WOLFRAM

Wolfram Innovator Award

Wolfram technologies have long been a major force in many areas of industry and research. Leaders in many top organizations and institutions have played a major role in using computational intelligence and pushing the boundaries of how the Wolfram technology stack is leveraged for innovation across fields and disciplines.

We recognize these deserving recipients with the Wolfram Innovator Award, which is awarded at the Wolfram Technology Conferences around the world.

Presented at the Wolfram Technology Conferences. Learn more

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.

2021

Fernando Sandoya

Principal Professor, Escuela Superior Politécnica del Litoral

Areas: Business Analysis, Data Science, Education, Machine Learning, Software Development

Fernando Sandoya currently teaches at the post-graduate level and oversees research and development of new products in context of consulting business. Among his notable projects are the development and implementation of an intelligent assistant for optimal sequencing of production in the largest food manufacturer in Ecuador (PRONACA); the development and implementation of a system for optimization of the reverse logistics of used tires across Ecuador (SEGINUS); the development of descriptive and predictive analytical model for land transportation of containers to the Ports of Guayaquil (Spurrier Group); and professional training programs in business intelligence, data science, machine learning and models for Ecuadorian universities. Dr. Sandoya is currently working to develop a machine learning system for Redclic and holds development contracts with an additional dozen companies.

2020

Omar Olmos

Instituto Technologico y de Estudios Superiores de Monterrey

Areas: Computational Physics, Data Science, Education, Machine Learning, Mathematics Courseware Design, Physics

Omar Olmos is north regional director of science and engineering for the Monterrey Institute of Technology, where he uses Mathematica for a range of education and research tasks. In addition to developing interactive examples, tutorials and other student resources, he uses Wolfram Language machine-learning analytics to predict student performance. Omar has also used Mathematica to model electromagnetic waves interacting with nanostructures, performing numeric experimentation to study new nanoscale optical effects.

2019

Dr. Jane Shen-Gunther

Doctor, Brooke Army Medical Center

Areas: Biomedical Research, High-Performance and Parallel Computing, Image and Signal Processing, Machine Learning, Molecular Biology

Dr. Jane Shen-Gunther is a medical doctor and researcher for the US Army, specializing in gynecologic oncology and obstetrics. She recently started using Mathematica and the Wolfram Language to advance her team’s research in HPV detection, automating the analysis of several gigabytes of image and instrument data and generating interactive visual reports for both patients and physicians. Dr. Shen-Gunther has also deployed her predictive model in the Wolfram Cloud to share access with other physicians. Her work has led to improved patient interactions, as well as better prediction of pap outcomes that impact underdeveloped countries.

2019

Flip Phillips

Professor of Motion Picture Science, Rochester Institute of Technology

Areas: Computational Humanities, Computational Thinking, Computer Graphics and Visual Arts, Education, Machine Learning

Flip Phillips is a professor, researcher and former Pixar animation scientist who uses Wolfram technology to integrate real-world computation into his psychology and neuroscience curriculum. Through his course, students get unique hands-on experience with computational thinking and machine learning, completing cross-disciplinary projects ranging from predicting voter behavior to identifying fruit from sensor readings. Phillips makes use of Wolfram connected devices for gathering data and frequently publishes his work in the Wolfram Cloud. He has used Mathematica extensively for his research on perception, psychological aesthetics and cortical plasticity. He has also written several packages for extending the Wolfram Language’s rendering capabilities.

2019

Dr. Joo-Haeng Lee

Senior Research Scientist, Electronics and Telecommunications Research Institute

Areas: Computer Graphics and Visual Arts, Computer Science, Machine Learning

Dr. Joo-Haeng Lee is a researcher specializing in human-machine interaction, robotics and computational art. He has used Mathematica to develop several unique geometric algorithms for camera calibration and Bézier curves/surfaces. Most recently, he utilized the Wolfram Language to develop PixelSwap, an algorithm for pixel-based color transition that can be used for both aesthetic images and synthetic learning sets for deep learning. Dr. Lee regularly uses Mathematica visualization for technical illustrations and his artworks for exhibitions.

2019

Tom Burghardt

Emeritus Professor of Biochemistry, Mayo Clinic Rochester (Department of Biochemistry and Molecular Biology)

Areas: Biomedical Research, Education, Machine Learning, Molecular Biology

Tom Burghardt is a researcher at the #1 ranked Mayo Clinic, where he has spent the last three decades studying myosin and muscle tissue. In his 100+ publications, he uses Mathematica extensively for advanced statistics and modeling—tracing all the way back to a 1985 signal processing computation done in SMP, a precursor to Mathematica. Burghardt’s most recent innovation uses feed-forward neural networks developed in the Wolfram neural net framework to help create models for inheritable heart disease using a worldwide database of cardiac muscle proteins. His ultimate goal is to make these models available in the Wolfram Cloud for other researchers to explore and use.

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. Tarkeshwar Singh

Quantitative Analyst and Software Engineer, Quiet Light Securities

Areas: Authoring and Publishing, Finance, Machine Learning, Risk Management

Dr. Singh is a quantitative analyst and software engineer at Quiet Light Securities and an early adopter of Wolfram Finance Platform. In conjunction with the CTO, Robert Maxwell, Dr. Singh brought Finance Platform on board to support daily derivative trading operations by developing extensive strategies and volatility surface models, as well as performing backtesting with intraday market tick data. He also provided daily snapshots of company-wide risk through CDF documents that provided insights and satisfied compliance requirements. He also developed an internal training program to bring quants up to speed with Wolfram technologies. In the future, he hopes to utilize the machine learning capabilities of the Wolfram Language to develop advanced trading algorithms through neural networks.

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