application to PhD programs
Dec. 14th, 2006 07:53 pm![[personal profile]](https://www.dreamwidth.org/img/silk/identity/user.png){kind=small}
guslIt seems I'm only applying to CMU this year (but at multiple departments).
Today, I decided that I'm not applying to Psychology, since it seems like the only faculty I could connect with are not interested in mentoring a thesis on methodology (model selection or model induction). Also, a lot of the work that they do is running experiments, whereas I am a theorist. I am really an AI person. Ion described me as being far on the Computer Science end among the ACT-R people. I don't disagree.
So this means that I'm only applying for MLD (Computational and Statistical Learning), HCII, and Philosophy (Logic, Methodology & Computation).
Please comment!
My goals and research interests
As an AI person, I have long been interested in the parallels between human and machine intelligence. For instance, learning (human and machine) can be seen as a general information process: learning is compression (philosophers of science will know that a unified theory provides a shorter way to describe the data). Furthermore, machines can be programmed to use the same representations as humans doing same task, as is evidenced by the existence of cognitive models (and unintentionally, many a program written by human programmers).
Machine Learning teaches us how to do induction effectively and accurately. Philosophy of science teaches us how to do induction "correctly". Active learning teaches us how to explore optimally (i.e. maximize useful information gain), and this knowledge is just as useful for the scientist choosing what experiment to perform next. In practice, however, much science uses intuitive methodologies and "tacit knowledge". I see it as my job to formalize these practices, thus putting them under scrutiny. Cognitive scientists have intuitions about what makes good cognitive models, and don't yet use machine learning to select among competing models, or to induce new models from data. I see it as my job to change this.
Despite the similarities, there are tasks that humans do better than machines and vice-versa. Human-machine collaboration holds the promise of augmenting human intelligence. However, most of this collaboration is based on the "calculator model", in which machines are used in a very restricted way, and there is no "transparency" in human and machine knowledge: human and machine only interface successfully at a few select places in the information-processing.
It is not easy to design machines to help us with general reasoning tasks because such tasks consist of manipulating concepts, which are too numerous and too slippery to "teach" to a computer. Reasoning systems in restricted domains have achieved some degree of success (after significant ontology engineering work), but scaling this to domain-independent reasoning has proven to be a very hard problem.
Areas of interest:
* scientific discovery, machine learning, induction methodologies: how can we combine theory and data?
* information theory (MDL learning)
* active learning
Domains of interest (i.e. data sets I would love to have):
* cogsci: behavior logs AS WELL AS human-made cognitive models
* demographics
* medical data
* natural language
* computational biology
Research experience
SimStudent Project (Sep 2006 - present)
I have been working as a programmer for the SimStudent project, which induces production rules from demonstrations. The idea is to learn cognitive models from behavior data. On the machine learning side, it lacks dynamic feature selection: we have hard-coded a set of operators through which the demonstrations can be learned.
On the cognitive side, it's not a good model of human performance, since it doesn't enforce cognitive constraints: i.e. it doesn't account for the fact that the learner is a human being.
Master's thesis (Apr-Sep 2005):
My master's thesis was about formalizing derivations in physics.
* created an equational theorem-prover that shows the set of models under which equations hold.
* created an ontology of classical mechanics, and formalized some derivations.
* used the above derivations to discover new proofs
Automatic dictionary builder (Jan 2004):
* designed and programmed an automatic dictionary builder for sentence-aligned parallel corpora, by doing Portuguese-English word alignment. Used several heuristics, including cognate-matching, correlations in word-length and word-frequency, and bootstrapping from a small hand-made dictionary.
Today, I decided that I'm not applying to Psychology, since it seems like the only faculty I could connect with are not interested in mentoring a thesis on methodology (model selection or model induction). Also, a lot of the work that they do is running experiments, whereas I am a theorist. I am really an AI person. Ion described me as being far on the Computer Science end among the ACT-R people. I don't disagree.
So this means that I'm only applying for MLD (Computational and Statistical Learning), HCII, and Philosophy (Logic, Methodology & Computation).
Please comment!
My statement of purpose
My goals and research interests
As an AI person, I have long been interested in the parallels between human and machine intelligence. For instance, learning (human and machine) can be seen as a general information process: learning is compression (philosophers of science will know that a unified theory provides a shorter way to describe the data). Furthermore, machines can be programmed to use the same representations as humans doing same task, as is evidenced by the existence of cognitive models (and unintentionally, many a program written by human programmers).
Machine Learning teaches us how to do induction effectively and accurately. Philosophy of science teaches us how to do induction "correctly". Active learning teaches us how to explore optimally (i.e. maximize useful information gain), and this knowledge is just as useful for the scientist choosing what experiment to perform next. In practice, however, much science uses intuitive methodologies and "tacit knowledge". I see it as my job to formalize these practices, thus putting them under scrutiny. Cognitive scientists have intuitions about what makes good cognitive models, and don't yet use machine learning to select among competing models, or to induce new models from data. I see it as my job to change this.
Despite the similarities, there are tasks that humans do better than machines and vice-versa. Human-machine collaboration holds the promise of augmenting human intelligence. However, most of this collaboration is based on the "calculator model", in which machines are used in a very restricted way, and there is no "transparency" in human and machine knowledge: human and machine only interface successfully at a few select places in the information-processing.
It is not easy to design machines to help us with general reasoning tasks because such tasks consist of manipulating concepts, which are too numerous and too slippery to "teach" to a computer. Reasoning systems in restricted domains have achieved some degree of success (after significant ontology engineering work), but scaling this to domain-independent reasoning has proven to be a very hard problem.
Areas of interest:
* scientific discovery, machine learning, induction methodologies: how can we combine theory and data?
* information theory (MDL learning)
* active learning
Domains of interest (i.e. data sets I would love to have):
* cogsci: behavior logs AS WELL AS human-made cognitive models
* demographics
* medical data
* natural language
* computational biology
Research experience
SimStudent Project (Sep 2006 - present)
I have been working as a programmer for the SimStudent project, which induces production rules from demonstrations. The idea is to learn cognitive models from behavior data. On the machine learning side, it lacks dynamic feature selection: we have hard-coded a set of operators through which the demonstrations can be learned.
On the cognitive side, it's not a good model of human performance, since it doesn't enforce cognitive constraints: i.e. it doesn't account for the fact that the learner is a human being.
Master's thesis (Apr-Sep 2005):
My master's thesis was about formalizing derivations in physics.
* created an equational theorem-prover that shows the set of models under which equations hold.
* created an ontology of classical mechanics, and formalized some derivations.
* used the above derivations to discover new proofs
Automatic dictionary builder (Jan 2004):
* designed and programmed an automatic dictionary builder for sentence-aligned parallel corpora, by doing Portuguese-English word alignment. Used several heuristics, including cognate-matching, correlations in word-length and word-frequency, and bootstrapping from a small hand-made dictionary.
Great
Date: 2006-12-15 07:37 am (UTC)