Deep Learning

Human Brain Acitivity Detection (Ongoing)
Electroencephalography (EEG) is a non-invasive method of monitoring and recording electrical activity in the brain, which plays a crucial role in diagnosing and treating various brain-related disorders, particularly in critically ill patients. However, the manual interpretation of EEG data remains a major bottleneck in neurocritical care, as it is time-consuming, expensive, and prone to fatigue-related errors and inter-rater reliability issues. To address these challenges, there is a pressing need to develop automated methods for EEG analysis. The objective of this research is to develop a robust model trained on EEG signals to automatically detect and classify seizures and other types of harmful brain activity, aiming to assist doctors and brain researchers in providing faster and more accurate diagnoses and treatments. This work has significant implications for improving patient care in neurocritical settings, advancing epilepsy research, and supporting the development of new therapeutic interventions.
Frank (Haoyang) Ling, Sijun Tao, Yifei Zhang
Code
Human Brain Acitivity Detection (Ongoing)
NPHardEval: Benchmarking Reasoning Ability of Large Language Models via Complexity Classes

NPHardEval serves as a comprehensive benchmark for assessing the reasoning abilities of large language models (LLMs) through the lens of computational complexity classes. This repository contains datasets, data generation scripts, and experimental procedures designed to evaluate LLMs in various reasoning tasks. The benchmark offers several advantages compared with current benchmarks:

  • Data construction grounded in the established computational complexity hierarchy
  • Automatic checking mechanisms
  • Automatic generation of datapoints
  • Complete focus on reasoning while exclude numerical computation
Lizhou Fan, Wenyue Hua, Lingyao Li, Frank (Haoyang) Ling, Yongfeng Zhang
PDF Code Project
NPHardEval: Benchmarking Reasoning Ability of Large Language Models via Complexity Classes
Movie IR System
In the modern entertainment landscape, finding the perfect movie across numerous streaming platforms has become a challenging task. Current recommendation systems often fall short, relying on generic algorithms or user ratings that overlook individual preferences. Our project addresses this challenge by redefining the movie discovery experience. Unlike traditional approaches, our innovative pipeline combines exact matching with semantic understanding and refined re-ranking. This approach not only achieves a notable increase of 0.11 in nDCG@10 over the baseline BM25 but also aligns more closely with user intent and content relevance.
Frank (Haoyang) Ling, Jia Zhu
Code
Movie IR System
Machine-Generated Text Detection for ChatGPT
  • Discriminated statistical information of machine-generated text to propose an explainable classifier, achieving comparable predictability to BERT-based models.
  • Performed a thorough analysis of data augmentations on response length, identifying that truncated sentences can decrease the model performance by around 5%
Frank (Haoyang) Ling, Haoquan Zhou, Kexuan Huang
PDF Code Dataset Project
Machine-Generated Text Detection for ChatGPT
Contrastive Learning on Graph Representation

Augmented graph data with random masking and self-attention after comparative analyses
to enhance the model’s robustness, surpassing baseline models in 5 out of 9 datasets.

Code Weekly Reports
Contrastive Learning on Graph Representation