IYRC Spring 2024 conference abstracts and presentation videos are now available!
The PDFs provided are abstracts only, showcasing research selected for presentation at the conference. They do not hold the same credit as full papers.
The PDFs provided are abstracts only, showcasing research selected for presentation at the conference. They do not hold the same credit as full papers.
PHOTOS FROM IYRC SPRING 2024 (IN PERSON DAY)
IYRC SPRING 2024 Keynote Speaker
DR. MASAAKI KITAJIMA
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June 8, 2024
The University of Tokyo Masaaki Kitajima is a Project Professor at the University of Tokyo. He earned his PhD from the University of Tokyo in 2011, following which he held postdoc positions at the University of Arizona from 2011 to 2013 and at the Singapore-MIT Alliance for Research and Technology from 2014 to 2016. After working as an Assistant and Associate Professor at Hokkaido University in Japan for 8 years, he started his current appointment at the University of Tokyo in March 2024. His research focuses on environmental virology, particularly in the field of wastewater-based epidemiology. His impactful contributions have led to his recognition as a Highly Cited Researcher by Clarivate for two consecutive years (2022 and 2023). |
IYRC SPRING 2024 BEST ABSTRACT PRESENTATION award winners
PAREKH, Ananya
What Predicts Individual Variation in Speech Motor Learning?
NAIR, Advaith
Analyzing the Prevalence of Computer-Based Activity in Dementia-Alzheimer's throughout California
DONG, Ashley
Linking Neurodegeneration and the Gut-Brain-Axis: Rutin and Apigenin Attenuate Cytotoxic and Inflammatory Effects of Gut Dysbiosis in Colon Fibroblast Tissue Model
KIM, Chaewon
Unveiling the Impact of Neuronal Cell Death Debris on Endothelial Cells and the Protective Role of Methylene Blue Using Novel Transwell Models
BHIMIREDDY, Nikhil
Analysis of Western and Mediterranean Diets on Brain Function and Cognition: A Systematic Review
CHOI, Jennifer
Analyzing the Effect of Aspartame Composites on Viability of Human Brain Cells
KADHAR, Rehaan
Methodology to Solve Computational Power and Time Constraints - Predicting Hypersonic Performance Efficiency in Scramjet Engine Through the Development of Machine Learning Algorithms and Reduced Order Models Trained Through Comprehensive Computational Fluid Dynamics Analysis