February 2, 2021
Collocated with the Thirty-Fifth Conference on Artificial Intelligence (AAAI-21)
External Site: https://aaai-uc.github.io/2021.html
The Undergraduate Consortium (AAAI-UC) offers undergraduate students an opportunity to enrich their conference experience by:
- presenting and receiving critical feedback about their work in a professional, academic setting;
- meeting prospective graduate advisors;
- receiving mentoring about the advantages (and disadvantages) of pursuing graduate studies in AI as well as practical early career advice;
- expanding their professional network to include AI experts, current graduate students, and undergraduate peers; and
- providing advice, tools, and resources for successfully applying to and attending graduate school in an AI-related research.
The fourteen students accepted to participate in this program will also participate in the AAAI-21 Poster Sessions on Thursday, February 4 (see accepted list below). All interested AAAI-21 undergraduate student registrants are invited to observe the presentations.
The full Undergraduate Consortium schedule is available in the AAAI Virtual Conference site.
AAAI gratefully acknowledges the generous grants from AI Journal and the National Science Foundation, which make this program possible.
AAAI-21 Undergraduate Consortium Accepted Papers
UC-10: Efficient Robust Music Genre Classification with Depthwise Separable Convolutions and Source Separation
Gabriel A Mersy
UC-29: Text Analysis for Understanding Symptoms of Social Anxiety in Student Veterans
Morgan Byers, Vangelis Metsis
UC-30: Use of Computer Vision to Develop a Device to Assist Visually Impaired People with Social Distance
Lucas G Nadolskis
UC-34: Investigating Methods of Balancing Inequality and Efficiency in Ride Pooling
Naveen J Raman
UC-40: Bison Hacks the Yard: Assisting Underrepresented Students Overcome Impostor Syndrome with Augmented Reality and Artificial Intelligence
Nicole M Sullivan
UC-46: Analyzing Games with a Variable Number of Players
Madelyn Gatchel
UC-47: Affect-Aware Machine Learning Models for Detecting Deception
Leena Mathur
UC-49: Probabilistic Robustness Quantification of Neural Networks
Gopi Kishan, Apurva Narayan
UC-56: Evolving Spiking Circuit Motifs Using Weight Agnostic Neural Networks
Abrar Anwar
UC-59: The Price of Anarchy in ROSCAS with Risk Averse Agents
Christian Ikeokwu
UC-66: Predictive Agent-Based Modeling of Natural Disasters Using Machine Learning
Favour Nerrise
UC-71: Exploration of Unknown Environments Using Deep Reinforcement Learning
Joseph McCalmon
UC-76: Using Remote Sensing Imagery and Machine Learning to Predict Poaching in Wildlife Conservation Parks
Rachel Guo
UC-77: MOTIF-Driven Contrastive Learning of Graph Representations
Arjun Subramonian