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Announcing the WiDS Datathon 2021 Excellence in Research Award Results

8/17/2021

 
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The WiDS Datathon Excellence in Research Award encourages deeper exploration and the development of collaborative data science innovations. Seventeen research papers were submitted from WiDS Datathon participants across eight countries including India, Indonesia, Israel, Pakistan, Saudi Arabia, Spain, the United Kingdom, and the United States. Subject matter experts from the WiDS Datathon Committee, National Science Foundation Big Data Innovation Hubs, and the MIT GOSSIS community evaluated the papers on their potential for real-world impact, rigor in scientific methodology, and clarity of communication. 
Congratulations, ScientificMind!
Congratulations to the winning team, ScientificMind members Maya Saghiv and Pavel Vodolazov, both from Israel. Maya and Pavel met and began collaborating in the WiDS Datathon, placing third on the Kaggle leaderboard. Maya says, ​
“What started as team formation in Kaggle became a fruitful professional relationship, to the extent that we also decided to continue together to submit a paper for the WiDS Datathon Excellence in Research Award. We are very honored and proud to have joined the WiDS Datathon 2021. We worked very hard on our solution for the WiDS Datathon, and were lucky to get a winning place. We were encouraged to submit a paper sharing more details about our work and contributing to the broader development of collaborative data science in the health data community, hoping to make a greater impact.”
Maya and Pavel share their #WiDStory, and invite you to read their research paper: Bridge Over Troubled Data – Practices for Handling Missing Data in Intensive Care Unit Predictive Modeling.
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Maya Saghiv and Pavel Vodolazov

​Honorary Mentions to First-Time Kaggle Participants and a Joint Academia-Government Team
In addition to the team receiving the 2021 WiDS Datathon Excellence in Research award, two additional teams are being recognized for their papers:

Team Parameters Patrol from the United States and the United Kingdom including teammates Stacy Forsyth, Natalie Pirkola, Elena Barbulescu, and Kate Tereshchenko of IBM submitted a research paper, Feature Engineering to Improve Performance. They explain how the WiDS Datathon was the perfect opportunity for collaboration, saying,
“We decided the WiDS Datathon 2021 project was interesting and right for us given our health care background and desire to learn more in data science/machine learning...All four team members were very excited to participate on this project and we started right away. We got together and proceeded to meet multiple times per week to explore the data and work in Kaggle. It was a first time Kaggle competition for all of us...We all feel we improved data science skills as a result and it was a rewarding experience.”
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Stacy Forsyth, Natalie Pirkola, Elena Barbulescu, and Kate Tereshchenko

​Team SIMD-UCLM (Intelligent Systems and Data Mining Lab) from Spain included teammates Juan Carlos Alfaro, Cristina Romero-Gonzalez, PhD, and Julia Flores. This same team has also organized a prior WiDS regional event in Albacete, Spain. For their Excellence in Research submission, they combined insight and knowledge from their backgrounds in both government and academia, submitting their paper, WiDS 2021 - How (good) can Diabetes Mellitus be predicted from ICU data? | Exploratory Data Analysis + Models evaluation. Julia says,  
“The main topic of WiDS suits our research interests as in SIMD we have always been focused on machine learning and applications. We have participated in the last 4 editions of the WiDS Datathon. We find it very encouraging and motivating to learn by participating in this annual challenge.”
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Juan Carlos Alfaro, Cristina Romero-Gonzalez, PhD, and Julia Flores

​Honing skills for beginners and women in data science
The WiDS Datathon continues to encourage women to compete as a way to hone their data science skills using novel datasets and a social good challenge. Participant surveys tell us that:
  • 32% of Excellence in Research Award participants are beginners in data science
  • 42% had not participated previously in any machine learning or predictive analytics competitions 
  • 73% datathon participants said they gained new skills in data science and 58% said it increased their confidence

A few of these beginners are young women, as young as high school students. One such student, Tamar Peleg of Tenafly High School in New Jersey, submitted a research paper with a unique twist covering the Prediction of Immunosuppression and Diabetes Mellitus Using Data.  We encourage secondary school students to consider careers in data science, artificial intelligence (AI), and related fields with our WiDS Education Outreach program, and we invite you to sign up to learn more about how to support young students in your community.

Congratulations to all who participated in the WiDS Datathon Excellence in Research opportunity this year! Stay tuned this fall for updates and announcements about the WiDS Datathon 2022.
The WiDS Datathon Excellence in Research Award 2021 was hosted by the National Science Foundation West Big Data Innovation Hub and the WiDS Datathon Committee, in collaboration with the Midwest Big Data Innovation Hub, Northeast Big Data Innovation Hub, and South Big Data Innovation Hub (NSF Awards 1916573, 1916481, 1915774).

Related Stories:
  • Collaborators Who Met During 2021 Datathon Win Excellence in Research Award
  • WiDS Datathon 2021 Results and Impact
  • Learn from WiDS Datathon 2021 Winners, including a Kaggle Grandmaster

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  • Home
  • About
    • Blog
    • WiDStory
    • News
    • Research
    • Sponsors
    • Collaborators
    • Contact
    • Donate
  • Conferences
    • WiDS Stanford 2023 Agenda
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    • WiDS Regional Events 2023
    • Ambassadors 2023 >
      • Ambassador Advisory Council
    • WiDS Ambassador Program
    • Past Conferences >
      • WiDS 2022
      • WiDS 2021
      • WiDS 2020
      • WiDS 2019
      • WiDS 2018
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    • Conference Committee
  • Datathon
    • Datathon Details
    • Datathon Resources >
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  • Education
    • Workshops >
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    • Next Gen >
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