PhD in Complex Systems and Data Science

• CSYS/CS 5060: Advanced Evolutionary Robotics Explores the automated design of autonomous machines using evolutionary algorithms. Covers relevant topics in evolutionary computation, artificial neural networks, robotics, simulation and xenobots. Students complete weekly programming assignments, formulate research a research hypothesis, and use their system to test that hypothesis. Credit not awarded for more than one of the following: CSYS 5060, CS 5060, CS 3060. Prerequisite: Graduate student. Cross-listed with: CS 5060. Not on the current schedule. View in catalogue
• CSYS/CS 5540: Advanced Machine Learning Provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (linear regression, logistic regression, neural networks, support vector machines, decision tree, ensemble models, random forest); unsupervised learning (clustering, dimensionality reduction, kernel methods); Also introduces deep learning such as convolutional neural networks and discusses recent applications. Credit not awarded for more than one of the following: CSYS 5540, CS 5540, CS 3540. Prerequisites: Knowledge of statistics as from STAT 2510, knowledge of linear algebra as from MATH 2522 or MATH 2544; Graduate student. Cross-listed with: CS 5540. Fall 2026 TTh 1:15-2:30p · Prof. S. Wshah View in schedule
• CSYS/MATH 5766: Chaos, Fractals, and Dynamical Systems Discrete and continuous dynamical systems, Julia sets, the Mandelbrot set, period doubling, renormalization, Henon map, phase plane analysis, and Lorenz equations. Credit not awarded for both CSYS 5766 and MATH 3766. Prerequisite: Graduate student or Instructor permission. Cross-listed with: MATH 5766. Not on the current schedule. View in catalogue
• CSYS/CS 5909: Natural Language Processing Provides a comprehensive, breadth-first introduction to natural language processing (NLP), an interdisciplinary field at the intersection of computer science, linguistics, and artificial intelligence. Students will explore both classical approaches and modern deep learning techniques, including large language models, through an integrative and hands-on learning experience. Project-based, emphasizing practical engagement with real-world textual datasets and the development of strong research practices for rigorous research projects. Prerequisite: Programming experience in Python. Cross-listed with: CS 5909. Fall 2026 TTh 10:05-11:20a · Prof. J. Lovato View in schedule
• CSYS/CS 6520: Evolutionary Computation Theory and practice of biologically-inspired search strategies including genetic algorithms, genetic programming, and evolution strategies. Applications include optimization, parameter estimation, and model identification. Significant project. Students from multiple disciplines encouraged. Pre/co-requisites: Familiarity with programming, probability, statistics. Cross-listed with: CS 6520. Fall 2026 TTh 8:30-9:45a · Prof. N. Cheney View in schedule
• CSYS/CS 6540: Deep Learning Introduction to Deep Learning algorithms and applications, including basic neural networks, convolutional neural networks, recurrent neural networks, deep unsupervised learning, generative adversarial networks and deep reinforcement learning. Includes a semester team-based project. Prerequisite: CS 3540. Cross-listed with: CS 6540. Not on the current schedule. View in catalogue
• CSYS/CS 6570: Social Computing Systems Social computing systems include online social networks, microblogging systems, social recommendation platforms, etc. Via a research-centric lens, explores the underlying nature/structure of social computing systems, studies various issues that plague them, and explores the methods by which researchers investigate such systems. Prerequisites: Proficiency in graph theory and computer programming (preferred language Python); knowledge of CS 3240 (or equivalent) and CS 2300 (or equivalent) assumed. Cross-listed with: CS 6570. Not on the current schedule. View in catalogue
• CSYS/CEE/STAT 7980: Applied Geostatistics Introduction to the theory of regionalized variables, geostatistics (kriging techniques), special topics in multivariate analysis. Applications to real data subject to spatial variation are emphasized. Prerequisites: Programming skills (such as in Python or Matlab) and content knowledge of multivariate statistics (such as STAT 5230) are assumed. Cross-listed with: CEE 7980, STAT 7980. Not on the current schedule. View in catalogue
• STAT 5510: Probability Theory Distributions of random variables and functions of random variables. Expectations, stochastic independence, sampling and limiting distributions (central limit theorems). Concepts of random number generation. Prerequisites: Graduate student or Instructor permission; content knowledge of MATH 2248, STAT 2510 assumed. Fall 2026 TTh 10:05-11:20a · Prof. J. Young View in schedule
• STAT 6300: Bayesian Statistics Introduction to Bayesian inference. Posterior inference, predictive distributions, prior distribution selection. MCMC algorithms. Hierarchical models. Model checking and selection. Use of computer software. Prerequisite: Content knowledge of STAT 5510 assumed. Fall 2026 TTh 1:15-2:30p · Prof. J. Young View in schedule
• CMPE/CS 5611: Machine Learning for Engineers Offers a rigorous introduction to Machine Learning and its application in the engineering sciences. Topics include supervised, unsupervised, and reinforcement learning. Introduces data-driven modeling techniques used in engineering and physical sciences. Applications include networks, power systems, mechanical dynamics, fluids, and device sensors. Provides introduction to computer vision for autonomy and robotics applications. Knowledge of calculus, linear algebra, probability is assumed. Credit not awarded for both CS 5540 and CS 5611. Cross-listed with: CS 5611. Fall 2026 MWF 9:40-10:30a · Prof. A. Pandey View in schedule
• CSCS 7010: Computational Hu&SocSc1 First of a two-semester sequence. Case studies cover computational approaches in humanities and social sciences as presented by faculty across the disciplines. Field trips to industry, non-profit and public sector sites highlight computational approaches in their day-to-day working contexts. Individual and paired assignments introduce ethical questions and epistemological debates. Fall 2026 W 12-3p · Prof. I. Nelson View in schedule
• CSCS 7020: Computational Hu&SocScII Second of a two-semester sequence. Case studies cover computational approaches in humanities and social sciences as presented by faculty across the disciplines. Field trips to industry, non-profit and public sector sites highlight computational approaches in their day-to-day working contexts. Individual and paired assignments introduce ethical questions and epistemological debates. Not on the current schedule. View in catalogue
• BIOL 6200/PBIO 6800: Ecological Genomics An exploration of the merger of ecology and genomics to address the genetic basis of adaptive variation in natural populations. Emphasis on integrating quantitative approaches and hands-on analysis of large genomic and ecological data sets. Pre/co-requisites: BCOR 2300, BCOR 2100, or STAT 1410; basic knowledge of statistics, probability, genetics, and evolution required; familiarity with programming in R or bash is recommended. Cross-listed with: PBIO 6800. Not on the current schedule. View in catalogue

This course list evolves and not all courses will be offered in any given semester. Other courses (including special topics) may be approved by the CSDS Curriculum Committee.