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The 38th Annual AAAI Conference on Artificial Intelligence

February 20-27, 2024 | Vancouver, Canada

  • AAAI-24
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New Faculty Highlights (NFH-24)

This year, AAAI is continuing its invited speaker program, highlighting AI researchers who have just begun careers as new faculty members or the equivalent in industry. 

New Faculty Highlight talks will be allotted 30 minutes each; the aim is for these talks to broadly survey the candidate’s research to date. Several talks will be released online and publicized each day of the AAAI conference, following which they will be available archivally as part of the conference program. Invited speakers will be further invited to contribute an article to a corresponding series in AI Magazine. 

Program Overview and Schedule

New Advances in Safe and Efficient Large Language Models
Hongyang Zhang

Towards Reproducible, Automated, and Scalable Anomaly Detection
Yue Zhao

Fair and Optimal Prediction via Post-Processing
Han Zhao

Continual Learning in an Open and Dynamic World
Yunhui Guo

The Role of Over-Parameterization in Machine Learning – The Good, the Bad, the Ugly
Fanghui Liu

Demystifying Algorithmic Fairness in an Uncertain World
Lu Cheng

Making Natural Language Reasoning Explainable and Faithful
Xinya Du

Towards Human-like Learning from Relational Structured-Data
Quanming Yao

Recent Advancements in Inverse Reinforcement Learning
Alberto Maria Metelli

Scaling Offline Evaluation of Reinforcement Learning Agents through Abstraction
Josiah Hanna

Towards Trustworthy Deep Learning
Lily Weng

Data-Efficient Graph Learning
Kaize Ding

Harmonious Mobility for Robots That Work with and around People
Christoforos Mavrogiannis

Fostering Trustworthiness in Machine Learning Algorithms
Mengdi Huai

Towards Reliable Learning in the Wild: Generalization and Adaptation
Huaxiu Yao

Algorithmic Foundation of Federated Learning with Sequential Data
Mingrui Liu

Fairness with Censorship: Bridging the Gap between Fairness Research and Real-world Deployment
Wenbin Zhang

Learning Representations for Robust Human-Robot Interaction
Yen-Ling Kuo

Collaborative Learning across Heterogeneous Systems with Pre-Trained Models
Trong Nghia Hoang

From Statistical Relational to Neuro-Symbolic AI
Giuseppe Marra

Symbolic Reasoning Methods for AI Planning
Gregor Behnke

Combating Insider Threat in the Open-World Environments: From Long-Tailed Data to Long-Tailed Model
Dawei Zhou

Towards Reliable Semantic Vision
Tejas Gokhale

Exploiting Data Geometry in Machine Learning
Melanie Weber

Quantifying Political Polarization through the Lens of Machine Translation and Vicarious Offense
Ashiqur Khudabukhsh

When Causal Inference Meets Graph Machine Learning
Jing Ma

Understanding Surprising Generalization Phenomena in Deep Learning
Wei Hu

Empowering Graph Neural Networks from a Data-Centric View
Wei Jin

Large Models, Limited Resources: Navigating Cross-Modality Research in the New Era
Yu Wu

Towards Holistic, Pragmatic and Multimodal Conversational Systems
Pranava Madhyastha

Interactive Theorem Provers: Applications in AI, Opportunities, and Challenges
Mohammad Abdulaziz

Provable and Efficient Machine Learning Algorithms for Optimization, Sampling, and Decision Making
Pan Xu

For More Information 

Inquiries concerning submissions and suggestions for the new faculty highlight program may be directed to the program cochairs at aaai24fhchairs@aaai.org. All other inquiries should be directed to AAAI at aaai24@aaai.org. 

New Faculty Highlight Co-Chairs 

Guy Van den Broeck, University of California, Los Angeles 
Vibhav Gogate, The University of Texas at Dallas 

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