A few years ago, after too many instances of starting a book and then realizing that I’d read it before, I began to keep a list of the books I’ve read, making a brief note in it each time I finish something. The list makes it easy to look back on what I’ve read in … Continue reading A random walk through bookshelves — books and movies 2015
I’ll continue describing a graduate biophysics course I taught in Spring 2015. In Part I, I wrote about the topics we covered. Here, I’ll focus on the structure of the course — books, assignments, in-class activities, and the students’ final project — and note what worked and didn’t work. (What didn’t work: popsicle sticks.) Click … Continue reading Recap of a graduate biophysics course — Part II
In Spring 2015 I taught a graduate biophysics course for the first time. It was a first in several ways: the course didn’t exist before, so I developed it from scratch, and it was also the first graduate course I’ve taught in my nine years as a professor! I’ve been thinking for months that I … Continue reading Recap of a graduate biophysics course — Part I
I was sad to learn that Steven Vogel passed away yesterday. He was a giant in the field of biomechanics, and his books on the subject are brilliant, fascinating, and fun. I’ve lost count of how many people I’ve run into who, like me, have found these books deeply inspirational. The first one I read … Continue reading In memoriam: Steven Vogel
In Part I, I wrote about how I started exploring the topic of machine learning, and we looked briefly described one of its main aims: automating the task of classifying objects based on their properties. Here, I’ll give an example of this in action, and also describe some general lessons I’ve drawn from this experience. … Continue reading Learning about (machine) learning — part II
Machine learning is everywhere these days, as we train computers to drive cars, play video games, and fold laundry. This intersects my lab’s research as well, which involves lots of computational image analysis (e.g.). Nearly everything my students and I write involves writing or applying particular algorithms to extract information from data. In the past … Continue reading Learning about (machine) learning — Part I
I spent much of my undergraduate life in UC Berkeley’s Astronomy department. I was an astrophysics and physics double major for quite a while, and I spent countless hours working with our undergraduate-built rooftop radio telescope (shown), both helping build it and serving as a teaching assistant in the laboratory course we designed around it. … Continue reading Berkeley astronomy news (rotten eggs part 2?)
I’m a bit behind in writing summaries of recently published papers from my group. Here’s one that’s a few months old — I’m spurred to write now since I just learned two days ago that it got onto the cover of JACS, the flagship journal of the American Chemical Society: M. D. Hammers, M. J. … Continue reading Seeing the smell of rotten eggs
A long post, in which you’ll have to slog or scroll through several paragraphs to get to the real question: can we navigate using fallen sticks? These days we seem to be inundated with deeply flawed scientific papers, often featuring shaky conclusions boldly drawn from noisy data, results that can’t be replicated, or both. I … Continue reading On the replication crisis in science and the twigs in my backyard
Two posts ago, I wondered about how long the average peer-review of a journal article takes to write. Most people I know reported “a few hours” as the average time, with the upper end of the range being a day or two. I emailed several journals — mostly ones that I’ve reviewed papers for or … Continue reading Review times revisited
The Eighteenth Elephant 