Teaching
CSE 6010/CX 4010 Computational Problem Solving
Last taught: Fall 2023Description: This course is designed for students with limited background in
traditional computer science
to prepare for upper-division and graduate coursework
computational science and engineering or computer science.
Methods of evaluation include quizzes or exams
for more basic course material along with a series of practically-oriented
programming assignments.
Course Topics:
- Elementary data structures, such as arrays, lists, trees, graphs, hash tables, heaps
- Algorithms and their analysis
- Hardware, computer architecture, and operating systems and concurrency issues
- C programming and software development
- Software engineering (code design, debugging, testing, documentation)
CSE 8802-IPL InQuBATE Project Laboratory
Last taught: Spring 2023Description: The objective of this course is to act as a bridge between core curricula and thesis development in integrative and quantitative biosciences. Students in this course will work in groups to identify problem areas of interest, conduct an individual literature review, and develop an individual or collaborative research project proposal involving the analysis of high-dimensional biological datasets. Methods of evaluation include a literature review assignment, and research proposal assignment, and participation.
Course Topics:
- Writing a literature review
- Generating new research ideas
- Identifying potential areas for a new research contribution and translating to a specific proposal idea
- Writing a research proposal
- Communicating research project plans and progress in oral and written forms
PHL 6000: Responsible Conduct of Research
Last taught: Summer 2021Description: The primary aim of this course is to identify and discuss ethical issues that graduate students confront relating to their research. Topics addressed include those recognized by U.S. funding agencies and Georgia Tech as crucial to being a responsible researcher. Methods of evaluation include participation in discussion forums and an original paper on ethical issues in research.
Course Topics:
- Identifying key research ethics topics areas
- Foundational concepts in th realm of research ethics
- Strategies for handling ethical challenges that can arise in research
CS 7001 Introduction to Graduate Studies
Last taught: Fall 2020; co-taught with Annie AntónDescription: This course helps new Computer Science Ph.D. students become better prepared for their graduate-school careers. Responsible conduct of research training is included along with opportunities to get to know faculty and other students and tools to succeed as a Ph.D. student. Methods of evaluation includes a mixture of assignments, mini-projects, and class participation.
Course Topics:
- Research productivity skills
- How to read a research paper
- How to write a research paper
- How to review a research paper
- How to generate and evaluate research ideas
- How to manage citations
- How to prepare and present a good talk
- How to prepare and present a good poster
- Responsible conduct of research
CSE 8803 Biomedical Modeling (special topics)
Last taught: Spring 2019Description: This course is focused on modeling and simulation in the biomedical context with the goal of understanding how models are created, solved, and used. At the same time, developing skills in interpreting results obtained using biomedical models and articulating strengths and limitations of modeling approaches also is emphasized. Methods of evaluation includes a mixture of assignments, a project, and class participation.
Course Topics:
- Modeling cellular dynamics using differential equations: FitzHugh-Nagumo model and phase space, Hodgkin-Huxley and other neural models, cardiac models, other systems (e.g., pancreatic beta cells)
- Modeling tissue-level dynamics: cardiac fibers/tissue, networks of cells
- Discrete-time approaches to modeling dynamics: cellular automata, timed automata
- Subcellular modeling: cell signaling models, intracellular calcium
- Solving biomedical models: issues in numerical integration of differential equations, modeling anatomical structures
- Parameter estimation for biomedical models: Genetic algorithms, data assimilation, Bayesian approaches, uncertainty quantification, identifiability issues
- Applications of biomedical models: Cardiac arrhythmias, diabetes, epilepsy, circadian rhythms, cyber-physical systems, others