Jingjing Guo(郭晶晶)

About Me

Hi there!

I am Jingjing(Jacqueline) Guo from Chongqing, China. Currently, I am a first-year PhD student at the University of Glasgow, super fotunate to be supervised by Dr. Marwa Mahmoud. I also work online as a research assistant at the Chinese University of Hong Kong, very fortunate to be supervised by Prof. Xiangyu Yue and co-supervised by Prof. Sicheng Zhao. My ultimate goal in life is to expand the range of my possibilities and to use everything I have to be committed to social equality and better communication between people.

Previously, I was an algorithm engineer at AI Lab of AICFE at Beijing Normal University. I obtained a Bachelor's degree in Computer Science & Technology at Lanzhou University, China in 2022, with a background of independent interdisciplinary research between computer science and psychology. I particularly focused on Emotion research with artificial intelligence technology, which also drew my attention to multimodal machine learning.

As you might notice, despite having a STEM background, I have a strong humanitarian side. I love and am willing to support those who dare to question authority, break constrains&norms, and explore various possibilities. Besides, I am enthusiastic about photography(Here's my portfolio 😆) as well, with the possibility to make it my second career.

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News

2023.9 Arrived in Glasgow! Start my journey to becoming a brilliant PhD!
2023.6 So excited to receive a fully funded scholarship to support my incoming PhD study at the University of Glasgow!!! I am so thrilled! 🥹🤩🥳Thank you CSC and UofG!
2023.5 Joined Beijing Normal University as an algorithm engineer! Exploring a whole new field!
2022.10 Started working with Prof. Xiangyu Yue and Prof. Sicheng Zhao! New Adventures ahead！
2022.6 Graduated from Lanzhou University with Excellence! I'll miss you, my youth.
2022.3 Named as Chun-Tsung Scholar! This is the closest I've ever been to Mr. Tsung-Dao Lee @theNobelPrize 🥹 ！Thank you CURE.

Selected Recognition and Awards

Research and Innovation
    Scholarship: China Scholarship Council and the University of Glasgow joint scholarship, 2023
    Honour: Excellent Bachelor's Thesis, 2022
    Honour: Chun-Tsung Scholar, 2022
    Scholarship: EIT Digital Master School scholarship, 2022
    Grant: Chun-Tsung Endowment, 2021
    Honour: Excellent Innovation and Entrepreneurship Training Program, 2020
    Grant: National Innovation and Entrepreneurship Training Program, 2018

Photography and Videography
    Honour: The First Price in the 4th College Student Cyberculture Festival Competition, 2019

Professional Service

IT Support, Global Chinese Conference on Computers in Education(GCCCE), 2023
PC Member, International Conference on Affective Computing and Intelligent Interaction(ACII) 2023

Interesting Projects

Emotion Analysis Models of Chinese Handwriting Based on Machine Learning

The initial concept for this project had been echoing in my mind for years, and I finally started this project in 2021, with a query against the old saying, "Handwriting presents the personality of one"(字如其人). With the maturation of emotion theory and the boom in artificial intelligence technology, research on emotions based on machine learning methods has begun to emerge. However, it is surprising to find that there is almost no handwriting-based analysis of emotional orientation, which is a mysterious area to be explored. After a survey of the literature, the possibility of a correlation between handwriting and the emotions of the writer is very high. This research makes a hypothesis that "Chinese handwriting and the emotions of writers are correlated".

Thanks to the Hui-Chun Chin and Tsung-Dao Lee Endowment Program, this project was funded from 2021 to 2022, and I was supervised by Prof Yonggang Lu during the whole year. Moreover, I was offered an internship opportunity at Wangxuan Institute of Computer Technology, Peking University under the supervision of Prof Zhouhui Lian, who always supported me and helped me a lot. During the internship, I conducted an experiment to collect data from the first stage. However, as there were some errors in the experiment that could not be avoided, a second stage of data collection was carried out. With the data, four machine learning models were used to train and predict. In the end, a preliminary result showed that there is a correlation between Chinese handwriting and the emotions of the writer.

Even though this project was first met with a great deal of scepticism, for instance, people saying it had no prior study, no research significance or future development, and it had too much whimsy in the ideas, I persisted in independently practising my idea. Finding an issue that attracts people and tackling it with zeal, in my opinion, already encompasses the significance of this deed.

This project still has a long way to go, and it might take ages to build a pretty complete dataset. I will keep perfecting the result and hope there will be a polished outcome present soon. Feel free to contact me if you have any question or thought.

Deep Fusion of Multimodal Bio-signals for Emotion Recognition

For the detection of emotion, I was keen to use bio-signals to aid detection in the project of "Emotion Analysis Models of Chinese Handwriting Based on Machine Learning" to increase the confidence of the emotion data. However, it failed due to the lack of equipment. So I decided to make up for the regret in my Bachelor's thesis. This project was supervised by Prof Xiaowei Zhang, and he helped me gain access to the DEAP dataset.
Studies show that human bio-signals are closely related to emotion, and emotional states can be effectively recognized by detecting human bio-signals. However, the complexity and non-stationary nature of bio-signals usually affect the effect of emotion recognition. Nevertheless, the performance of the emotion recognition model can be effectively improved by integrating bio-signals of multiple modalities, modelling the correlation between signals, and then utilizing the complementarity of bio-signals of multiple modalities.

Given this, from the perspective of multi-modal fusion, Deep Canonical Correlation Analysis (DCCA) was used to comprehensively explore Electroencephalogram (EEG) and Electrooculogram (EOG) signals. The correlation between EEG and EOG and the synergistic representation was obtained, which realized more accurate emotion recognition than unimodal. Firstly, this thesis respectively extracts the initial features of EEG signal and EOG signal by fast Fourier transform and wavelet transform. Then, the correlation between the initial features of the two modalities is fully modelled by constructing the DCCA model, and the feature vectors mapped by the two modalities are integrated to generate a more specific joint fusion representation. Through fusion classification experiment on DEAP, the results show that: multimodal emotion recognition effect than uni-modal signal is improved, which further verified the multimodal fusion bio-signal to realize the superiority and necessity of emotion recognition.

Red Herring

Do not go gentle into that good night
by Dylan Thomas
Do not go gentle into that good night,
Old age should burn and rave at close of day;
Rage, rage against the dying of the light.
Though wise men at their end know dark is right,
Because their words had forked no lightning they
Do not go gentle into that good night.
Good men, the last wave by, crying how bright
Their frail deeds might have danced in a green bay,
Rage, rage against the dying of the light.
Wild men who caught and sang the sun in flight,
And learn, too late, they grieved it on its way,
Do not go gentle into that good night.
Grave men, near death, who see with blinding sight
Blind eyes could blaze6 like meteors and be gay,
Rage, rage against the dying of the light.

Sometimes I upload videos about my research and life, and you can find them on my Youtube channel.

I STRONGLY CONDEMN FEUDAL DROSS.
强烈谴责封建糟粕。
I STRONGLY OPPOSE ANY FORM OF OPPRESSION AND DISCRIMINATION AGAINST WOMEN.
强烈反对任何形式的对女性的压迫和歧视。

Thank you for coming! Do contact me if you are hesitating.