Contact department for description.

Contact department for description.

This laboratory course introduces the statistical computing language R and provides hands-on experience using R to screen and adjust data, examine deterministic functions and probability distributions, conduct classic one- and two-sample tests, utilize bootstrapping and Monte Carlo randomization procedures, and conduct stochastic simulations for ecological modeling.

This course provides students with an understanding of basic statistical concepts critical to the proper use and understanding of statistics in ecology and conservation science and prepares students for subsequent ECO courses in ecological modeling.

Provides graduate students with a broad sampling of new and cutting-edge research related to environmental conservation to help foster critical thinking and provide a more expansive view of natural resources research. Seminars will be given by departmental faculty and faculty from other departments, both on campus and from other institutions. The seminars will be designed for both students who plan a research career and those who plan a more applied path.

Introduction to the research process in the natural resources sciences. Focus on research philosophy, concepts, and design, progressing from development of hypotheses, questions, and proposals, to grants and budgeting, and delivery of such research products as reports, publications, and presentations.

Description not available at this time

This course introduces the students to the design of embedded systems with a focus in unprecedented cyber-physical systems and internet of things applications. It takes a holistic approach to design end-to-end systems by addressing challenges at the hardware, software, and network layers of the stack. Special attention is paid to design trustworthy systems for applications running on commodity platforms and operating systems.

This course introduces the students to the design of embedded systems with a focus in unprecedented cyber-physical systems and internet of things applications. It takes a holistic approach to design end-to-end systems by addressing challenges at the hardware, software, and network layers of the stack. Special attention is paid to design trustworthy systems for applications running on commodity platforms and operating systems

The course will focus on advanced analytical tools for modeling and analysis of modern networks including: network optimization, queuing theory, game theory, mean field theory, and matching theory. Examples of resource allocation problems in ultra-reliable low-latency networks, virtual networks, multi-access edge networks, 5G/6G networks will be discussed by using these tools.