Ms. Navneet Gandhi | Gas Sensor Awards | Best Researcher Award

Ms. Navneet Gandhi | Gas Sensor Awards | Best Researcher Award 

Ms. Navneet Gandhi, IIITDM jabalpur, India

Navneet Gandhi is an aspiring semiconductor researcher currently pursuing a Ph.D. at IIITDM Jabalpur, India, with a strong focus on advanced nanoelectronic devices and sensor technologies. Her doctoral research centers on the simulation, fabrication, and machine learning-aided optimization of junctionless FET-based sensors, emphasizing negative capacitance and strain silicon approaches. With a Master’s degree in Embedded Systems and VLSI Design from SVITS Indore and a Bachelor’s degree in Electronics and Telecommunication Engineering from LNCT Indore, Navneet has built a solid academic foundation. Her research interests span simulation and modeling of NC-FET-based biosensors and gas sensors, the use of AI techniques in semiconductor device analysis, and the exploration of next-generation device architectures such as nanosheets, forksheets, and FerroFETs. Additionally, she is engaged in the fabrication of nanomaterial-based sensors. Navneet combines strong theoretical expertise with hands-on experience, aiming to contribute significantly to the advancement of sensor technology and nanoelectronics.

Professional Profile:

GOOGLE SCHOLAR

ORCID

Summary of Suitability: Navneet Gandhi – Best Researcher Award

Navneet Gandhi is a highly promising researcher in the field of semiconductor devices, nanosensors, and machine learning-assisted modeling. With a solid academic background and deep-rooted research expertise, she is making significant contributions to the advancement of next-generation sensor technologies.

📚 Education Background

  • 🎓 Ph.D. (Pursuing) | 2021 – 2024
    Institute: IIITDM Jabalpur, India
    Thesis: Simulation, Fabrication, and Machine Learning-Aided Optimization of Advanced Junctionless FET-Based Sensors With Negative Capacitance and Strain Silicon Approach

  • 🎓 Master of Engineering (M.E.) | 2011 – 2014
    Specialization: Embedded System and VLSI Design
    Institute: SVITS, Indore, India
    Percentage: 79.6%
    Thesis: Design of Voice Morphing System Using FFT

  • 🎓 Bachelor of Engineering (B.E.) | 2006 – 2010
    Specialization: Electronics and Telecommunication Engineering
    Institute: L.N.C.T, Indore, India
    Percentage: 79.78%

  • 🏫 Intermediate (12th) | 2005 – 2006
    Board: Govt. G. H. S. School, Khirkiya (M.P)
    Percentage: 88%
    Subjects: Physics, Chemistry, Mathematics, English, Hindi

  • 🏫 High School (10th) | 2003 – 2004
    Board: Govt. G. H. S. School, Khirkiya (M.P), India

🏆 Achievements, Awards & Honors

Academic Excellence:

  • Consistently performed with distinction in both undergraduate and postgraduate studies (Above 79% in B.E. and M.E.) 🎖️

  • 88% in Intermediate with strong fundamentals in science and mathematics 📐🔬

🌟 Research Contributions (Ph.D. Focus):

  • Advanced research in simulation and fabrication of Negative Capacitance FET-based sensors

  • Integration of Machine Learning and Deep Learning in semiconductor device analysis 🤖📊

  • Exploration of emerging technologies including NC-FETs, Nanosheets, Forksheet, and FerroFETs

🔬 Interdisciplinary Skills:

  • Simulation ⚙️

  • Nanomaterials fabrication 🧪

  • Sensor modeling 📉

  • AI-based device optimization 🧠

Publication Top Notes:

Self-heating and interface traps assisted noise behavior analysis of JL-FinFET H2 gas sensor

Proof of concept: comparative study of machine learning models for optimization and performance evaluation of DM RSD JLNC-FinFET biosensor

Revealing the Reliability Performance of a Dielectric-Modulated Negative Capacitance Junctionless FinFET Biosensor

Junctionless negative capacitance FinFET-based dielectric modulated biosensor with strain silicon integration at different FE thickness

A proof of concept for reliability aware analysis of junctionless negative capacitance FinFET-based hydrogen sensor

Unveiling the Self-Heating and Process Variation Reliability of a Junctionless FinFET-Based Hydrogen Gas Sensor

Demonstration of a Junctionless Negative Capacitance FinFET-based Hydrogen Gas Sensor: A Reliability Perspective

Self-Heating and Interface Traps Assisted Early Aging Revelation and Reliability Analysis of Negative Capacitance FinFET

Mr. Xueye Chen | Flexible Sensing Awards | Best Researcher Award

Mr. Xueye Chen | Flexible Sensing Awards | Best Researcher Award 

Mr. Xueye Chen, ludong university, China

Chen Guorong is a Ph.D. professor and Master’s supervisor, currently serving as the Associate Dean of the School of Computer Science and Engineering (School of Artificial Intelligence) at Chongqing University of Science and Technology. Recognized as a Chongqing Leading Talent in Technological Innovation and a Chongqing Academic and Technical Leader, he has made significant contributions to the fields of artificial intelligence and safety production informatization. He has held esteemed academic roles, including Vice Chair of the IEEE Geoscience and Remote Sensing Society (GRSS) Chongqing Chapter and General Chair of the IEEE International Conference on Industrial Cyber-Physical Systems and Intelligent Manufacturing (IICSPI) 2025. With a research portfolio encompassing over 20 provincial and ministerial-level projects, 40+ SCI-indexed publications, and 10 authorized patents, he has been honored with nine provincial/ministerial scientific awards. As a visiting scholar at the University of Ottawa, Canada, Chen has fostered international academic collaboration. His pioneering work in smart emergency response and safety production has driven industry-academia partnerships, influencing technological innovation and contributing significantly to global research and societal advancements.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award 

Dr. Chen Guorong is a highly accomplished researcher with a strong background in Artificial Intelligence and Safety Production Informatization. His extensive leadership roles, research achievements, and contributions to technological innovation make him an outstanding candidate for the Best Researcher Award.

🎓 Education & Work Experience

  • Ph.D., Professor, and Master’s Supervisor at Chongqing University of Science and Technology.

  • Associate Dean of the School of Computer Science and Engineering (School of Artificial Intelligence).

  • Visiting Scholar at the University of Ottawa, Canada.

🏆 Awards & Honors

  • Chongqing Leading Talent in Technological Innovation.

  • Recognized as a Chongqing Academic and Technical Leader.

🔬 Research & Achievements

  • Research Interests: Artificial Intelligence & Safety Production Informatization.

  • Led 20+ provincial/ministerial-level research projects.

  • Published 40+ SCI-indexed papers.

  • Holds 10 authorized patents.

  • Recipient of 9 provincial/ministerial-level scientific awards.

  • Pioneered industry-academia collaboration in smart emergency response and safety production, contributing to global innovation.

🌍 Academic & Professional Engagements

  • Vice Chair of IEEE GRSS Chongqing Chapter.

  • Head of Technology & Informatization Group, Chongqing Emergency Management Expert Committee.

  • Vice Chairman of Chongqing Petroleum and Gas Society.

  • General Chair of IEEE IICSPI 2025.

Publication Top Notes:

Fabrication and energy collection of PDMS/dimethylsilicone oil superhydrophobic high tensile film

Porous Carbon Nanoparticle Composite Paper Fiber with Laser-Induced Graphene Surface Microstructure for Pressure Sensing

A pressure sensor made of laser-induced graphene@carbon ink in a waste sponge substrate using novel and simple fabricaing process for health monitoring

Numerical simulation and performance study of three-dimensional variable angle baffle micromixer

A novel dual-mode paper fiber sensor based on laser-induced graphene and porous salt-ion for monitoring humidity and pressure of human

Machine learning and genetic algorithm as tools for single and multi-objective shape optimization of micromixers with Cantor fractal structure

A novel micromixer based on coastal fractal for manufacturing controllable size liposome