Coding camps

Houston startup aims to arm the energy industry's workforce with coding skills

Daytum exists to train coding experts in oil and gas. Getty Images

Nearly 2,000 miles separate the energy industry of Houston and Silicon Valley where startups have cropped up to help manage the thousands of data points collected on oil rigs each day. The different geographies have developed their own dialects: data scientists on the West Coast talk about how operations should be, according to their models, while the lingo of Third Coast engineers and technicians centers on oil-specific operations.

Last year, while working in natural resource investing from Houston, Kunal Rayakar realized he had heard from a number of students who could, uniquely, speak both. The reason: They knew coding languages, which meant they could analyze their own data and bridge that gap between the coasts. When Rayakar followed the trail of students to the University of Texas at Austin, he found two engineering professors, John Foster and Michael Pyrcz, who were teaching their students data skills as part of the curriculum. They started talking, and eventually Foster and Rayakar founded daytum — and soon after, Pyrcz joined, too.

"The intention is to give people more awareness of the data that comes through, so they can make faster decisions," Rayakar says.

An education program for workers in the energy industry, daytum hosts workshops and an online learning network for technicians and engineers to better understand the data they're working with. This, Rayakar says, helps them exercise more control over the work — especially for those whose preliminary training in the field was before data became indispensable to the job. The professors and Kunal host two– or five-day workshops, and just a few weeks ago, they held both introductory and intermediate courses at the University of Houston.

The professors teach Python, a common programming language. Although there's a learning curve to studying Python, it's not as confusing as some of the tools, like MATLAB, that engineers studied in their undergraduate educations. But students don't actually have to become Python experts at all — instead, they use Jupyter, an online digital notebook that can import Python packages, which are large and ready-made coding sequences.

Often, these are free and available to download on sites like Github. Daytum professors teach packages that are useful for analyzing and visualizing the data they work with in the field, and students leave having a usable workstation on their computers, ready to be installed and implemented in their work.

"People really enjoyed the courses," Rayakar says. "We were really happy."

But right now, daytum's main goal is to continue to grow its workshops — including introducing Austin bootcamps, to engage people in learning, and to empower oil industry technicians to navigate the industry's digital transition more smoothly.

"By building longer-term solutions and cultures, we can build better educations," Rayakar says.

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Building Houston

 
 

This UH engineer is hoping to make his mark on cancer detection. Photo via UH.edu

Early stage cancer is hard to detect, mostly because traditional diagnostic imaging cannot detect tumors smaller than a certain size. One Houston innovator is looking to change that.

Wei-Chuan Shih, professor of electrical and computer engineering at the University of Houston's Cullen College of Engineering, recently published his findings in IEEE Sensors journal. According to a news release from UH, the cells around cancer tumors are small — ~30-150nm in diameter — and complex, and the precise detection of these exosome-carried biomarkers with molecular specificity has been elusive, until now.

"This work demonstrates, for the first time, that the strong synergy of arrayed radiative coupling and substrate undercut can enable high-performance biosensing in the visible light spectrum where high-quality, low-cost silicon detectors are readily available for point-of-care application," says Shih in the release. "The result is a remarkable sensitivity improvement, with a refractive index sensitivity increase from 207 nm/RIU to 578 nm/RIU."

Wei-Chuan Shih is a professor of electrical and computer engineering at the University of Houston's Cullen College of Engineering. Photo via UH.edu

What Shih has done is essentially restored the electric field around nanodisks, providing accessibility to an otherwise buried enhanced electric field. Nanodisks are antibody-functionalized artificial nanostructures which help capture exosomes with molecular specificity.

"We report radiatively coupled arrayed gold nanodisks on invisible substrate (AGNIS) as a label-free (no need for fluorescent labels), cost-effective, and high-performance platform for molecularly specific exosome biosensing. The AGNIS substrate has been fabricated by wafer-scale nanosphere lithography without the need for costly lithography," says Shih in the release.

This process speeds up screening of the surface proteins of exosomes for diagnostics and biomarker discovery. Current exosome profiling — which relies primarily on DNA sequencing technology, fluorescent techniques such as flow cytometry, or enzyme-linked immunosorbent assay (ELISA) — is labor-intensive and costly. Shih's goal is to amplify the signal by developing the label-free technique, lowering the cost and making diagnosis easier and equitable.

"By decorating the gold nanodisks surface with different antibodies (e.g., CD9, CD63, and CD81), label-free exosome profiling has shown increased expression of all three surface proteins in cancer-derived exosomes," said Shih. "The sensitivity for detecting exosomes is within 112-600 (exosomes/μL), which would be sufficient in many clinical applications."

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