My Team 2025
Ongoing Phd Students
Aisyahtul Mardhiah binti Mokhtar Alfakari (Malaysia)
Doctorate (PhD)
2024
Project Name: Understanding Bribery Practices from a Neuroscience Cognitive Perspective
Aisyahtul was a graduated Master student under the supervision of Dr. Syaheerah and is continuing with her PhD. Her Masters project focuses on data visualization embedded with time series prediction regarding the attrition and promotion issues among the permanent USM staff. The dashboard that describes the attrition pattern and promotion structure that happened in USM has been developed successfully using PowerBI. In order to touch the prediction element of data science, the attrition trend using the time series prediction models has been built using Python and embedded into PowerBI for the best visual. For her PhD, she is exploring multimodal bribery tendency prediction using eye-tracking and survey questions.
Nahvin Muthusamy (Malaysia)
Doctorate (PhD)
2024
Project Name: Modelling Speaker Behavioural Profile for an Enhanced Context in Speech using Deep Learning
Under the guidance of Dr. Syaheerah, Nahvin Muthusamy leads a pioneering speech analysis research endeavor. Beyond conventional transcription, Nahvin explores linguistic nuances, cultural elements, emotions, personal traits, cognition, and speaker reliability.
Employing a diverse dataset and a survey-based approach, Nahvin annotates and timestamps speech data. His research questions the effectiveness of an AI system in analyzing multiple factors, addressing challenges in dataset collection, and benchmarking against existing techniques, utilizing a CNN-based approach on spectrogram representations.
Nahvin’s project contributes significantly to advancing speech analysis techniques, fostering natural human-machine interaction, and enabling innovative applications in various domains. The project’s vision is to revolutionize technology, communication, and our understanding of human behavior through comprehensive speech analysis.
Samira Elsamad (Lebanon)
Doctorate (PhD)
2024
Project Name: Profiling Bribery Tendencies: A Multi-Modal Neuroimaging Approach Using EEG & fMRI.
Samira Elsamad is currently pursuing her PhD under the supervision of Dr. Syaheerah at Universiti Sains Malaysia (USM). Her research focuses on understanding the behavioral and neural factors that influence corrupt decision-making, particularly in the context of bribery. By leveraging EEG brain imaging and machine learning, she aims to develop a predictive model that identifies bribery tendencies based on decision-making processes. This work contributes to the broader study of social neuroscience and the computational modeling of unethical behaviors. Previously, during her Master’s degree, Samira was part of the data science team at the International Mobility & Collaboration Centre (IMCC) at USM, where she worked on an interactive dashboard to enhance internationalization strategies. Her role included evaluating USM’s global partnerships using novel data-driven approaches, reinforcing her expertise in data science applications for strategic decision-making.
Li Dongliang (China)
Doctorate (PhD)
Project Name: LLM-based Virtual Patients for Clinical Interview Training
Under the guidance of Dr. Syaheerah, Li Dongliang’s research focuses on the application of LLMs in clinical education—specifically, developing intelligent virtual patients to assist medical students in history taking training. Traditionally, such training requires significant involvement from experienced physicians, which is often limited by faculty availability and institutional resources.
With the emergence of large language model (LLM), this research seeks to transform conventional virtual patients into intelligent, responsive agents capable of simulating complex clinical scenarios with realism and consistency. The goal is to create virtual patients that can not only provide accurate and medically coherent responses, but also dynamically adapt to students’ questioning style, reasoning depth, and communication skills.
This work is significant because it addresses the resource bottleneck in clinical education and offers scalable, personalized training experiences. By leveraging LLMs, the project aims to improve the fidelity and effectiveness of virtual patient simulations—helping students build competence and confidence before entering real clinical settings.
Assad H. Thary Al-Ghrairi
Doctorate (PhD)
Project Name: Deepfake Detection
I am Assad H. Thary Al-Ghrairi, currently a PhD student in the School of Computer Sciences at USM. I am also a member of the teaching staff at the College of Science at Al-Nahrain University, Iraq, Baghdad. I am interested in research in computer science, pattern recognition, image processing, artificial intelligence, multimedia, remote sensing, and satellite image classification. Deepfake technology has advanced significantly, posing challenges in distinguishing real from manipulated content, particularly in facial images and videos. Artificial intelligence (AI) plays a crucial role in detecting deepfakes through techniques such as Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs), which analyze facial features and patterns for inconsistencies. Key indicators of deepfake content include unnatural facial movements, artifacts in skin tone, and audio-visual synchronization issues. However, the evolving nature of deepfake creation tools makes detection increasingly difficult, necessitating continuous adaptation of detection methods. Effective AI models require extensive datasets of both real and deepfake content to improve accuracy. As this technology progresses, ongoing research and development in detection strategies are essential to mitigate the risks associated with manipulated media.
Ongoing MSc Students
Masters
2025
Project Name: Optimising Radiomics for Prostate Cancer Diagnosis Through Deep Learning-Based Lesion Segmentation and Feature Selection:
This project focusing on optimising the clinical radiomics workflow that combines deep learning-based lesion segmentation and feature selection of prostate cancer under the host Sultan Qaboos University and Hospital. The project includes testing of multiple segmentation models and strategies using multiparametric pelvic MRI to identify the best performaning setup in segmenting prostate lesion with highest DSC. The pipeline eliminated manual segmentation, reducing variability, and improved efficiency. Feature selection methods identified key predictive radiomics features, effectively reducing redundancy while preserving clinical relevance. This integrated approach aims to improve the reliability and clinical applicability of radiomics for non-invasive prostate cancer diagnosis.
Masters
2025
Project Name: Analyzing Expats’ Sentiments in Expats’ Social Media Groups in Oman
Muhammad Al Mubarak Zainal Abeeden is a master student for Master of Science in Data Science and Analytics at Universiti Sains Malaysia (USM). He graduated with a Bachelor of Science in Mathematics (Honours) from the University of Malaya.
His research interest is in the application of advanced machine learning and Natural Language Processing (NLP) techniques to extract insights from unstructured text data. This is shown in his final year project, “Analyzing Expats’ Sentiments in Expats’ Social Media Groups in Oman.” In this study, he developed a detailed NLP pipeline for discourse analysis of the r/Oman subreddit. The project successfully used the BERTopic model for robust topic discovery and the state-of-the-art RoBERTa transformer model for sentiment classification.
One of the key findings of his work was the discovery of a significant shift to highly negative sentiment in expat-related posts concerning specific topics, such as employment contracts and traffic regulations. This finding demonstrates a powerful methodology for providing actionable insights to policymakers and community organizations, highlighting areas of specific concern for the expatriate community.
Before his postgraduate studies, Muhammad had a lot of professional experience as a Senior Workforce Management Team Lead and Analyst in companies such as foodpanda and Accenture. His work involved data-driven forecasting, capacity planning, and business analysis, showcasing a strong foundation in applying analytical skills to solve complex operational challenges. His technical skills include Python (Pandas, NLTK), SQL, and data visualization tools, reinforcing a strong blend of academic theory and practical application in data science.
Masters
2025
Project Name: An Exploratory Study for Characterizing and Predicting Prostate Abnormalities Using MRI-based Radiomics and Artificial Intelligence:
This project focuses on using radiomics features extracted from prostate MRI scans such as T2-weighted and ADC imagesto identify and predict clinically significant prostate cancer. The study applies extraction of 3D radiomic features with PyRadiomics and employs machine learning models such as Random Forest, XGBoost, and Logistic Regression to build predictive frameworks. The research aims to enhance early detection strategies and support radiologists in non-invasive diagnosis using AI-driven methods.
Masters
2024
Cyberbullying Bystanders
Project Name: Improving Cyberbullying DL Detection Models Using Bystanders and LLM-based Techniques
Hazem, an entrepreneur, MSc student at USM, and co-founder of YourVoice, enabling the blind to control their devices by voice.
He earned his BSc in Computer Science (Honours) in 2024, majoring in Intelligent Computing. During his journey, Hazem gained expertise in AI through internships, including ViTrox Corporation, where he applied state-of-the-art computer vision to detect semiconductor defects. His award-winning Final Year Project developed tools to filter inappropriate web content for children.
Currently, his research focuses on automating bystander stance labeling using Large Language Models (LLMs) to improve cyberbullying detection.
Alongside his research he has co-founded his startup YourVoice, in October 2024. Where it is a software that enables the blind and disabled to work, study and have fun online by naturally conversating with YourVoice and it executes then replies to them the results without needing to look at the screen. It comes with its own rehabilitation program to prepare the blind and disabled for the real-world.
Graduated Undergraduate Students
Ryyan Rashid (India)
Undergraduate
2024
Cyberbullying Team
Project Name: Enhancing Cyberbully Detecting using Images
Ryyan Rashid is a committed undergraduate student majoring in computer science with a specialization in intelligent computing at the University of Sains Malaysia. Possessing a robust background in computer science, Ryyan has demonstrated proficiency in developing fully functional web applications and a successful history of constructing various neural networks, including RNN, LSTM, and Transformers, for Natural Language Processing projects.
Fueled by a passion for innovation and technology, Ryyan is motivated to contribute to the future of the field. During a six-month tenure as a researcher at Zhang Lab – Harvard Medical School, he actively applied his expertise in Machine Learning, Artificial Intelligence, Image Processing, and Neural Networks, resulting in meaningful advancements.
Presently, Ryyan is engaged in a project focused on leveraging image data in conjunction with textual data to detect cyberbullying and explore the role bystanders play in such scenarios.
Ammar Nor Shahrin (Malaysia)
Undergraduate
2023
Project Name: Mental Health Dashboard (MENDA)
Ammar has developed MENDA (Mental Health Dashboard), a tool tailored for educational institutions with the primary goal of offering a comprehensive overview of students’ mental health. In response to the escalating mental health concerns among students at SMK Datuk Hj. Mohamed Nor Ahmad, Ammar’s MENDA seamlessly integrates yearly student survey data, including GAD-7 and PHQ-9 surveys. This information is presented in a user-friendly dashboard powered by PowerBI.
MENDA’s dashboard provides visualizations, group comparisons, and the capability to identify students exhibiting depressive and anxiety symptoms. Additionally, Ammar has incorporated a machine learning model for early case identification. By utilizing various features from student surveys, this proactive approach aims to predict potential mental health issues, not only supporting students with existing symptoms but also emphasizing prevention.
Teachers can leverage MENDA to gain profound insights into their students’ mental health, enabling them to effectively provide the necessary support and intervention.
Cheam Yu Chein (Malaysia)
Undergraduate
2023
Trust Modeling Team
Project Name: ِِAutomated Meme Classification Model
Cheam Yu Chein, a third-year student from USM, is set to embark on a research internship in Madrid, Spain, facilitated by the Erasmus+ Program. With the program’s support, Cheam aims to develop an automated classification model during the internship, focusing on categorizing memes as either hateful or not hateful. The research will explore the model’s performance with different inputs, such as text, image, or a combination of both.
The overarching goal of Cheam’s research is to determine the effectiveness of the classification model in various input scenarios. The anticipated outcomes of this investigation hold potential significance in advancing the development of a predictive model for evaluating trustworthiness. The research aligns with broader efforts to contribute valuable insights to the ongoing progress in trustworthiness prediction models.
Belén Ferrón Hurtado (Spain)
Undergraduate
2022
Cyberbullying Team
The aim of Belén’s FYP is to reduce cyberbullying on Twitter by taking into account bystander roles and the effect of their contagiousness.
Nur Syahirah Binti Mohd Nasir (Malaysia)
Undergraduate
2022
Cyberbullying Team
Project Name: Web-based Data Extraction System
Nur Syahirah has developed a Web-based Data Extraction System. This system is designed to collect data from the Twitter platform based on researchers’ specifications and filters. It serves as a valuable resource for researchers, especially those focusing on cyberbullying, providing access to social media data that includes information about onlookers and bystanders. Users have the ability to filter data according to their requirements, including specified dates and times. The system offers outputs in two formats: CSV and HTML tables, enhancing flexibility for researchers in obtaining desired data. Nur Syahirah’s system contributes to the efficiency of gathering and analyzing social media data, supporting research endeavors in the domain of cyberbullying and related studies.
Deborah Ooi (Malaysia)
Undergraduate
2019
Project Name: The Effect of Vocal Cues on Trust at Zero Acquaintance
Undergraduate Research
Graduated Phd Students
Haifa Saleh Alfurayj (KSA)
Doctorate (PhD)
2025
Project Name: The Bystander Contagion in Enhancing Cyberbully Modelling
Haifa’s project aims to enhance cyberbullying detection by exploring the causative factor of cyberbullying spreading, known as Bystander Contagion. This often-overlooked aspect plays a crucial role in the escalation of cyberbullying behavior. Haifa plans to investigate the detection of cyberbullying through the evaluation of users’ profiles, activities, and social media history, with a specific focus on bystander contagion. The goal is to identify influential cyberbullies or bystanders, enabling the suspension of their accounts to effectively reduce cyberbullying incidents.
In developing the cyberbullying detection model, Haifa will address key points, including the influence and characteristics of bystanders, measurement of bystanders’ contagiousness in cyberbullying diffusion, and identification of the most influential users capable of influencing others. The project will utilize datasets from the Twitter platform, with a focus on Arabic language tweets, acknowledging the unique challenges posed by the complex nature of cyberbullying in Arabic. Haifa’s methodology involves labeling and annotating the data, preprocessing procedures, and applying natural language processing, sentiment analysis, and machine learning with feature extraction for classification purposes.
Mustafa Al Qudah (Jordan)
Doctorate (PhD)
2025
Project Name: Stress Modelling Using Multimodal Cues (co-supervision with Dr. Sufril)
Mustafa Al Qudah is engaged in a project centered on human stress and emotion detection. The focus of this endeavor is the detection of stress using multimodal cues, specifically facial expression and temperature. To achieve this, four thermal/visible cameras will be strategically placed in a room to capture participants’ faces as they move. Additionally, a remote photoplethysmography (PPG) camera-based technique will measure participants’ heart rates during the experiment. The experimental setup involves the use of acoustic stimuli to gradually induce stress in participants. User states will be recorded both before and after exposure to the stimuli. The classification of stress and other affective states will rely on deep learning algorithms, showcasing their effectiveness in classifying thermal images. The project aims to include a comparison with alternative classification approaches to provide a comprehensive evaluation of the proposed methods.
Cristina Luna Jimenez (Spain)
Doctorate (PhD)
2023
Trust Modeling Team
Project Name: Multimodal Trust Modelling at Zero-acquaintance (co-supervision with Assoc. Prof. Dr. Fernando Fernandez)
Christina Luna Jimenez delved into the intricate concept of trustworthiness during her research attachment. The focus was on annotating emotional videos at zero acquaintance in a cross-cultural context, with the intention of translating these annotations into a machine-readable format using machine learning tools.
Christina, alongside researchers from Spain, Malaysia, and Hungary, gathered annotations from diverse cultural backgrounds. However, the final experiments exclusively utilized annotations from Malaysian and Hungarian contributors. The findings revealed that perceived characteristics such as eloquence, attractiveness, and authenticity significantly influenced trustworthiness annotations, displaying high correlation values. Notably, these influences varied across cultures; for instance, kindness played a more crucial role for Hungarians than for Malaysians in determining trustworthiness.
Badr Lahasan (Yemen)
Masters
2017
Project Name: Elastic-bunch Graph Matching-based Models to Recognize Faces Exposed to Occlusion, Expression and Illumination
Badr’s research interest lies in biometrics: particularly the study focuses on unconstrained facial recognition – occluded images with additional challenges such as expression, variations and single sample per subject. Badr has attempted to study occluded images coupled with expression, a new challenge few research in this area considers.
Bisan A. N. Alsalibi (Palestine)
Doctorate (PhD)
2017
Project Name: Membrane-Inspired Bat Algorithm and Feature Fusion to Recognize Faces in the Wild
Bisan Alsalibi received her first degree from the Islamic University of Gaza, Computer Engineering Department, Palestine in 2009 and her master degree from the Computer Science School in USM, Malaysia, 2013. She worked as a research assistant in USM in 2013. She is currently a PhD candidate at USM funded by MIS scholarship. Her general research interest is bio-inspired computing, membrane computing, graph theory and computer vision. In short, she is interested in recognizing objects under uncertainty which is being a challenge to even humans by means of modeling biologically motivated approaches. Bisan graduated in 2017 and is now a teaching fellow in SOCS USM.
Graduated MSc Students
Graduated MSc Students
Masters
2025
My name is Tay Saik Yue, 27 years old this year. I was born and raised in Penang where I completed my education up to Form 6. After that, I decided to pursue my bachelor’s degree in the “Land Below the Wind”, which is also known as Sabah. I completed the course “Mathematics with Economics” in Universiti Malaysia Sabah (UMS) with a CGPA of 3.61. My time in Sabah has allowed me to be more adventurous and independent as I was far away from home. I took up the sport of scuba diving and turned it into a hobby. Hopefully in the future, turn it into something that I can do every day. As “data” was getting more attention and turning into a trending topic, I decided to take up the MSc “Data Science and Analytics” in Universiti Sains Malaysia (USM) to improve my knowledge and see where it might lead me in the future. 😊
Masters
2025
Michael Lo Yin Kai is a masters student for MSc Data Science and Analytics in Universiti Sains Malaysia. His CDS590 project (2024/2025) was supervised by Prof. Dr. Syaheerah Lutfi. His research interest are application of LLM in document management and application of machine learning for material failure prediction. He holds a M.Sc. by Research in Mechanical Engineering from Universiti Teknologi PETRONAS. His expertise includes machine learning, NLP, and finite element simulations. He has published research on pipeline failure prediction using artificial neural networks and has industry experience in project management, engineering design, and process optimization.
Masters
2025
I am Khalil Benhammou, a 26-year-old Moroccan currently pursuing a Master’s in Data Science and Analytics at Universiti Sains Malaysia. I hold a Master of Science in Computer Science from the International University of Casablanca. Previously, I worked as a Front-End Engineer at ALGO Consulting Group in Morocco.
Bader Husni Zyoud (UAE)
Masters
2024
Trust Modeling Team
Project Name: Culturally Responsive Zero-Trust Model
Meet Bader Zyoud, an insightful Information Security Engineer based in the UAE. As he nears completion of his Masters, Bader has immersed himself in pioneering research at the intersection of cybersecurity and organizational culture. Beyond being a proficient Information Security Engineer, Bader is a driven professional with a keen interest in understanding how information security culture influences the implementation of the zero-trust model within organizations.
In his ongoing research, Bader investigates the intricate dynamics between information security culture and the zero-trust paradigm, unraveling the nuanced ways in which these elements intersect to fortify an organization’s cyber defenses. His commitment to academic excellence, coupled with real-world experience, positions Bader as a thought leader poised to shape the future of cybersecurity in the UAE. Stay tuned for his insightful contributions to the evolving landscape of information security and the zero-trust model.
Zita Gayakpa (Togo)
Masters
2024
Project Name: IMCC Bantu International Student Assistance
IMCC Bantu aims to revolutionize the support system for full-time international students by leveraging machine learning techniques to match them with the perfect helper. Zita’s innovation addresses the critical need for personalized assistance during the challenging transition to a new educational environment.
Machine learning algorithms analyze various parameters, including cultural background, language proficiency, to identify the most suitable mentors or guides for incoming international students. This tailored matching process ensures that newcomers receive guidance and support from someone who understands their unique circumstances and can provide relevant assistance.
This initiative is crucial as it fosters a smoother integration process, easing the students’ adaptation to a new country. By utilizing machine learning, the IMCC Bantu project facilitates connections based on shared backgrounds that will help reducing the stress often associated with studying abroad. Ultimately, this personalized assistance contributes to their overall well-being and successful integration into their new academic community.
Eugene Kwok (Malaysia)
Masters
2024
Project Name: An Interactive International Mobility Forecasting Dashboard Using Power BI
Eugene is working on a project for International Mobility & Collaboration Centre (IMCC), USM.
His project consists of developing a dashboard for the mobility department by using the inbound mobility data and outbound mobility data. Besides that, he has to make enrollment predictions for the enhance resource planning for the IMCC.
The first benefit for his client is that visualization of data allows management to make informed decision and secondly, it enhance resource planning.
Muhamad Aizat Bin Abdurahim (Malaysia)
Masters
2023
Project Name: Prediction of Pin Failure in Test Interface Unit Using Machine Learning Approach
Muhamad Aizat has developed a machine learning project that will detect a pin failure from testing product data that focused on enhancing current processes which is manual effort of debugging why the pin was fail and replace the pin. The process is lengthy where during the events of products fail in testing, a technician who is debugging the TIU will first address the issue by manually locate the failure pins. The criteria of the pin failure will be determined by test program, and it’s embedded into software that the machine runs. While testing, if the scenario happens, the software will notify user to check the pin of TIU if there is any pin failure. After that the technician will debug the pin and replace it. To improve this process, the project embedded machine learning to analyze the data by predicting the failure before it actually fails. It will tells the user that certain criteria of data will likely fail the testing and notifies the location of pin that going to cause the failure.
Graduated MSc Students
Graduated MSc Students
Ibrahim Abba (Nigeria)
Masters
2023
Project Name: Facial Image Recognition of Boko Haram Terrorists using Transfer Learning and Convolutional Neural Network
Motivated by a personal encounter with the devastating impact of Boko Haram’s insurgency in Maiduguri, where Ibrahim’s home was attacked and razed in December 2021, this research addresses the urgent need for enhanced security measures. Leveraging images from the Nigerian Army’s wanted list and other online sources, the proposed Convolutional Neural Network (CNN) employs transfer learning from the ImageNet dataset to discern Boko Haram members from the community.
Emphasizing that many Boko Haram faces are familiar due to recruitment from the local area, the model focuses on precise facial image recognition. Through careful data collection, including images from known Boko Haram individuals, and fine-tuning the VGG16 architecture pretrained on ImageNet, the model achieves a remarkable 95% accuracy. This work not only advances counter-terrorism strategies but also stands as a personal commitment to fortifying security in regions affected by extremist activities, where community-specific knowledge is pivotal for effective identification of the boko haram terrorist group.
Mohammad Nifhail Bin Zambri (Malaysia)
Masters
2021
Project Name: Prediction of Interestingness in Movie Trailers
MSc. Research
Hassan Adamu (Nigeria)
Masters
2021
Project Name: Mining Public Sentiment From Twitter for COVID-19 Relief and Using Machine Learning Techniques.
MSc. Mix Mode
Sarmla Tharishny Kolasingam (Malaysia)
Masters
2020
Trust Modeling Team
Project Name: Trust Modelling based on Facial Expression using Deep Learning
K. Sarmla investigated trust detection using facial expression towards developing a trust prediction model, for her Masters. In general, behavioral judgement of an individual and decisions on whether to interact or not are highly influenced by trust. Many trust and reputation models were developed in the past but based only on numeric paradigm. The desire to trust is subjective to each individual and is found to be highly affected by one’s facial features. However, do expressions play a role? This serve as the motivation for her to study the effect of human’s facial expressions on trustworthiness of a person by using deep learning based algorithm – Convolutional Neural Network (CNN).
Michael Kong (Malaysia)
Masters
2018
Project Name: Classification of Oscillatoriales Cyanobacteria Using Multimodal Features
Michael Kong received his Bachelor of Computer Science with distinction from the University of Wollongong in 2016 and his Master of Science (Computer Science) from Universiti Sains Malaysia in 2018. His research for his masters thesis focused on image recognition in the area of microbiology, particularly for cyanobacteria recognition. In his free time, he enjoys learning about new and upcoming web technologies as well as photography.
Zaaba Ahmad (Malaysia)
Masters
2015
Project Name: Prediction Models of Extraversion and Neuroticism of Malaysian Facebook Users
Zaaba is a Masters candidate who has worked on a research in-line with the trending of Social Media era where he predicted personality traits through the usage behaviour of social media. His study was to demonstrate how people’s behaviour in social media can be used to predict user personality traits. He has been inspired to work in this area as social media has become universal and important platform for networking and content sharing. Zaaba is now teaching in a local public University.
Najlaa Saddiq Mokhtar (Malaysia)
Masters
2015
Project Name: A Computational Model for Detection Learning
Najlaa Sadiq Mokhtar is a MSc. candidate who embarks on an interesting research in the field of Human Computer Interactions, focusing in Affective Computing (personality, behaviour and emotion analysis) in learning, which. In short, she attempts to investigate the effects of emotions in learning. She hopes to build an affect-sensitive synthetic tutor that could detect and respond appropriately to learner’s state. The emotions involved would be those that accompany learning. Najlaa is now a senior software engineer in a private company.
Amal Azazi Abdulaziz (Yemen)
Masters
2015
Project Name: Automatic Person-independent Detection of 3D Facial Expressions using Optimizition Algorithm Based on Conformal Mapping and Differential Evolution
Amal Aziz’s research interests lay in the field of computer vision, image processing and pattern recognition. Particularly, she is passionate about human emotion recognition towards enhancing human-computer interaction. Her Master’s research is on automatic facial expression recognition in 3D faces. Her intentions are to work in analyzing the verbal and non-verbal human emotion cues to build a multi-model emotion recognition system. Applying such a system in a particular application, such as: gaming, tutoring, etc, to improve the human emotional experience with machines is her ultimate goal. Amal successfully completed her Masters (by research) and is currently embarking PhD. She investigates facial expression recognition impairments in drug users. She would move on to investigate the contribution of gaze in this study. Amal is currently undergoing PhD in Australia with a full scholarship.
Ren Xiaowan (China)
Masters
2014
Project Name: The Impact of Age in Social Media Selection and Emotional Sharing Among Chinese People in Mainland China
Laura Ren’s interests lie within the area of Social Informatics, which is the study of social phenomena, behaviours and structure through the use of technology. Her work focuses in interaction in the Chinese cultural context, using social media features. Currently she is a MSc. Informatics candidate at PPSKOMP, USM. The idea of making machines understand people’s language and emotion has attracted her attention.