Computer Science VS Computer Engineering (Academic Research ? Similar?)

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Discussion Overview

The discussion centers around the similarities and differences in academic research between Computer Science (CS) and Computer Engineering (CE). Participants explore the nature of research opportunities in both fields, particularly in areas like robotics and artificial intelligence (AI), and consider the implications for students entering college.

Discussion Character

  • Exploratory
  • Debate/contested
  • Technical explanation

Main Points Raised

  • Some participants note that there are overlaps in research areas for CS and CE, particularly in fields like robotics and AI, but emphasize that the specifics can vary significantly by institution.
  • One participant mentions that CE is often not a standalone department and that research opportunities may be influenced by the funding and faculty available in related departments like CS or Electrical Engineering (EE).
  • There is a suggestion that while CS majors may have limitations in industry compared to CE graduates, the distinction may blur in academic settings.
  • Concerns are raised about the focus on application-specific AI research at certain universities, with a desire for more fundamental research in AI and neuroscience, such as neural networks and cognitive models.
  • Another participant points out that strong programs in Computational Neuroscience and Cognitive Science exist at some universities, indicating that interdisciplinary approaches may yield relevant research opportunities.
  • There is a discussion about the potential for students from CS or CE backgrounds to engage in research in psychology or biology, suggesting that domain-specific knowledge can be acquired alongside technical skills.

Areas of Agreement / Disagreement

Participants express differing views on the extent of overlap between CS and CE research, with some suggesting significant similarities while others highlight the variability based on institutional structures and funding. The discussion remains unresolved regarding the best paths for students interested in interdisciplinary research.

Contextual Notes

Limitations include the variability of departmental structures across universities, the influence of funding on research opportunities, and the potential lack of standalone CE projects due to the field's relative newness.

avant-garde
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Computer Science VS Computer Engineering (Academic Research...? Similar?)

Are there many overlaps in academic research for CS and CE? It seems that for the schools I've researched (UT, Stanford, CMU, etc...) the "research" sections of their webpages for both CS and CE seemed very similar (robotics, AI, etc).

I'm about to go to college next year so I was wondering if someone with insider knowledge in one or both of these two fields would differentiate the research that goes on between the two? Are they essentially the same? I was under the impression that a CS major won't be able to do as much as a CE grad in industry but when it comes to academia the lines blur? Just making an educated guess here.



*edit: oh and I realized that when they say "AI" research, these universities are working on very application-specific AI. I was hoping for AI research on a more fundamental/algorithmic level such as Numenta's htm's, neural networks, and Blue Brain, but it seems that we don't know enough about the brain yet? So do you think for the next few decades we will still see application-specific AI rather than a more general AI?
 
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avant-garde said:
I'm about to go to college next year so I was wondering if someone with insider knowledge in one or both of these two fields would differentiate the research that goes on between the two?
It depends a ton on the school, as compE is almost never a standalone department. At mine, compE is run out of CS/EE, so research opportunities come out of one of the departments. Robotics attracts a lot of compE's cause it blends CS and EE, but it's run by whichever department has the faculty and funding to support it. I've seen computer architecture type projects in CS, but FPGA stuff in EE-> but other schools run it differently. Yeah, it all boils down to who has funding, and there aren't really standalone "compE" projects as the field is far too new to have many researchers in the field.


I was hoping for AI research on a more fundamental/algorithmic level such as Numenta's htm's, neural networks, and Blue Brain, but it seems that we don't know enough about the brain yet
You're probably looking in the wrong sections/at the wrong schools. Minnesota has a strong Computational Neuroscience/Cognitive science program,as does Stanford last I knew, and I saw an AI model of attention coming out of the neuropsychology program at the University of Texas. You'll need to think outside the box a bit, but there's definitely work on this.
 


story645 said:
You're probably looking in the wrong sections/at the wrong schools. Minnesota has a strong Computational Neuroscience/Cognitive science program,as does Stanford last I knew, and I saw an AI model of attention coming out of the neuropsychology program at the University of Texas. You'll need to think outside the box a bit, but there's definitely work on this.

Ah... so anything with the word "computational" plus some subject in biology, eh?
Not many undergraduate programs have those, so I guess it's still Computer Science at the undergrad level...
 


avant-garde said:
Not many undergraduate programs have those, so I guess it's still Computer Science at the undergrad level...
Grab an undergrad psychology course or two geared at cognitive neuropsych from a philosophical approach. I'm doing undergrad research in psychology that looks at different models (including a neural net variant) of attention, so I know what you want is out there, but yeah it's as likely to be coming out of the psych or bio department as the CS or EE department. You need to keep in mind that you don't necessarily need to have a major in psych or bio to do work on those projects; the professor may love the fact that you're CS/compE/EE and make you get up to speed on the domain specific knowledge.
 

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