Events

Speaker: Wei Yuan

Speaker: Haiyang Jiang

As the EMCR Informal Talk series are designed to share peoples’ own lives, keeping the Informal Talk as an in-person only event best aligns with its personal nature and can maximize interactions with the audience.

Speaker: Yunzhi Zhang (Stanford University)

Speaker: Tharita Tipdecho

Speaker: Dr Jianling Wang (Google Deepmind)

Speaker: Tianyu Gao (Princeton University)

Speaker: Dr Feng Liu (The University of Melbourne)

Speaker: Hung Vinh Tran

This two-day face-to-face course will bring industry professionals together for dialogue and sharing of knowledge to better understand the operation of bushings for power transformers, as well as the design, maintenance and testing thereof.

Speaker: Dr Qitian Wu (MIT & Harvard)

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.

Speaker: Wenting Zhao (Cornell University)

Speaker: Jiahao Zhang (Pennsylvania State University)

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.

Speaker: Jack (John) Morris (Cornell University & Meta AI)

This talk offers a comprehensive review of GNN applications in epidemic modeling, presenting a hierarchical taxonomy for both epidemiological tasks and modeling techniques.

We introduce Dispersing Prompt Expansion DiPEx, a self-supervised prompt learning strategy that overcomes the limitations of manually crafted text queries in VLMs, which often miss objects due to semantic overlap diminishing detection confidence.

This PhD study aims to tackle the common challenges by designing novel training strategies for deep learning models trained on imperfect datasets.

While Large Language Models (LLMs) exhibit strong zero-shot capabilities, carefully designed inference-time strategies are crucial for unlocking their full potential. This talk delves into two tasks where this is particularly evident: text ranking and retrieval-augmented generation (RAG).

This thesis proposes several key works as solutions in decentralized POI recommender systems.

The central focus of our research: developing large language models and foundational models specifically tailored for graph data.

In this talk, I will outline the progress in LLM research, and the opportunities they afford to multilingual settings.

In this talk, I will discuss an alternative approach through inverse rendering, which enables machine learning models to extract explicit physical representations from raw, unstructured image data, such as Internet photos and videos.