UCLA NRT MENTOR Program - Research Layers and Cores

Here are suggested courses for each of the research layers and cores. Students can suggest additional or alternative courses in line with the program goals deepending on course offerings in various departments. This list will be updated on a quarterly basis.

Research Layer 1: Genomics and Genetics.

  • CS: CS CM221 Introduction to bioinformatics;
  • CS 224 Com-putational genetics;
  • CS 225 Computational methods in genomics;
  • CS 229 Computational biology.
  • Statistics: Stats M254 Statistical methods in computational biology. Human Genetics:
  • HG236A Advanced human genetics A: Molecular aspects;
  • HG236B Advanced human genetics B: Statistical aspects;
  • HG M278 Statistics of DNA Microarray.
  • Psychology: Psych 205G Behavior genetics.

    Research Layer 2: Brain Imaging and Multi-modal prediction.

  • Statistics: Stats 233 Statistical methods in biomedical imaging.
  • Psychology: Psych 204B Theories of learning;
  • Psych 205B Human neurophysiolo-gy;
  • Psych 205L Cognitive neuroscience;
  • Psych M213 Neuroimaging and brain mapping;
  • M219 Principles and applications of magnetic resonance imaging;
  • M248 Introduction to biological imaging;
  • Psych 265 Computational methods for neuroimaging;
  • M424 Functional MRI journal club.

    Research Layer 3: Mobile Sensing and Individual Behaviors.

  • Math 266 (A-E) Applied differential equations;
  • Math 269 (A-E) Numerical Analysis;
  • Math 238AB Dynamical systems.
  • Geography: Geog M205 Spatial statistics;
  • Geog 208 Geographic data visualization and analysis.
  • Psychology: Psych 259 Quantitative methods in cognitive psychology.
  • Sociology: 213B Applied event history analysis.
  • Neuroscience: NS240 Phenotypic measurement of complex traits.

    Research Layer 4: Social Networks.

  • Math 276 Topics in network science;
  • Stats 218 Statistical Analysis of Networks;
  • CS M276A Pattern recognition and machine learning;
  • ECE: 232E Graphs and network flows.
  • Sociology: SOC 208A, 208B. Social network methods;
  • Communication: Communication 156 Social networking;
  • Statistics: Stats M231 Pattern recognition and machine learning;
  • Stats 170 Introduction to time-series analysis;
  • Stats 234 Statistics and information theory.

    Core Area A: Mathematical Modeling and Network Analysis.

  • CS: CS M296A Advanced modeling methodology for dynamic biomedical systems;
  • Math: Math 209B Crytographic Protocols;
  • Math 270A Techniques of Scientific Computing; Math 270B/C Computational Linear Algebra;
  • Math 273 Optimization and Calculus of Variations;
  • Sociology: Soc M213A Introduction to demographic methods;
  • Soc 213C Population Models and Dynamics.
  • Political Science: PoliSci 204A,B,C Game Theory in Politics I, II, III;
  • Economics: Econ 213B General equilibrium and game theory;
  • Econ 412 Fundamentals of big data.

    Core Area B: Scalable Machine Learning and Big Data Analytics.

  • CS: CS 239 Data science in software engineering;
  • CS 249 Big data analytics;
  • CS 240A Databases and knowledge bases;
  • CS 240B Data stream management systems and data mining applications;
  • ECE: ECE 239AS Neural networks \& deep learning;
  • ECE 231A Information theory: channel and source coding.
  • Math: Math 285J Seminar in machine learning.
  • Statistics: Stats 236 Introduction to Bayesian statistics;
  • Stats 165 Statistical methods and data mining.
  • Economics: Econ 413 Data analytics and big data.

    Core Area C: Biomedical Applications and Social Outcomes.

  • Bioengineering: BE M227 Medical information infrastructure;
  • M217 Biomedical imaging;
  • Anthropology: ANTH 229 Current problems in biological anthropology;
  • Sociology: SOC 212C Study design and other issues in quantitative data analysis; Soc 234 Sociology of development;
  • Political Science: PoliSci 200C Causal inference for social science; PoliSci 200E Experimental design for social science; PoliSci M208E Bayesian Econometrics.

    Technical Communication.

  • CS: CS495 Teaching assistant seminar: how to explain things;
  • Math: Math 495: Teaching college mathematics;
  • ECE: ECE375 Teaching apprentice practicum.


  • Ethics in Multidisciplinary Data Science and Engineering. Engineering 183 Engineering and society;
  • Psychology: Psych 267 Neuroethics;
  • NS207 Integrity of scientific investigation: education, research, and career implications;
  • MIMG C234 Ethics and accountability in biomedical research;
  • C250 Research integrity in cellular biology, molecular biology, and biochemistry research.