Meet Your Senior Research Scientist: Dr. Jonathan Edelen

Meet the RadiaSoft team in this ongoing Q&A series, where we introduce you to our stellar employees. Learn about their work, their background, and some of the things that make them who they are. Today, Jon Edelen, talks about his multi-faceted job at Radiasoft, a few of the interesting projects he’s worked on, and some things he wishes he more people knew about accelerators.

What do you do at RadiaSoft?

All sorts of things. I work on everything from helping to manage the company to supporting experimental efforts at national labs and at our sister company RadiaBeam. On the technical side, I work on RF controls systems, signal processing, and machine learning for accelerator diagnostics and controls. With the latter, I focus on anomaly detection, which is what we call trying to find faulty behavior in the instrument. I also model particle accelerators and have a particular interest in electron sources, including thermionic cathode physics.

What’s your educational and career background?

I graduated from Rensselaer Polytechnic Institute in 2009 with a Bachelor’s degree in Electrical Engineering. I did two years of magnetic signatures modeling for submarines before heading to graduate school at Colorado State University. I worked on design and optimization of the CSU Free Electron Laser (FEL) and on the physics of electron back-bombardment in thermionic cathode RF guns. After graduate school I was selected for the Bardeen Fellowship at Fermilab, which got me into the RF controls group and into working on RF modeling and combined RF/beams simulations. I also participated in high-power commissioning of a new radio frequency quadrupole (RFQ) and medium-energy beam transport system. In 2017, I started at RadiaSoft where I started developing symplectic space-charge algorithms and modeling of field emission in thermionic energy converters.

What’s the biggest misconception about your field and why?

There are a lot more particle accelerators out there than people think. There are the big facilities like CERN or SLAC, but accelerators are used in all kinds of industry applications, from medical diagnostics to food safety. They come in all sizes, too, from kilometer-scale instruments to tabletop devices.

Where did you grow up?

I grew up in a small town in Connecticut. We had a tiny high school class of 60–Everyone knew everything about everyone else. Small town life, eh?

Before joining RadiaSoft, what’s the strangest or most interesting job you held?

When I worked for the Navy I used to participate in sea trials for submarines and surface ships. It was kind of a fun experience being part of the analysis team. Usually there was only a 2-3 day window to do everything we needed to with the ship / boat and get them on to the next thing, so the schedules were tight.

Who is your favorite scientist from history and why?

Lenhard Euler is a favorite. He’s known for the Euler Equation, which addresses complex numbers and is critically important for advanced physics and mathematics. We use this equation almost daily in anything that involves electromagnetic fields, which is most things in particle accelerator science. Georg Cantor is another honorable mention. Cantor proved that there are both countable and uncountable infinities, which is a splendid thing to think about.

Tell us about one of your current projects.

I am continuously working on a ton of different projects. I am leading the design and commissioning of a RF control system for a C-Band LINAC. I am the Primary Investigator for an SBIR-funded project for building machine learning tools for large accelerator facilities, with the focus split between anomaly detection and control systems. Some of my other projects include optimizing an electron LINAC for a high efficiency Free Electron Laser experiment, helping to integrate hysteresis models in a magnetostatics simulation code, and working on a currently-languishing paper on the variation in the work function on thermionic cathodes.

What is a talent, secret superpower, or fun fact about yourself that people wouldn’t guess?

I know the secrets of farm-stand produce fraud! When I was in high school, I worked on a farm during the summers. The farm had a produce stand that sold peaches from Georgia. I spent a lot of time peeling the “Georgia” stickers off the peaches so that we could sell them as “Connecticut peaches,” which are not a real thing.

What’s your favorite Slack emoji and why?

Scuttleberg!!!! Because who doesn’t love a dancing scientist crab?!

What’s something you wish people understood better about RadiaSoft?

We’re known largely for our software development and our expert consultants for particle accelerators, but we also have a unique expertise in plasma physics, vacuum nanoelectronics, FPGA’s, and machine learning. We do much, much more than GUI’s.

Want to learn more about the RadiaSoft team? Visit our team page for full bios.

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Brightness in Undulators: Challenges & Opportunities

The Importance of Brightness in Undulators

X-ray light sources are a key instrument for scientific discovery in disciplines ranging from biology to materials science to organic chemistry. These light sources are powered by undulators and are used, among other purposes, to examine the ultrafine structures of samples that are under investigation. The ability to measure such fine structures necessitates a high signal-to-noise ratio in the X-ray beam. The beam’s brightness dominates this signal-to-noise ratio, making that brightness a fundamental figure of merit for the undulator. Brightness is such an important feature that the ability to accurately calculate it determines how far beamline designs can advance.

Current and Potential Brightness of Light Sources

The light sources currently in use show wide variation in brightness and photon energy, with significant improvements anticipated as devices and facilities are updated. Figure 1 (at the top of this article) shows that the brightness of beams produced should increase by two orders of magnitude in the near future. Reaching these hoped-for brightness levels will require overcoming serious difficulties that are rooted in both the beamlines and their sources.

Challenges to Increasing Brightness

One of the challenges to increasing brightness in the beamlines is that their complicated behaviors require high-level mathematics to predict, which in turn requires training and retaining people who can do that work. Brightness calculations require knowing the emitted radiation of the undulator, the photon beam size and divergence, the electron beam energy spread, the photon flux, etc. The math gets twisted pretty fast. Photon flux, for example, is represented by the formula in Figure 2.

Figure 2. Photon Flux Calculations

The components of the photon flux formula are themselves complicated. The formulas for , , , and and are demonstrated in Figure 3.

Figure 3. Additional Calculations

The shifting variables and nested complexity of these mathematical formulas make them difficult to work with or discuss in depth in a blog post. (The references listed at the end of this article provide details for further study). For the purposes of this piece, it’s enough to recognize this barrier to advancement in the field.

Moreover, light sources themselves are enormous and intricate devices; they can cover acres of ground and have thousands of delicate moving parts that require indescribably-minute adjustments in real time. These adjustments must be done by a phalanx of highly-trained engineers and support staff. Suffice to say that any experiment run in such devices is enormously difficult and expensive. Scientists will need to find pathways to lower barriers in both the beamline calculations and the light-source generation in order to continue advancing the field.

Opportunities for Lowering Barriers

The value of high-stakes scientific experiments is, of course, in their complexities. It is neither possible nor desirable to remove them. Lowering barriers to navigating them should be a focus among the community.

The obvious solution is to use software and computational power to handle the calculations and model the behavior of the beamlines and light-source devices. Modeling instead of executing “real” experiments reduces the expense and increases accuracy by orders of magnitude. The sticky bit is that these software programs can be challenging to learn, hard to use, and difficult to share. To truly lower these barriers, another boost is needed. Bringing the simulation codes into a browser or other GUI-based platform can be that boost.

Wrapping the legacy codes in a GUI makes them accessible to scientists and engineers at all stages of their careers. Removing the necessity of learning command-line operations means that any of the multiple codes suitable for X-ray brightness calculations can be used with equal ease. “Drag and drop” inputs make adjustments fast and cheap and multiple iterations easy. GUIs also provide greater visualization capabilities as well as ease of sharing between collaborators.

Using GUI technology to facilitate calculating and modeling tasks is not a silver bullet that removes all barriers. But it is a powerful tool that should be recognized and utilized as scientists seek to achieve the ever-brighter beams that are needed.

Conclusion

Synchrotron radiation production is a hugely important engine for scientific productivity, with applications ranging from basic science to medicine to industry. It is and will continue to be ever-more important to lower barriers both to accurate calculations of brightness and modeling of beamlines in order to advance the development of light sources. Browser-based or GUI technology can lower barriers dramatically and will be an ever-bigger part of the progress in the field of beamline generation. Improving our ability to efficiently quantify the performance of light sources will bring a bright new tomorrow to the light-source community.

References & Resources

The information in this article is drawn from several sources. Please see the references below for further information.

Nash, O.Chubar, N. Goldring, D.L. Bruhwiler, P. Moeller, R. Nagler and M. Rakitin, “Detailed X-ray Brightness Calculations in the Sirepo GUI for SRW,” AIP Conference Proceedings 2054, 060080 (2019); https://doi.org/10.1063/1.5084711

Nash, O.Chubar, D.L. Bruhwiler, M. Rakitin, P. Moeller, R. Nagler, and N. Goldring “Undulator radiation brightness calculations in the Sirepo GUI for SRW,” Proc SPIE 11110, 111100L(2019); http://doi.org/10.1117/12.2530663

M.S. Rakitin, P. Moeller, R. Nagler, B. Nash, D.L. Bruhwiler, D. Smalyuk, M. Zhernenkov and O. Chubar, “Sirepo: an open-source cloud-based software interface for X-ray source and optics simulation,” Journal of Synchrotron Radiation 25, 1877 (2018); https://doi.org/10.1107/S1600577518010986

K.-J. Kim, “Brightness, coherence and propagation characteristics of synchrotron radiation,” NIM A 246, p71 (1986)

This work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award #DE-SC0011237

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Meet Your Software Developer: Evan
Carlin

Meet the RadiaSoft team in this ongoing Q&A series, where we introduce you to our stellar employees. Learn about their work, their background, and some of the things that make them who they are.
Today, Evan Carlin, talks about software development at Radiasoft, some interesting projects he’s worked on, and a few of software problems that he’s solved.

What do you do at RadiaSoft?

I solve problems by writing code. Most of my time is spent working on the Sirepo framework. I have worked on projects like adding the ability to run simulations on the Cori supercomputer at NERSC and allowing users to dynamically compile the FLASH code through the Sirepo interface. I also help with some other software development such as deploying a server running NVIDIA IndeX and setting up MongoDB and sirepo-bluesky on our Sirepo Jupyter server.

Outside of programming, I’m involved in the Social Justice Committee where we try to make RadiaSoft and the larger physics community more inclusive.

What’s your educational and career background?

I went to college in Tacoma Washington at the University of Puget Sound. I wanted to study economics, but the first 15 minutes of my introductory economics class taught me that I did not enjoy economics. That semester I was also in a Computer Science 101 course and I was hooked from the first assignment: modifying a Java program to move a turtle around the screen. After college, I worked at a consulting company doing a variety of programming projects. I then worked at Google, trying to improve customer support experience and internal tools.

What’s the biggest misconception about your field and why?

That you need to be good at math. Computer science departments are sometimes found inside of math departments and many people think you need to be good at math to excel at programming. Both fields share problem-solving and abstract-thinking skills, but you do not need to know much about math for most programming jobs. I took one math class in college, and I only took it so I could take a calculus-based physics class. If you like solving puzzles and don’t mind staring at a screen for hours on end, then you may be a good programmer.

Where did you grow up?

I grew up in RadiaSoft’s hometown, Boulder, Colorado. I spent my entire childhood there except for when I lived in Padova, Italy when I was 12.

Before joining RadiaSoft, what’s the strangest or most interesting job you held?

In college I worked in a “keychain factory.” Really, it was the basement of a house near mine with a long wall of tables stacked with boxes of keyrings and car-logo medallions. It was a great college job because I could just show up and work a few hours whenever I had time. The downside was that making keychains is about as exciting as it sounds, and it really hurts your fingernails after a while.

Who is your favorite scientist from history and why?

They aren’t technically scientists, but Adam Savage and Jamie Hyneman. They’re the cohosts of the TV show MythBusters, and were my earliest science influencers. A lot of what they did on their show used the scientific method and taught me how to break a problem down to understand it. I would love to someday own a shop like theirs and spend my time tinkering.

Tell us about one of your current projects.

I am working on a project called NDVIZ. The goal is to use 3D-visualization software to interact with very large datasets. I’m getting our software deployed so our collaborators at Oak Ridge National Laboratory can try it out.

I spent more hours than I care to admit trying to create a working Docker image [a piece of software that acts like a template for building new applications] that could run the service and properly visualize the data using NVIDIA IndeX. I had the container running and IndeX was reachable, but the data that was visualized was completely black instead of lit up. I tried twiddling all sorts of parameters, using different GPU drivers, manually building different software, and even reaching out to folks at NVIDIA. Ultimately the answer, as it usually is, was in the code. There were some example Dockerfiles that showed how to properly build an image and once I adapted them everything worked. I learned a good lesson, I should’ve taken some time to explore the code I was given before jumping in and solving the problem how I thought it should be solved.

What is a talent, secret superpower, or fun fact about yourself that people wouldn’t guess?

This might be a talent, a superpower, or just completely useless depending on who you ask, but I can get anyone who is willing to try to enjoy country music. People have been scarred by the pop-country on the radio. Once you dive in and explore country music, you’ll realize that it is a vast genre. Recently I’ve been listening to a lot of James Hand. His voice and style of play are not the most accessible but his lyrics are haunting.

What’s your favorite Slack emoji and why?

My favorite is the man dancing. I like to use it instead of thumbs up when I’m happy or in agreement with something.

What’s something you wish people understood better about RadiaSoft?

I wish people knew that even though the main focus of RadiaSoft’s work is particle physics, we are solving many difficult software problems, too. For example, we have written a distributed job-management system that uses asynchronous Python. We learned many interesting bits about Python through that project that I would like to someday share with the larger software community.

Want to learn more about the RadiaSoft team? Visit our team page for full bios.

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Meet Your CTO: Rob Nagler

Meet the RadiaSoft team in this ongoing Q&A series, where we introduce you to our stellar employees. Learn about their work, their background, and some of the things that make them who they are.

Today, Rob Nagler talks about the strategy of software programming, some interesting projects he’s worked on, and a few of the many companies he’s founded.

What do you do at RadiaSoft?

I help people with software and hardware. The software might be accounting systems or how to use a new tool. I also try to eliminate bottlenecks for the rest of the software team. Programmers should be programming, not dealing with license or hardware issues.

What’s your educational and career background?

I took my first computer course when I was nine years old, back when computers filled up entire rooms. I taught myself how to build electronics and software programs until high school when I took my first Basic and Fortran classes. I got degrees at UCSD and Stanford in computer engineering.
Although my focus is primarily software, I have been involved with hardware throughout my career. I’ve worked for big and small companies, but mostly, I’ve worked at startups, many of which I founded.

What’s the biggest misconception about your field and why?

People think programming is difficult. In many ways it is, but it often comes down to solving simple problems in a structured and specific way. I think the “structured” and “specific” parts are what trips people up. Sometimes they give too explicit instructions, and other times the instructions are too vague. Getting it Goldilocks-right is about taking the time to find the simplest way of talking about a problem. When this happens, the software writes itself.

Where did you grow up?

I grew up in East Meadow, NY. It’s a small town on Long Island with many Levitt homes, where most people would commute by train to work.

Before joining RadiaSoft, what’s the strangest or most interesting job you’ve held?

Over the years I’ve started 15 or so companies. Many of these startups operated concurrently so at times I wore (and still wear) different hats. I’ve had to act as a fiduciary for one company while negotiating with another company I owned.
One of my more fun startups was a nonprofit designed to encourage kids to ride their bikes to school. At one time 50 schools were running the program. Technology was involved: the kids had RFID tags on their bike helmets, and a solar-powered RFID reader was installed at the schools to count them as they arrived.

Who is your favorite scientist from history and why?

While there are interesting historical computer scientists, I prefer thinking about the people I’ve worked with who are not famous such as David Cheriton, Tom Lyon, Paul Moeller, Roger Sumner, and Ion Yadigaroglu. These people are my favorites because of how they influenced me and shaped my career.

Tell us about one of your current projects.

I am working on improving our accounting at RadiaSoft. I like this project because I can solve a problem for people without a lot of complex software. The problem has a lot of moving parts, but the solution is relatively simple and eliminates most of the need for manual entry. Not only is manual entry tedious for people, it’s error prone. Now, we can take our data from our timekeeping system, generate some inputs to Quickbooks, Paychex, and Excel, and eliminate hours and hours of manual entry from one system to another. It makes me happy to solve a direct problem for someone.

What is a talent, secret superpower, or fun fact about yourself that people wouldn’t guess?

Fun fact: I lived in Switzerland for 12 years. I was not the type of person to travel after college. Rather, I just went to work. A friend of a friend needed some help running a software company in Zurich so I hopped on a plane after a couple of phone calls. I had never been to Switzerland before. I didn’t even have a visa, which resulted in a rather sticky situation with the Fremdenpolizei.

What’s your favorite Slack emoji and why?

I don’t like emojis. Bah humbug. I am old fashioned, and I use emoticons. 🙂

What’s something you wish people understood better about RadiaSoft?

We are a small company with many different projects, which can be quite complicated to manage. While our project deliverable is usually a research paper, we try to make sure we also add some features to our flagship product, Sirepo. Doing this benefits the larger scientific community because Sirepo is open source. In this way, we’ve been able to grow Sirepo from a simple application to the rich, multi-faceted scientific gateway it is today.

Want to learn more about the RadiaSoft team? Visit our team page for full bios.

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Research Spotlight: Averaged Invariants in Storage Rings with Synchrotron Motion

When we design a storage ring particle accelerator, we start from certain basic assumptions that are relaxed as we make progress in the design.

We begin by looking at the betatron motion—the horizontal and vertical oscillations that arise from alternating gradient strong focusing. This motion will tell us whether we can stably store a beam. We also look at synchrotron motion—the longitudinal oscillations of the particles in the beam because of time-of-flight dependence on energy and the radiofrequency cavities that accelerate the particles. One of the assumptions we start with is that the betatron and synchrotron motion is independent, or that they are uncoupled.

The benefit of assuming that they are uncoupled is that it allows us to start with three one-dimensional problems instead of one three-dimensional problem. However, they aren’t actually uncoupled. Betatron motion can be linearly coupled in, for example, circular optics or modified with nonlinear dynamics, such as with nonlinear integrable optics. Chromaticity—the dependence of the betatron frequency on the particle’s energy—also can couple the synchrotron motion with the betatron motion, so-called synchro-betatron coupling. This is a coupled nonlinear system that can lead to chaotic dynamics, emittance growth, and other bad things affecting the quality and lifetime of the beam.

Averaged Hamiltonians and Toy Models

Spurred by RadiaSoft’s collaboration on the FAST/IOTA project at Fermilab, we looked into if some sort of general statement can be made about the synchro-betatron coupling.

When we study the long-term dynamics in a storage ring, like the IOTA ring, we want to study the single-turn map. The single-turn map tells us, given some initial position of a particle, what its final position will be after going through the ring once. It turns out, the single-turn map contains all the dynamics of the accelerator, and analyzing it will let us make long-term predictions about the behavior of a beam of particles.

The dynamics of particles around a storage ring are described by a Hamiltonian which generates the single-turn map. This can get deep into the world of symplectic maps and Lie algebras, but the big picture idea is that if we can understand the dynamics of that Hamiltonian, we can understand the dynamics of the particles in the ring, whether the trajectories are stable or not, and so on.

To do this, RadiaSoft scientists first computed an analytic model to predict an averaged Hamiltonian for a particle accelerator ring. The theoretical calculation is high-level and generic, and in practice is probably tricky to calculate anything concrete for a real accelerator. But it does show that, while we might not be able to compute an averaged Hamiltonian in practice, it at least in principle exists. That existence is enough to suggest various stability properties of the accelerator.

What we found is that, outside of resonance issues that arise from the synchrotron tune being a rational number, we can compute a period-averaged Hamiltonian that tells us something about the average motion of the particles over many synchrotron oscillations. We lose some short-time wobbles and wiggles in the trajectory, but we can say broadly whether the dynamics will be well-behaved or not by looking at this averaged Hamiltonian.

To confirm our computation, we built a toy model of a nonlinear ring with synchrotron motion. This was straightforward to implement in a Jupyter notebook. We used a toy 1D single-turn map Hamiltonian that includes chromaticity, linear focusing, and an octupolar nonlinear term for the transverse dynamics, and a thin RF cavity and linear momentum compaction for the longitudinal dynamics.

So the model includes integrable nonlinear transverse dynamics (but no chaos since the octupole term is included as a constant-focusing term), as well as nonlinear synchrotron motion through a nonlinear RF cavity potential.

While the model may not be perfect for a particle accelerator, it lets us compute something with pen and paper and compare it to the simulations.

We found that our averaged Hamiltonian is very well-conserved in our toy model with linear synchrotron motion. In this case, we computed the averaged Hamiltonian analytically, because our simple model allowed that.

We then compared this averaged Hamiltonian, turn-by-turn, with the unaveraged perpendicular Hamiltonian—that is, the Hamiltonian that contains all the chromatic terms and transverse dynamics, but not the synchrotron motion. What we found is that the perpendicular Hamiltonian has a periodicity with the synchrotron motion, suggesting the existence of some underlying invariant. We also found that the averaged Hamiltonian is very close to invariant over the synchrotron period, suggesting it is that invariant.

We can also compare this near-conservation to the synchrotron period directly.

The conservation is in the so-called normal coordinates (see, e.g., É. Forest for a discussion of general linear normal forms) of the linear synchrotron motion, so when we extend to the nonlinear motion we don’t expect conservation since we have not computed the nonlinear normal forms.

In a perturbation theory sense, computing the linear normal forms transforms the linear oscillations to a constant phase advance, so when we look at added nonlinear effects we haven’t canceled out sideband effects and we expect to see oscillations at harmonics of the fundamental. When we add nonlinear synchrotron motion, to account for the curvature in the RF cavity fields, we find oscillations in the invariant commensurate with twice the synchrotron period. This gives us a tell-tale signature that some invariant exists, but we aren’t computing the full normal coordinates, which can be hard to impossible to do effectively for more realistic systems.

Signs of a Hamiltonian in a Real System

Now that we have an intuition for what will happen if we try to apply this reasoning to a complex system, we looked at an integrable Rapid Cycling Synchrotron design developed by Jeff Eldred at Fermilab.

We used Synergia to track the single-particle dynamics, and analyzed the on-momentum invariants for the characteristics we expected from our toy model. Sure enough, we see oscillatory, but bounded, behavior in the invariants that oscillates with the synchrotron period. This bounded behavior suggests there’s an underlying period-averaged Hamiltonian like the one we computed for the toy model, and that the averaged trajectories are integrable, even with the synchrotron motion.

We found a periodic bursting behavior in the non-conservation of the Hamiltonian and second invariant of the lattice as computed in the usual on-momentum linear Twiss coordinates we might be familiar with for computing Courant-Snyder invariants. The periodicity suggests the existence of an underlying invariant, as simply averaging H over the period shows that there is not some spurious drift. The bursting in non-conservation coincides with the particle going from positive to negative energy offset. This corresponds to going to a region where the vertical and horizontal chromaticities are approximately equal, which permits exactly integrable dynamics for off-momentum particles, and a region where the chromaticities are not equal, but this is a subject for another paper.

Conclusion

This computation has strong implications for future nonlinear integrable optics accelerators like the iRCS we studied in this paper. A big concern was what synchro-betatron coupling will do to the integrability of nonlinear integrable optics, and in this paper we offer a mathematical treatment to better understand that question.

Our initial studies suggest that the dynamics will remain regular and well-behaved over long times, and that while we should look out for what synchro-betatron coupling could do, it’s not an immediate show-stopper. It also suggests a path forward for better understanding synchro-betatron coupling in general.

Read the full paper on arXiv here or check out the JINST publication here.

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Meet Your Software Developer: Paul Moeller

Curious about our team of software developers at RadiaSoft? You can meet them, one-on-one, in this ongoing Q&A series. Every month, we introduce you to another member of the RadiaSoft team and they tell you about their work, background, and some of the things that make them who they are.

Today, Paul Moeller talks about the advice he’d give to software developers, the new machine learning app he’s working on, and the thing he wants most from Sirepo users.

What do you do at RadiaSoft?

As a principal software developer, I work on Sirepo. That entails bringing various scientific codes to our browser-based framework and putting nice user interfaces on them. Almost all of them are open-source; some scientists created this software themselves or it passed through time with different people maintaining it. (For example, Zgoubi, a Fortran code, has been around since the ’70s and that’s one of the ones Sirepo supports.)

These scientific codes are out there for anyone to get, but it’s really hard for a regular person to actually run them. They’d have to get it to their computer, compile everything, and make it work. You might have to write a file and feed it into it, for example. We at RadiaSoft try to take all of that information out and build a schema for it, which is like making a map of all the inputs for a code and then arranging them to make logical sense and build a user interface for it. The nice thing about what we do, what Sirepo does, is make it so users don’t need to go through all that, they can just run these codes from their browsers.

What’s your educational and career background?

I always knew that I wanted to study computer science, even when I was in high school. I enjoyed working on software. So I went to a small undergrad called Clarke College in my hometown of Dubuque, Iowa. I also had a music minor in undergrad. Then I got my master’s degree at Loyola University in Chicago and got a job right after that in Chicago. I went to work for a company that wrote software for manufacturing, accounting, and logistics, similar to SAP and Oracle. I worked there for four years, then got married and moved to Boulder, Colorado.

I knew I wanted to live in Boulder because I had a sister who lived here for a while and always thought it was a great place. My wife went to CU then. I met Rob Nagler, our CTO. and joined him at Bivio Inc., and the company evolved into a consulting services business. We met David and that’s how we joined RadiaSoft. This is all over a 20 year span.

What’s some advice you’d give to other software developers?

In general, computer scientists and developers get caught in the language and the technology being used rather than the problem they are trying to solve. You can’t have one hammer that hits every nail. There’s a big advantage to using different technologies for different issues, but also understand that the technology you’re using today will be very different or even obsolete in a few years. Remember that clients often come to you with the technology they want, rather than the problem they have.

Before joining RadiaSoft, what’s the strangest or most interesting job you’ve held?

Back in 1990, when I was in undergrad, I spent the summer working for Central City Opera as a festival staff member. A friend of mine from college and I traveled out to Colorado from Iowa and mopped rehearsal floors, picked up garbage, manned phones, and ushered the performances—all for $25 a day. As an usher I saw around 10 performances of La Traviata, Cosi Fan Tutte, and The Merry Widow each.

If you could invite a pioneer from your field to dinner, who would it be and why?

I would invite a young Steve Jobs, when he was young and crazy, right after he left Apple and when he was doing stuff with Pixar and Next. That would be an interesting time in his life to have a conversation. I really like his approach to making products because they are superior to their competitors. I’d be very curious to figure out what it is about him and how he makes decisions that result in such amazing products. That’s really the million dollar question.

Tell us about one of your current projects.

One project I’m working on is called Webcon. One of our senior research scientists, Jon Edelen, is heading that one up. It’s making a web app that lets you classify or apply machine learning to a dataset. It is a general purpose app which is kind of neat, as opposed to a lot of the work we do which is very specific, related to a beamline or a particular machine.

In Activait, you can upload a bunch of data, select what parts of the data you’re interested in, visualize it, partition it, and classify it. You can discover information from the raw data through classification and correlations.

In general, Activait can be used with any sort of data. It’s not only accelerator-science specific. There was one dataset I was looking at which tried to figure out whether you have diabetes or not. There were lots of inputs, like glucose levels. You let the machine learning just crunch away at it and predict. Machine learning is new to me and it’s an exciting project. It’s a product that’s live, but that we’re all actively working on. So it’s going to get better and better.

What is a talent, secret superpower, or fun fact about yourself that people wouldn’t guess?

I run a lot. If I’m not working, I’m most likely running. On weekends, I run short races, virtual races, these days. In the summertime, I’ll run 50 miles a week, fewer in bad winter weather. It’s definitely something I enjoy, but even five years earlier I would have said it was soccer. But eventually I got old enough that I thought, it’s better just to run rather than put myself in harm’s way.

What’s your favorite Slack emoji and why?

This is not one I use a lot, but one I like is the Walking the Dog emoji. It comes up a lot on our software team Slack channel because many people are walking dogs at various times. It’s what we use when we’re unavailable. Otherwise, I just stick to the thumbs-up emoji.

What’s something you wish people understood better about Sirepo?

I wish people understood that we’re always developing Sirepo and continually improving it. If you’ve used Sirepo and you think it’s nice, but it doesn’t do X thing. Let us know! I would love more feedback from active Sirepo users. It’s not a typical customer-service black hole with Sirepo. One of us will get back to you and you might even change the program for the better. We are totally open to supporting someone’s wishlist. Sirepo’s target customers are such a small portion of the world, it is something we can do. We’re very focused on a specific area, so the more buy-in we can get from people in those areas the better we can make the product for them.

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