Weekly Hours: this depends if you are talking classes or just working on research.
1. Classes full time and teaching : 1-3 hours per week on research (i.e if you take any credit hours on research)
2. Classes and research : 10-15 hours per week on research
3. Research : 40 hours per week
These are average times and the key point about academia and research is that you have times where you are super busy and productive with crunch times and other times where productivity is at a lull. We need to learn how to deal with both of those times. if you only have an hour or two - learn how best to utilize that time. maybe reading a paper is useful, or learning how to code etc... I also find that spending a few hours together on a task is way more efficient than spending half hour here, half hour there etc... but thats just how i work and you dont have to work the same as me.
Communication: regularly checking slack and email is a must. A lot of our meetings are scheduled on slack and that’s also where you can ask if folks are around to ask questions.
Lab notes: it is in your best interest to keep regular notes that you can follow along and be aware of whats going on in the group. it also helps you to go back to what you did in the recent past and what changes you did etc...
Chain of command: there is typically no chain of command as defined by hierarchy. If you are trying to do some task and there is someone in the lab, you can always ask anyone in our group and work together, if its me or a postdoc or another grad student or an undergrad or even high school student to get things running. We can all learn something from everyone.
Meetings: 1-1 meetings with me are once per week. Then we have our regular group meetings (RKE Group) where I expect you to attend once per week. There is also the larger group meeting (HENP Group) and our journal clubs that are very good for you to attend in the beginning and must attend as you pass our qualifier exam.
If you are a PhD student before your qualifier exam (i.e before you become a PhD candidate) - i ask that you present roughly once a month or more at the RKE group meeting and atleast once per semester at the HENP group meeting
After your qualifier exam - I expect an update typically weekly (slides or verbal) in the RKE meeting and once in two to three weeks at the HENP meeting.
If you are part of an experiment collaboration say CMS or sPHENIX - you absolutely must all weekly meetings related to your physics working group and detector subsystems. You should also attend the general collaboration meetings so that you are aware of
I understand that there are a lot of meeting but its basically how physics is done now.
Department Events: Attend all department colloquiums. We also have seminars related to our research sometimes that are advertised.
Equipment/Supplies; Just ask me if you need anything.
Time off: 2 weeks for the summer and 2 weeks over Christmas. These are the usual holidays. We don’t typically have timesheets or fixed days on/off since this is academia. as long as you keep me updated on whats going on, i usually dont care where you are doing your work at.
Computation: A lot of our work involves computation and programming in some form or another - be it data analysis with c++, machine learning in python, phenomenology studies of implementing your calculation in a markov chain monte-carlo model etc... Now, no one is born fully adept at programming and its like learning any human language - you need lots of hands on practice to actually become proficient at it. We are also physicists yeah... we code because it makes our life easier and at this time in our understanding of nature, it is literally impossible to calculate and compute most what we need by hand on a black board. the days of a scientist working by themselves on a blackboard in our field are long in the past. Im fully understanding that when you start undergrad or grad school not everyone has the same level of computation literacy. Please be honest and let me know upfront what you know and what you are comfortable with and we can work together.
Grad Students: This is something that i truly believe in and often tell to students - there needs to be a mentality shift when you go from undergrad to grad. In undergrad, you essentially have some tasks, homeworks for example, that when you finish, you move on to the next one. Research is rarely like that. we never really finish finish a task as it just ebbs and flows along the path of research as we find and learn more and more. Having an inherent curiosity and drive towards your research topic is crucial for having a fun and exciting research experience. That said, doing research is not always fun and exciting. there will be moments that are difficult for whatever reason and as a group we will help you over that.
Mentoring Process: One of the only expectations I have is for folks to attend group meeting in-person if you are not sick and if you are around as its easier to discuss progress and the next step. If the work gets done, I don’t care if you do it at the desk assigned to you on campus, or at home, or at a coffee shop or wherever. Just be aware that as you start working on research, it is helpful to be on campus since a lot of the senior folks, including senior grad students, postdocs and especially faculty are often here. That way, you can come by and ask questions and we can easily and quickly help you to move forward. My level of interaction and mentorship is usually very hands off. I give you a project and we discuss the steps, I point out the people working on it and you start doing the necessary legwork, learning from papers, existing measurements etc. and ask about the next steps. The more you engage with me in terms of asking questions, presenting updates, general follow up, the more I engage with you!
During the course of your research experience (mainly for the PhD students), my aim is to guide you to the point where our discussions transition from a one-directional (me to you) list of tasks and learning, to a bi-directional discussion of the topics. Everyone takes a different path to this point but reaching this moment is when i feel like my goal of being your advisor is done. I absolutely enjoy this feeling and it is the cornerstone of my mentoring thought process.
If this method of hands-off approach or whatever it is about our mentoring relationship does not work or you have some thoughts/comments about it, please feel free to come to me and im happy to discuss and learn.
Most importantly - I firmly believe that happy and excited people do good work. If anything is causing you discomfort or trouble with regards to work, please let me know (if you feel comfortable with that). We also have resources on campus to help if needed. If you are miserable, then i am not interested in whatever work you are doing because that defeats our collective purpose to do science for science's sake and have fun!
Weekly Hours: this depends on how many research credits you are taking
1 credit corresponds to roughly 2-3 hours of time on your end
A lot of your time will be spend learning the fundamentals of our research and how to actually make progress. I believe that learning an effective method of approaching research and work ethic is as important as the actual work that you end up doing.
First and foremost - an important learning is to ask questions - it shoudnt matter if what we discuss in a group meeting is going above your head or you think others will think poorly of you for asking questions (no one in our group should think so), please stop the proceedings and say that you dont understand. learning is how anyone gets further in their career and we are all in different stages of our learning.
Secondly, as a physics major, you definitely need to consider taking some amount of programming courses from the CS department. computational literacy is as important to science in our current generation as is a good grasp of mathematical foundation (integrals, differentials equations etc...).
Lastly - make sure that you explore whats in front of you! definitely do not limit yourself to one area of study.
This is about the use of generative AI in your work setting - be it as help in coding or writing. While I have personal reservations (accuracy, technicality and morality) related to the software, it is quite clearly useful as a toolkit for several purposes. I do not have any issue if folks use it for their work with some boundaries that I would like to discuss here.
Code - In general I would say that if people use it to supplement their code, then they should comment and make clear which part of the code is ML generated, what model and what prompt etc.. (and the date you used it). My suggestion is only to use ML as example snippets but not to generate a whole program. A common statement for any tool is that if you do not understand the code that is displayed, then you should not use it until you have tested it to make sure that it does what you want it to do and you understand it. That is how we learn. I want to note one thing that is important is that these current models are *very* good at things they are already trained on. This means that cutting edge science is not part of the training by definition!
Writing - A main focus of your education and an important part of your PhD training is to learn how to write/communicate science well. So especially, your first draft should be all your own work, and I need to see that draft. Then you are welcome to ask AI for suggestions on how to phrase certain sentences better, but there must then be a footnote on the front page or whatever, acknowledging that an AI tool was used to improve the presentation of the results but the original work is your own. Some phrasing like this seems to be becoming a common standard at journals.