Update: 2022 Student Data Science Competition Awards

The month-long 2022 Student Data Science Competition culminated in the final event on April 17, 2022, where prizes totaling $7,000 were awarded to the winning teams.

Competition Setting: The Networks of Texas A&M’s Research

Increasingly, we want our research to address societal grand challenges through convergence that integrates knowledge, methods, and expertise from different disciplines to form novel frameworks that catalyze discovery and innovation. The theme of the 2022 Data Science Competition was “Exploring the Networks and Impact of Texas A&M’s Research”. The competition asked participants to describe and visualize the interdisciplinary nature and impact of Texas A&M’s research by combining bibliometric data with data on grants, policy, partnerships, and media. Participants were asked consider how they would describe Texas A&M’s research to university leaders, state representatives, or funding agencies, or the public, to answer:

  • How have patterns of research involving multiple disciplines evolved at Texas A&M? 
  • What have been the successes for Texas A&M research in solving complex problems, and which new collaborations across disciplines could strengthen Texas A&M’s response to emerging societal challenges?
  • Where has Texas A&M research been represented in public discourse that sets priorities for progress, and where can this representation be increased?

The competition was held over a five-week period in March and April. From an initial registration 94 participants, there were 9 team submissions from which 7 were selected for presentation to the judges at the final event on April 17, 2022.

The Winning Teams

First Place: Cute-Cumber: Left to Right: Zhenlei Song, Ziyi Zhang, Chaoyi He, Diya Li, with Dr. Nick Duffield.

First Place ($2,000): Cute-Cumber: Ziyi Zhang, Chaoyi He, Electrical & Computer Engineering, College of Engineering; Diya Li, Zhenlei Song, Department of Geography, College of Geosciences.

Link Prediction in Collaboration Networks

Link prediction for collaboration networks is one of the most popular topics, which aims to forecast the future links from the structure of the current network. Nowadays, collaboration networks can evolve continuously over time with the addition and deletion of links and nodes, meanwhile, the features of links and edges are changing over time. In this project, we developed a TGN-Scholar model to address the above challenges, which achieves an excellent performance on TAMU Scholar dataset. The results are helpful for researchers in TAMU to know which people, groups, and labs are potential collaborators in the future.”

Second Place: Data Noobs: Rohit Dube, Tushar Pandey, Abhijit Mahapatra, Swarnabha Roy, Sambandh Bhusan Dhal

Second Place ($1,500): Data Noobs: Sambandh Bhusan Dhal, Computer Engineering Program, College of Engineering, Abhijit Mahapatra, Data Science Program, College of Engineering, Rohit Dube, Industrial Engineering Program, College of Engineering, Swarnabha Roy, Computer Engineering Program, College of Engineering, Tushar Pandey, Department of Mathematics, College of Science.

Normalized impact score of topics as a metric to analyze funds and publications in departments

“In the competitive world of academia, it’s important to be aware of your peers and realize potential of collaborating with others working on similar research. The aim is to look at bibliometric data, analyze connections between different research papers and departments in order to maximize the impact of research and find areas of potential collaboration.The analysis related to funding/grant, publication and citations led us to create a powerful metric to determine significance of specific topics within a department. This metric can be used as a tool to make a case for high impact research funding.

Third Place: Team Aster: Jung Hoon Seo and Jiaxin Du, with Dr. Nick Duffield

Third Place ($1,000): Team Aster: Jung Hoon Seo, Department of Computer Science, College of Engineering, Jiaxin Du, Department of Urban and Regional Science, College of Architecture.

Locations of case studies conducted by Texas A&M University scholars

“Most scholars restrain their case study to certain geographical areas, such as a community, a city or a region. Little is known about how the case study location varies and how severe is the geospatial equity problem. In this research, we identified the case study locations of the Texas A&M University (TAMU) scholars’ research. We found that the majority of case studies conducted by Texas A&M University researchers were in the state of Texas state. The results provide suggestions for future research and collaboration. We suggest the research director and agencies create opportunities to encourage them to cooperate. So the future research would be spatially equitable, more generalizable, and have higher influences.”

Joint Fourth Place: Analytics Wizards: Pratiwi Fudlailah, Tatthapong Srikitrungruang, Aditya Pandit (remote)

Joint Fourth Place ($500): Analytic Wizards: Pratiwi Fudlailah, Department of Ocean Engineering, College of Engineering, Tatthapong Srikitrungruang, Department of Industrial and Systems Engineering, College of Engineering, Aditya Yogesh Pandit, Department of Industrial Engineering, College of Engineering.

Texas A&M Research that embraces UN Sustainable development goals

“The project aims to understand how the research conducted at Texas A&M University aligns with the UN Sustainable development goals. The UN Sustainable development goals (UN SDGs) aim to resolve 17 issues such as, elimination of poverty, climate change, gender equality etc. We analyze the UN SDG based research at Texas A&M on parameters such as, countries sponsoring various research topics, funding and collaboration with other institutes, citation and altmetric scores of the research and trends in such research, along with gaps in research in some areas. Using API in Python to extract data from various sources and Tableau to create colorful visualizations, we were able to better understand how the research at Texas A&M University could further help in creating a better and sustainable world”.

Joint Fourth Place: Bean Queens: Bean Queens: Jacob Mashburn, Mansi Bezbaruah, Ben Warren, Thomas Yahl, Arpan Pal

Joint Fourth Place ($500): Bean Queens: Mansi Bezbaruah, Arpan Pal, Thomas Yahl, Ben Warren, Jacob Mashburn, Department of Mathematics, College of Science. Summary:

Identifying Sub-field Collaborations

Special Team Prizes and Midpoint Prizes

  • Best Presentation Design ($500): Cute-Cumber
  • Best Use of Additional Data ($500): Bean Queens
  • Best Supplementary Materials ($500): Data Noobs
  • Midpoint Event Winning Teams ($250 each team): Cute-Cumber, Data Noobs, Pattern Pros (Srujana Reddy Katta, Suma Katabattuni.Nitesh Kumar Reddy Geereddy

Judges and Organization

The panel of judges comprised Arash Abdollahzadeh, Machine Learning Engineer, Chevron; Nick Duffield, TAMIDS Director and Professor in the Department of Electrical and Computer Engineering; Bruce Herbert, Director of the Office of Scholarly Communications; Darren Homrighausen, Instructional Associate Professor, Department of Statistics; Venkatesh Shankar, Director of Research at the Center for Retailing Studies and Professor, Mays Business School; Shuiwang Ji, Professor and Presidential Impact Fellow in the Department of Computer Science & Engineering; and Jian Tao, Assistant Professor in the Department of Visualization and TAMIDS Assistant Director for Project Development. Detailed biographies of the organizers, judges and speakers are available here

Industry Sponsor Presentation from Chevron

Arash Abdollahzadeh ’20, Machine Learning Engineer, Chevron

Arash Abdollahzadeh ’20, Machine Learning Engineering at Chevron and graduate of the Texas A&M BS in Electrical and Computer Engineering program gave an informative presentation on Data Science careers at Chevron, which generated many questions from the student audience.


  • TAMIDS gratefully acknowledges support for the 2022 Data Science Competition: Chevron, the Texas A&M Division of Research, The Texas A&M Libraries, and the Texas A&M Departments of Electrical and Computer Engineering, Statistics, and Visualization.