Sandeep Jain | Engineering and Technology | Best Researcher Award

Dr Sandeep Jain, Sungkyunkwan University, Republic of Korea, South Korea

Dr. Sandeep Jain is a metallurgical engineer and researcher with expertise in machine learning applications in alloy design, lightweight materials, and high-entropy alloys. He holds a Ph.D. (2023) and M.Tech. (2017) from IIT Indore and a B.E. in Mechanical Engineering (2013). Currently a Postdoctoral Researcher at Sungkyunkwan University, South Korea, Dr. Jain focuses on designing multicomponent alloys and optimizing manufacturing processes. He has published extensively, including works on machine learning-driven phase prediction and flow stress modeling. Dr. Jain is a guest editor, reviewer for leading journals, and recipient of prestigious awards like the Global Best Achievement Award 2024. 🧪🤖🌍

Publication Profile

Orcid

Education

Dr. Sandeep Jain is a dedicated scholar with a robust academic background in engineering. 🎓 He earned his Ph.D. (2017-2023) and M.Tech (2015-2017) in Metallurgical Engineering and Materials Science from the prestigious Indian Institute of Technology Indore, achieving impressive CGPAs of 8.67 and 8.75, respectively. 📘✨ His journey in engineering began with a B.E. in Mechanical Engineering from MBM Engineering College, Jodhpur (2009-2013), where he secured a commendable 68% score. 🔧📚 Dr. Jain’s academic excellence reflects his passion for materials science and mechanical engineering, laying a solid foundation for impactful contributions to his field. 🚀🔬

Research Experience

Dr. Sandeep Jain, currently a Postdoctoral Researcher at Sungkyunkwan University, South Korea 🇰🇷, specializes in designing lightweight multicomponent alloys and optimizing injection molding processes using machine learning 🤖. As a Research Associate at IIT Delhi 🇮🇳, he analyzed the mechanical and creep behavior of Ni-based superalloys and pioneered sustainable rose gold plating methods 🌟. His tenure at IIT Indore included designing lightweight Ni-based alloys and conducting advanced phase equilibria studies 🔬. Dr. Jain’s expertise extends to simulation tools like ANSYS Fluent, XRD, and EBSD, contributing to innovative and sustainable material development 🌍.

Teaching Experience

Dr. Sandeep Jain has an extensive teaching background in materials science and engineering. As a Teaching Assistant at the Indian Institute of Technology Indore (Dec 2017–Nov 2022 and July 2015–June 2017), he contributed to courses like Solidification and Phase Field Modelling, Computational Methods for Materials, and Physical Metallurgy. His expertise also spans practical modules, including Mechanical Workshop, Casting and Welding Lab. Earlier, he served as a Guest Faculty at Govt. Engineering College, Ajmer (Aug 2013–June 2014), teaching Material Science, Engineering Mechanics, Strength of Materials, and more. Dr. Jain’s dedication to education blends technical knowledge with hands-on experience. 🎓🛠️📚

Awards / Fellowships

Dr. Sandeep Jain has earned prestigious accolades for his outstanding achievements in academia and research. In 2024, he was honored with the Global Best Achievement Awards 🎖️🌟, recognizing his contributions to his field. His academic journey has been supported by prestigious fellowships, including the Ph.D. Fellowship 🧑‍🎓📚 and the M.Tech. Fellowship 🎓🔬, both awarded by the Ministry of Human Resource Development (MHRD), Government of India. These honors highlight his dedication, innovation, and excellence in advancing knowledge and contributing to societal progress. Dr. Jain’s achievements continue to inspire and set benchmarks for aspiring scholars worldwide. 🚀📖

Research Focus

Dr. Sandeep Jain’s research focuses on the development and application of machine learning techniques to predict mechanical properties in lightweight alloys and high entropy alloys. His studies include hardness prediction, flow stress, phase prediction, and the influence of processing methods like friction stir processing. These investigations aim to enhance the performance of advanced materials such as Al-Mg-based alloys and CoCrFeNiV high entropy alloys. His work bridges the gap between experimental studies and computational simulations, contributing valuable insights into alloy design and optimization. 🌟🔍📊

Publication Top Notes

A Machine Learning Perspective on Hardness Prediction in Advanced Multicomponent Al-Mg Based Lightweight Alloys

A Machine learning perspective on hardness prediction in multicomponent Al-Mg based lightweight alloys

Enhancing flow stress predictions in CoCrFeNiV high entropy alloy with conventional and machine learning techniques

Influence of Friction Stir Processing on Novel Designed Aluminium-Based Alloy to Enhance Strength and Ductility

Machine learning-driven insights into phase prediction for high entropy alloys

Effect of Ta on the Evolution of Phases and Mechanical Properties of Novel Seven Components Fe–Co–Ni–Cr–V–Al–Ta Eutectic High Entropy Alloys: Experimental Study and Numerical Simulation

Yiming Xu | Tech Innovations | Best Researcher Award

Mr Yiming Xu, Cranfield University, United Kingdom

Yiming Xu is a Ph.D. candidate in Energy at Cranfield University (2020-2024) with a focus on AI for energy flexibility and decarbonisation. He holds an MSc in Advanced Mechanical Engineering from Cranfield University and a BEng in Mechanical Engineering from Nanjing University of Aeronautics and Astronautics. Yiming has contributed to Innovate UK projects, presented at conferences such as ICAE and ISGT, and published papers on energy trading. He has interned at DJI Technology Co., Ltd, and holds patents in finger flexibility devices and mountain-climbing aids. Proficient in Python, C++, and data visualization, he is also an amateur Muay Thai fighter. 🧠🔋🤖📚🥊

Education

With a PhD in Energy from Cranfield University (2020-2024) 🎓, He focused on AI for energy flexibility modelling and decarbonisation 🌱, vehicle-to-vehicle energy trading, and EV owner behaviour analysis 🚗. He presented at ICAE, ISGT, ICPADS, and other seminars 🎤. My MSc in Advanced Mechanical Engineering (2019-2020) included a thesis on peer-to-peer energy trading for EVs ⚡ and courses like CFD and risk engineering 📚. During an AI exchange at Imperial College London (2018), I designed computer vision algorithms for a robotic arm 🤖. My BEng from Nanjing University (2015-2019) involved a thesis on 3D printing and courses in mechanics and materials 🛠️.

Experience

During my internship at DJI Technology Co., Ltd in Shenzhen, China, from June to August 2018, I participated in the global young engineer competition ROBOMASTER, working with a team that included top universities from China and overseas. I served as venue maintenance personnel in the ROBOMASTER machinery group, responsible for debugging mechanical organs and sensors, and maintaining the visual recognition module of the referee system. I inspected and maintained over 50 units of equipment, resolving issues more than 10 times, ensuring the smooth operation of the event. 🌍🤖🔧👨‍🔧📷✅

Research Projects

As a Research Assistant on three Innovate UK projects, I optimized energy flow management in urban EV charging with Lesla Ltd (Aug 2023 – Jan 2024), designing AI models to schedule charging behavior and forecast energy demand 📈🔋. I established a smart home EV charger system for Entrust Smart Home Ltd (Jan 2021 – Mar 2021), focusing on app design and peer-to-peer energy trading 📱🏠. Additionally, I worked with SNRG Ltd and Electric Corby CIC (Oct 2020 – Mar 2021) on advanced grid services, analyzing driving behavior data and designing trading algorithms 🚗💡. All projects met quality standards and were successfully delivered ✅.

Research focus

Yiming Xu’s research primarily focuses on vehicle-to-vehicle (V2V) energy trading, particularly through innovative auction models and flexible trading platforms. His work explores sustainable energy solutions, fraud prevention, and efficient market mechanisms in V2V energy exchanges. Xu’s studies integrate advanced technologies like the K-factor approach and double auction systems to enhance energy trading efficiency and security. His research contributions are significant in the fields of smart grids, green computing, and sustainable energy, aiming to develop robust frameworks for future energy systems. 🌍🔋🚗💡📉🔒