SEARCHThe View from Eigenvector
EigenNews and EigenViews from Barry and the Eigenvector staff.
Year 30
Dec 27, 2023
I believe that days go slow and years go fastAnd every breath’s a gift, the first one to the last – Luke Bryan, ‘Most People are Good’ Eigenvector Research starts its 30th year this January. Reflecting on that made me think of the Luke Bryan quote above. Some days have been long, for sure. For […]
Transparency
Dec 26, 2023
Jonathan Stratton of Optimal posted a nice summary on LinkedIn of the 2nd Annual PAT and Real Time Quality Summit which took place in Boston this month. In it he included the following bullet point: “The age-old concept of data modeling with spectroscopy has been revitalized through the integration of Machine Learning and AI initiatives. […]
In-Person, Live Online or Recorded?
Sep 25, 2023
At Eigenvector Research we offer many courses in chemometrics and machine learning and several ways to take them. As such, I often get variations on the question “what’s the difference between your in-person classes and your online classes?” I’ll address this by starting with what stays the same. We use the same notes, and have […]
Why Solo?
Aug 30, 2023
Eigenvector Software Explained
Jun 15, 2023
Eigenvector Research produces a variety of software products for chemometrics and machine learning and we often get asked how they work together. Here’s the roadmap! We have two main packages for modeling, our MATLAB® based PLS_Toolbox, and our stand alone Solo (with versions for Windows, macOS and Linux). Solo is the compiled version of PLS_Toolbox, […]
Evaluating Models: Hating on R-squared
Jun 16, 2022
There are a number of measures that are used to evaluate the performance of machine learning/chemometric models for calibration, i.e. for predicting a continuous value. Most of these come down to reducing the model error, the difference between the reference values and the values predicted (estimated) by the model, to some single measure of “goodness.” […]
Python is free
Apr 5, 2022
PLS_Toolbox is not free. But you don’t have to be a dedicated data scientist to use PLS_Toolbox (or its stand-alone equivalent Solo). Many of its users are, but the real expertise of most users is in something else such as analytical instrumentation (typically spectroscopy) or the specific problem they are working on (e.g. chemical process […]
We used to call it “Chemometrics”
Feb 23, 2022
The term chemometrics was coined by Svante Wold in a grant application he submitted in 1971 while at the University of Umeå. Supposedly, he thought that creating a new term, (in Swedish it is ‘kemometri’), would increase the likelihood of his application being funded. In 1974, while on a visit to the University of Washington, […]
Under Same (Old) Management
Oct 21, 2021
That’s not a headline you see very often. Usually it’s “Under New Management.” But here at Eigenvector Research we’re proud of our stability. I wrote the first version of our MATLAB-based PLS_Toolbox while I was in graduate school thirty-one years ago. I still oversee its development along with our other software products. In 1990 Partial […]
Chimiométrie 2020: Models, Models Everywhere!
Feb 4, 2020
Domain Knowledge and the New “Turn Your Data Into Gold” Rush
Jan 29, 2020
A colleague wrote to me recently and asked if Eigenvector was considering rebranding itself as a Data Science company. My knee-jerk response was “isn’t that what we’ve been for the last 25 years?” But I know exactly what she meant: few people have heard of Chemometrics but everybody has heard about Data Science. She went […]
Eigenvector Turns 25
Jan 1, 2020
Eigenvector Research, Inc. was founded on January 1, 1995 by myself and Neal B. Gallagher, so we’re now 25 years old. On this occasion I feel that I should write something though I’m at a bit of loss with regards to coming up with a significantly profound message. In the paragraphs below I’ve written a […]
The Software Sweet Spot for Metabolomics
Aug 21, 2019
I attended Metabolomics 2019 and was pleased to find a rapidly expanding discipline populated with very enthusiastic researchers. Applications ranged from developing plants with increased levels of nutrients to understanding cancer metabolism. Metabolomics experiments, however, produce extremely large and complex data sets. Consequently, the ultimate success of any experiment in metabolomics hinges on the software […]