Links and Resources

In my research we take advantage of a number of openly available tools to help us do everything from taking notes, to making graphs, to building complex statistical models. I have a preference for open tools so, in addition to referencing these tools in published papers, I would like to acknowledge these tools and their creators here. I am deeply indebted to an awful lot of people who make these tools possible. 

• Mozilla

• R and the R Foundation for Statistical Computing

• R Studio

• Python

• The Anaconda Distribution

• Notepad++

• Inkscape

• The Gnu Image Manipulation Program

• GitHub
  
  
  

• My GitHub Repository

• My Published Papers

• My Lectures and Videos

(My institution asks for an executive summary of teaching activities as part of our annual review. Here is a current draft for my educational activities.)

Philosophy: Teaching is a big part of what attracted me to being a professor all the way back to graduate school. Currently, I have the joy of teaching students how to be rigorous scientists in the movement science PhD program, and how to be critical consumers of research in the physical therapy doctoral program (DPT). These are very different skillsets, and I love the challenge of needing sufficient depth of knowledge to mentor student PhD students through esoteric study designs and statistical analyses, while at the same time having sufficient mastery over the material to de-mystify research and present sound foundational principles to clinicians.

Outputs: For the DPT program, I have designed content and provided lectures for the "evidence" thread, designed content and provided lectures for research ethics, and created a series of asynchronous modules on social constructs in medicine. For the movement science PhD program, I have revised the syllabus for and co-taught "Instrumentation and Measurement in Movement Science" with Dr. Jacob McPherson, and I regularly facilitate directed readings in data analysis and methodology (e.g., machine learning and statistical parametric mapping). With respect to mentorship, I have served as a mentor for one KL2 awardee, as a mentor for three post-doctoral trainees, as the primary supervisor for three pre-doctoral trainees, and have served on thirty dissertation committees. I also maintain an un-monetized YouTube channel where various lectures I have created and workshops I have led are freely available (https://tinyurl.com/5n88msx4). I use YouTube as a platform to quickly and easily share this information with students and collaborators (e.g., rather than re-explain Type I versus Type III sums of squared errors every year). An added benefit of making this content openly available is that it has proven to be beneficial for people all over the world (I currently have 1.1k subscribers and my statistics videos acquired 19.7k views in the past year).

(My institution asks for an executive summary of research activities as part of our annual review. Here is a draft of my current research activities.)

My masters’ (2009) and doctoral degrees (2012) are in cognitive neuroscience, where my work focused on modeling behavioral and physiological data over time, complex crossover designs with multiple repeated measures, and modeling different sources of variance in a dataset (e.g., random samples of subjects and stimuli in an experiment). I then completed my post-doctoral training in rehabilitation science at the University of British Columbia (2014) and earned an accreditation as a professional statistician (PStat®) from the American Statistical Association (2019).

My unique expertise in neurological rehabilitation, study design, and applied statistics makes me a valuable team scientist for many projects in physical therapy and neurology. For my part, I have helped bring in over 5 million dollars of extramural funding to WashU as a co-investigator/collaborator on grants from the National Institutes of Health, the Department of Defense, and private foundations. I am particularly proud of a P50 application that I submitted with my colleaguesin 2025 entitled “Data Analytics and Precision Rehabilitation [DAPR]” with a budget of 6.7 million USD and an impact score of 36. If funded, the DAPR project would create large scale collaboration between participating research centers, allowing us to leverage true “big data” approaches to precision medicine in rehabilitation.

As of 2025-04-30, my work has been cited over 7,400 times, with an h-index of 42 (estimated from Google Scholar). I also maintain a GitHub repository where code, data, and tutorials I have created are available (https://github.com/keithlohse). 

(My institution asks faculty to report on creating a culture of inclusive excellence as part of our annual review. Here is a draft of my current statement.)

At a high level, I have a deep and abiding commitment to everyone having equal opportunities in life. We are unfairly born into different circumstances and as much as we might strive for meritocracy, no one’s successes are completely their own and neither are their failures. I think it is imperative that we make high quality education available to everyone, giving all students the greatest chance of realizing their full potential and the ability to use their skills for the greater good. To that end, I value making science accessible to everyone. As a small step in this direction, I use open source software and share data when possible (making analyses and processes more accessible for all), I try to share my published work through pre-prints and open-access publications (so that results and implications of data are freely available), and I share many of the lectures I give/workshops I have led on an unmonetized YouTube channel (making my pedagogical content more accessible).

In my research and in my teaching, I think it is also important to recognize that we are all products of history; a history that marginalized large groups of people (often brutally) through colonization, class struggle, and slavery. Social and political acts that may seem antiquated continue to have ripple effects today and, tragically, some of these struggles are still ongoing. To that end, I conduct research and teaching through the lens of social (and political) determinants of health. Health is a complex system with interacting factors at physiological, psychological, and sociological levels.

At WashU, I have developed medical (for DPT students) and research curricula (for PhD students) that advance equity, diversity, and inclusion. In collaboration with health disparities researchers, I have created modules to explain complex data analytic topics like mediation, moderation, latent variables, and confounding through the lens of health disparities, and evaluate the impact that ML/AI can have on marginalized groups.

(My institution asks faculty to report on entrepreneurial activities as part of our annual review. Here is a draft of my statement on entrepreneurship.)

At present, I have no entrepreneurial responsibilities. However, I would be remiss if I did not comment on how I do not like “entrepreneurship” and its growing presence in research. To my mind, entrepreneurship prioritizes profit and proprietary control, which clashes with the values of collaboration and accessibility in open science. For an entrepreneur, protecting intellectual property is crucial for maintaining a competitive advantage and generating profit. In contrast, open science advocates transparency and sharing of research tools, outputs, and findings. There is also a well-documented history of commercial interests influencing the selective publication of favorable results, undermining the validity of scientific inquiry. Open science, in contrast, aims for unbiased dissemination of all findings. It is my personal view that the pursuit of patents and exclusive rights hinders the free flow of information, which in turn slows (or even stops) the collective advancement of scientific knowledge. I understand that as scientists we need to participate in the economic systems of society, but I think it is important that we balance the drive for profit with the principles of openness, collaboration, accessibility, and reproducibility. I think that adopting these scientific virtues will accelerate innovation and discovery in a way that privatization and entrepreneurship alone cannot.