The Institute for Data-Intensive Engineering and Science (IDIES) is dedicated to supporting the growing number of undergraduate researchers engaged in data-intensive projects
Through the annual Summer Student Fellowship (SSF) program, IDIES provides $6,000 in funding for students to undertake 10-week research projects from June to August.
Eligible projects must:
- Involve data science and data-intensive techniques
- Be mentored by a current IDIES member
- Be entirely student-led and student-designed
The ideal SSF project should:
- Align with the IDIES mission to enhance the JHU mission of “Knowledge for the World” by offering intellectual leadership in data-driven science, adaptive technologies, collaboration, consultation, and sustainable access to high-value datasets
- Have the potential to advance knowledge
- Challenge and seek to shift current research/practice through novel concepts, approaches, or methodologies
- Benefit society and contribute to achieving specific, desired societal outcomes
From the numerous applications received, the following four student researchers have been selected based on their project proposals and previous work in their field:
James D’Alleva-Bochain, mentored by Professor Brice Ménard, will be working on “Understanding Learning: An Exploratory Analysis of the Geometry of Neural Network Weights and Activation”
Alex Larson, mentored by Professor Tamás Budavári will conduct research on “Enhancing Post-Operative Efficiency in the Neurosciences Critical Care Unit Using Machine Learning and Data Science”
Srisha Nippani, mentored by Professor Mauro Maggioni, is investigating “A Non-Parametric Approach for Learning Interaction Laws in Agent-Based Systems”
Jooyoung Ryu, mentored by Professor Robert D. Stevens, will explore “AI-driven Echocardiographic Model for Early Identification of Stress Cardiomyopathy”
We extend a big congratulations to James, Alex, Srisha, and Jooyung. These student researchers exemplify the mission of IDIES and promise significant contributions to their respective fields.
