Publications in VIVO

Map of Science

Lladser, Manuel E.

Associate Professor

Positions

Associate Professor, Applied Mathematics
Assoc Faculty Director, Applied Mathematics
Associate Professor, Computer Science

Research Areas

Research

research overview

Dr. Lladser's research expertise spans applied mathematics and probability, with an emphasis on discrete probability and discrete mathematics. The overarching vision of his research and teaching is a synergism of mathematics and science. He is particularly drawn to problems arising in computational and molecular biology, mathematical data science, and computer science.

keywords

algorithmic probability, bioinformatics, computational biology, computational probability, discrete mathematics, discrete probability, mathematical biology, mathematical machine learning

Publications

selected publications

book
- Variables aleatorias y simulación estocástica. Ed. 10. 2011
- Algorithmic Probability and Combinatorics 2010
chapter
- MATHEMATICAL APPROACHES FOR DESCRIBING MICROBIAL POPULATIONS: PRACTICE AND THEORY FOR EXTRAPOLATION OF RICH ENVIRONMENTS. 85-103. 2013
- Toward the asymptotic count of bi-modular hidden patterns under probabilistic dynamical sources: a case study. 425-451. 2012
- Occupancy distributions in Markov chains via Doeblin’s ergodicity coefficient. 79-92. 2010
- Prediction of unseen proportions in urn models with restricted sampling. 85-91. 2009
- Uniqueness of polynomial canonical representations. 463-469. 2007
conference proceeding
- Sparsification of Phylogenetic Covariance Matrices of k-Regular Tree. Leibniz International Proceedings in Informatics. 4:1-4:17. 2024
- Hidden independence in unstructured probabilistic models. 23:1-23:13. 2020
- Metric-space Positioning Systems (MPS) for Machine Learning. 479-479. 2016
- FStitch. 174-183. 2014
- Semi-supervised Learning of a Markovian Metric. 466-471. 2008
journal article
- Interpretable metric learning in comparative metagenomics: The adaptive Haar-like distance. PLoS Computational Biology. 2024
- Getting the Lay of the Land in Discrete Space: A Survey of Metric Dimension and Its Applications. SIAM Review. 919-962. 2023
- Sparsification of large ultrametric matrices: insights into the microbial Tree of Life. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2023
- On latent idealized models in symbolic datasets: unveiling signals in noisy sequencing data. Journal of Mathematical Biology. 2023
- Levenshtein graphs: Resolvability, automorphisms & determining sets. Discrete Mathematics. 2023

Teaching

courses taught

APPM 3170 - Discrete Applied Mathematics
Primary Instructor - Spring 2019 / Spring 2023
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, well-ordering; 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 / Fall 2021 / Spring 2023
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 / Fall 2023 / Fall 2024
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 / Fall 2022
Introduces mathematical techniques, including generating functions, the first- and second-moment method and Chernoff bounds to study the most fundamental properties of the Erdos-Renyl 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 / Fall 2024
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 / Fall 2023 / Fall 2024
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 / Fall 2022
Introduces mathematical techniques, including generating functions, the first- and second-moment method and Chernoff bounds to study the most fundamental properties of the Erdos-Renyl 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 / Fall 2024
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.

APPM 6950 - Master's Thesis
Primary Instructor - Fall 2022 / Fall 2023 / Spring 2024 / Fall 2024
May be repeated up to 6 total credit hours.

APPM 8000 - Colloquium in Applied Mathematics
Primary Instructor - Fall 2022
Introduces graduate students to the major research foci of the Department of Applied Mathematics.

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 as an option for the capstone requirement. Department enforced prerequisites: 35 hours of Computer Science coursework including Foundation courses, Upper-Division writing, CS GPA 3.0. Department consent required, contact academic advisor for details. May be repeated up to 8 total credit hours.

MATH 4520 - Introduction to Mathematical Statistics
Primary Instructor - Fall 2023
Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Same as MATH 5520 and STAT 4520 and STAT 5520.

MATH 4540 - Introduction to Time Series
Primary Instructor - Spring 2020 / Spring 2024
Studies basic properties, trend-based 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 5520 - Introduction to Mathematical Statistics
Primary Instructor - Fall 2023
Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Department enforced prerequisite: one semester calculus-based probability course, such as MATH 4510 or APPM 3570. Recommended prerequisite: previous coursework equivalent to APPM 3570 or STAT 3100 or MATH 4510; minimum grade of C- for all. Same as STAT 4520 and MATH 4520 and STAT 5520.

MATH 5540 - Introduction to Time Series
Primary Instructor - Spring 2020
Studies basic properties, trend-based 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 / Fall 2021 / Spring 2023
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 4100 - Markov Processes, Queues, and Monte Carlo Simulations
Primary Instructor - Fall 2023 / Fall 2024
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 and APPM 5560.

STAT 4520 - Introduction to Mathematical Statistics
Primary Instructor - Fall 2023
Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Same as STAT 5520 and MATH 4520 and MATH 5520.

STAT 4540 - Introduction to Time Series
Primary Instructor - Spring 2020 / Spring 2024
Studies basic properties, trend-based 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 4720 - Open Topics in Statistics and Data Science
Primary Instructor - Fall 2024
Provides a vehicle for the development and presentation of new topics that may be incorporated into the core courses in statistics and data science. Department enforced prerequisite: variable, depending on the topic, see instructor. May be repeated up to 15 total credit hours. Same as STAT 5720.

STAT 5100 - Markov Processes, Queues, and Monte Carlo Simulations
Primary Instructor - Fall 2023
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. Recommended prerequisite: previous coursework equivalent to that of APPM 3570 or STAT 3100 or MATH 4510, with a minimum grade of C-. Same as APPM 4560, STAT 4100 and APPM 5560.

STAT 5520 - Introduction to Mathematical Statistics
Primary Instructor - Fall 2023
Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Department enforced prerequisite: one semester calculus-based probability course, such as MATH 4510 or APPM 3570. Recommended prerequisite: previous coursework equivalent to APPM 3570 or STAT 3100 or MATH 4510; minimum grade of C- for all. Same as STAT 4520 and MATH 4520 and MATH 5520.

STAT 5540 - Introduction to Time Series
Primary Instructor - Spring 2020 / Spring 2024
Studies basic properties, trend-based models, seasonal models modeling and forecasting with ARIMA models, spectral analysis and frequency filtration. Department enforced prerequisite: APPM 5520 or MATH 5520. Recommended prerequisite: previous coursework equivalent to STAT 4520 or MATH 4520 or STAT 5520 or MATH 5520; minimum grade of C- for all. Same as STAT 4540 and MATH 4540 and MATH 5540.

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Background

education and training

Ph.D., Ohio State University 2003
M.A., University of Wisconsin-Madison 2000
M.S., Univ de Chile (Chile) 1996

International Activities

geographic focus

Africa Continent
Chile Country
Europe Continent
France Country
Ghana Country

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Other Profiles

VIVO

Lladser, Manuel E.

Positions

Research Areas

Research

research overview

keywords

Publications

selected publications

book

chapter

conference proceeding

journal article

Teaching

courses taught

Background

education and training

International Activities

geographic focus

Other Profiles

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Google Scholar

ResearchGate

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