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Difference between Electrical and Computer Engineering

Electrical engineering students apply electrical, electronic and magnetic theory to solve problems related to the development, design and operation of electrical hardware and software, control systems, electrical machines and communication systems.

Computer engineering students design, develop and implement new and changing hardware and software technology, making computers faster, smaller, more effective and less expensive.

Because of the huge overlap and inter-dependency between the two areas, both are offered by the same department in majority of the US universities.

Electrical and Computer Engineering Research: The Big Picture

Research in Electrical Engineering spans a diverse set of intellectual disciplines and applications. The disciplines can be grouped into three overlapping and interrelated areas:

1 Physical Technology & Science

This area focuses on defining the device technology and circuit fabric of future electronic and photonic systems, which integrate the abstraction levels of materials, nanostructures, semiconductor devices, integrated circuits, power electronics and electronic system engineering. It also investigate physics, materials, devices, and systems using light and electromagnetism, for applications including sensing, imaging, communications, energy, biology, medicine, security, and information processing.

It is further divided into following sub-areas:

1.1 Integrated Circuit and Power Electronics

This area is concerned with the application-driven design of electronic circuits and systems, spanning a wide spectrum from low frequencies to mm-wave and THz. The research incorporates a variety of technologies, ranging from emerging nano and MEMS devices, nano-CMOS and BiCMOS processes, as well as discrete electronics for power conversion.

The specific research thrusts include: Mixed-signal integrated circuit design, Power electronics, Nanosystems and RF and mm-wave integrated circuit design.

1.2 Biomedical Devices, Sensors and Systems

Biological properties can be measured and altered using electronics, magnetics, photonics, sensors, circuits, and algorithms. Applications range from basic biological science to clinical medicine, and enable new discoveries, diagnoses, and treatments by creating novel circuits, devices, systems, and analyses.

Examples include: Measuring molecular concentrations, Building implantable bio-sensors and bio-stimulators, On-chip imaging and sensing, Wireless sensing and powering and Designing new algorithms and systems for early cancer screening and detection.

1.3 Energy Harvesting and Conversion

The study of electronics and photonics play a central role for energy harvesting and conversion. For example, the harvesting of solar energy requires significant advance in both electronics and photonics. In addition, the majority of electronics today are fundamentally limited by the energy they consume. Conversely, the availability of more varied energy sources would enable functionality and ubiquity of electronics not yet possible today. Even when power is readily available, some electronics must obey very strict thermal requirements, such as those that come in contact with the human body.

Examples include: Fundamental research into the nanoscale physics of electron-phonon energy interaction, Renewable energy research including photovoltaics, thermophotovoltaics and radiative cooling, Fundamental research into the nanoscale physics of electron-phonon energy interaction and Heat-sensitive electronics and their interaction with strict temperature environments such as car engines, the human body, or extraterrestrial applications.

1.4 Photonics, Nanoscience and Quantum Technology

Physics, materials, devices, and systems are investigated using light and electromagnetism generally, for applications including sensing, imaging, communications, computing, energy, biology, medicine, security, and information processing. Scientific work ranges from basic quantum mechanical processes in nanostructures to planetary science, incorporating technologies from nano- and micro-scale fabrication through radio and optical fiber communications to environmental probes.

Examples include:

  • Photonics: Devices, systems and applications involving electromagnetic waves and in particular, light. Applications include communicating information, where photonics plays a crucial role, medical instrumentation, imaging, sensing, and (photovoltaic) solar power generation.
  • Nanoscience & Engineering: Physics of nano-photonic structures (where the minimal feature sizes are at the single wavelength or even deep subwavelength scales); controllable fabrication of nanophotonic materials and structures; the applications of such structures in low-energy information processing and communications, high-efficiency energy conversion, sensing, and medicine.
  • Quantum Technologies: Study and employment of quantum mechanical properties of light and matter for applications including secure communications, quantum and classical computing, and sensing. Nanophotonics and nanoscience play crucial roles in building a platform for quantum technologies.
1.5 Nanotechnology, Nanofabrication and NEMS/MEMS

Nano- and micro-electromechanical systems (NEMS/MEMS) are useful for applications ranging from chemical sensors and relays to logic devices.

Examples include:

  • The design of MEMS accelerometers, gyroscopes, electrostatic actuators, and microresonators
  • Biosensors, magnetic biochips, magnetic integrated inductors and transformers
  • Flexible substrates for electronics, sensors, and energy conversion platforms
  • Nanofabrication and nanopatterning technologies
1.8 Electronic Devices

New and innovative materials, structures, process, and design technologies are explored for nanoelectronics, energy, environment, and bio-medical applications.

Examples include:

  • Silicon, germanium, and III-V compound semiconductor devices, metal gate/high-k MOS, and interconnects for nanoelectronics;
  • Device applications of new materials such as carbon (carbon nanotube, graphene)
  • New fabrication technologies for scaling logic and memory devices into the nanometer regime
  • Magnetic nanotechnologies and information storage.


2 Information Systems & Science

In addition to work on the core disciplines of information theory and coding, control and optimization, signal processing, and learning and inference, research in this area spans several application areas, including biomedical imaging, wireless communications and networks, multimedia communications, Internet, energy systems, transportation systems, and financial systems.

2.1 Control and Optimization

Optimal design and engineering systems operation methodology is applied to things like integrated circuits, vehicles and autopilots, energy systems (storage, generation, distribution, and smart devices), and financial trading. Optimization is also widely used in signal processing, statistics, and machine learning as a method for fitting parametric models to observed data.

Examples include:

  • Smart grid algorithms
  • Approximate dynamic programming
  • Dynamic game theory
  • Decentralized control
2.2 Information Theory and Applications

Core topics of information theory, including the efficient storage, compression, and transmission of information, applies to a wide range of domains, such as communications, genomics, neuroscience, and statistics.

Examples include:

  • Network information theory
  • Computational genomics
  • Information theory of high dimensional statistics
  • Machine learning
2.3 Communications Systems

Communications challenges exist over wireline, optical, and wireless links, and research in this area spans the study of fundamental limits, physical modeling, coding, networking and overall system design. The focus is on improving utility to users, power efficiency, and reliability.

Examples include:

  • Cellular and adhoc networks
  • Optical communications
  • Data center networking
  • Sensor networks
2.4 Societal Networks

Societal networks and urban systems can be improved using mathematical methods for analysis, design and optimization. Systems include transportation, health care, financial, water, waste management and emergency services, and they suffer from two major problems: a severe shortage of resource supply and excessive demand. Key outcomes involve using insights from data generated by these networks to improve the supply and curb demand, and large-scale deployments in cities around the world are a major result.

Examples include:

  • Designing “nudge” engines,
  • Personalized recommendation engines
  • Big data algorithms and systems
  • Mobile phone-based sensing algorithms
2.5 Signal Processing and Multimedia

Extracting or recovering useful information while reducing unwanted noise can be achieved using sophisticated mathematical methods and computation to process signals (audio, video, electromagnetic, biomedical, remote sensing, multimedia and others). Applications include multimedia compression, communications, networked media systems, augmented reality, and remote sensing.

Examples include:

  • Augmented and virtual reality
  • Compact descriptors for visual search
  • Personalized and immersive media
  • Computational imaging and display
2.6 Biomedical Imaging

Basic science questions, as well as clinical applications and translation, are applied to a broad range of imaging technologies – from devices to systems to algorithms – for biomedical applications ranging from microscopy to whole-body diagnostic imaging and image-guided interventions.

Examples include:

  • Positron emission tomography
  • Focused ultrasound surgery
  • Electrophysiology and Imaging
  • Computational microscopy
2.7 Data Science

All aspects of data and information are part of this research, including how to collect, store, organize, search, and analyze information. Recently there has been energized interest in information management because huge volumes of data are now available from sources such as web query logs, Twitter posts, blogs, satellites, sensors, and medical devices. The interest is not solely due to the volume, but because there has been a paradigm shift in the way data is used. In the past, data was used to verify hypotheses; today, mining data for patterns and trends leads to new hypotheses. The more data available, the finer and more sophisticated these hypotheses can be.

Examples include:

  • Automated data cleansing (e.g., entity resolution, graph alignment, etc.)
  • Scalable self-tuning optimization, machine learning, and data mining systems
  • Algorithms for analysis of large, dynamic networks
  • Next generation distributed large-scale computing and simulation environments


3 Hardware / Software Systems

Research in this area looks into new ways to design, architect, and manage energy-efficient systems for emerging applications ranging from the internet-of-things to big data analytics.

3.1 Energy-Efficient Hardware Systems

The exponential growth in performance and storage capacity has been the key enabler for information technology for decades. However, the end of voltage scaling in semiconductor chips has made all computer systems, from mobile phones to massive data centers, energy limited. Moreover, new nanosystems enabled by emerging nanotechnologies provide unique opportunities for revolutionizing energy-efficient architectures through new transistor and memory technologies and their massive and fine-grained integration. These shifts motivate new system architectures and vertical co-design of hardware, system software, and applications. This area looks at new ways to design, architect, and manage highly energy-efficient systems for emerging applications ranging from the internet-of-things to big data analytics.

Examples include:

  • Hardware design for specialized accelerators and programing models for heterogeneous computing
  • Scalable architectures with thousands of computing elements and massive memory capacity
  • Hardware architectures and systems software for cloud computing
  • Architectures for nanosystems enabled by emerging technologies
3.2 Software Defined Networking

Software defined networking (SDN) has emerged as a new paradigm of networking. Key aspects of SDN include separation of data and control plane, a well defined vendor-agnostic interface between the data and control plane (e.g. OpenFlow), and a logically centralized control plane that creates a network view for the control and management applications. Industry is embracing SDN because it enables competition and innovation and helps network operators reduce capex and opex and create revenue generating services.

Examples include:

  • Foundations of SDN including abstractions for forwarding and control planes; SDN building blocks that embody the abstractions; controller design and data consistency guarantees; SDN scalability, reliability, and security;
  • New network capabilities or services enabled by SDN including network virtualization, troubleshooting and verification; traffic engineering; and other automation and orchestration for cloud applications;
  • SDN for different types of networks, including data center, enterprise wired and wireless, cellular wireless, and service provider networks.
3.3 Mobile Networking

As personal computing and data move to the cloud, mobile wireless networks form the substrate over which users access these services. This research aims to design and build the next generation of wireless networks, taking a cross-disciplinary approach that tackles broad cross-cutting problems such as interference, mobility, and network complexity using tools from RF circuits, signal processing, communications and information theory, and distributed software systems.

Examples include:

  • New network designs that embrace and exploit interference instead of avoiding or ignoring it in order to create high capacity wireless networks
  • Programmable wireless infrastructure that expose interfaces for fine-grained control of the radio spectrum
  • Software systems that manage such large dense networks automatically and continuously to maximize spectral efficiency, facilitate seamless hand-off between networks, and enable new services to be deployed easily in wireless networks
3.4 Secure Distributed Systems

An increasing amount of computation is now hosted on private and public clouds, backed by warehouse-scale datacenters. At the same time, web-scale applications such as search, social networks, and software-as-a-service, are changing not just the way we use information, but also the way that people interact with each other. Secure Distributed Systems focuses on next generation computation, storage, and communication platforms that enable and simplify the development of such applications.

Examples include:

  • Resource efficient cloud computing
  • Software platforms for coordinating swarms of smart objects and connecting them with web-scale services
  • Network and web security protocols, cryptography
  • Security for embedded devices
  • New applications for cloud computing, e.g., distributed graphics
3.5 Embedded Systems

Today, there is computation in everything. Birthday cards can play songs, fireworks use microcontrollers rather than fuses for timing, homes and buildings are becoming “smart”, and we wear many computers in our pockets and on our wrists. These systems are characterized by a tight coupling of hardware capabilities, software functionality, application requirements, and physical form factor. They raise many open questions in power management, networking, security, privacy, and administration. Embedded Systems look at new, fundamental ways to design these systems that will make them robust, secure, and long-lived.

3.6 Integrated Circuits and Power Electronics

This area is concerned with the application-driven design of electronic circuits and systems, spanning a wide spectrum from low frequencies to mm-wave and THz. The research incorporates a variety of technologies, ranging from emerging nano and MEMS devices, nano-CMOS and BiCMOS processes, as well as discrete electronics for power conversion.

The specific research thrusts include:

  • Mixed-signal integrated circuit design
  • RF and mm-wave integrated circuit design
  • Power electronics
  • Nanosystems


Career in Electrical and Computer Engineering

Electrical engineering jobs involve designing, developing, testing, and supervising the manufacturing of electrical equipment, systems and components. This can include electric motors, radar and navigation systems, broadcast and communications systems, power generation equipment, or electrical systems for automobiles, aircraft and spacecraft.

Electrical engineers can also design and manufacture commercial electronic products such as computer hardware, wearable devices or smartphones. Electrical engineering job descriptions can include:

  • Improving or developing products by designing new ways of using electrical power
  • Participating in the manufacture, installation and testing of electrical components and devices
  • Developing manufacturing, construction and installation standards and specifications for electronic systems
  • Analyze and evaluate electrical and electronic devices or systems to recommend improvements, modifications or repair.

Starting your career as an Electrical Engineer, you can move on to become Senior (Electrical / System / Application / Power Control) Engineer, Electrical Design Engineer,  or Electrical Test Engineer. You can choose any vertical – Computer Networking, Electronics, IT, Design or Testing.

The average salary for an Electrical Engineer is ~$93,000.

Many consider Electrical Engineering to be one of the more interesting fields of engineering, with jobs that involve designing components for automobiles, communication systems, electronic equipment and power grids. The good news for students interested in this field is that the number of jobs for Electrical Engineering is expected to grow at 1% between 2017-2024.

There would be more job opportunities for Electrical Engineers in engineering service companies as bigger manufacturing companies are opting to reduce their costs by contracting to those electrical engineering service companies rather than directly hiring them. Such growth will be driven by rapid pace of technological innovation and development in consumer electronics, solar, semiconductors, communication technologies, robotics and electrical cars.

Top Universities for Electrical and Computer Engineering

  1. Massachusetts Institute of Technology (MIT)
  2. Stanford University
  3. University of California, Berkeley (UCB)
  4. University of California, Los Angeles (UCLA)
  5. Harvard University
  6. California Institute of Technology (Caltech)
  7. Georgia Institute of Technology
  8. University of Illinois at Urbana-Champaign
  9. University of Michigan
  10. Princeton University
  11. Carnegie Mellon University
  12. University of Texas at Austin
  13. University of California, San Diego (UCSD)
  14. Columbia University
  15. Cornell University
  16. Purdue University
  17. The Ohio State University
  18. Texas A&M University
  19. University of California, Santa Barbara (UCSB)
  20. University of Pennsylvania
  21. University of Southern California
  22. University of Washington
  23. University of Wisconsin-Madison
  24. Virginia Polytechnic Institute and State University
  25. Yale University
  26. Duke University
  27. Johns Hopkins University
  28. Michigan State University
  29. New York University (NYU)
  30. North Carolina State University
  31. Pennsylvania State University
  32. Rice University
  33. University of California, Irvine
  34. University of Florida
  35. University of Maryland, College Park
  36. Arizona State University
  37. Boston University
  38. Illinois State University
  39. Northwestern University
  40. Rensselaer Polytechnic Institute
  41. University of California, Davis
  42. University of Colorado Boulder
  43. University of Minnesota
  44. Brown University
  45. Drexel University
  46. Northeastern University
  47. The University of Arizona
  48. University of Illinois, Chicago
  49. University of Notre Damn
  50. The University of Tennessee, Knoxville


Thesis vs Internship-based MS

At many universities, a student may take one of two approaches to earn a master’s degree. In the “traditional” path, students choose to work on an industry or academic research project in a program that ends with a thesis and final exam. In the “industry” path, students collaborate with industry partners in concentrations of interest from the industry standpoint, perform an academic research project or take part in an internship.

Is it possible to change concentration / advisor after acceptance?

Yes, most universities tend to be very fluid when it comes to program tracks. You may, for example, apply to the digital department, get accepted, then after acceptance, turn it around and sign up for analog/mixed signal courses then declare that particular track as a concentration down the road.

Same goes with change of advisors. Many students change advisors in grad school simply because they discovered more interesting subject to study.

Should I be an Analog or a Digital Engineer?

Many Electrical Engineering departments categorize engineers Analog and Digital. This is similar in industry, and it has its importance.

From a career point of view, digital first, mixed-signal verification is an ideal path towards a growing need for more behavioral mixed-signal design and verification.

Overall, the engineers who practice in both domains offer more value.  A word of advice: Stay within this gray area and you will go far!

One of the real advantages between knowing how to implement solutions in both the analog and digital domains is that you can select the most elegant combination of both analog and digital processing to apply to a particular problem.

If you are a digital-only person you might be tempted to throw the fastest highest resolution ADC possible at the analog signal and then do all your algorithm processing in digital.

If you are analog-only, then you might be tempted to do all the processing in analog with hardly any digital processing.

Coming up with an overall solution that is optimized for power, performance, repeat-ability, cost and manufacture-ability is most likely a thoughtful combination of both analog and digital processing techniques.



  • Stanford University
  • U.S. Bureau of Labor Statistics

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