Events

Don't miss the 2024 Innovation Showcase, a unique opportunity for EECS students to showcase their end of year projects to industry partners and the UQ community.

This two-day face-to-face course will bring industry professionals together for dialogue and knowledge sharing to better understand the renewable energy technologies and their integration regarding renewable generator modelling, control techniques, frequency and voltage regulation aligning with grid codes.

This two-day face-to-face course will bring industry professionals together for dialogue and sharing of knowledge to better understand the fundamentals of power systems along with its modelling and operational aspects.

Our primary research question is how we can effectively utilize limited labelled data for irregular time series modelling?

In this presentation, we focus on expediting ViTs while maintaining high performance.

This presentation proposes a series of compression approaches to reduce the computation complexity and memory usage of deep models for efficiency improvement.

Entity Alignment (EA) is crucial for integrating heterogeneous knowledge graphs (KGs) into a unified knowledge base by identifying equivalent entities across them.

This talk explores the significance of personalization and its potential impact on mood improvement through AI-Composed Soundscapes.

This presentation explores the comparative efficacy and influencing factors of three major platforms: Search Engines, Symptom Checkers, and Large Language Model (LLM)-powered Conversational Agents.

Extensive experiments on public benchmark datasets have verified the effectiveness of the two proposed works in retaining excellent recommendation performance.

This presentation shows results both on synthetic data and real network+smart meter data from a Belgian system operator.

Experience new ways to interact with digital technology in this rapidly evolving field at the 2024 UQ Interaction Design Exhibit.

In this talk, we introduce the Sequential Recommendation problem and draw parallels between language modelling and recommender systems.

Plant disease recognition aims to identify disease through visual symptoms, and has a profound impact on agricultural production and food safety.

In this talk, we will discuss the key challenges in improving efficiency of LLM serving.

This presentation investigates the role of modularity in advancing neuroevolution algorithms and proposes novel techniques that exploit this property to evolve efficient neural networks.

This talk will discuss a flexible survival analysis method that simultaneously accommodates dependent censoring and eliminates the requirement for specifying the copula.

We target conveying more complex information into multi-modal dialogue generation with events, knowledge and reasoning.

With the massive development in Information Communication Technology (ICT) in the past decades, people nowadays are used to fast-paced and real-time information exchange and public opinion deliberation.

This PhD research investigates the critical yet under-explored role of metadata in evaluating the quality of repurposed data.

In this research, we explore the under-researched and challenging area of full-body manipulation tasks for robotics.

In this talk, I will introduce a few works that have been done in my group showing the trends from close-set to open-set and to open-world.

In this thesis, we comprehensively investigate the aforementioned challenges and build effective, secure federated online learning to rank methods.

This research addresses the imperative need for informative representation learning through multimodal data fusion techniques.

This seminar introduces several innovations to address challenges of federated GNN-based recommender systems including performance degradation, inflexible architectures, slow convergence, and the demand for substantial local storage resources.