Before I get flamed or whatever, it seems that the hardforums got me crickets so I'm posting here as well to see if I can get any input from someone. I'd be really thankful.
Thanks in advance.
Hi guys,
Currently I'm a 4th year Computing Science student who is graduating this April. Technically I'm in my 5th year, but more on that later.
My grades in my last 2 years will hopefully average out to about a 3.4-3.5.
I should mention that I changed my major in my 3rd year of University and I've essentially taken all Math,stat and CMPUT courses in my last 2 years. My current year (Fall 2009 and this term is Winter 2010) included the 8 required "higher" level Computing Science courses and one mathematical statistics course and a computational physics course.
God willing, I'm hoping to finish this year with a 3.9. That would be all the courses that show my potential to be an academic.
A little bit about the courses I took last term, all 5 were CMPUT courses. They involved:
Operating Systems
Processor Design
Algorithms (Flow networks) and Complexity Theory (from an Algorithms perspective, not an finite automaton perspective)
Machine Learning
Reinforcement Learning.
I did quite well in these courses.
This term, the 3 relevant computing courses are:
1. An Independent Study Computing course where I'm learning Probability Theory and modelling Hidden Markov Models.
2.A distributed computing class focusing on parallelization. It requires extensive knowledge of operating systems such as the mutual exclusion problem, and a good handle on threads. We study models like MPI, treadmarks, MapReduce, pthreads,etc.. 3. A Computing course focusing on implementing Algorithms for ACM competitions, with efficient implementations and good running times.
Needless to say I am humbled by the opportunities I am HOPING to be provided if I get admitted into graduate school... My institution has afforded me the great luxury of being well-versed, I daresay entry-level graduate, on these topics discussed above.
Since I skipped software engineering courses that teach you how to "use" SVN and "work" in teams on meaningless Java projects, I was able to take every course I felt that was "true" to Computing Science. No I'm not an elitist; I just believe that Computing Science isn't programming and I LOVE the science. There is nothing more I want to do than be a pure-bred Computing Scientist. My goal is to be a pivotal contributor to any field I hope to research. I want to inspire a new generation, just as exploring the beauty of the current generation has inspired me.
I am in the position to become a new-generation "hybrid" that focuses on many areas and tie them together. I should state I love all of the fields I discussed above equally, but Algorithms research isn't my goal. I hope to use them extensively in amortizing potential runtimes for things I do, but I don't want to research or create an approximation algorithm or explore such stuff.
And this is the problem I face.
My current gripe is that I love Probability Theory, but I feel the mathematics I need isn't there. As a result I want to "bridge" my math to acquire the appropriate level of sophistication so I can feel at home.
I have knowledge of Calculus 1,2,3, Ordinary and Partial Differential equations. What I would need is a healthy understanding of Topology, Measure Theory (with respect to Real Analysis) to satisfy the level of rigor I need to get a legitimate thesis. I know that,ultimately, this may reward me the most, since I would propose new work with a larger scale appeal if I discuss it in mathematics and show the applicability to areas in AI.
And that's the other thing, I want my Thesis to present original work.
The problem is I don't want to discuss just an application
of a novel statistical learning algorithm and provide results with insight. That's what may happen if I don't learn all the math I feel I need.
Another problem is that I am so in love with Operating Systems, Processor Design (all the low level GUTS) that I am having a hard time choosing what to do. I really love performance modelling and learning about why certain parellelization models failed and succeeded. The papers may be long but man they're so intuitive and beautiful. For example, the TreadMarks model was a failure in so many ways; but there were so many great ideas that were extracted from it that they paved the way (indirectly) for things like Google's MapReduce.
My University is world-class, I daresay top 5 in the world for Machine Learning/Statistical Learning. I know it's not as strong for Systems but I don't even know if I would do Systems research here.
I am really torn between the idea of highperformance systems/Virtual Machine research on Shared Memory Systems/Processor Design/Operating Systems type research, or Statistical Learning/Machine Learning.
Can anyone point me to some great Graduate schools that are strong in each? I would prefer not disclosing my institution out as I can easily be identified if someone was a little persistent.
I would really appreciate if someone could show me some processor design-type research areas in CS. They're really hard to find and I'm hoping to uncover one. Maybe that will satiate me the most. I really enjoyed learning about the concepts of what is required to have a high performance CPU.
Thanks for any assistance.
Thanks in advance.
Hi guys,
Currently I'm a 4th year Computing Science student who is graduating this April. Technically I'm in my 5th year, but more on that later.
My grades in my last 2 years will hopefully average out to about a 3.4-3.5.
I should mention that I changed my major in my 3rd year of University and I've essentially taken all Math,stat and CMPUT courses in my last 2 years. My current year (Fall 2009 and this term is Winter 2010) included the 8 required "higher" level Computing Science courses and one mathematical statistics course and a computational physics course.
God willing, I'm hoping to finish this year with a 3.9. That would be all the courses that show my potential to be an academic.
A little bit about the courses I took last term, all 5 were CMPUT courses. They involved:
Operating Systems
Processor Design
Algorithms (Flow networks) and Complexity Theory (from an Algorithms perspective, not an finite automaton perspective)
Machine Learning
Reinforcement Learning.
I did quite well in these courses.
This term, the 3 relevant computing courses are:
1. An Independent Study Computing course where I'm learning Probability Theory and modelling Hidden Markov Models.
2.A distributed computing class focusing on parallelization. It requires extensive knowledge of operating systems such as the mutual exclusion problem, and a good handle on threads. We study models like MPI, treadmarks, MapReduce, pthreads,etc.. 3. A Computing course focusing on implementing Algorithms for ACM competitions, with efficient implementations and good running times.
Needless to say I am humbled by the opportunities I am HOPING to be provided if I get admitted into graduate school... My institution has afforded me the great luxury of being well-versed, I daresay entry-level graduate, on these topics discussed above.
Since I skipped software engineering courses that teach you how to "use" SVN and "work" in teams on meaningless Java projects, I was able to take every course I felt that was "true" to Computing Science. No I'm not an elitist; I just believe that Computing Science isn't programming and I LOVE the science. There is nothing more I want to do than be a pure-bred Computing Scientist. My goal is to be a pivotal contributor to any field I hope to research. I want to inspire a new generation, just as exploring the beauty of the current generation has inspired me.
I am in the position to become a new-generation "hybrid" that focuses on many areas and tie them together. I should state I love all of the fields I discussed above equally, but Algorithms research isn't my goal. I hope to use them extensively in amortizing potential runtimes for things I do, but I don't want to research or create an approximation algorithm or explore such stuff.
And this is the problem I face.
My current gripe is that I love Probability Theory, but I feel the mathematics I need isn't there. As a result I want to "bridge" my math to acquire the appropriate level of sophistication so I can feel at home.
I have knowledge of Calculus 1,2,3, Ordinary and Partial Differential equations. What I would need is a healthy understanding of Topology, Measure Theory (with respect to Real Analysis) to satisfy the level of rigor I need to get a legitimate thesis. I know that,ultimately, this may reward me the most, since I would propose new work with a larger scale appeal if I discuss it in mathematics and show the applicability to areas in AI.
And that's the other thing, I want my Thesis to present original work.
The problem is I don't want to discuss just an application
of a novel statistical learning algorithm and provide results with insight. That's what may happen if I don't learn all the math I feel I need.
Another problem is that I am so in love with Operating Systems, Processor Design (all the low level GUTS) that I am having a hard time choosing what to do. I really love performance modelling and learning about why certain parellelization models failed and succeeded. The papers may be long but man they're so intuitive and beautiful. For example, the TreadMarks model was a failure in so many ways; but there were so many great ideas that were extracted from it that they paved the way (indirectly) for things like Google's MapReduce.
My University is world-class, I daresay top 5 in the world for Machine Learning/Statistical Learning. I know it's not as strong for Systems but I don't even know if I would do Systems research here.
I am really torn between the idea of highperformance systems/Virtual Machine research on Shared Memory Systems/Processor Design/Operating Systems type research, or Statistical Learning/Machine Learning.
Can anyone point me to some great Graduate schools that are strong in each? I would prefer not disclosing my institution out as I can easily be identified if someone was a little persistent.
I would really appreciate if someone could show me some processor design-type research areas in CS. They're really hard to find and I'm hoping to uncover one. Maybe that will satiate me the most. I really enjoyed learning about the concepts of what is required to have a high performance CPU.
Thanks for any assistance.