Category Archives: teaching

Screenshot 2023-05-16 at 8.36.51 PM

Math 100 student’s protein folding research

Madison Koskey had the highest score in the class on this project task:

Fold an idealized protein known as S64 to obtain a highest possible score in the hydrophobic-polar protein folding model in three dimensions.

This problem was studied by Lesh, Mitzenmacher, and Whitesides (2003).

Above we see Madison’s intricate folding of S64 which earned a score of 27.

To try your hand at rotating it in 3D, go directly to this link.

Slides for October 30


MATH 654 Fall 2022

Watch this space for updates on logic courses.

The undergraduate course MATH 454 (Axiomatic Set Theory) was taught in Fall 2021. Textbook: Schimmerling’s “A course on set theory”

A similar course at the graduate level was MATH 654 Fall 2020, which had the following readings:

  • Foundations of Mathematics, Kenneth Kunen, parts of chapters I-IV.
  • Modal logic for open minds, Johan van Benthem, some of chapters 1-11 and 16.
  • Logic and Proof, Lean tutorial, and The Natural Number Game

Math 654 in Fall 2022 will use the textbook by Ebbinghaus, Flum, and Thomas and some readings about Lean.


MATH 657 Spring 2020

Course title: “Recursive functions and complexity”

Textbook title: “A second course in formal languages and automata theory” by J. Shallit

Despite the intimidating titles this is just a graduate introduction to automata, computability, and complexity.
Possible additional topics: Automatic complexity and Python programming.

The Tukey test: starting with a known variance

Consider normal random variables $Y_i$ with means $\mu_i$, $1\le i\le k$.
The Tukey test often seems overly complicated, but it becomes clearer if we first assume that variance of $Y_i$, $\sigma^2$, is known.

Let’s draw $r$ samples from each, called $Y_{i,j}$, $1\le j\le r$.
Denote the sample averages by $\overline Y_{i+}$.
Let $W_i = \overline Y_{i+}-\mu_i$.
Let $Q=R$, the range, be defined by
R = \max_i W_i – \min_i W_i = \max_{i,j} |W_i-W_j|.
Suppose we understand the distribution of $R$ well enough to find a number $Q_\alpha$ such that
\Pr(R\le Q_\alpha) = 1-\alpha
Define the intervals
I_{i,j} = (\overline Y_{i+}-\overline Y_{j+} – Q_\alpha, \overline Y_{i+}-\overline Y_{j+} + Q_\alpha)
Then it is easily seen that
\Pr(\text{for all $i$, $j$, }\mu_i-\mu_j \in I_{i,j}) = 1-\alpha.
and hence for all $i$, $j$,
\Pr(\mu_i-\mu_j\in I_{i,j}) \ge 1-\alpha.
(Larsen and Marx make a mistake here, mixing up the last two equations.)
Thus, the hypothesis that $\mu_i=\mu_j$ can be rejected if we observe $0\not\in I_{i,j}$.

Now, if the variance is unknown, we instead define $Q=R/S$ where $S$ is a certain estimator of the variance.
An important point is that we want to understand the joint distribution of $R$ and $S$, which is easiest if they are independent.
We do have an estimator of $\sigma^2$ that’s independent of the $W_i$, namely the residual sum of squares (a.k.a. sum of squares for error),
\mathrm{SSE} = \sum_i \sum_j (Y_{i,j}-\overline Y_{i+})^2
So we take $S^2$ to be a suitable constant time $\mathrm{SSE}$.
Namely, we want $S^2$ to be an unbiased estimator of $\sigma^2/r$, the variance of $W_i$. And we know that $\mathrm{SSE}/\sigma^2$ is $\chi^2(rk-k)$ distributed.
This leads us to define
S^2 = \mathrm{MSE}/r
where $\mathrm{MSE} = \mathrm{SSE}/(rk-k)$.
A point here is that $\mathrm{SSE}/\sigma^2$ is a sum of $k$ independent $\chi^2(r-1)$ random variables (one for each $i$), hence is itself $\chi^2(rk-k)$.


Graduate Program in Logic

The Department of Mathematics at University of Hawaii at Manoa has long had an informal graduate program in logic, lattice theory, and universal algebra going back to Alfred Tarski’s student William Hanf.
Starting in 2016, things are getting a little more formal.

We intend the following course rotation (repeating after two years):

Semester Course number Course title
Fall 2015 MATH 649B Graduate Seminar
Spring 2016 MATH 649* Applied Model Theory
Fall 2016 MATH 654* Graduate Introduction to Logic
Spring 2017 MATH 657 Computability and Complexity

*Actual course numbers may vary.

Faculty who may teach in the program

David A. Ross, Professor
Bjørn Kjos-Hanssen, Professor
Mushfeq Khan, Temporary Assistant Professor 2014-2017
Achilles Beros, Temporary Assistant Professor 2015-2017