Ms. Akanksha Dwivedi | Parallel Computing | Excellence in Research Award

Research Scholar | Indian Institute of Technology Jodhpur | India

Ms. Akanksha Dwivedi is a doctoral research scholar in Computer Science and Engineering at the Indian Institute of Technology Jodhpur, where she works under the guidance of Dr. Dip Sankar Banerjee at the Systems for Performance, Analysis, and Data Engineering Lab. She holds a Master of Technology in Mechatronics, Robotics, and Automation from the Center for Advanced Studies, Lucknow, and a Bachelor of Technology in Electronics and Communication Engineering from Dr. APJ Abdul Kalam Technical University, Lucknow. She has served as a Teaching Assistant at IIT Jodhpur and RSVS Lucknow, as well as a Project Associate at the National Institute of Technology Uttarakhand, contributing to projects in deep learning for speech decoding and precision health technologies. Her research interests include high-performance computing, scalable parallel algorithms, data analytics, artificial intelligence for healthcare applications, robotics, and sensor technologies. She has published in reputed venues such as Future Generation Computer Systems and IEEE High Performance Extreme Computing, with additional contributions in AI-driven healthcare sensors and sustainable materials. Akanksha has received prestigious fellowships including the Anusandhan National Research Foundation project fellowship and the Ministry of Education doctoral fellowship. She has been honored with awards for innovative ideas, international travel grants, and recognition in hackathons and debate competitions, as well as achievements in sports at the state level. Her research skills span programming in C, Python, and CUDA, parallel computing with OpenMP, data analysis, robotics systems, and advanced tools such as MATLAB and Docker, reflecting her strong technical foundation and multidisciplinary expertise.

Featured Publications

Dwivedi, A., & Banerjee, D. S. (2024, December 4). MST in incremental graphs through tree contractions. In Proceedings of the 28th IEEE High Performance Extreme Computing Conference (HPEC), Boston, USA.

Dwivedi, A., Sharma, S., & Banerjee, D. S. (2023, March 3). Efficient parallel algorithms for large tree contraction. In Proceedings of the Student Research Symposium, International Conference on High Performance Computing (HiPC).

Dr. Wen Zhang | Batteries deep learning | Best Researcher Award

Doctorate at Yeungnam University | South Korea

Professional Profile

Google Scholar

🎓 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