Algorithms in Action

John McGowan '81 draws a block diagram for his software product Math Recognizer for a technical presentation, showing how it is integrated with current analysis tools.

As a mathematical software engineer, John McGowan ’81 spends his days happily immersed in numbers. When pressed to identify the genesis of his interests, however, he admits he’s not sure. “I can’t recall a time when math and science weren’t a passion for me,” he confesses. “I can say that I distinctly remember reading popular science books on physics when I was 6 or 7.”

McGowan pursued his love of physics through secondary school, attending Taft, he says, because of its excellent reputation in math and science. “I remember taking AP physics with Eric Drake—he was a terrific teacher—and AP calculus with Dan Comiskey, who really helped me get my arms around the material.”

McGowan maintained his commitment to science into college and graduate school, earning his bachelor’s in physics from the California Institute of Technology and his doctorate from the University of Illinois at Urbana-Champaign.

While working on a Ph.D. in experimental high-energy physics, McGowan did a great deal of advanced statistical analysis and programming, and though he found the work engaging, he grew worried about the long-term practicality of his pursuits. “I realized that employment opportunities in particle physics were going down, not up, so I began to seek more practical applications for my knowledge,” he says.

His search was wide-ranging. His analytical skills—together with interests in gesture recognition, speech recognition, and video compression technologies—led McGowan through several start-ups, then on to stints at NASA and Hewlett Packard Labs and finally to Apple, where he spent two years as a human interface device algorithm engineer, researching and developing the algorithms that enable the company’s famous touch interface of single and double finger taps and swipes.

During this time, McGowan also worked with his father’s company, the GFT Group, using his expertise in speech recognition to create Petrana, an artificial personal assistant that communicated to users in spoken English, allowing them to execute a series of computer operations hands-free. Petrana and GFT are now dormant, having been halted after the death of McGowan’s father in 2008.

In February 2017, McGowan launched Mathematical Software, a start-up dedicated to researching and developing tools and algorithms that can be used to automate complex data analysis. He anticipates that the company’s first software will be released this spring, and is currently seeking customers.

McGowan says his ideal clients are companies doing research and development in data-intensive areas such as medicine or pharmaceuticals, finance, and engineering. These firms gather complex data, he says, which they often have difficulty analyzing. “They must invest massive amounts of time and expertise to find a mathematical model that can interpret their data,” he explains, “which is where we come in. Our software can take the data, do pattern recognition, and then return an appropriate mathematical model to perform the analysis they require.”

For example, McGowan says, blood coagulation systems are complicated and not well understood. And if blood clots at the wrong time inside the body, heart attacks and strokes can result. “Our software could analyze the data and provide a good mathematical model of coagulation, thereby enabling researchers to suggest diet modifications or drugs that might affect the clotting mechanisms,” he says.

Mathematical Software’s programs will operate on the client’s system so that they may analyze data without compromising proprietary information, McGowan continues, and will be sold on a satisfaction basis. “If our program analyzes your data and says, ‘I know what’s going on here,’ then you pay,” he says. “But if it doesn’t work, you owe nothing.”

McGowan is eager to get the software into circulation. “We anticipate helping many companies with complicated data analysis that will allow them to solve problems that have previously proved intractable.”

—Lori Ferguson