UMass Boston

Applicants must also meet general graduate admission requirements in addition to the following program-specific requirements:

• Applicants will be required to determine the track they are interested in pursuing (Data Analytics, Bioinformatics, or Computational Physics) and demonstrate adequate preparation at the undergraduate level in the form of relevant coursework and research experience.
• Given the multi-disciplinary nature of the Computational Science program, we expect that our applicants will be undergraduates with bachelor of science degrees in computer science, mathematics, biology, chemistry, physics, or graduates with master’s degrees in these areas.
• Applicants are required to take the general GRE test.
• The program requires three letters of recommendation submitted with the application.

Transfer Requirements

Students who transfer to the Computational Science program will receive transfer credit or advanced standing for their previous work if they can demonstrate course equivalency. Credits for previous work will be given at the discretion of the Program Committee. Transfer students will still be required to pass written and oral qualifying exams and fulfill all other candidacy requirements.

Deadlines: February 1 (priority deadline) or April 1 (final deadline) for fall; October 1 (priority deadline) or December 1 (final deadline) for spring

Application Fee: The nonrefundable application fee is $75. UMass Boston alumni and current students that plan to complete degree requirements prior to graduate enrollment can submit the application without paying the application fee.

Program Cost Information: Bursar's website

Core Courses (19 Credits)

• CS 624 - Analysis of Algorithms 3 Credit(s)
• MATH 625 - Numerical Analysis 4 Credit(s)
• MATH 626 - Numerical Linear Algebra 4 Credit(s)
• MATH 647 - Probability Models 4 Credit(s)
• MATH 648 - Computational Statistics 4 Credit(s)

Track Courses (9 to 12 Credits)

Complete three track courses; 2 from the primary track and 1 from the other two tracks.

Data Analytics Courses:

• CS 638 - Applied Machine Learning 3 Credit(s)
• CS 670 - Artificial Intelligence 3 Credit(s)
• CS 671 - Machine Learning 3 Credit(s)
• CS 672 - Neural Networks 3 Credit(s)
• CS 675 - Computer Vision 3 Credit(s)
• CS 724 - Topics in Algorithm Theory and Design 3 Credit(s)
• MATH 655 - An Introduction to Statistical Machine Learning 4 Credit(s)

Bioinformatics Courses:

• BIOL 572 - Molecular Biology (Lecture only) 3 Credit(s)
• BIOL 612 - Advanced Cell Biology 3 Credit(s)
• BIOL 617 - Biostatistics and Experimental Design Lab 1 Credit(s)
• BIOL 625 - Genomics and Biotechnology 3 Credit(s)
• BIOL 674 - Cell Signaling 3 Credit(s)
• BIOL 677 - Advanced Eukaryotic Genetics 3 Credit(s)
• BIOL 681 - Network Biology 3 Credit(s)
• CS 612 - Algorithms in Bioinformatics 3 Credit(s)
• CS 638 - Applied Machine Learning 3 Credit(s)
• CS 666 - Biomedical Signal and Image Processing 3 Credit(s)
• CS 675 - Computer Vision 3 Credit(s)
• BIOL 370 Molecular Biology (Undergraduate Courses - see Undergraduate Catalog for descriptions)

Computational Physics Courses:

• CHEM 601 - Thermodynamics & Kinetics 4 Credit(s)
• CHEM 602 - Quantum Mechanics & Spectroscopy 4 Credit(s)
• CHEM 608 - Data Analysis in Chemistry 4 Credit(s)
• PHYSIC 611 - Theory of Classical Mechanics and Fluid Mechanics 4 Credit(s)
• PHYSIC 613 - Quantum Mechanics, Atomic, and Molecular Physics 4 Credit(s)
• PHYSIC 614 - Thermodynamics and Statistical Mechanics 4 Credit(s)
• PHYSIC 616 - Mathematical Methods for Physicists 4 Credit(s)
• PHYSIC 638 - Quantum Measurement Theory 4 Credit(s)
• PHYSIC 662 - Computational Science 4 Credit(s)

Electives (9 to 12 Credits)

Complete 2 courses from the list of electives. Additional track courses from above may be applied toward this requirement with permission of the graduate program director.

• BIOL 612 - Advanced Cell Biology 3 Credit(s)
• BIOL 681 - Network Biology 3 Credit(s)
• CS 630 - Database Management Systems 3 Credit(s)
• CS 636 - Database Application Development 3 Credit(s)
• CS 651 - Compiler 3 Credit(s)
• CS 680 - Object-Oriented Design and Programming 3 Credit(s)
• CS 681 - Object-Oriented Software Development 3 Credit(s)
• CS 682 - Software Development Laboratory I 3 Credit(s)
• PHYSIC 645 - Cancer Biophysics 4 Credit(s)
• Undergraduate Courses (see Undergraduate Catalog for descriptions):
• ENGIN 442 - Internet of Things
• ENGIN 446 - Computer Architecture Design
• ENGIN 448 - Operating Systems

Research Seminars (2 Credits)

1 Seminar course

• INTR-D 601 - Integrative Biosciences Graduate Program Seminar 2 Credit(s)

Dissertation (26 Credits)

Complete 26 credits of dissertation research (INTR-D 899) by registering for a science dissertation course with your faculty advisor. Between 3-8 dissertation credits should be taken each semester until the requirement is satisfied.

For more information on curriculum, including course descriptions and degree requirements, visit the Academic Catalog.

The Computational Sciences PhD Program at UMass Boston coordinates and promotes multidisciplinary exchange of ideas among researchers and graduate students. The program involves faculty and graduate students in various departments at the College of Sciences and Mathematics. Departments currently participating in the program include: Biology, Chemistry, Computer Science, Engineering, Physics and Mathematics.

The program emphasizes interdisciplinary research, crossing the boundaries between physical, computational, and mathematical sciences to address challenging problems in science and engineering. At its core, the program builds a strong foundation on the mathematical and the computational background that are fundamental to data-oriented interdisciplinary research and then branches out to more specialized concentrations. The core of the program revolves around scientific computing, numerical analysis and optimization, and probability and statistics. The program provides specialized tracks in Data Analytics, Computational Physics, and Bioinformatics. Graduates from the program are expected to be well positioned for securing jobs in academia or industries seeking specialized expertise in data analytics and algorithm development.

Program Learning Outcomes:

1. Students will have developed a strong foundation in applied math, statistics, algorithms, and data sciences through core and track specific courses.
2. Students will have gained hands on experience in state-of-the-art technologies relevant to the student’s area of research (e.g., Genomics, Proteomics, High-performance parallel computing, Mass Spec, Quantum Computing) through both course work and research.
3. Students will have engaged in cross-disciplinary research in their respective tracks:
1. Data Analytics:
2. Bioinformatics
3. Computational Physics.
4. Students will have gained training as multi-lingual scientist, that can effectively develop and communicate complex ideas across disciplines.
5. At its core, the program promotes data-intensive research, and all students will have worked closely with real world data to develop scientific applications.
6. Students will have developed strong programming skills and the ability to work with high-performance computing and large-scale data sets.
7. Ultimately, students will have produced original, multi-disciplinarily, and data-oriented research published in high-impact journals.

Complete 62 to 67 credits from at least 10 courses including at least 34 credits of course work from five core courses, three track courses, two electives, 2 credits of seminar, and 26 credits of dissertation research.

Track: Students must choose a track in Data Analytics, Bioinformatics, or Computational Physics.
Doctoral candidacy: Pass a comprehensive examination after completion of 30 credits of course work. This examination consists of two parts: written and oral. Passing the written examination is a prerequisite to enter the oral examination.
Dissertation: Compose and defend a dissertation based on original research.

Minimum grade: No course with a grade below B may be applied toward program requirements.
Statute of limitations: Seven years.

Graduate Program Director Daniel Pomerleano
daniel.pomerleano [at] umb.edu
(617) 287-7486