Dr. Wen Zhang | Batteries deep learning | Best Researcher Award
Doctorate at Yeungnam University | South Korea
Professional Profile
🎓 Educational Background
Wen Zhang (张雯) has pursued a diverse and enriching academic journey, demonstrating her passion for design and engineering. She earned her Bachelor’s degree in Industrial Design from Chengdu Neusoft University in China, graduating in June 2021 with a GPA of 2.73/4.0. Following this, Wen advanced her studies in Mechanical Engineering at Yeungnam University, South Korea, where she completed her Master’s degree in August 2024 with an impressive GPA of 4.05/4.5. She is now delving deeper into her field by pursuing a Doctoral degree in Mechanical Engineering at the same university, starting in September 2024.
💻 Skills and Expertise
Wen Zhang possesses a robust set of skills and expertise that align perfectly with her academic and professional pursuits.
🌐 Language Proficiency
As a native Mandarin speaker, Wen excels in communication in her mother tongue. Additionally, she has demonstrated fluency in English, underscored by her impressive TOEFL score of 92, which highlights her strong linguistic and cross-cultural communication abilities.
🛠️ Software Proficiency
Wen has mastered a wide array of software tools critical for design and engineering. Her expertise includes CAD (Computer-Aided Design) for technical and industrial design applications, Photoshop (PS) and Illustrator (AI) for advanced graphic design, CorelDRAW (CDR) for vector illustration, and After Effects (AE) for motion graphics and video editing. She is also skilled in Python programming, showcasing her versatility in computational tasks and problem-solving.
Publications Top Noted📝
Emerging two-dimensional (2D) MXene-based nanostructured materials: Synthesis strategies, properties, and applications as efficient pseudo-supercapacitors
Authors: Rui Wang, Won Young Jang, Wen Zhang, Ch Venkata Reddy, Raghava Reddy Kakarla, Changping Li, Vijai Kumar Gupta, Jaesool Shim, Tejraj M Aminabhavi
Journal: Chemical Engineering Journal
Year: 2023
Lithium-Ion Battery Life Prediction Using Deep Transfer Learning
Authors: Wen Zhang, RSB Pranav, Rui Wang, Cheonghwan Lee, Jie Zeng, Migyung Cho, Jaesool Shim
Journal: Batteries
Year: 2024