School of Natural Sciences and Mathematics
Department of 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.
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
Professors: Swati Biswas, Min Chen, Pankaj Choudhary, Baris Coskunuzer, Mieczyslaw Dabkowski, Vladimir Dragovic, Sam Efromovich, Yulia Gel, Wieslaw Krawcewicz, Susan Minkoff, L. Felipe Pereira, Dmitry Rachinskiy, Viswanath Ramakrishna, Janos Turi, John Zweck
Associate Professors: Maxim Arnold, Yan Cao, Liang Hong, Oleg Makarenkov, Tomoki Ohsawa, Anh Tran
Assistant Professors: Carlos Arreche, Noirrit Chandra, Ronan Conlon, Rizwanur Khan, Qiwei Li, Stephen McKeown, Chuan-Fa Tang, Jiayi Wang, Nathan Williams, Nan Wu, Yunan Wu
Professors Emeriti: Larry Ammann, Ali Hooshyar, Patrick Odell, John Van Ness
Clinical Professor: Natalia Humphreys
Clinical Associate Professor: Mohammad Akbar
Clinical Assistant Professor: Wenyi Lu
Professors of Instruction: Anatoly Eydelzon, Manjula Foley, Bentley Garrett, Yuly Koshevnik
Associate Professors of Instruction: Mohammad Ahsan, Kelly Aman, Malgorzata Dabkowski, Rabin Dahal, Derege Mussa, My Linh Nguyen, Jigarkumar Patel, Julie Sutton
Assistant Professors of Instruction: Anani Komla Adabrah, Iris Alvarado, Saikat Biswas, Hui Ding, Adannah Duruoha, Kemelli Estacio-Hiroms, Huizhen Guo, Shengjie Jiang, Joselle Kehoe, Runzhou Liu, Neha Makhijani, Irina Martynova, Diarisoa Mihaja Rakotomalala, Adrian Murza, Ajaya Paudel, Octavious Smiley, Nasrin Sultana, Che-Yu Wu
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 II1
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
FIN 6381 Introductory Mathematical Finance
or 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 Structures2
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
Professors: Swati Biswas, Min Chen, Pankaj Choudhary, Baris Coskunuzer, Mieczyslaw Dabkowski, Vladimir Dragovic, Sam Efromovich, Yulia Gel, Wieslaw Krawcewicz, Susan Minkoff, L. Felipe Pereira, Dmitry Rachinskiy, Viswanath Ramakrishna, Janos Turi, John Zweck
Associate Professors: Maxim Arnold, Yan Cao, Liang Hong, Oleg Makarenkov, Tomoki Ohsawa, Anh Tran
Assistant Professors: Carlos Arreche, Noirrit Chandra, Ronan Conlon, Rizwanur Khan, Qiwei Li, Stephen McKeown, Chuan-Fa Tang, Jiayi Wang, Nathan Williams, Nan Wu, Yunan Wu
Professors Emeriti: Larry Ammann, Ali Hooshyar, Patrick Odell, John Van Ness
Clinical Professor: Natalia Humphreys
Clinical Associate Professor: Mohammad Akbar
Clinical Assistant Professor: Wenyi Lu
Professors of Instruction: Anatoly Eydelzon, Manjula Foley, Bentley Garrett, Yuly Koshevnik
Associate Professors of Instruction: Mohammad Ahsan, Kelly Aman, Malgorzata Dabkowski, Rabin Dahal, Derege Mussa, My Linh Nguyen, Jigarkumar Patel, Julie Sutton
Assistant Professors of Instruction: Anani Komla Adabrah, Iris Alvarado, Saikat Biswas, Hui Ding, Adannah Duruoha, Kemelli Estacio-Hiroms, Huizhen Guo, Shengjie Jiang, Joselle Kehoe, Runzhou Liu, Neha Makhijani, Irina Martynova, Diarisoa Mihaja Rakotomalala, Adrian Murza, Ajaya Paudel, Octavious Smiley, Nasrin Sultana, Che-Yu Wu
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.
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 5343 Algorithm Analysis and Data Structures1, 2
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 I1
STAT 5352 Probability and Statistics II1
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 Algebra1
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
Professors: Swati Biswas, Min Chen, Pankaj Choudhary, Baris Coskunuzer, Mieczyslaw Dabkowski, Vladimir Dragovic, Sam Efromovich, Yulia Gel, Wieslaw Krawcewicz, Susan Minkoff, L. Felipe Pereira, Dmitry Rachinskiy, Viswanath Ramakrishna, Janos Turi, John Zweck
Associate Professors: Maxim Arnold, Yan Cao, Liang Hong, Oleg Makarenkov, Tomoki Ohsawa, Anh Tran
Assistant Professors: Carlos Arreche, Noirrit Chandra, Ronan Conlon, Rizwanur Khan, Qiwei Li, Stephen McKeown, Chuan-Fa Tang, Jiayi Wang, Nathan Williams, Nan Wu, Yunan Wu
Professors Emeriti: Larry Ammann, Ali Hooshyar, Patrick Odell, John Van Ness
Clinical Professor: Natalia Humphreys
Clinical Associate Professor: Mohammad Akbar
Clinical Assistant Professor: Wenyi Lu
Professors of Instruction: Anatoly Eydelzon, Manjula Foley, Bentley Garrett, Yuly Koshevnik
Associate Professors of Instruction: Mohammad Ahsan, Kelly Aman, Malgorzata Dabkowski, Rabin Dahal, Derege Mussa, My Linh Nguyen, Jigarkumar Patel, Julie Sutton
Assistant Professors of Instruction: Anani Komla Adabrah, Iris Alvarado, Saikat Biswas, Hui Ding, Adannah Duruoha, Kemelli Estacio-Hiroms, Huizhen Guo, Shengjie Jiang, Joselle Kehoe, Runzhou Liu, Neha Makhijani, Irina Martynova, Diarisoa Mihaja Rakotomalala, Adrian Murza, Ajaya Paudel, Octavious Smiley, Nasrin Sultana, Che-Yu Wu
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.
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 I3
STAT 5352 Probability and Statistics II4
ACTS 6301 Principles of Long Term Actuarial Mathematics I5
ACTS 6303 Principles of Long Term Actuarial Mathematics II5
ACTS 6304 Principles of Short Term Actuarial Mathematics I6
ACTS 6305 Principles of Short Term Actuarial Mathematics II6
ACTS 6307 Advanced Statistics for Risk Modeling7
ACTS 6310 Advanced Predictive Analytics8
Prescribed Elective Courses: 12 semester credit hours
For the prescribed elective courses select four courses from the following:
ACTS 6302 Investment and Financial Markets9
ACTS 6306 Theory of Credibility6
ACTS 6308 Actuarial Financial Mathematics
ACCT 6301 Financial Accounting10
ACCT 6305 Accounting for Managers10
FIN 6301 Financial Management10
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 Economics11
OPRE 6301 Statistics and Data Analysis12
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 I13
STAT 6338 Advanced Statistical Methods II
STAT 6347 Applied Time Series Analysis13
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
Professors: Swati Biswas, Min Chen, Pankaj Choudhary, Baris Coskunuzer, Mieczyslaw Dabkowski, Vladimir Dragovic, Sam Efromovich, Yulia Gel, Wieslaw Krawcewicz, Susan Minkoff, L. Felipe Pereira, Dmitry Rachinskiy, Viswanath Ramakrishna, Janos Turi, John Zweck
Associate Professors: Maxim Arnold, Yan Cao, Liang Hong, Oleg Makarenkov, Tomoki Ohsawa, Anh Tran
Assistant Professors: Carlos Arreche, Noirrit Chandra, Ronan Conlon, Rizwanur Khan, Qiwei Li, Stephen McKeown, Chuan-Fa Tang, Jiayi Wang, Nathan Williams, Nan Wu, Yunan Wu
Professors Emeriti: Larry Ammann, Ali Hooshyar, Patrick Odell, John Van Ness
Clinical Professor: Natalia Humphreys
Clinical Associate Professor: Mohammad Akbar
Clinical Assistant Professor: Wenyi Lu
Professors of Instruction: Anatoly Eydelzon, Manjula Foley, Bentley Garrett, Yuly Koshevnik
Associate Professors of Instruction: Mohammad Ahsan, Kelly Aman, Malgorzata Dabkowski, Rabin Dahal, Derege Mussa, My Linh Nguyen, Jigarkumar Patel, Julie Sutton
Assistant Professors of Instruction: Anani Komla Adabrah, Iris Alvarado, Saikat Biswas, Hui Ding, Adannah Duruoha, Kemelli Estacio-Hiroms, Huizhen Guo, Shengjie Jiang, Joselle Kehoe, Runzhou Liu, Neha Makhijani, Irina Martynova, Diarisoa Mihaja Rakotomalala, Adrian Murza, Ajaya Paudel, Octavious Smiley, Nasrin Sultana, Che-Yu Wu
Mathematics Faculty With Research Interests in Bioinformatics and Computational Biology: Swati Biswas, Yan Cao, and Min Chen
Biology Faculty
Professors: Rockford K. Draper, Juan E. González, Kelli Palmer, Lawrence J. Reitzer, Stephen Spiro, Li Zhang, Michael Qiwei Zhang
Associate Professors: Heng Du, Tae Hoon Kim, Faruck Morcos, Duane D. Winkler, Zhenyu Xuan
Assistant Professors: Nicole De Nisco, Nikki Delk, Nicholas Dillon, Purna Joshi, Darshan Sapkota
Professors Emeriti: Lee A. Bulla, Donald M. Gray
Associate Professors Emeriti: Gail A. M. Breen, Dennis L. Miller
Clinical Professor: David Murchison
Research Assistant Professors: Li Liu, Ru-Hung Wang
Professors of Instruction: Scott A. Rippel, Uma Srikanth
Associate Professors of Instruction: Wen-Ju Lin, Elizabeth Pickett, Ilya Sapozhnikov
Assistant Professors of Instruction: Ida Klang, Meenakshi Maitra, Caitlin Maynard, Iti Mehta, Ramesh Padmanabhan, Jing Pan, Ruben D. Ramirez, Eva Sadat, Subha Sarcar, Michelle Wilson, Zhuoru Wu
Senior Lecturer: Wen-Ho Yu
UT Dallas Affiliated Faculty: Leonidas Bleris, Sheena D'Arcy, Stephen D. Levene
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.
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 Structures1
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 Structures1
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.
Doctor of Philosophy in Mathematics
75 semester credit hours minimum beyond the baccalaureate degree
Department Faculty
Professors: Swati Biswas, Min Chen, Pankaj Choudhary, Baris Coskunuzer, Mieczyslaw Dabkowski, Vladimir Dragovic, Sam Efromovich, Yulia Gel, Wieslaw Krawcewicz, Susan Minkoff, L. Felipe Pereira, Dmitry Rachinskiy, Viswanath Ramakrishna, Janos Turi, John Zweck
Associate Professors: Maxim Arnold, Yan Cao, Liang Hong, Oleg Makarenkov, Tomoki Ohsawa, Anh Tran
Assistant Professors: Carlos Arreche, Noirrit Chandra, Ronan Conlon, Rizwanur Khan, Qiwei Li, Stephen McKeown, Chuan-Fa Tang, Jiayi Wang, Nathan Williams, Nan Wu, Yunan Wu
Professors Emeriti: Larry Ammann, Ali Hooshyar, Patrick Odell, John Van Ness
Clinical Professor: Natalia Humphreys
Clinical Associate Professor: Mohammad Akbar
Clinical Assistant Professor: Wenyi Lu
Professors of Instruction: Anatoly Eydelzon, Manjula Foley, Bentley Garrett, Yuly Koshevnik
Associate Professors of Instruction: Mohammad Ahsan, Kelly Aman, Malgorzata Dabkowski, Rabin Dahal, Derege Mussa, My Linh Nguyen, Jigarkumar Patel, Julie Sutton
Assistant Professors of Instruction: Anani Komla Adabrah, Iris Alvarado, Saikat Biswas, Hui Ding, Adannah Duruoha, Kemelli Estacio-Hiroms, Huizhen Guo, Shengjie Jiang, Joselle Kehoe, Runzhou Liu, Neha Makhijani, Irina Martynova, Diarisoa Mihaja Rakotomalala, Adrian Murza, Ajaya Paudel, Octavious Smiley, Nasrin Sultana, Che-Yu Wu
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
Professors: Swati Biswas, Min Chen, Pankaj Choudhary, Baris Coskunuzer, Mieczyslaw Dabkowski, Vladimir Dragovic, Sam Efromovich, Yulia Gel, Wieslaw Krawcewicz, Susan Minkoff, L. Felipe Pereira, Dmitry Rachinskiy, Viswanath Ramakrishna, Janos Turi, John Zweck
Associate Professors: Maxim Arnold, Yan Cao, Liang Hong, Oleg Makarenkov, Tomoki Ohsawa, Anh Tran
Assistant Professors: Carlos Arreche, Noirrit Chandra, Ronan Conlon, Rizwanur Khan, Qiwei Li, Stephen McKeown, Chuan-Fa Tang, Jiayi Wang, Nathan Williams, Nan Wu, Yunan Wu
Professors Emeriti: Larry Ammann, Ali Hooshyar, Patrick Odell, John Van Ness
Clinical Professor: Natalia Humphreys
Clinical Associate Professor: Mohammad Akbar
Clinical Assistant Professor: Wenyi Lu
Professors of Instruction: Anatoly Eydelzon, Manjula Foley, Bentley Garrett, Yuly Koshevnik
Associate Professors of Instruction: Mohammad Ahsan, Kelly Aman, Malgorzata Dabkowski, Rabin Dahal, Derege Mussa, My Linh Nguyen, Jigarkumar Patel, Julie Sutton
Assistant Professors of Instruction: Anani Komla Adabrah, Iris Alvarado, Saikat Biswas, Hui Ding, Adannah Duruoha, Kemelli Estacio-Hiroms, Huizhen Guo, Shengjie Jiang, Joselle Kehoe, Runzhou Liu, Neha Makhijani, Irina Martynova, Diarisoa Mihaja Rakotomalala, Adrian Murza, Ajaya Paudel, Octavious Smiley, Nasrin Sultana, Che-Yu Wu
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