- Led Collaborative Projects: Spearheaded academia-industry projects involving Computer Vision and Multi-Agent Reinforcement Learning, collaborating with external partners such as IMDA and DSTA.- Team Leadership: Directed and managed teams of PhD students, delegating tasks effectively and ensuring timely completion of project milestones.- Problem-Solving: Facilitated discussions with team members to identify and resolve project issues, promoting innovative solutions and technical problem-solving.- Code Validation: Conducted thorough reviews and validations of team members' code, ensuring accuracy, efficiency, and adherence to best practices.- Project Coordination: Coordinated activities with external industry partners, overseeing project scoping, data preparation, and model development phases.- Research Support: Provided guidance and support to team members on research projects involving Multi-Agent Reinforcement Learning and Large Language Models.- Communication Skills: Maintained regular communication with external partners to align project goals, expectations, and deliverables, fostering strong industry-academic relationships.- Technical Expertise: Applied advanced knowledge in Computer Vision and Reinforcement Learning to develop and refine models, contributing to the success of collaborative projects.- Accomplishments: Successfully delivered project outcomes that met stakeholder requirements, demonstrating practical applications and impact of advanced machine learning techniques.

- Research and Development: Studied the integration of Deep Convolutional Neural Networks (CNNs) with Gaussian Processes (GPs) for image classification tasks.- Model Generalization: Demonstrated that combining GPs with CNNs improves the model's generalization on unseen data, enhancing its robustness.- Model Calibration: Showed that the integration of GPs with CNNs improves model calibration, ensuring that the estimated class probabilities are consistent with real-world occurrences.- Out-of-Distribution Detection: Proved that the combination of GPs and CNNs helps detect out-of-distribution data through reliable predictive uncertainty.- Internship Report: Authored a comprehensive internship report detailing the research findings, which received the maximum score.- Master's Degree: Successfully obtained a master's degree, leveraging the achievements and findings from the internship.

- Collaborative Development: Worked with team members to develop a C++ tool for the Human Resource Department to automatically extract information from Curriculum Vitae files in various formats such as DOCX and PDF.- Data Structuring: Designed and implemented a solution to convert extracted information from Curriculum Vitae to a structured table format for easier data management and analysis.- Implementation: Coded and integrated various features of the tool, ensuring robust performance and accuracy in data extraction.- Quality Testing: Conducted software quality testing and incorporated feedback from human recruiters to enhance the tool's functionality and user experience.- Professional Development: Gained hands-on experience in the complete software development lifecycle, including project management, design, coding, testing, and deployment.