Thank you, Chris Gotwalt, on your 25th anniversary at JMP!
On the occasion of Chris Gotwalt’s 25th anniversary at JMP, I want to make everyone aware of how important his work has been to us.
Chris has led our Statistical Development group for many years, and recently expanded it with two new development managers, Clay Barker and Laura Lancaster. Chris has also been at the forefront of JMP’s leadership in statistical software, implementing a huge number of important methods in JMP, as detailed below.
On May 16, 2001, Chris started at JMP as a graduate student with a statistical computing fellowship through the Department of Statistics at NC State University, where he completed his Ph.D. Under Dennis Boos, his education was very strong in computing, numerical methods, and mathematical statistics. Since his arrival at JMP, he has been at the center of much of what we have developed statistically.
Chris’s most recent project was the new Bayesian Optimization platform. Chris authored all the numerics, and Kasia Dobrzycka developed the UI. He had to solve many hard problems to make the Gaussian Process model more general, including:
- Handling categorical factors or responses.
- Batch augmentation, so that more than one run at a time can be materialized.
- Handling many factors, even more than 20.
- Handling multiple responses with competing goals with complicated sparsity
- Handling goals that matched the target rather than minimizing or maximizing.
- Handling randomization restrictions, including blocking and split-plotting.
- Handling cardinality constraints, using custom candidate sets.
The result is the Bayesian Optimization platform, a showcase feature of JMP Pro 19. Chris worked closely with Eastman Chemical and Procter & Gamble to help solve some of their thorniest optimization problems.
But one of the most valuable contributions Chris has made was assembling the talented team of statistical developers, testers, and documenters that are at the heart of our product.
Thank you, Chris, for helping make JMP a great product that our users treasure. Because of your leadership, JMP has become (and has remained) a leader in statistical computing.
-John Sall
A while ago, I compiled the following list of Chris’s accomplishments to nominate him for an award. It is a good representation of the impact he has had at JMP.
Mixed models
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When Brad developed software for optimal design of split-plot experiments, we realized that there were two big missing links to make it practical. First, we needed to make the right-sized tests for fixed effects. Earlier, in 1995, Kenward and Roger extended the work of Harville and Kackar to produce a right-sized denominator degrees of freedom for fixed-effects tests, but the calculation was complex. Chris’s ingenuity was much needed to make these calculations more tractable and efficient. The second problem was dealing with negative variance components. To get the right-sized tests, you also have to structure the problem to let the variance component used in the REML and K&R calculations stay negative. Letting them go negative sounds strange, but the components are really covariances in a compound symmetry model, and to make unbiased estimates, simulation studies have proven that keeping them negative is the right approach to get the tests sized right. With the DOEs coming out of optimal experimental design with small sample sizes, the probability of them going negative is increased. Chris reordered all the calculations with some innovative computational tricks for REML to support this without needing the n-by-n matrices that PROC MIXED needed to do the same trick. With these innovations, analyzing optimal split-plot experiments suddenly became very practical.
While researching mixed models and the properties of the Kenward-Roger adjustments for REML tests, Chris noticed some similarities in the calculations to those that Firth researched in bias reduction. This led to Chris proving that the REML method was equivalent to a Firth estimate, and thus endowed with all its properties. Although REML was long respected as producing the best mixed model estimates, it was derived as a practical method in order to reproduce method-of-moments calculations for balanced cases, rather than as a formal bias-reduction optimization method. Chris’s work legitimizes REML as a method of principle, as well as practice.
Later, Chris was challenged with fitting most of the repeated-measures covariance structures that PROC MIXED supported, but taking advantage of both sparsity and the latest numerical techniques. The result is an engine that produces estimates many times faster than the modern engines in SAS HPMixed. It enabled estimation of large variance components models in complex industrial production situations.
The mixed model algorithms Chris developed are at the foundation of multiple important platforms in JMP Pro; they are the basis of the spline models in the Functional Data Explorer, the core of the Generalized Linear Mixed Models platform, and the new Mixed Model Variable Selection platform. Laura Castro-Schilo and I identified a mathematical connection between SEM models and linear mixed models. The initial two releases of SEM were based on a transformation of the SEM model fitting problem into a special (but very complicated) case of a linear mixed model.
Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Chris with Florian Vogt at Discovery Summit Europe in 2023.Neural boosting
When we needed to produce better quality neural networks a few years ago, Chris noticed that the previous developers’ routines had spent too much time calculating derivatives and doing Newton-Raphson for large numbers of hidden layer nodes. Chris greatly sped up the calculations by using SR1. However, he also noticed that a boosting strategy allowed one to solve the problem in a stage-wise fashion, where a few hidden nodes were added at each stage, estimating only a few parameters at a time, with early stopping by cross-validation to produce one of the best neural nets. It was state-of-the-art until the more recent deep-learning convolution nets were developed.
Simulation sampling
Our group needed a way to calculate defect probabilities in the extreme tails of multivariate normals. We supported robust process engineering simulation models that optimized for low defect rates with respect to specification limits in multivariate simulations, and produced surrogate experiments to do the minimization. The key to making this work was to produce decent probabilities for points up to 12 standard deviations away. Chris used various theoretical results to develop a weighted sampler to calculate these probabilities, using a radial stratification method.
MSA profiling
In measurement systems analysis, Chris worked with Laura Lancaster and had several valuable breakthroughs. One was the development of a Bayesian approach to producing positive variance components for MSA. Another was the ability to hook a smart simulation engine to a profiler to get instant answers for shift detection in control charts, given the MSA calculations. Now there is an easy way to translate the MSA analysis to answer real questions about the ability to detect shifts in processes.
Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Chris chats at Discovery Summit 2016Text mining
The dimension reduction needed for latent semantic analysis in text mining requires a very fast sparse SVD. Chris developed that by using updated Lanczos methods, but then noticed that it would be possible to do a centered version of it to make it equivalent to principal components without sacrificing the speed of the sparse calculations. The new centered singular vectors should represent the text space better than the current state of the art. It also allowed us to use the sparse calculations in our principal components and association analysis platforms. Then Chris realized that the same sparsity patterns that he noticed when implementing latent class analysis (resolving multinomial mixture distributions) could be applied to become a fast clustering and topic-generation feature in text mining, a fast LCA alternative to latent Dirichlet allocation.
Functional Data Explorer
Chris led the development of the Functional Data Explorer. While taking on a minor role in assisting with its initial development, he worked directly with customers to design an easy-to-use tool that tremendously simplified the solution of complex product and process design problems across many industries.
Statistical software
The key to much of the success of JMP software has been a fast, accurate numerical computational foundation, and Chris designed and implemented most of it, as well as organizing better user interface development.
Leadership
As the director of Statistical Development for JMP, Chris has built and led a strong team of developers. JMP software has become important for engineers and scientists in industry, especially in the sectors of semiconductors, electronics, consumer products, chemicals, and pharmaceuticals. Chris has especially led in the areas of mixed models, reliability, and process quality. He has also become an effective communicator in the industry.
And more
Here are a few more innovations from Chris’s CV:
- Developed the sparse matrix library to speed linear model calculations.
- Developed SR1 and GMRES solver for penalized regression, with Clay Barker in GenReg.
- Developed Numerics for the Control Chart Power calculations.
- Extended the Portnoy-Sahai technique for estimating variance components from random effect models. These estimates are in the JMP Variability platform and give strictly positive estimates, which, although biased, have lower MSE than REML estimates.
- Developed a two-stage stratified sampling algorithm for model-based rare event simulation. The first stage uses equal sampling in the strata to determine the variance-minimizing sample sizes to use in the second stage. This is the core numerical algorithm for Profiler Simulator in JMP. (It also became important later for multivariate capability calculations.)
- Designed algorithms for the analysis of computer experiments, including minimum potential (of electrostatic particles) designs and D-optimal designs for Gaussian process models.
- Developed a combined quadrature and optimization algorithm for computing Bayesian D-optimal design of experiments for nonlinear and generalized linear models.
- Developed methods for computing the moment matrix that is exact for mixture problems and a simulation approach for more general linearly constrained problems. It has led to designs that give more accurate predictions on average and, thus, require fewer runs than earlier approaches.
- Invented a reparameterized approach to finding D-optimal and I-optimal designs that exposed the inherent sparsity of the problem. It led to update times that are O(1) for main effects models and O(p) for interaction models. Previous algorithms using low-rank updates alone were always O(p2). These algorithmic improvements greatly extend the size of the designs that are possible to compute.
- Generalized Huber regression to define a likelihood function of a multivariate distribution whose MLE estimates outlier-robust covariance matrices.
- Developed a comprehensive framework for fitting structural equation models (SEMs), which are the primary models used in psychology and behavioral sciences, that handles large datasets with grouping structure and arbitrary constraints. The framework exploits inherent sparsity and is faster than any other competing product we are aware of. This was probably the best-engineered project I ever worked on.
- Developed the JMP’s methods for the analysis of conjoint studies, which are used extensively in consumer research, and collaborated with Bradley Jones on the design computations for conjoint studies.
- Developed JMP’s algorithms for modern time series forecasting based on exponential smoothing models.
More recently
Another notable work from Chris was his implementation of REML mixed model estimation for wide problems, when a random effect is involved in a genomics model, but there are tens to hundreds of thousands of gene expressions as responses. Chris adapted REML algorithms for a small n but large p to make it practical.
Chris started at JMP on May 16, 2001, as a graduate student with a statistical computing fellowship through the Department of Statistics at NC State University, where he completed his Ph.D. Since his arrival at JMP, he has been at the center of much of what we have developed statistically.
Company CultureFeature StoryJMP NewsRe: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Happy 25th Anniversary, Chris! It's clear from this article and from the passion and energy you bring every day that you’ve made an incredible impact on JMP and far beyond. Happy to celebrate this milestone. Cheers!
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Congratulations on a quarter century with JMP, Chris! This is such a great article showcasing your many contributions as a researcher, developer, development manager, and customer collaborator over the years. It's always amazing to see what customers can accomplish with your many innovations - here's to many more!
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Congratulations, Chris! Your innovations are truly brilliant and constantly redefine the ceiling of use and capabilities in JMP giving us the coolest things to share with our customers. You're also like the Dos Equis guy...every time we catch up you've done something amazing and interesting in your personal life too. Cheers to you and your many accomplishments!
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Congratulations on 25 years, Chris!! This is an impressive list of accomplishments that we all benefit from. Personally, I can confirm that jaws drop at SEM demos when people see the speed at which our models converge!
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Huge congratulations Chris. In addition to the great technical achievements, the impact you have on a day in the life of our users is incredible. Enabling them make smarter decisions with ease and speed. Helping us to achieve a high level of trust and respect to drive forward TSR.
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Congratulations Chris. It would be impossible to overstate how much I’ve enjoyed spending time with you on both work and non-work related things. The volumes I’ve learned from you is invaluable as have been our non-stat related conversations. Viva le Pompe!
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Congratulations, Chris! I admire how you can explain deep, complicated theory and make it sound easy. I have greatly enjoyed our conversations about statistical and mundane topics. Here's to another 25 years!
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!What a nice compliment to a brilliant mind. I've always enjoyed Chris's ability to not look like the smartest guy in the room, and his easy conversation and teaching ability of complex ideas. Personally I've gained great benefit from this 
Congratulations on your milestone anniversary. Hope to see you soon.
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!
Happy Anniversary, Chris!! I am so appreciative of your contributions and friendship. Congratulations!!
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Congratulations on your 25th anniversary! On top of it all, you make working at JMP joyful and wonderful.
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Congratulations on this milestone anniversary, Chris! We are so fortunate to have your many wonderful talents—not just your technical brilliance, but also your sense of humor, kindness, and thoughtfulness that contribute in a big way to JMP’s amazing culture. It has been a pleasure to work with these past many years and hope it continues for many more!
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Congrats, Chris! I truly appreciate how you represent JMP to our users; the way you speak about your work reflects the same cutting-edge ambition we see in our innovative customers. Your contribution to JMP is enormous - thank you for all that you do!
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Massive congratulations on 25 years, and thank you for the incredible, oversized impact you've had on the software and everyone who's ever touched it. Our users are lucky to have someone so dedicated to helping them solve their problems, and all of us have been very fortunate to work alongside you. I'm sure I'm not alone in saying that the support and encouragement you've given me has shaped my career in ways that will ripple through the rest of my life.
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!So many impactful achievements that have made uncovering insights easier for JMP users. It’s always a pleasure to connect. Congratulations on this milestone 25th anniversary!
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Congratulations, Chris!
I just remember this quote.
“I've learned that people will forget what you said, people will forget what you did, but people will never forget how you made them feel.”
― Maya Angelou
I was sincerely amazed when you asked me about how Korean users would respond to some features of Bayesian Optimization during your visit to Korea for attending Discovery Summit.
THANK YOU!
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Huge congratulation Chris! This list is almost unbelievable. You had and have such big influence on what we know nowadays as JMP software, is it the statistical achievement or the people you build arround you. But besides that I got a lot of great memories from our connections and time we spend together. Thanks a lot for being such a great person!
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Way to go Chris. Congratulations on your work anniversary and for all your accomplishments.
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Congratulations, Chris. I’m impressed by your long list of achievements, but above all, I’d like to thank you for being so approachable and easy to get along with, both with your colleagues and our clients; alongside your scientific expertise, that’s what makes you so exceptional.
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Happy 25th anniversary!
Thank you for everything you bring to JMP, to JMP users, and to all employees. I have personally learned so much from you that it’s impossible to quantify, and I can’t thank you enough. Your contributions are truly impressive, and your attentiveness to customers and their analytical needs is so much appreciated!
And putting the statistics aside, thank you for all those wonderful moments we shared together!!!
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Congratulations, Chris! You are a true pillar of JMP, constantly raising the bar for what’s possible with JMP Pro. Your work makes a real difference and helps all of us shine in our conversations with customers :smiling_face_with_smiling_eyes:.
It’s a pleasure to celebrate you. I truly appreciate everything you bring, both professionally and personally. Wishing you continued happiness and good health—I’m very much looking forward to all the projects ahead.
Thank you,
Jessica
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Chris, Truly exceptional work and career thus far! Your deep statistical and numerical coding, research, leadership, passion, pleasant demeanor, communication skills, and humor are unparalleled and such an asset to JMP. Very glad to have you as a colleague, friend, and fellow problem solver as we take on challenges ahead together.
Re: Thank you, Chris Gotwalt, on your 25th anniversary at JMP!Dear Chris, the fact you agreed to join a yoga session with Manuela, Jessica and me and the fact barely anybody understands what you come up with while jogging or skiing is proof you're exceptional. Congratulations on 25 years at JMP - off to many more!!!
