Dr. Lladser's is a probabilist and computational biologist. The overarching vision of his research and teaching is a synergism of mathematics and science. Currently, his research is mostly focussed in developing methods for dimensionality reduction of symbolic datasets, as well as assessing contamination in discrete datasets.
APPM 3170  Discrete Applied Mathematics
Primary Instructor

Spring 2019
Introduces students to ideas and techniques from discrete mathematics that are widely used in science and engineering. Mathematical definitions and proofs are emphasized. Topics include formal logic notation, proof methods; set theory, relations; induction, wellordering; algorithms, growth of functions and complexity; integer congruences; basic and advanced counting techniques, recurrences and elementary graph theory. Other selected topics may also be covered.
APPM 3570  Applied Probability
Primary Instructor

Fall 2019 / Fall 2020
Studies axioms, counting formulas, conditional probability, independence, random variables, continuous and discrete distribution, expectation, joint distributions, moment generating functions, law of large numbers and the central limit theorem. Degree credit not granted for this course and ECEN 3810 or MATH 4510. Same as STAT 3100.
APPM 4560  Markov Processes, Queues, and Monte Carlo Simulations
Primary Instructor

Fall 2018
Brief review of conditional probability and expectation followed by a study of Markov chains, both discrete and continuous time, including Poisson point processes. Queuing theory, terminology and single queue systems are studied with some introduction to networks of queues. Uses Monte Carlo simulation of random variables throughout the semester to gain insight into the processes under study. Same as APPM 5560 and STAT 4100.
APPM 4565  Random Graphs
Primary Instructor

Spring 2021
Introduces mathematical techniques, including generating functions, the first and secondmoment method and Chernoff bounds to study the most fundamental properties of the ErdosRenyl model and other celebrated random graph models such as preferential attachment, fixed degree distribution, and stochastic block models. Same as APPM 5565.
APPM 4720  Open Topics in Applied Mathematics
Primary Instructor

Spring 2018 / Spring 2019
Provides a vehicle for the development and presentation of new topics that may be incorporated into the core courses in applied mathematics. Department enforced prerequisite: variable, depending on the topic, see instructor. May be repeated up to 15 total credit hours. Same as APPM 5720.
APPM 5560  Markov Processes, Queues, and Monte Carlo Simulations
Primary Instructor

Fall 2018
Brief review of conditional probability and expectation followed by a study of Markov chains, both discrete and continuous time, including Poisson point processes. Queuing theory, terminology and single queue systems are studied with some introduction to networks of queues. Uses Monte Carlo simulation of random variables throughout the semester to gain insight into the processes under study. Same as APPM 4560, STAT 4100 and STAT 5100.
APPM 5565  Random Graphs
Primary Instructor

Spring 2021
Introduces mathematical techniques, including generating functions, the first and secondmoment method and Chernoff bounds to study the most fundamental properties of the ErdosRenyl model and other celebrated random graph models such as preferential attachment, fixed degree distribution, and stochastic block models. Same as APPM 4565.
APPM 5720  Open Topics in Applied Mathematics
Primary Instructor

Spring 2018 / Spring 2019
Provides a vehicle for the development and presentation of new topics that may be incorporated into the core courses in applied mathematics. Department enforced prerequisite: variable, depending on the topic, see instructor. May be repeated up to 6 total credit hours. Same as APPM 4720.
CSCI 4950  Senior Thesis
Primary Instructor

Fall 2018 / Spring 2019
Provides an opportunity for senior computer science majors to conduct exploratory research in computer science. Department enforced restriction, successful completion of a minimum of 36 credit hours of Computer Science coursework and approved WRTG. May be repeated up to 8 total credit hours.
MATH 4540  Introduction to Time Series
Primary Instructor

Spring 2020
Studies basic properties, trendbased models, seasonal models, modeling and forecasting with ARIMA models, spectral analysis and frequency filtration. Same as MATH 5540 and STAT 4540 and STAT 5540.
MATH 5540  Introduction to Time Series
Primary Instructor

Spring 2020
Studies basic properties, trendbased models, seasonal models, modeling and forecasting with ARIMA models, spectral analysis and frequency filtration. Department enforced prerequisite: MATH 4520 or MATH 5520 or APPM 4520 or APPM 5520. Same as MATH 4540 and STAT 4540 and STAT 5540.
STAT 3100  Applied Probability
Primary Instructor

Fall 2019 / Fall 2020
Studies axioms, counting formulas, conditional probability, independence, random variables, continuous and discrete distribution, expectation, joint distributions, moment generating functions, law of large numbers and the central limit theorem. Degree credit not granted for this course and ECEN 3810 or MATH 4510. Same as APPM 3570.
STAT 4540  Introduction to Time Series
Primary Instructor

Spring 2020
Studies basic properties, trendbased models, seasonal models modeling and forecasting with ARIMA models, spectral analysis and frequency filtration. Same as STAT 5540 and MATH 4540 and MATH 5540.
STAT 5540  Introduction to Time Series
Primary Instructor

Spring 2020
Studies basic properties, trendbased models, seasonal models modeling and forecasting with ARIMA models, spectral analysis and frequency filtration. Department enforced prerequisite: APPM 5520 or MATH 5520. Same as STAT 4540 and MATH 4540 and MATH 5540.