The talks will typically take place on Tuesdays at 4:00-5:00pm in Adel Room 164. Please contact Nandor Sieben if you would like to give a talk or have a question about the colloquium.

**Speaker:** Dana Ernst
**Title:** Sabbatical report

**Abstract:**
I’ll spend the first several minutes summarizing my sabbatical experience and then dive into one of the projects I worked on. In particular, I’ll discuss some recent results my collaborators and I obtained concerning the structure of impartial games and rulesets, as well as the structure preserving maps between them. In some sense this work can be thought of as formalizing and extending some folklore from combinatorial game theory. This work was initiated at the Combinatorial Game Theory Colloquium in the Azores that I attended in January, and is joint work with Bojan Bašić, Paul Ellis, Danijela Popović, and Nándor Sieben.

**Speaker:** Jim Swift
**Title:** Synchrony and Anti-Synchrony in Coupled Cell Networks

**Abstract:**
The internal state of a cell in a coupled cell network is often described by an element of a vector space. Synchrony or anti-synchrony occurs when some of the cells are in the same or the opposite state. Of special interest are the evenly tagged anti-synchrony subspaces in which the number of cells in a certain state is equal to the number of cells in the opposite state.
We apply these results to systems of coupled van der Pol oscillators, and coupled Lorenz equations.

This is joint work with Eddie Nijholt and Nándor Sieben

**Speaker:** Robert Buscaglia
**Title:** Activities in Data Science and Statistical Applications

**Abstract:**
The discussion will include a survey of projects from the Buscaglia group conducted during the 2022-2023
academic year. This includes results from collaborative projects with Forestry, Biology, Biochemistry, and
Medicine. Important results will be discussed stemming from standard statistical methods, including
linear modeling and generalized linear modeling, along with some non-traditional techniques such as
Functional Data Analysis. Supervised and unsupervised machine learning methodologies and results will
also be introduced. Key published findings, updates on current publications, and grant progress and
upcoming submissions will be discussed for each major project. The presentation will include results from
work completed with two NAU DoMS students (one undergraduate research, one graduate research) and
the final product for the Hooper Undergraduate Research Award completed by Avery Bell, a BS Data
Science student. A small review of the projects conducted in the Data Science capstone will also be
presented.

**Speaker:** Kayode Isaac Oshinubi (SICCS)
**Title:** Predicting the changes and transition between endemic and epidemic phases of an infectious disease outbreak in some countries

**Abstract:**
The objective of this study is to develop a robust method for predicting the changes and transition between endemic and epidemic phases of an infectious disease, using COVID-19 outbreak as an example.

We define indicators for detecting changes and transitions between endemic and epidemic phases using seven scalars calculated from daily reported news cases: variation coefficient, entropy, dominant/subdominant spectral ratio, skewness, kurtosis, uniformity index, and normality index. The indicators chosen are related to the form of the empirical distribution of new cases seen over a fourteen-day period chosen to smooth out the influence of weekends when fewer new cases are registered.

We used the Principal Component Analysis (PCA) to create a score from the seven proposed indicators that allows an acceptable level of forecasting performance by providing a realistic retro-predicted date for the rupture of the stationary endemic model corresponding to the entry into the epidemic exponential growth phase. The first principal component (a linear combination of the seven indicators) explains a considerable portion of the observed variance and can thus be used as a predictor of the event studied (in this case, the presence of an epidemic wave). This score is used to forecast the limits of the several phases of the COVID-19 outbreak in various nations following endemic and epidemic transitions and changes.

We were able to build a new forecasting strategy for predicting an epidemic wave that comes after an infectious disease’s endemic stationary period. This research offers a valuable tool for early epidemic detection, aiding in effective public health responses.

**Speaker:** Nandor Sieben
**Title:** Sabbatical report

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**Speaker:** Michele Torielli
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**Speaker:** Jaechoul Lee
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**Speaker:** Sam Harris
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**Speaker:** Nellie Gopaul
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**Speaker:** Elisa Palezzato
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**Speaker:** Angie Hodge-Zickerman and Cindy York (Zoom)
**Title:** Mathematics Assessment in the Age of ChatGPT

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**Speaker:** Shafiu Jibrin
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