Degree Information

Master of Science in Electrical and Computer Engineering
Northeastern University, Boston
September 2020 - December 2022

Academic Journey

My graduate studies at Northeastern University were perfectly timed with the AI revolution! Being in Boston during this period meant access to one of the world’s premier tech ecosystems, surrounded by brilliant minds from MIT, Harvard, and numerous innovative companies.

Coursework & Specializations

Machine Learning & AI Focus

  • Advanced Machine Learning: Deep dive into neural networks, optimization, and modern ML techniques
  • Computer Vision: Image processing, object detection, and visual recognition systems
  • Signal Processing: Digital signal analysis and filtering techniques
  • Pattern Recognition: Statistical methods for data classification and clustering

Engineering Foundations

  • Digital Systems Design: Hardware-software co-design and optimization
  • Advanced Algorithms: Computational complexity and algorithm design
  • Communication Systems: Digital communications and information theory
  • Control Systems: Feedback control and system optimization

Practical Applications

  • Embedded Systems: Real-time computing and edge device programming
  • VLSI Design: Integrated circuit design and optimization
  • Wireless Communications: RF systems and antenna design
  • Digital Image Processing: Advanced techniques for medical and industrial imaging

Research & Projects

Graduate Research Focus

My research centered on the intersection of electrical engineering and artificial intelligence, particularly focusing on:

  • Hardware acceleration for machine learning algorithms
  • Edge computing for real-time AI applications
  • Signal processing for biological and medical data
  • Computer vision applications in industrial settings

Key Academic Projects

Real-time AI on Edge Devices

Developed optimized neural networks that could run efficiently on resource-constrained hardware, bridging the gap between powerful AI models and practical deployment.

Medical Signal Analysis

Applied advanced signal processing techniques to analyze physiological signals, contributing to healthcare monitoring applications.

Computer Vision for Manufacturing

Created vision systems for quality control in manufacturing environments, combining electrical engineering principles with modern AI techniques.

Academic Excellence

Research Collaboration

  • Lab Research: Active participation in cutting-edge research projects
  • Industry Partnerships: Worked on projects with local Boston tech companies
  • Cross-disciplinary Work: Collaborated with computer science and bioengineering teams
  • Publication Opportunities: Contributed to research papers and conference presentations

Technical Skills Developed

  • Programming: Advanced Python, MATLAB, C++ for engineering applications
  • Hardware: FPGA programming, embedded systems design
  • Simulation: SPICE, ModelSim, and other engineering simulation tools
  • AI/ML: TensorFlow, PyTorch, and specialized libraries for engineering applications

Boston Tech Ecosystem

Industry Connections

Being at Northeastern in Boston provided incredible opportunities:

  • Co-op Program: Hands-on industry experience during studies
  • Tech Meetups: Regular networking with Boston’s vibrant AI community
  • Startup Scene: Exposure to innovative companies in Cambridge and Boston
  • Research Institutions: Collaboration opportunities with nearby world-class universities

Professional Development

  • Technical Conferences: Attendance at major engineering and AI conferences
  • Industry Seminars: Regular talks by leaders in technology and engineering
  • Career Fairs: Access to top-tier technology companies
  • Mentorship: Guidance from faculty with industry experience

Transition to AI/ML Career

Academic Foundation

The electrical and computer engineering background provided a unique perspective on AI/ML:

  • Hardware Understanding: Deep knowledge of how AI algorithms execute on real hardware
  • Signal Processing: Strong mathematical foundation for understanding neural networks
  • System Design: Holistic view of how AI systems integrate into larger engineering systems
  • Optimization: Engineering mindset focused on efficiency and performance

Practical Applications

The degree perfectly prepared me for roles that require:

  • Edge AI development: Understanding both the algorithms and the hardware constraints
  • Production ML systems: Engineering perspective on scalability and reliability
  • Interdisciplinary projects: Bridging AI/ML with other engineering domains
  • Technical leadership: Comprehensive understanding of both theory and practice

Academic Impact

Research Contributions

  • Conference presentations on AI applications in engineering
  • Collaborative research with medical and industrial partners
  • Open-source contributions to engineering and ML communities
  • Mentoring of undergraduate students in research projects

Skills Integration

The program perfectly combined:

  • Theoretical rigor from electrical engineering principles
  • Practical experience through hands-on projects and co-ops
  • Modern applications in AI and machine learning
  • Industry relevance through partnerships and real-world problems

“The best engineers understand not just what to build, but how it works at every level - from the physics of semiconductors to the mathematics of neural networks.”

Why Northeastern?

Co-operative Education

Northeastern’s famous co-op program provided invaluable industry experience:

  • Real-world application of classroom learning
  • Professional network development
  • Industry insights into current challenges and opportunities
  • Career preparation through practical work experience

Research Excellence

  • State-of-the-art facilities for engineering and computing research
  • Faculty expertise spanning traditional engineering and emerging AI fields
  • Industry partnerships providing access to real problems and data
  • Interdisciplinary collaboration across different engineering departments

Boston Advantage

  • Tech hub location with access to cutting-edge companies
  • Research ecosystem with nearby MIT, Harvard, and other institutions
  • Professional opportunities in a thriving technology market
  • Cultural richness of a world-class city for personal growth

This graduate education provided the perfect foundation for a career at the intersection of engineering and artificial intelligence, combining rigorous technical training with practical experience and exposure to the latest developments in AI and machine learning.