This is a mixture of:

  • my personal philosophy on a PhD and advisor/advisee relationships;
  • my expectations for students in my lab;
  • my expectations for myself as an advisor; and
  • light discussion of structures and systems in the lab that support the above points.

While this is primarily written for students interested in joining the lab, I hope it’s also useful as a reference point for those evaluating different advising styles more broadly.

What’s your advising philosophy?

Ah, the classic question that seems to keep popping up.

I’ve been fortunate to learn from a range of mentors with very different approaches - some deeply hands-on, other less so; some focused on technical skills, others on big-picture thinking. Across these experiences, I’ve tried to pull out what I thought worked well and what didn’t. As such, this document reflects the values and practices I hope to bring into my own advising, based largely on my own experiences.

Of course, this is partly biased by what worked well for me. I’ve tried my best to avoid this bias, by talking a lot with colleagues on their advising experiences and reflecting on my own experiences as a mentor/advisor.

In one or two places, I will dive into some deeper details on how I try to structure aspects and thought processes. These sections will be lower-level than the remainder of this document, but I think they are useful to give a glimpse into the types of structures that we will use in the lab to help develop and discuss ideas.

Table of Contents

Philosophy

What is the point of a PhD?

A PhD is a training ground for independent researchers. The goal is not just to complete projects or publish papers, but to learn how to define problems, manage uncertainty, and contribute new and meaningful ideas to the world.

My hope is that every student who comes through our lab leaves with the ability (and confidence) to do world-class research. The best way to become that kind of researcher is to try. Not through perfection or constant success, but through thoughtful work, persistence, and ambition.

That said, a PhD is not a commitment to an academic career. The skills you’ll develop - deep thinking, problem-solving, communication, and self-direction - are valuable across many paths. I seriously considered leaving academia during my PhD, and I think that reflection was healthy and important. I’ll support you wherever your goals lead.

Defining world-class research

When I say “world-class research,” I don’t mean publishing n papers per year at top conferences. I mean work that is thoughtful, rigorous, and can stand the test of time.

To me, that involves:

  • Identifying problems that matter - either practically or theoretically;
  • Developing deep understanding of both the problem and the surrounding literature;
  • Situating your work in the broader context of the field;
  • Advancing knowledge through new ideas, theory, methods, or empirical results; and
  • Communicating those contributions clearly and convincingly to others.

That may sound abstract - and it is. Great research looks different in different areas. Still, the core idea is simple: we aim to do careful work on important questions, and share it in a way that others can understand, trust, and build on.

To ground this philosophy, I want to share a personal example. One of the major projects from my PhD was a new localization technique that removed a key limitation present in much of the prior work. It took time to get there - longer than many of the faster-turnaround projects my peers pursued - and once I had the core result, I didn’t rush it out. I invested substantial time in writing, analysis, and experimental validation to make sure the contribution was clear, well-supported, and compelling. I wanted to really convince the community that this was a meaningful advance.

Fast-forward some time and I receive an email telling me that the work received the Best Paper Award from IEEE Transactions on Robotics, the flagship journal in our field. More importantly, it remains one of the results I’m most proud of. One carefully executed paper can shape a field (and a career) more than five rushed ones.

This is the kind of work I want to enable. My hope is to create an environment where students are supported in taking the time and space to pursue deep, ambitious research that genuinely matters.

The magnitude of “world-class problems” and the myth of breakthroughs

Having defined what I mean by world-class research, I want to address a common myth: the idea that you need to solve everything at once, or that every project must be a breakthrough. Most reasonable people, after reflecting on this, recognize this to be false but it still can often sit in the back of our minds and can manifest as various forms of doubt, imposter syndrome, or perfectionism.

Major research problems - those that are impactful to society and push the our understandings - rarely are solved in a single work. In practice, most real progress comes through slow, deliberate work: refining ideas, uncovering structure, building tools, and connecting dots over time.

What we often call a “breakthrough” is usually the result of accumulated insight. It might be a paper that synthesizes prior work, reframes a problem in a compelling way, bridges disciplines, or advances an existing line of inquiry - but it often rests on years of less visible effort.

I say this because it’s easy to become paralyzed by the myth of the breakthrough: the idea that if you’re not solving everything at once, your work doesn’t matter. That’s false. Chipping away at hard problems is not just acceptable - it’s the norm (just look at the history of deep learning). Persistent, thoughtful progress is what good research usually looks like.

How to evaluate good problems?

Low-level discussion follows.

There’s no single formula for what makes a good research problem - but there are useful patterns. One framework I like (shared by Robert Mahony) evaluates problems along five axes:

  • Impact: Is this problem important? Will solving it matter to others - scientifically, societally, or practically?
  • Novelty: Does it advance how we think about a problem? Does it offer a new perspective, method, or insight?
  • Feasibility: Can we make real progress in a reasonable amount of time? Is the scope right - not too narrow, not too vast?
  • Interest: Are you genuinely excited by it? Will you care enough to stick with it when it gets hard?
  • Resources: Do we have (or can we find) the tools, time, and collaborators needed to work on it effectively?

The balance of these factors changes over time. For a first-year student, feasibility and interest often matter most - especially when building confidence and momentum. Your first project doesn’t have to be world-changing. My own first research problem wasn’t that impactful but it gave me a solid foundation to build on.

Choosing good problems is a really important topic, so for more discussion of this I recommend checking out my collection of PhD advice. There’s a lot of writing on this and the existing corpus of advice is largely in agreement, so I won’t go into any more detail here.

Expectation setting: background and development

Student background

Of course, nobody shows up knowing everything. I generally do not expect a very specific background from students, as I think that effort, motivation, and working intelligently are more important than having a specific set of skills from the get-go. A key part of your PhD will be building new technical skills - especially in areas like optimization, probability, machine learning, and robotics systems. This is going to be difficult, but highly rewarding.

You will need to take ownership of that growth: reading, implementing, debugging, asking questions, and seeking out gaps in your own understanding. That’s part of what it means to become an independent researcher.

That said, it is very helpful for students to arrive with a solid foundation in programming and a willingness to engage deeply with mathematics and systems engineering. At some point in your PhD, you will likely spend substantial time on all of these topics, so a good base (and more importantly, knowing that you enjoy doing these things) will go a long way.

Student technical development

While you’re responsible for your growth, you won’t be doing it in a vacuum. A high functioning lab provides a supportive environment for learning. Some things the lab will provide include:

  • Reading groups focused on foundational topics and emerging research;
  • Weekly group meetings that include technical talks and project updates;
  • Shared tools, codebases, and best practices;
  • Opportunities to work with and learn from more senior students and collaborators;
  • Regular one-on-one meetings where I can recommend resources, help unblock technical challenges, etc.

Note that while the group is young, I will be more directly involved in all of these activities. I.e., I will be involved in reading group and technical discussions.

There will also inevitably be types of mentorship that I cannot provide (e.g., specific expertise in a niche area, or the perspective of a senior researcher). In these instances, I will do my best to connect you with others who can help. It is helpful here if you can identify what you need and ask for it.

Expectation setting: what problems we work on

Working outside our lab’s core areas

For many reasons, it makes sense to do work that can draw on the core competencies of our lab. This has both scientific benefit (we can make a sustained push along a particular line of inquiry) and practical benefit (we can leverage in-house expertise, tools, and resources).

However, it is also important to explore ideas outside our core areas. This can lead to new insights, unexpected connections, and open up new research directions. This type of exploration is encouraged, though it should be done thoughtfully and strategically. This type of work is taking a bit more of a shot in the dark, so it is useful to think through the risks, payoffs, and possible fallbacks before diving in.

Ideally, the research we do should be a bit of a mix of the two: bringing new outside ideas into our core areas, and applying our core skills to new domains.

And if you ever feel stuck - whether it’s a paper you don’t understand, a system that won’t work, or a gap in your background - please ask. You’re not expected to already know everything. You’re expected to be proactive, resourceful, and willing to learn. I may not have the answers, but I can help you find resources, suggest approaches, or connect you with others who can help. This sounds obvious, but I think worth re-iterating.

Independence and choosing research topics

A core goal of the PhD is learning how to define and pursue your own research agenda. My role is to help you get there - by guiding problem selection early on, helping develop your ideas, and offering strategic feedback as your independence grows.

Early in the program, I’ll likely suggest a few well-scoped problems to help you gain traction. These are meant to build technical skills, familiarity with the literature, and confidence. Over time, I expect students to take increasing ownership: identifying research questions, framing them clearly, designing experiments or theory, and driving day-to-day progress.

Even the most senior researchers seek feedback and collaboration. But by the later stages of your PhD, you should be operating independently, with me acting more as a sounding board - someone who can help refine ideas, flag blind spots, and challenge assumptions.

Funding and research direction

Like any lab, our research will be shaped in part by funding opportunities. I work actively to secure support that aligns with our scientific interests and values, and I aim to keep student-led inquiry at the center of our work.

In robotics, most funding agencies allow significant flexibility in how research goals are pursued. That flexibility is something I take seriously. When we propose work to a sponsor, I try to frame it in a way that reflects our broader interests so that there is room for exploration, creativity, and intellectual ownership by students.

In practice, this often means that:

  • You will have space to define your own research directions, even within funded projects;
  • We can typically carve out subproblems or extensions of a project that align closely with your interests;
  • If a project ever feels too constrained, we will work together to find a better framing or explore adjacent ideas that satisfy both the scientific and sponsor goals.

I will also actively pursue more flexible or exploratory sources of funding, such as university seed grants or fellowships to support student-initiated ideas.

At the end of the day, I want you to work on problems that you find exciting and important. We’ll be strategic about matching those ideas to funding opportunities, but the driving force should always be meaningful research, not just fulfilling a contract.

Proposing ideas

I expect students to start proposing their own ideas from day one. Problem formulation is a core research skill. This takes time, feedback, and repeated practice to develop.

In my own PhD experience, one of the most valuable aspects of our lab culture was that students were expected to take intellectual ownership of their work. It was hard - especially early on - but that challenge accelerated my growth as a researcher and independent thinker. I aim to foster a similar environment.

Your early ideas don’t need to be polished or correct. What matters is engaging actively with the research process: spotting gaps, questioning assumptions, and thinking creatively about where to go next.

Proposal sketches

Low-level discussion follows.

One you have some medium confidence and high interest in a problem, it is good to try to formalize it into a (very brief) proposal. This is a particularly good exercise for first-year students, but is encouraged for all students. This should be a ~1-2 page document that outlines:

  • The problem you want to solve
  • Why it matters (impact)
  • How it relates to existing work (literature and novelty)
  • A sketch of your approach (methods)
  • What would success look like (evaluation)
  • What resources you need (time, tools, collaborators)
  • What skills you need to develop (background, tools, etc.)
  • Any initial thoughts on feasibility and risks

This proposal doesn’t have to be a pristine piece of writing, just communicate the ideas clearly and give us a shared understanding of what you want to do. We can iterate on it together.

If there are gaps you are having trouble filling in the proposal, we can also try to close them together. A lot of the proposal writing process is about critically thinking about the problem and how you want to approach it, to force you to face what is currently unclear and to structure your thoughts.

This is largely related to the Heilmeier Catechism, which is a popular framework for constructing and evaluating research proposals.

Expectation setting: progress

What does progress look like?

Progress in research is often nonlinear and unpredictable. Some weeks feel like breakthroughs, others are spent debugging a tool, reading papers, or reframing the problem entirely. That’s normal.

Two questions can help you reflect on whether you’re moving forward:

  • Time: Are you consistently spending focused time on your research?
  • Value: Is that time moving your project or understanding in a meaningful direction?

Not all progress looks like new results. It can include:

  • Developing a deeper understanding of the literature;
  • Asking sharper and more focused research questions;
  • Writing or refactoring tools that improve your workflow;
  • Explaining your work more clearly than before;
  • Gaining autonomy in how you scope, plan, and execute your work.

One of the most valuable weeks I spent during my PhD involved nothing but reading. I went deep into the literature for my specific field, identified patterns and gaps that weren’t commonly acknowledged by other practitioners, and developed a much clearer view of where my work could contribute. I wrote no code, proved no theorems, and produced no paper. Still, this time fundamentally shaped my future research and gave me the necessary background to make meaningful progress.

In my mind, one key is to - with some frequency - ask: What is the best use of my time right now? And then act on that. Don’t do this every day, otherwise you will never get anything done. But do it often enough that you are relatively confident that you are working on the right things.

At a high level, some soft benchmarks can be useful. A common goal is to contribute to 2–3 paper submissions per year, including both first-author and collaborative work. But this is not a hard rule. One strong, well-executed paper may matter more than several minor ones, and some years will be heavier on exploration, learning, or tool-building. A PhD should have a minimum of 3-4 good first-author papers.

Expectations by stage

This is a rough outline of what I expect from students at different stages of their PhD. Of course, every student and project is different, so this is just a general guideline. We’ll adapt it to fit your specific situation and goals (and departmental requirements, if relevant).

Year 1: Focus on building technical skills, understanding the literature, and developing confidence in your ability to define and pursue research problems. A great goal is to complete a first project that results in a paper submission. This could be a small but well-scoped problem that you can execute independently with some guidance. You should also be able to explain your work clearly and articulate its contributions.

Year 2-3: You should just be getting the abilities to independently scope and execute an interesting project. I will provide guidance on topics like framing, methodology, and potential pitfalls, but you should be in the driver’s seat for project scoping. If there are certain areas to explore, collaborations to start, or tools to build, this can be a good time to do so. Midway through year 2, I strongly suggest you start drafting a proposal for your thesis topic. This is not a final declaration of the work you will do - it can change completely and parts can be added or removed - but this is the time to start really establishing this high-level vision for what you want to do.

Year 4+: By this point, you should be operating largely independently, with me acting as a sounding board for ideas, feedback on papers, and high-level guidance. You should be able to define your own research agenda, identify important problems, and execute projects with minimal oversight. My goal here is to enable you to do your best work (likely by largely stepping back and giving you the space and resources needed).

Expectation setting: mentorship and communication

Mentorship style

There’s no one-size-fits-all approach to mentoring. Every student is different, and my style will adapt to fit your goals, working style, and stage of progress. That said, there are core principles that guide how I work with students:

Student development: As you progress, I expect you to take increasing ownership of your research - choosing problems, developing ideas, and charting your own path with growing independence.

Independence: I’ll provide mentorship, structure, and feedback - but I expect students to be self-directed, take initiative, and actively drive their own learning and progress. The whole point of a PhD is to learn how to do independent research.

Whole-person support: PhD students are people. I care about your well-being, not just your productivity. If you’re struggling - with work, health, family, or motivation - I want you to be able to bring that up. We can adjust, recalibrate, and figure out how to move forward in a sustainable way.

Level of involvement: I aim to be an engaged mentor, especially early on. I expect to work closely with students on ideas, experiments, and papers. As you grow more confident and experienced, I’ll step back and give you more space. We’ll calibrate together how much guidance and feedback is helpful at each stage.

What I’ll work on with you: I will help scope problems, refine ideas, and discuss experimental design. I also put a strong emphasis on communication skills. I’ll work with you on writing, giving talks, and clearly articulating your contributions.

Communication and calibration: I will be transparent about my expectations, and I’ll ask you to do the same. I want you to raise questions if expectations or ideas are unclear. Open, regular communication is key to building a productive advising relationship - and to making sure you’re getting the right kind of support at the right time.

Career development: I’ll support you in whatever path you choose: academia, industry, startups, or elsewhere. That includes helping find opportunities, preparing applications, doing mock interviews, and connecting you with colleagues in relevant fields, companies, and institutions.

Communication

Clear, frequent communication is critical for good advising. I aim to meet with each student regularly - typically once per week - to discuss research updates, give feedback, and help work through open questions. These meetings are also a space to talk about broader topics like career planning, skill development, or general questions about research.

Communication shouldn’t be limited to scheduled meetings. I encourage you to reach out (Slack or email) when you’re stuck, excited about a new idea, or unsure how to proceed. You don’t need a polished result to start a conversation. Often the best discussions come from messy, unfinished thoughts.

I try to be responsive and available, especially during crunch periods. If something is urgent or time-sensitive (e.g., for a deadline or blocking issue), just flag it clearly and I’ll prioritize it.

Feedback

Feedback and iteration is at the center of doing good research. Nothing is correct on the first try (including this document).

I give direct, detailed feedback - particularly on writing, presentations, and framing research ideas. I take your work seriously, and my goal is to push you to think clearly, sharpen your arguments, and communicate precisely. One of the greatest complements given to researchers is that they are “clear thinkers.” This is very much a trainable skill, and I want all students to develop it.

One of the most productive things you can do is help me understand what kind of feedback you want. Are you looking for high-level input on direction, or line edits on a nearly-final talk? Are you trying to decide between ideas, or polish a contribution? Letting me know helps me meet you where you are.

Also, the status of your work matters. A rough draft invites different comments than a polished one. If you want feedback on a specific aspect, let me know. I’m happy to help you refine your ideas, but I can only do that if I understand what you’re looking for.

I also welcome disagreement. If you think I’m wrong about something, say so. Research is about making arguments and having those arguments tested. Thoughtful pushback is a sign that you’re thinking deeply and independently.

Ultimately, I want our communication to be honest, frequent, and intellectually engaged. If anything ever feels off, please bring it up. Feedback should go both ways.

How to meet

Spending a little bit of time before meetings can make them much more productive. It is good to have a list of prioritized topics to discuss. If you send them ahead of time I can also prepare some thoughts or resources in advance.

If there is a problem you are facing, it can be really helpful if you can answer a few questions before we meet:

  • What have you tried so far?
  • What is getting in the way of tackling this?
  • What support do you need?
  • What would you do if you were in my seat?
  • Is there anything else I should know?

This helps me understand the context, quickly rule out any obvious solutions that you have already tried, and focus our discussion on how I can best support you.

Misc. but important

Time management and work-life balance

A PhD is a marathon, not a sprint. I expect students to work hard and take their research seriously, but I also expect you to rest, take care of yourself, and maintain a life outside the lab. Sustainable progress comes from consistency, not burnout.

There will occasionally be intense pushes before deadlines, but those should be rare, not the default. If you’re feeling overwhelmed or burned out, I want to hear about it. We can revisit timelines, adjust priorities, and find a better pace. Long-term health (mental and physical) is important.

Authorship and collaboration

I believe in giving credit where credit is due. If you contribute meaningfully to a project (through ideas, implementation, writing, or experimentation) you should be recognized as a co-author. I generally follow common authorship conventions in robotics, but I’m always open to discussing authorship decisions transparently and early.

I also strongly encourage collaboration - both within our lab and across labs, institutions, and disciplines. Many of the best ideas emerge from unexpected conversations, and I’ll support you in building those connections and pursuing joint work when it advances the science.

Reproducibility and research artifacts

Our goal is to produce research that others can build on. This means writing clean, well-documented code, organizing experiments clearly, and making results reproducible.

I do not expect perfection, but I do expect students to value clarity, maintainability, and reusability in their code and data practices. If others can’t understand, reproduce, or build on your work, it limits its impact.

Internships and external opportunities

Many students find value in internships or external research opportunities during their PhD. I spent a summer during my PhD as an intern, and it helped reaffirm my interest in academic research. I did not serve as a visiting student (or similar) during my PhD, but know many people who did and were able to get a lot out of it.

If these are of interest to you, I’m happy to support these opportunities. I’m lucky to have a network of colleagues in industry and academia, and I can make introductions where appropriate.

Time is finite (my limitations)

In an ideal world, I would have unlimited time to work with each student, providing detailed feedback on every idea, paper, and experiment. In reality, time is finite. I have many different responsibilities, including teaching, administration, fundraising, and service. It’s also important to me to spend time with my family (and students should similarly value time outside the lab).

As such, while I will do my best to support you, this only works if you take ownership of your work. I will do what I can to support you, provide feedback, and help you grow as a researcher, but I expect you to come prepared to meetings, actively drive your own research, and take initiative in your own development.

At the end of the day, we will both be time-limited. I will do my best to prioritize my time to help you and to make the best use of your time. In exchange, I ask that you be proactive, resourceful, and try to do the same for me. One of the great skills of a PhD is learning how to “manage up” - i.e., how to get the most out of your advisor’s time and expertise. This is a skill that will serve you well in your career, so I encourage you to practice it.

Lab structures and systems

Because of the constraints on time, a lot of my life is spent in prioritization and efficiency mode. Many of the lab structures and systems I put in place are designed to help us all work efficiently and effectively. I generally believe in minimal bureaucracy and structures, and I try to only put systems into place that help focus thoughts in an efficient way with minimal overhead. If the lab structures aren’t serving that purpose, we should find a system that does.

Closing Notes

Grad school should be fun! It’s not easy, but it should be intellectually rewarding and personally fulfilling. I loved my PhD experience, and I hope to create an environment where you can equally enjoy the experience. I will do what I can to create an enjoyable, productive, and intellectually rich environment and I will ask you to do the same.