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Speech, Lexicon and Modeling Lab

Guidelines: Doing a PhD with Kevin Tang

I’m always interested in working with and learning from highly motivated and hard working PhD students. Supervising a PhD student is a major investment of time, money, and energy, therefore I would like to outline my expectations for prospective students and the kind of support SLAM and UF would provide.

SLAM will provide you with the necessary support and guidance to help us succeed together. You will benefit from our biweekly individual and group lab meetings as well as the many UF resources, some of which are outlined below. To help you become a successful academic as well as a competitive job candidate by the time you graduate, you would need to acquire the following key skills during your PhD program. By the end of the second year, you should have made solid progress on each front.

Writing and communicating results

You will need to be able to write coherently, accurately, and to deliver writing on time (meeting deadlines). You don’t have to aim to be a great writer; clarity and coherence are enough. Grammatical sentences are desirable. If you are not a native speaker, most of your common errors would need to be ironed out by the time you graduate.

Resources and Support:

  • LIN Introduction to Grad Research
  • ENC 5319 Scholarly Writing for Publication
  • Reading books on how to write and reading good writing in reading groups

Data analysis and programming skill

  1. You will need to acquire programming ability in R and, at least, Python. R, specifically, will be used for statistical data analysis. Python will be used for all other programming activities.
  2. You will need to acquire a fair amount of knowledge of statistical theory and methods.
  3. You will need to be able translate your findings in forms of plots and other visualization methods.
  4. Depending on your PhD topic, you might need to acquire other programming techniques/languages, e.g. web programming.

Resources and Support:

Experimental and/or computational modeling skills

  1. You will need to be able to understand the design of experiments, how to implement them from scratch and by yourself, as well as the evaluation of the results.
  2. The same applies to computational modelling type of experiments.
  3. Depending on your project topic, you will need to learn various experimental instruments (e.g., ultrasound, EPG, eye-tracking, EEG…), methods (e.g., priming, lexical decision, forced alignment, ABX, self-paced reading…), and software (e.g., PsychoPy, jspsych, Experigen, Tensorflow, Kaldi…)

Resources and Support:

  • UF courses:
    • LIN Computational Linguistics (Python – basic NLP/comp. ling.)
    • CAP 5771 Introduction to Data Science
    • CAP 6610 Machine Learning
    • LIN Psycholinguistics
    • PSY 3213L Laboratory Methods in Psychology
    • SOP 4214C Research Methods in Social Psychology
    • CAP 5108 Research Methods for Human-Centered Computing
    • LIN Corpus Linguistics/Data Driven Learning (Corpus Linguistics methods)

Domain-specific knowledge

  1. You will need to acquire complete control over the literature in the area related to your PhD topic, and in all closely related areas.
  2. You will need to know what the current issues are, what the major problems/questions in the current issues are, and what the current proposed solutions/answers are.
  3. After a couple of years of doing a PhD, you are expected to know more than anyone on your specific research topic.

Resources and Support:

Professional attitude

You will need to uphold a professional attitude. This includes:

  1. being able to set yourself deadlines and actually meet them. This will involve prioritizing your academic and personal schedule appropriately.
  2. being responsible and highly committed. This means you are responsible for your work and other professional activities. You should not give up without seriously trying to the best of your ability.
  3. being meticulous and having a high standard — when you think you have completed a task, ask yourself if it was done correctly and in the best possible way.
  4. being able to communicate professionally with other researchers (e.g., using the right register/formality in both spoken and written communication)
  5. being emotionally mature, e.g., being able to handle constructive criticisms and failures, take them on board and move forward.
  6. being supportive of other lab members, specifically junior lab members. At SLAM, we value and support our undergraduates as potential and active researchers. I expect you to interact with junior lab members in a productive and educational manner.

Code of conduct

You need to make sure you do not do anything, inadvertently or not, that amounts to misconduct of any kind, such as harassment and scientific fraud.

  1. SLAM is a harassment-free group for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity and religion (e.g., see https://mc-stan.org/events/stancon-code_of_conduct)
  2. SLAM values good scientific conduct and practices (see https://www.dfg.de/en/research_funding/principles_dfg_funding/good_scientific_practice/index.html)

 

Acknowledgment: This document is heavily based on Prof. Shravan Vasishth’s PhD guide: http://www.ling.uni-potsdam.de/~vasishth/doingaphdwithme.html