2024

David G. Stork

Stanford University

Areas: Computer Graphics and Visual Arts, Computer-Aided Education, Engineering, Image Processing, Machine Learning, Materials Science, Mathematics, Visualization

David G. Stork is an adjunct professor of electrical engineering, symbolic systems and material science and engineering, as well as an adjunct lecturer in computational mathematics and engineering at Stanford University, where he considers Mathematica to be a valuable teaching tool and resource. Here, he developed and teaches Computational Symbolic Mathematics, a Mathematica-based course for using computer algebra for solving difficult non-numerical mathematical problems. Stork is a graduate in physics from the Massachusetts Institute of Technology (MIT) and the University of Maryland. He has held faculty positions at Wellesley and Swarthmore Colleges; Clark, Boston and Stanford Universities; and the Technical University of Vienna. Stork has been a long-time friend of Wolfram, using Mathematica in teaching and research. He holds 64 US patents and has published over 220 scholarly papers and nine books and proceedings volumes, including Pattern Classification, Second Edition and Pixels & Paintings: Foundations of Computer-Assisted Connoisseurship.

2024

Pedro Fonseca

SUEZ

Areas: Computational Thinking, Engineering, Image Processing

Pedro Fonseca earned his degree in environmental engineering with a specialization in sanitary engineering from Universidade NOVA de Lisboa, Portugal. He has since built an international career focused on the detailed engineering of water treatment plants within the SUEZ Group, with professional experiences in Paris, France; Virginia, United States; and Lisbon, Portugal. Since 2012, Fonseca has managed the hydraulic discipline, contributing significantly to the research and development of new products and leading the basic and detailed hydraulic design of water treatment plants around the world.

Fonseca’s passion for education drives his engagement with Wolfram Language, which he first encountered in 2006 (Version 5.2) while pursuing a second degree in applied mathematics. Over the years, he has integrated Wolfram technologies, including Mathematica and System Modeler, into various aspects of his work and personal projects. These tools play a crucial role in his product development efforts, such as data mining, algorithm development and the creation of digital twins for design verification and optimization. Fonseca has also actively participated in multiple Wolfram Research activities, primarily in France, including boot camps, summer schools and product demonstrations.

2023

Martijn Froeling

Assistant Professor, University Medical Center Utrecht

Areas: Image Processing, Research and Analysis, Software Development

Martijn Froeling is an assistant professor specializing in quantitative neuromuscular magnetic resonance imaging (MRI) at the University Medical Center Utrecht. His work revolves around enhancing MRI techniques to better understand muscle function and diseases.

MRI scans provide valuable data, but they need careful processing and analysis. That’s where Froeling’s QMRITools paclet comes in. The paclet is a handy toolkit for experimental design, data analysis and teaching. Since its launch in 2012, it has been used in over 50 scientific papers. Originally created to analyze muscle diffusion-weighted imaging data, QMRITools has expanded its scope. It now includes features like cardiac analysis (including tagging and T1 mapping), Dixon reconstruction, EPG modeling and fitting, J-coupling simulations and more.

The paclet currently offers over 450 custom functions, making it a valuable resource for researchers. Plus, there’s extensive documentation with more than 750 pages, and each toolbox comes with demonstrations. With these tools, Froeling aims to simplify quantitative MRI analysis, benefiting our understanding of muscle injury and disease.

2022

William A. Sethares

Professor, Electrical and Computer Engineering, University of Wisconsin–Madison

Areas: Computational Humanities, Computational Thinking, Computer-Aided Education, Courseware Development, Engineering, Image and Signal Processing, Image Processing, Signal Processing

Bill Sethares is a researcher and professor of electrical and computer engineering at the College of Engineering at the University of Wisconsin–Madison, focusing on signal processing with applications in acoustics, image processing, communications and optimization.

At the University of Wisconsin–Madison, Sethares attracts students from majors beyond engineering with his computationally rich image processing course material and project-based learning (all Wolfram Language–based, of course!). Sethares is a founding member of the LEOcode project and brings computation to art historians in the form of applications used to find patterns in watermarks and canvases. These can help to identify and date historical papers and paintings.

2020

Ariel Sepúlveda

Pronto Analytics Inc.

Areas: Business Analysis, Image Processing, Industrial Engineering, Software Development

Ariel Sepúlveda is the founder and president of Pronto Analytics, an organization dedicated to helping other organizations standardize the generation of analyses and reports for supporting decision-making processes. He is an industrial engineer who has used Wolfram technology throughout his educational and professional career. He has used the Wolfram Language in many fields, including quality control, retail analytics, image processing and manufacturing applications. Ariel’s most recent project is D4CR, a Wolfram Language–powered application that interprets natural language queries to analyze data and generate standardized reports.

2020

Virgilio Gomez Jr.

Quality Aspirators

Areas: Biomedical Research, Image Processing, Mechanical Engineering

Virgilio Gomez Jr. is a mechanical engineer who frequently uses Mathematica for research and development. As a graduate student, he used Mathematica to implement closed-form solutions for three-dimensional vibrations of elastic bodies. In his time as a research development mechanical engineer at Quality Aspirators, he has used Wolfram Language image processing in several projects, most recently for quantifying the aerosol spray generated during a certain dental procedure. This work aided in the design and testing of Safety Suction, a device for removing blood- and bacteria-carrying particles from the air to create a more hygienic environment, helping reduce the spread of COVID-19.

2019

Mihai Vidrighin

R&D Lead, PsiQuantum

Areas: Electrical Engineering, Image Processing, Physics, System Modeling

Mihai Vidrighin is a researcher in photonics who has used Mathematica extensively throughout his career. During his PhD thesis, he used Mathematica to run simulations and data analytics involving quantum thermodynamics, and he continues to recommend the system to colleagues. He currently leads a team developing a photonics component for generating single photon pairs with new accuracy and scale. In this project, he has used the Wolfram Language to build an extremely comprehensive model for nonlinear and quantum optics to describe photon-pair generation and quantum optics circuits. Vidrighin has also written several Wolfram Language packages for quantum optics simulation and electron microscope image processing.

2018

Nicholas Mecholsky

Research Scientist, Vitreous State Laboratory
Adjunct Assistant Professor, Catholic University of America

Areas: Authoring and Publishing, Image Processing, Machine Learning, Nuclear Engineering, Optimization, Physics, System Modeling

Nicholas Mecholsky is a research scientist and professor focusing on optimization and physical modeling. In addition to demonstrating high-level math and physics concepts to his students with the Wolfram Language, he has utilized it in research publications on subjects ranging from animal flocks to autonomous cars to thermoelectric transfer. He is currently involved in a joint project with the US Department of Energy and Vitreous State Laboratory using Wolfram Language image processing and machine learning to model, analyze and predict crystallization phenomena in nuclear tank waste. The project has significantly improved the efficiency of vitrification (transformation into glass), helping to make safer nuclear waste storage a reality.

2017

Dr. Massimo Fazio

Professor, University of Alabama at Birmingham

Areas: Biotechnology, Image Processing, Mechanical Engineering

Dr. Fazio is an assistant professor at the University of Alabama at Birmingham whose main focus is optical imaging. His research using Wolfram technologies led to several significant NIH grants, including the 2017 Xtreme Research Award from Heidelberg Engineering at the Association for Research in Vision and Ophthalmology (ARVO) meeting. This award was granted to Dr. Fazio for creating a custom clinical imaging protocol for glaucoma patients that provides an estimate of the eye-specific mechanical response to time-varying intraocular pressure. Additionally, he created an image processing algorithm that quantifies the 3D structure of the optic nerve from OCT clinical data entirely in the Wolfram Language.

2015

Kale Wallace

District Engineer, Samson Energy

Areas: Chemical Engineering, Image Processing

Kale Wallace first started using Mathematica in university courses and has since used it in his work at Southwestern Energy and Samson Energy for data handling and image processing. At Southwestern Energy, Wallace built a well productivity prediction model analyzing millions of lines of data and using machine learning to predict well performance based on drilling and completion parameters. He has also created field-development visualizations showing wells brought online and their corresponding production and cashflow. His replication of the ARIES economics engine in Mathematica allowed probabilistic (Monte Carlo) economics methods, full-field development scenarios, break-even calculations, and go-forward recommendations to be evaluated much more quickly than could be done in ARIES.

2013

Bart ter Haar Romeny

Eindhoven University of Technology

Areas: Biotechnology, Image Processing, Mathematics

A professor in biomedical image analysis, Bart ter Haar Romeny uses Mathematica to design brain-inspired image analysis methods for computer-aided diagnosis. He is an enthusiastic teacher, and introduced Mathematica as a design tool in the curriculum for all students of his department and in most projects in his group. He advocates that Mathematica is ideal for designing innovative algorithms and for “playing with the math.” His PhD students van Almsick, Duits, Franken, (now Professor) Florack, Janssen, and Bekkers substantially contributed to the Mathematica packages on brain-inspired computing. He cochaired with Markus van Almsick the International Mathematica Symposium 2008 in Maastricht and teaches a popular national course on biologically inspired computing (book written in Mathematica), which was thrice awarded the BME Teaching Award.

2013

Stefan Braun

Managing Director of SmartCAE

Areas: Aerospace, Biotechnology, Chemical Engineering, Control, Data Mining and Analysis, Engineering, Finance, Financial Risk, High-Performance and Parallel Computing, Image Processing, Industrial Engineering, Interface Design, Materials Science, Mathematica Consulting, Mechanical Engineering, Pharmaceutical, Physics, Risk Analysis, Signal Processing, Structural Engineering

Stefan Braun is recognized for using Mathematica in industrial applications. He has used Mathematica and the SmartCAEFab in more that 150+ industrial projects in different application areas. SmartCAE’s software solutions allow practical users to simulate complex applications problems, with a lot of parameters, without being a simulation or Mathematica expert.

2011

Ronald Kurnik

Roche Molecular Systems

Areas: Chemical Engineering, Image Processing, Pharmaceutical, Signal Processing

Chemical engineer Ronald Kurnik develops medical devices, using Mathematica for rapid prototyping of algorithms for signal and image processing and for quantitative chemical reaction modeling. His work has led Roche to file for 15 patents, 7 of which have been issued so far.