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Allen School’s Gabriel Erion awarded prestigious F30 fellowship from National Institutes of Health

Gabriel Erion, an M.D.-Ph.D. student currently doing his Ph.D. research with Allen School professor Su-In Lee in the Artificial Intelligence for Medicine and Science (AIMS) Lab, has earned the prestigious Ruth L. Kirschstein National Research Service Award from the National Institutes of Health. The Kirschstein-NRSA, also known as the F30, was created to enhance research and clinical training of promising predoctoral students who are enrolled in a combined M.D.-Ph.D. training program and plan to pursue careers as physician-scientists. 

The University of Washington is one of about 50 schools around the country that have a Medical Scientist Training Program — an M.D.-Ph.D. program supported by federal funding from the NIH. The program can last eight or more years; students spend the first two years of their training in medical school, mostly in a classroom setting. They then transition to a Ph.D. and finish the entire degree before returning to medical school for two years of primarily hospital-based clerkships. After graduating, students often split their time between research and practicing medicine. Erion was the first MSTP student at the University of Washington to join the Allen School for a Ph.D. in Computer Science & Engineering.

Erion initially planned to study biology in college and then go to medical school, but his love for building computers inspired him to take math and computer science classes, too. He switched his major to applied math, but still wanted to combine that with experience in health care. 

“An M.D.-Ph.D. was one of the best ways I found to combine these fields, and the University of Washington had one of the best programs for someone with my interests,” Erion said. “Unlike many other schools, the UW program didn’t limit MSTP students to biology departments, and was supportive of my idea to do a math and computer science based Ph.D.”

Erion and Lee’s research on CoAI: Cost-Aware Artificial Intelligence for Health Care, in collaboration with Dr. Nathan White from the University of Washington Department of Emergency Medicine, helped him secure the fellowship. CoAI is a machine learning method for making cost-sensitive risk scores in clinical settings that maintains or improves accuracy while dramatically reducing the time it takes to predict a variety of patient outcomes.

Medical risk scores are used by doctors to help assess a patient’s risk of having a disease. There are hundreds of such risk scores, and they’re used for a wide range of purposes. Erion’s research focuses on three areas: predicting trauma patients’ risk of bleeding disorders, which is used by doctors to anticipate needs for resources like blood transfusions; predicting in-hospital death of patients in intensive care units, which is used to determine who needs the most attention from doctors and nurses; and predicting whether primary care patients will live for 10 years after their visit — an outcome which is not often assessed in clinics, but could be useful as a general indicator of patient health for primary care doctors. 

CoAI was created to help doctors with these time-consuming risk assessments by providing a general-purpose machine learning framework for building low-cost clinical risk scores. The F30 grant will enable Erion and his team to create methodological developments and experiments to demonstrate CoAI’s value in a range of important clinical scenarios. For example, predicting a trauma patient’s risk of bleeding disorders is tedious right now with the Prediction of Acute Coagulopathy of Trauma (PACT) score, but CoAI could make it more efficient. 

“The PACT score variables are some of the most time- and effort-intensive to gather, which we determined by surveying EMTs, paramedics, and nurses in our region. PACT requires blood pressure and heart rate measurements as input, as well as the Glasgow Coma Score, which is a 15-point scale that takes time and cognitive effort to calculate,” Erion explained. “We wanted to see if we could predict bleeding disorders as well as or better than PACT, while reducing the effort required for emergency medical service providers to gather necessary data for the score.”

This ease of use means the tool would be more likely to be deployed in practice, saving EMTs valuable time in the ambulance. The team conducted a survey that showed providers would prefer a risk calculator for which inputs would only take less than a minute to gather. 

“We showed that CoAI can actually provide more accurate predictions than the PACT score with only 50 seconds of data-gathering, which is almost 10 times less than the PACT score was calculated to take,” Erion said. “It does this by automatically determining which variables are easy to measure, for example, data from the dispatcher that is pre-provided, or data like age that take only seconds to gather. We also demonstrated similar performance improvements for the ICU and primary care datasets.”

Prior to Erion earning the NIH fellowship, he and the CoAI team captured the Madrona Prize at the Allen School’s 2019 Research Showcase.

Congratulations, Gabe! 

June 16, 2020

Allen School and Madrona Venture Group highlight student and faculty innovation at 2019 Research Showcase

Man standing in front of PowerPoint slide titled "Wearable and Mobile Devices"
Professor Tim Althoff presents his research on data science for human well-being during the luncheon keynote

Every fall, the Allen School’s Industry Affiliates program hosts a research showcase to highlight the ways in which our faculty and student researchers are advancing the frontiers of computing. The day-long event features sessions devoted to various topics in computing and culminates in an open house and poster session that gives our industry partners, alumni, and friends an opportunity to learn more about the latest innovations emerging from Allen School labs.

Among the many highlights of the 2019 Research Showcase, which was held Wednesday in the Paul G. Allen Center and Bill & Melinda Gates Center on the University of Washington’s Seattle campus, was a keynote by professor Tim Althoff. Althoff, who joined the Allen School faculty last year, combines techniques from data mining, social network analysis, and natural language processing to generate actionable insights about people’s physical and mental health.

For example, Althoff is pursuing ground-breaking research that aims to use data generated by people’s everyday behavior to better understand the level and variance of physical activity of populations around the world. As part of this work, he found that the inequality of physical activity within a country is a predictor of obesity rates. Althoff believes that such insights can inform how our environment influences our behavior and health, and in the future could support the data-driven design of cities.

“This research is uniquely enabled by the massive digital traces generated by wearables and mobile devices,” explained Althoff. “It revealed the existence of a health inequality that we were previously unaware of.”

Madrona Prize winners Joseph Janizek (left) and Gabriel Erion (center) of the CoAI team with Madrona’s Tim Porter

For another project, Althoff analyzes online search engine interactions to gauge the impact of sleep on cognitive performance in the workplace and among athletes. He is also exploring a data-driven approach to mental health counseling to identify the most effective conversational strategies to support peer-to-peer counseling and improve client outcomes. 

In addition to Althoff’s talk, the program included in-depth sessions in which participants had an opportunity to explore the latest developments across a variety of domains, including data management, programming languages and software engineering, robotics, systems, augmented and virtual reality, ubiquitous computing, machine learning, deep learning for natural language processing, and the intersection of computation and biology. At the end of the day, Allen School leadership and representatives of Madrona Venture Group announced the recipients of the 14th annual Madrona Prize and the People’s Choice Award — a tradition in which we celebrate the innovative contributions of our student researchers with prizes and public bragging rights.

This year’s grand prize winner, CoAI: Cost-Aware Artificial Intelligence for Health Care from the Allen School’s Laboratory of Artificial Intelligence for Medicine and Science (AIMS) led by Professor Su-In Lee, was chosen by Madrona Venture Group for combining excellence in research with the potential for commercial success. CoAI is a machine learning method for making cost-sensitive predictions in clinical settings that maintains or improves accuracy while dramatically reducing the time it takes to predict a variety of patient outcomes. The team, which includes Lee, Allen School Ph.D./M.D. students Gabriel Erion and Joseph Janizek, and Drs. Carly Hudelson and Nathan White of UW Medicine, developed CoAI to integrate with existing machine learning packages with just a few lines of code to improve patient care when it comes to time-sensitive clinical prediction tasks in all areas of medicine.

Katie Doroschak (center) demonstrates molecular tagging using nanowire-orthogonal DNA strands to the Madrona team

Madrona also recognized three runners-up that also exemplify high-quality research combined with commercial potential:

AuraRing: Precise Electromagnetic Finger Tracking via Smart Ring, from the UbiComp Lab, by Electrical & Computer Engineering Ph.D. students Farshid Salemi Parizi and Alvin Cao; Allen School alumnus Eric Whitmire (Ph.D., ‘19), now a research scientist at Facebook Reality Labs; Allen School Ph.D. student Ishan Chatterjee; GIX master’s student Tianke Li; and professor Shwetak Patel, who holds a joint appointment in the Allen School and Department of Electrical & Computer Engineering

Molecular Tagging with Nanopore-orthogonal DNA Strands, from the Molecular Information Systems Lab, by Allen School Ph.D. students Katie Doroschak and Melissa Queen; Chemistry undergraduate Karen Zhang; Allen School master’s student Aishwarya Mandyam (B.S., ‘19); research scientist Jeff Nivala; Allen School affiliate professor Karin Strauss, Principal Research Manager at Microsoft Research; and Allen School professor Luis Ceze.

HomeSound: Exploring Sound Awareness in the Home for People Who Are Deaf and Hard of Hearing, from the Makeability Lab, by Allen School Ph.D. students Dhruv Jain and Kelly Mack; Human-Centered Design & Engineering Ph.D. student Steven Goodman; professor Leah Findlater of the Department of Human-Centered Design & Engineering; and Allen School professor Jon Froehlich.

Farshid Salemi Parizi lets a guest take AuraRing for a spin

Calling the Allen School showcase “one of the highlights of our year,” Madrona managing director Tim Porter said, “The Allen School at the UW is an incredibly important resource for our region and as the school has grown and actively attracted researchers from many different areas, we have seen the breadth and depth of innovation grow.”

HomeSound also took home the coveted People’s Choice Award, which is voted on by attendees at the open house as their favorite poster or demo of the evening. The runner-up for People’s Choice was ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks. The team behind ALFRED spans the Allen School’s Robotics and Natural Language Processing groups, including Allen School Ph.D. students Mohit Shridhar and Daniel Gordon; Allen School postdoc Jesse Thomason; former postdoc Yonatan Bisk, currently a visiting researcher at Microsoft; Winson Han and Roozbeh Mottaghi of the Allen Institute for Artificial Intelligence; and Allen School professors Luke Zettlemoyer and Dieter Fox.

“Our students and faculty aim for real-world impact, and it really shows in the presentations we saw this week,” said Hank Levy, director of the Allen School. “We’re pleased that so many of our industry partners could join us to learn about the exciting developments happening in our labs — developments that not only will advance our field, but also have the potential to improve millions of people’s lives. I want to thank Madrona Venture Group, in particular, for their friendship and support to the school and our students throughout the years.”

Dhruv Jain (center) of the Makeability Lab explains People’s Choice winner HomeSound to attendees

This is the 14th year in which Madrona has formally recognized student research with commercial potential emerging from the Allen School.

Read more in the Madrona press release here, and check out GeekWire’s coverage of Althoff’s keynote here and the poster session here. See a complete list of past Madrona Prize winners here, and learn more about the Allen School’s Industry Affiliates program here.

Thanks to Madrona and to all of our industry partners, alumni and friends who showed up yesterday in support of our students, and congratulations to the winners — see you next year!

November 22, 2019