Computational Science and Modeling Program- CSMP
Executive Director: A. Gelb (CSMP and Mathematics)
Executive Committee: P. Chin (CSMP and Engineering), D. Lacy (CSMP and Government), C. Meyer (CSMP and Engineering), J. O’Malley (CSMP and Biomedical Data Science), Hélène Seroussi (CSMP and Engineering, R. Singal (CSMP and Business Administration)
Professors B. Chaboyer (CSMP and Physics & Astronomy), A. Chakrabarti ( CSMP and Computer Science), G. Cybenko (CSMP and Engineering), L. Debo (CSMP and Operations Management), E. Demidenko (CSMP and Biomedical Data Science), C. Eskey (CSMP and Radiology), D. Giannakis (CSMP and Mathematics), S. Guha (CSMP and Biomedical Data Science), J. Gui (CSMP and Biomedical Data Science), S. Hassanpour (CSMP and Biomedical Data Science), N. Jacobson (CSMP and Biomedical Data Science), K. Keller (CSMP and Engineering), J. Lipson (CSMP and Chemistry), K. Lynch (CSMP and Physics and Astronomy), T. MacKenzie (CSMP and Biomedical Data Science), D. Mierke (CSMP and Chemistry), M. Morlighem (CSMP and Earth Sciences), P. J. Mucha (CSMP and Mathematics), S. Pauls (CSMP and Mathematics), D. Perovich (CSMP and Engineering), C. Ramanathan (CSMP and Physics and Astronomy), L. Ray (CSMP and Engineering), D. Rockmore (CSMP, Mathematics and Computer Science), E. Santor Jr. (CSMP and Engineering), R. Sarpeshkar (CSMP and Engineering), S. Schnell (CSMP, Mathematics, Provost), R. Shumsky (CSMP and Operations Management), J. Smith (CSMP and Decision Science), T. Tosteson (CSMP and Biomedical Data Science), L. Viola (CSMP and Physics),
Associate Professors D. Chakrabarty (CSMP and Computer Science), J. Emond (CSMP and Biomedical Data Science), H. R. Frost (CSMP and Biomedical Data Science), F. Fu (CSMP and Mathematics), C. H. Pries (CSMP and Biological Sciences), W. Jarosz (CSMP and Computer Science), Y. Lee (CSMP and Mathematics), G. Luke (CSMP and Engineering), C. Nadell (CSMP and Biological Sciences), J. Paydarfar (CSMP and Surgery), D. Schultz (CSMP and Microbiology and Immunology), V. Vaze (CSMP and Engineering), S. Vosoughi (CSMP and Computer Science), J. Whitfield (CSMP and Physics), J. Zhao (CSMP and Engineering), O. Zhaxybayeva (CSMP and Biological Sciences)
Assistant Professors H. Chang (CSMP and Quantitative Social Science), C. Chen (CSMP and Engineering), B. Ferguson (CSMP and Engineering), M Fitzpatrick (CSMP and Engineering), N. Ju (CSMP and Mathematics), B. Keller (CSMP and Earth Sciences), E. Levien (CSMP and Mathematics), Y. Li (CSMP and Engineering), J. Mahlmann (CSMP and Physics and Astronomy), W. Marrero (CSMP and Engineering), P. Martinez Camblor (CSMP and Anesthesiology), B. Mazaheri (CSMP and Engineering), Y. Nakayama (CSMP and Engineering), A. Pediredla (CSMP and Computer Science), B. Plancher (CSMP and Computer Science), S. Robertson (CSMP and Epidemiology), P. Robustelli (CSMP and Chemistry), J. Siderius (CSMP and Business Administration), L. Song (CSMP and Biomedical Data Science), G. Wang (CSMP and Health Policy and Clinical Practice), Y. Yan (CSMP and Computer Science), Y. Yang (CSMP and Computer Science), S. Zhao (CSMP and Biomedical Data Science)
Computational science is concerned with all questions related to the computer-aided solution of problems from the modeling of natural, social, and engineering sciences. At a fundamental level, areas of computational science and modeling research include the study of dynamical systems, optimization, inverse problems and inference, statistical science, signal and image processing, network analysis, data mining, quantum computing, and machine learning. More generally, computational science and modeling are inherent to a wide variety of scientific disciplines, including the many areas of biology, computer science, climate studies, earth science, engineering, geology, and across the social science and business disciplines. These activities are occurring with increasing interconnectedness and generality across Dartmouth College, the professional schools (Geisel, Thayer, and Tuck) and Dartmouth Health. Dartmouth faculty engaged in computational science and modeling research regularly receive research funding from a number of research agencies, including the NSF (across the DMS, CISE and ENG directorates) as well as the NIH, DOD, and DOE.
Requirements for the Doctor’s Degree (Ph.D.)
The Dartmouth CSMP PhD program is interdisciplinary with intentional overlap between the three areas of scholarship: Natural & Physical Sciences, Operations Research & Decision Sciences, and Social & Medical Sciences. The infrastructure is created to enable more opportunities for collaboration and co-mentoring of PhD students. The infrastructure will also help facilitate institutional efforts in applying for various interdisciplinary federal funding programs. While all of the CSMP teaching faculty listed above have demonstrated research interests and expertise in at least one of the three areas, many have overlapping interests that continue to evolve and become more interdisciplinary, which is consistent with national and international research trends and critical to solving the next generation of problems.
Over the first two years students should complete two classroom courses in each of the three areas below. Students may in some cases choose different courses to fill these basic requirements upon advisory team consent. Students may also, with advisory team consent, replace up to one course per area with an alternative means of obtaining and demonstrating proficiency in the area. NOTE: some courses are offered every other year. *Not offered in 2026/2027.
(a) Foundations in computational methods. Students choose two courses from:
CHEM 101.6, COSC 236*; COSC 240*; COSC 179* (ENGS 106*); COSC 271*; EARS 113*; ENGS 96 (Fall 26); ENGS 100*; MATH 116 (Spring 27).
(b) Foundations in modeling. Students to choose two courses from: MATH 106 (Winter 27), MATH 136 (Fall 26); MATH 146 (Spring 27); PHYS 101 (Fall 26); PHYS 103*; PHYS 107*; ENGS 102*.
(c) Scientific applications of modeling. Students choose two courses, preferably from within the same general thematic thrust. For example, a student in the natural and
physical sciences thrust could pick two courses from CHEM 101.5 (Fall 26), EARS 107 (Winter 27); EARS 159*; PHYS 100 (Fall 2026); ENGS 200* (PHYS 110*); PHYS 116*; PHYS 118*.
Attendance in CSMP 150 (Faculty research seminar) is required in the fall of the first year. First year research rotations: Each student will engage in a different research rotation in each term (Fall, Winter, Spring) of their first year.
Alternative core courses (as agreed upon by the advisory committee) of the following alternatives may be used, but no more than 4 courses from each prefix over two years, (a) Foundations in computations methods, (b) Foundations in modeling, (c) Scientific applications of modeling:
ENGS 91: Numerical Methods in Computation (Fall 26) (a)
ENGS 92: Fourier Transforms and Complex Variables (Fall 26) (a)
ENGS 93: Statistical Methods in Engineering (Fall 26) (a)
ENGS 101 Principles of Reinforcement Learning (Fall 26) (c)
ENGS 104 Optimization Methods for Engineering Applications (Fall 26) (a)
ENGS 108 Applied Machine Learning (Fall 26) good for (c)
ENGS 109 (HdSL) High Dimensional Sensing and Learning (Spring 27) (c)
QBS 108 Machine Learning (Spring 27) (c)
QBS 119 Foundation of Biostatistics 1 (Fall 26) (c)
QBS 120 Foundations of Biostatistics 1 (Mathematical edition) (Fall 26) (c)
Elective Courses offered 26/27:
COSC 149 Algorithmic Foundations Seminar (Fall 26)
COSC 189 Topics in Applied Computer Science (Spring 27)
COSC 189.37 - Visual Computing Seminar (Fall 26, Winter 27)
COSC 189.40 - Distributed Computing: Algorithms and Verification (Fall 26)
COSC 232 Advanced Algorithms (Fall 26)
COSC 273 Computational Aspects of Digital Photography (Fall 26)
COSC 274 Machine Learning and Statistical Data Analysis (Fall 26, Spring 27)
COSC 278 Machine Learning and Statistical Data Analysis (Winter 27), Deep Learning (Spring 27)
COSC 287 - Rendering Algorithms (Winter 27)
ENGG 193: Statistical Methods in Engineering (Fall 26, Winter 27))
EARS 160 Earth System Modeling
PSYC 164 Computational Methods
PSYC 174 Computational Neuroscience: Brain Engineering
PSYC 178 Computational Foundations for Human and Systems Neuroscience
MATH 177 Methods of Statistical Learning for Big Data cross listed with QBS 177
QBS 124 Advanced Biomedical Data Analysis (Spring 27)
QBS 122 Modeling Complex Data (Spring 27)
QBS 145 Computational Immunology (Fall 26)
QBS 177 Methods of Statistical Learning for Big Data (Winter 27)
Moreover, current ECON and GOVT undergraduate courses may in some cases be used to satisfy core requirements as agreed upon by the advisory team.