Avirup Roy | Machine Learning |Machine Learning Research Award

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Mr. Avirup Roy | Machine Learning |Machine Learning Research Award

PhD Student, Michigan State University, United States

Dr. Avirup Roy is a dedicated researcher and engineer specializing in networked embedded and wireless systems. Currently pursuing his PhD at Michigan State University, his work focuses on developing self-learning mechanisms for embedded hardware systems with limited computational resources. With a solid foundation in electronics and communication engineering, Avirup has gained extensive experience in both academia and industry, contributing to projects ranging from smart malaria detection to automated power management systems. His technical skills span machine learning, embedded systems, cloud computing, and web development. Beyond his professional life, Avirup is passionate about Indian classical music, photography, and swimming. 🌟📚🎵📷🏊‍♂️

Profile

ORCID

 

Education🎓

Michigan State University, East Lansing, MI, US PhD in Electrical and Computer Engineering (2020-Present). Dissertation: Self-learning mechanisms for Embedded hardware systems with limited computational resources. GPA: 3.75/4Maulana Abul Kalam Azad University of Technology, Kolkata, WB, India Bachelor of Technology (BTech) in Electronics and Communication Engineering (2013-2017)

Experience💼

Graduate Research Assistant, Michigan State University (Sep 2020 – Jul 2023),Developed an android and website application for smart malaria detection involving cloud database integration. Graduate Teaching Assistant, Michigan State University (Aug 2023 – Present), Instructed and graded labs for Embedded Cyber-physical Systems, VLSI Systems, and Digital Control courses. ICER Cloud Computing Fellow, Michigan State University (Sep 2023 – Present), Implemented Azure cloud resources in semi-supervised federated learning for embedded devices. Programmer Analyst, Cognizant Technology Solutions (Dec 2017 – Jul 2020), Developer and support analyst for ASP.NET based applications of MetLife Inc. Intern, Calcutta Electric Supply Corporation (CESC) Limited (Jul 2016 – Aug 2016), Worked on automated power management systems using SCADA communication. Intern, Bharat Sanchar Nigam Limited (BSNL) (Jun 2015 – Aug 2015), Explored general trends in wireless communication. Undergraduate Researcher, Maulana Abul Kalam Azad University of Technology (2015-2016), Presented research at various international conferences and served as the vice-president of SPIE Student Chapter.

Research Interests🔍

Embedded Machine Learning: Focused on developing efficient learning algorithms for resource-constrained devices.
Networked Embedded Systems: Exploring self-learning mechanisms and their applications in real-world scenarios.
Cloud Computing: Leveraging cloud resources for semi-supervised federated learning.
VLSI Systems: In-depth study and teaching of Very-Large-Scale Integration systems.
Cyber-Physical Systems: Research on embedded systems interacting with physical processes.

Awards🏆

National Social Entrepreneurship Programme (2014): Secured 2nd position for the ‘Hand-Made Paper Industry’ project.
SPIE Smart Structures and Non-destructive Evaluation Conference (2016): Presented research in Las Vegas, Nevada.
EAPE Conference (2015): Presented research on emerging areas of photonics and electronics.
Graduate Fellowships: Awarded multiple fellowships during PhD for research and teaching excellence.

Publications

Semi-Supervised Learning Using Sparsely Labelled Sip Events for Online Hydration Tracking Systems
A. Roy, H. Dutta, A. K. Bhuyan, and S. K. Biswas, 2023, International Conference on Machine Learning and Applications (ICMLA).
Cited by: 3 articles.

An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation
Roy, A., Dutta, H., Griffith, H., & Biswas, S., 2022, Sensors.
Cited by: 5 articles.

Rania Sefti | Data Science | Best Researcher Award

Published on by Computer Scientists Awards

Ms. Rania Sefti | Data Science | Best Researcher Award

Phd student, Université Mohammed Premier Oujda, Morocco

Sefti Rania is a passionate researcher specializing in numerical analysis, optimization, and image processing. With a robust academic background and extensive teaching experience, she is currently pursuing a Ph.D. in a joint program between Morocco and France. Her research focuses on developing advanced methods for medical image segmentation using deep learning techniques.

Profile

Scopus

 

Education 🎓

Ph.D. in Mathematics and Computer Science (Specialization: Numerical Analysis and Optimization, Image Processing, Deep Learning), Mohammed First University, Oujda, Morocco, University of Orleans, France (Since 2020). Master in Numerical Analysis and Optimization (Honors: Good), Mohammed First University, Oujda, Morocco (2019). Bachelor’s Degree in Mathematical Sciences and Applications (Honors: Fairly Good), Mohammed First University, Oujda, Morocco (2017). High School Diploma in Experimental Sciences (Honors: Good), Ibn El Haytam High School, Nador, Morocco (2012)

Experience 💼

Adjunct Lecturer at Mohammed First University, Oujda, Morocco (2020 – Present). Higher School of Technology (Specialty: MCT and LPMI). Faculty of Sciences (Specialty: SVT and SMPC). Modules taught include Mathematics and Analysis with a total of over 200 hours of instruction. Reviewer for numerous articles in Mathematics and Computer Science since 2022

Research Interests 🔬

Numerical Analysis and Optimization, Image Processing, Deep Learning, Medical Image Segmentation.

Awards 🏆

Numerous Publications in renowned journals and conferences in the field of numerical analysis and optimization. Presentation Awards for contributions at international conferences such as MACMAS, NT2A, and SMAI-SIGMA

Publications

A CNN-based spline active surface method with an after-balancing step for 3D medical image segmentation, Mathematics and Computers in Simulation. Link – Cited by:

C2 composite spline methods for fitting data on the sphere, Springer special volume of the SEMA-SIMAI Springer Series. (Accepted in June 2023) – Cited by:

PID-Snake: Progressive Iterative Deformation of a Snake model for segmentation of a variety of images, Journal of Computational and Applied Mathematics. (Submitted in June 2024) – Cited by:

Fine-tuned cubic generalized composite spline interpolation with optimal parameter, Mathematics in Computer Science. (Submitted in June 2024) – Cited by:

A deep network-based spline active contour method for medical image segmentation, Springer special volume of the SEMA-SIMAI Springer Series. (Submitted in 2024) – Cited by: