This new seminar series covers a variety of high-level (meta) topics that we all do as robotics researchers. The primary audience is all current students, but everyone is invited.
We hope cover a range of topic such as:
- Finding a research problem
- Giving presentations
- Navigating the job market
- Writing papers
- Programming for researchers
- Time management for students
Speak: Do you have something you're passionate about? Let me know, we'd love to hear about it.
Help: Are you able to organize a talk? Help out with snacks? Spend a few minutes with setup? Let me know.
Ten lessons for becoming (and staying) productive as a graduate student (Felix Duvallet).
Thursday August 14th, 3pm. NSH 1305.
Being productive as a graduate student can be hard, yet our success often depends on it. Becoming (and staying) productive sometimes feels akin to black magic, and it's unclear where to start. In this talk, I'll share ten lessons that might help you become more productive. Rather than focusing on quick and dirty hacks that almost never work, we'll try to understand the heart of the problem (human psychology) and come up with systems that enable us to become slightly more productive versions of ourselves in the long term.
For this talk, you must bring a physical writing implement (i.e., pen or pencil) and something paper-based to write on (no computers).
About the speaker: Felix is trying to stay productive while finishing up his thesis. He works on getting robots to understand language; sometimes they even listen.
Reflections on Choosing Academia (Illah Nourbakhsh). Hosted by Alex Styler.
Wednesday September 3rd, 3pm. Location TBD
Many grad students face serious decisions points regarding the academia-industry divide, and they do so with little experience from their possible futures. I, for instance, came from a family where a PhD was not a goal by any means, and where a corporate job was the default path forward. My pathway to my current position was somewhat unconventional, including a startup company as well as stints at JPL and NASA/Ames along the way, and so I have some experience with a variety of possible futures you can face. In this talk I concentrate on the tradeoffs I believe best characterize the set of options you face as you leave RI with your PhD, and I also describe the ways in which I was ill-prepared for faculty life.
How to Keep Thinking (Matt Mason).
Monday October 20, 3:30pm. NSH 1305.
Abstract coming soon.
In this talk, we'll learn what sets good code apart from bad, and some basic best practices so your code is well-structured, less buggy, and easier to modify and reuse. Also, since good code and well-tested code are usually one and the same, we'll spend some time covering the basics of writing unit tests.
The format will be roughly 45 minutes of lecture, followed by a 30-45 minute workshop where we'll practice what we've learned. If you plan on attending the workshop, bring a laptop with MATLAB installed (any version is ok).
About Jessica: Before coming to CMU, I spent 5 years working as a developer, tester, and dev-ops engineer at ThoughtWorks and GrubHub. I hope some of what I've learned in industry will be helpful for you on your academic projects!
Disseminating good research is often as important as creating good research. I will present some collected wisdom and share my personal experiences on making and giving research talks.
For most, finding a viable thesis topic is a singular experience. It must be cutting edge but it must fit within a large set of constraints dictated by pragmatics. While you might work on a research team, your own ideas must be front and center in a Phd thesis. While your advisor might help you with organizing principles and background, he or she can't (or shouldn't) hand you a pre packed set of problems to solve. Understandably, a significant question has to do with how to get started and how to proceed with promising ideas.
In my talk I will summarize the process that I go through with students on the process of finding, iterating on, and running with a thesis topic. I will explain how this process is an example of the fact that the artifacts of science are quite different from the process of science.