• Skip to main content
  • Skip to secondary menu
  • Skip to primary sidebar
  • Skip to footer
  • IEEE.org
  • IEEE Xplore
  • IEEE Standards
  • IEEE Spectrum
  • More Sites

IEEE Potentials Magazine

The magazine for high-tech innovators

  • Home
  • Theme
    • Features
    • Columns/Departments
  • About Us
  • Contact
  • Associated Links
    • Potentials at IEEE Students
    • Potentials Media Guide
  • Highlighted Articles
  • Call For Papers
  • Recent Issues
    • Nov/Dec 2025
    • Sept/Oct 2025
    • July/Aug 2025
    • May/June 2025
    • March/April 2025
    • Jan/Feb 2025

IEEE Student Activities– What I learned as an engineering project manager

September 8, 2023 by Sherwin Razmy

After I graduated with a degree in engineering, I worked for several years in the U.S. Navy, then returned to school for my master’s of business administration (MBA) and my doctorate in law (JD). One should separate oneself by earning a graduate degree. An MBA goes hand in hand with an engineering bachelor’s for anyone who will be working for an engineering corporation. More than anything, know business management in order to be successful. Managing people is the key to success in a business.

For more about this article see link below.
https://ieeexplore.ieee.org/document/10237367
For the open access PDF link of this article please click here.

Filed Under: Past Columns / Departments

Primary Sidebar

Current Issue

Get the entire issue now.

About the Magazine

IEEE Potentials Magazine is the publication dedicated to undergraduate and graduate students and young professionals. IEEE Potentials explores career strategies, the latest in research, and important technical developments. Through its articles, it also relates theories to practical applications, highlights technology’s global impact, and generates international forums that foster the sharing of diverse ideas about the profession.

POPULAR ARTICLE

Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.

Read More…

Search

Past Issues

Footer

IEEE Potentials Magazine is a member benefit for IEEE Student members.

The magazine is archived in IEEE Xplore, and articles from all issues are available for download.

Home | Sitemap | Contact & Support | Accessibility | Nondiscrimination Policy | IEEE Ethics Reporting | IEEE Privacy Policy | Terms

© Copyright 2025 IEEE - All rights reserved. A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.