jiang
@sxj220069
Joselle Kehoe
@jxk061000
Runzhou Liu
@rxl210013
Neha Makhijani
@nxm165130
Irina Martynova
@ixm140930
Diarisoa Mihaja Rakotomalala
@dmr150130
Adrian Murza
@acm170730
Ajaya Paudel
@abp062000
Octavious Smiley
@oxs230011
Nasrin Sultana
@nxs190039
Che-Yu Wu
@cxw153530
Admission Requirements
In addition to the University's
general admission requirements
, applicants are required to submit the following materials as part of their application file for holistic admission consideration:
Three letters of recommendation.
A statement of purpose/essay.
A resume or CV.
Deadline:
The university application deadlines apply with the exception that, for the upcoming Fall term, all application materials must be received by December 15 for first-round consideration of scholarships and fellowships. See
Graduate Admissions
for additional deadlines.
Degree Requirements
The University's general degree requirements are discussed on the
Graduate Policies and Procedures
page.
The student must arrange a course program with the guidance and approval of the Graduate Advisor for Data Science and Statistics. A minimum of 75 semester credit hours beyond the bachelor's degree is required.
The following seven courses have to be taken by each student:
STAT 6331
Statistical Inference I
STAT 6337
Advanced Statistical Methods I
STAT 6338
Advanced Statistical Methods II
STAT 6339
Linear Statistical Models
STAT 6340
Statistical and Machine Learning
STAT 6344
Probability Theory I
STAT 6342
Deep Learning
Each student should take at least three courses approved by the Graduate Advisor for Data Science and Statistics from the following list:
STAT 6332
Statistical Inference II
STAT 7330
Bayesian Data Analysis
STAT 7331
Multivariate Analysis
STAT 7334
Nonparametric and Robust Statistical Methods
STAT 7336
Nonparametric Curve Estimation
STAT 7338
Time Series Modeling and Filtering
STAT 7339
Advanced Regression Modeling
STAT 7340
Functional Data Analysis
STAT 7345
Advanced Probability and Stochastic Processes
MATH 6322
Mathematical Foundations of Data Science
Electives and Dissertation
An additional 15-21 semester credit hours designed for the student's area of specialization are taken as electives in a degree plan designed by the student and the Graduate Advisor for Data Science and Statistics (or the student's PhD advisor). This plan is subject to approval by the Department Head. The student must pass a PhD Qualifying Examination and the oral examination in accordance with departmental policies in order to continue in the PhD program. Finally, a dissertation is required and must be approved by the graduate program.
There must be available a dissertation research advisor or group of dissertation advisors willing to supervise and guide the student. A dissertation Supervising Committee should be formed in accordance with the UT Dallas policy memorandum (
UTDPP1052
).
Research
Within the Mathematical Sciences Department opportunities exist for research in a wide range of areas within the mathematical sciences. Some specific examples are given below. The opportunity to take coursework in several of the other University programs also allows the student to prepare for interdisciplinary research. Such coursework must be approved by the assigned Graduate Advisor.
Some of the broad research areas represented in Mathematics are as follows: Algebraic and Complex Geometry, Analysis and its Applications, Control Theory and Optimization, Dynamical Systems and Ordinary Differential Equations, Differential Geometry, Mathematical Physics, Mathematical Methods in Medicine and Biology, Geosciences, and Mechanics, Numerical Analysis and Scientific Computing, Partial Differential Equations, and Topology.
Some of the broad research areas represented in Data Science and Statistics are as follows: probability theory, stochastic processes, statistical inference, asymptotic theory, statistical methodology, time series analysis, Bayesian analysis, robust multivariate statistical methods, nonparametric methods, nonparametric curve estimation, sequential analysis, biostatistics, survival analysis, statistical genetics and genomics, and bioinformatics.
For a complete list of faculty and their areas of research, visit
math.utdallas.edu/people/faculty
Certificates
Graduate Certificate in Data Science
12 semester credit hours
The Department of Mathematical Sciences, in cooperation with the Department of Computer Science, offers a graduate certificate in Data Science.
Admission Requirements
Students must gain admission to a graduate program at UT Dallas and have the pre-requisites needed to take the certificate courses.
Certificate Requirements
Students must complete the following four courses with a GPA of 3.0 or better.
CS 6307
Introduction to Big Data Management and Analytics for non CS-Majors
CS 6375
Machine Learning
MATH 6312
Combinatorics and Graph Theory
STAT 6340
Statistical and Machine Learning
Graduate Certificate in Biomedical Artificial Intelligence
15 semester credit hours
The Graduate Certificate in Biomedical Artificial Intelligence offers a specialized curriculum designed to enhance expertise in AI applications within the biomedical field. This program provides essential skills in programming, biostatistics, and machine learning, preparing you for advanced roles in healthcare and biomedical research.
Admission Requirements
Two semesters of calculus, one semester of linear algebra, and one semester of statistics at the undergraduate level. Those with only one semester of calculus can take
MATH 5303
to fulfill the additional mathematics requirements.
Certificate Requirements
The certificate requires 15 semester credit hours (5 courses) and can be completed in three semesters.
BIMS 5304
Introduction to Human Health Research
or
STAT 5304
Introduction to Human Health Research
STAT 5305
Informatics and Programming for Biomedical Sciences
STAT 6305
Biostatistics and Epidemiology
STAT 6306
Artificial Intelligence for Human Health with Lab
STAT 6342
Deep Learning
Updated: 2025-11-04 12:22:28 v14.8bd3f4
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