Frequently Asked Questions

“SPARC” stands for “Safe and Performant Autonomous Robotics and Control.

The “SPARC” is pronounced in the same way as “spark” and is a reference to the spark of innovation that drives our ideas and the spark of electricity that drives our robots.

The SPARC Lab logo features the letters “sparc” in lowercase, blue stylized font over five grey circles. The colors reflect Tufts’ palette while symbolizing rigor, calm, and the safety of our systems. The blue letters over top of the five grey circles represent our robots’ tracks driving on the five populated continents (cf. the Olympic rings), emphasizing our goal of broad deployment of safe and performant robots at scale.

The smaller SPARC Lab icon is a blue “s” superimposed over a grey “p,” representing the balance between safety and performance. The overlapping letters also resemble a simplified yin-yang, reflecting the tradeoff and interconnected nature of these two key concepts.

In our lab we need to do a little bit of everything. As academic researchers, we develop theory for “why” things should work. As roboticists, we make the theory work in practice through software and hardware implementation.

Robotics requires a range of abilities and every person will have different skills and preferences. What is important is that you have an interest and respect for every part of the process: theory, software, and hardware. We can always learn what we need to know along the way!

I think that the purpose of a PhD is to learn how to be a researcher. Publishing papers is necessary to achieve that, but publishing in itself is not our main goal. Therefore, while specific technical skills are important, what matters more is your ability to acquire new ones. I think that the three most important qualities for a PhD student are passion, perseverance, and ability to collaborate. While courses and grades in a relevant field can reflect these traits, I also value things like personal projects, statements of purpose, community outreach, work experience, and more. I try to assess applicants holistically and avoid letting any single factor (e.g. GPA or test scores) carry too much weight.

In addition to those three qualities, it is critical that students’ research interests align with the lab’s focus. For me, a good fit isn’t just about specific skills but about pursuing work that excites and inspires you. That’s why I place a strong emphasis on ensuring that there’s an alignment of research interests between the applicant and the lab. I want to make sure that the lab is a good fit for you.

For undergraduate, masters, and visiting researchers, who have shorter and more explicit project timelines, the focus is slightly different. We prioritize the same core qualities as above, but the applicant’s demonstrated technical ability to complete the proposed project becomes primarily important, as there is less time to explore and acquire foundational skills.

Our research bridges robotics, control theory, and machine learning, so it is useful to have a background in each of those three subjects as well as a solid understanding of the underlying math.

In terms of math our work relies on: linear algebra, differential equations, analysis, optimization, and probability theory. But you don’t need to know all of this on day one! You’ll pick up what’s important along the way.

Here are some great resources to get you started if you haven’t had the opportunity to take these classes yet: the matrix cookbook, understanding analysis, convex optimization, feedback systems, Steve Brunton’s youtube channel.

Also, for more academic resources, you can check out our research library page.

For work in the lab it is useful to have familiarity with software languages and packages including: C++, Python, Matlab, ROS2, Docker, pyTorch, the C++ Eigen matrix library, openCV, IsaacSim, MuJoCo. It isn’t expect that you have familiarity with all of these, but it’s useful to know what they are.

For hardware and design knowledge, it can be very helpful if you’re familiar with 3D design tools (e.g., solidworks) and manufacturing methods (e.g., 3D printer, laser cutter, water jet cutter, mill, lathe, CNC) as well as having a familiarity with standard hand tools (e.g., drills, wrench, allen keys) and electronics (e.g., soldering, circuit design).

Also, a huge part of science is in communicating results, so therefore it is critical that we, as researchers, develop our communication skills. This includes public speaking and designing slide decks for presentations. Video editing and figure design are also surprisingly important skills for a robotics researchers as videos and figures can really help to communicate a result. For scientific communication in math, it is incredibly useful to know how to use latex and I would recommend using Overleaf as your latex editor that has a lot of google-docs-style functionality.

SPARC Lab generally publishes our research at the following venues:

• Control Theory Venues:
  • CDC (spring deadline, international control conference), ACC (fall deadline, American control conference), LCSS (short-form journal), TAC (long-form journal)
• Robotics Venues:
  • RSS (winter deadline, small + prestigious conference), IROS (spring deadline, large international conference), ICRA (fall deadline, large international conference), RAL (short-form journal), TRO (long-form journal)
• Machine Learning:
  • CORL (summer deadline, robotics + learning conference), L4DC (late fall deadline, control + learning conference), ICML (winter deadline, large learning conference)

Lead author’s of accepted research submissions will be funded by the lab to present their work at the associated conference.

In our lab, we believe impactful research is grounded in these core principles:

1. Clarity and Accessibility
   • A great paper is not dense for its own sake. The best work communicates ideas cleanly and directly. We aim to:
     • Distill complex ideas: Present the essential insight in a straightforward way.
     • Maintain core rigor: Include all complexity needed to support the claims and nothing extraneous.
     • Ensure accessibility: Make the central idea understandable even to researchers outside the immediate subfield.
2. Rigor
   • Strong research is built on precise reasoning, careful validation, and intellectual honesty. We aim to:
     • Build on solid foundations: Use correct theory, assumptions, and models that reflect the real system.
     • Validate thoroughly: Stress-test ideas across scenarios, edge cases, and failure modes, and compare rigorously against alternative methods.
     • Prioritize accessible correctness: Ensure proofs, derivations, experiments, and evaluations hold up under scrutiny while maintaining the clearest presentation possible.
3. Reproducibility
   • Reproducibility is the foundation of scientific progress. A strong paper allows others to independently verify its results. We aim to:
     • Be transparent: Provide complete details of methods, experimental setups, and algorithms.
     • Enable verification: Ensure results can be reliably repeated by others in the field.
4. Provides Generalizable Insight
   • The impact of a paper should extend far beyond its initial publication. A truly great result provides a generalizable insight that can be applied broadly. We aim to:
     • Provide broad utility: Offer rigorous takeaways that inform future work and apply across domains.
     • Create lasting impact: Contribute ideas that endure beyond any single task, system, or dataset.

In robotics, complexity is inevitable, but confusion is optional. We strive to extract, validate, and communicate the simple, actionable ideas that make safe autonomy possible in the real world.

Industry research often has more funding and larger teams, so for big projects that need results quickly, industry can usually move faster than academia. Where academia shines is in its ability to think deeply about difficult problems over long periods of time. Without the pressure to deliver a product every quarter, a researcher can spend a PhD or more exploring a question that might be too uncertain or long-term for industry to take on.

The lack of immediate product pressure also grants a crucial freedom: academia isn’t shaped by immediate customer needs. That freedom allows researchers to look for fundamental insights and broadly useful ideas instead of solutions tailored to a specific product or company. And unlike industry, academic research is centered on openly sharing knowledge. Publishing work, releasing code, and teaching others help the entire field move forward rather than keeping advances within a single organization.

At the same time, industry sets an inspiring example for academia. Industry teams excel at building reliable systems under real constraints, deploying ideas at scale, and solving concrete problems that affect millions of people. These accomplishments can motivate academic researchers to ground their work in real-world needs and to aim for ideas that eventually make a practical impact.

In short, academic research shouldn’t try to compete with industry on speed or scale. Instead, it can leverage its freedom and long timelines to tackle the big, foundational challenges that industry might not have the time or flexibility to pursue. For example, while industry might focus on making a robot safe and performant in a cage, academia is uniquely positioned to explore the much harder problem of making robots safe enough to work freely in the real world.

The primary responsibility of masters and undergraduate students is generally course work, but I am more than happy to have you spend some time working with us in the lab. The potential roles are called “Research aFfiliate (RF)” and “Research Issistant (RI)”. RFs are volunteers whose primary responsibility is to learn; whereas RIs are compensated (financially and/or with course credit) and are given explicit research expectations that they must meet throughout the term.

Research aFfiliate (RF):

• Volunteer position
• Focus on learning, gaining research experience, and exploring research topics.
• Access to robot equipment at the discretion of the PI, PhD students, or RAs.
• No explicit research expectations; this role is intended as a learning resource.

Research Intern (RI):

• Compensated position
• Receive financial compensation or course credit at the student’s discretion. Consider research to be a part-time job or a class.
• General access to lab space, computational equipment, and the robot for their research project.
• Expected to balance coursework with research responsibilities (≥ 5hrs/week), spend dedicated time in lab, and attend regular meetings.

In general, RFs and RIs are able to switch between the two roles at the discretion of Prof. Cosner depending on their time, background, and success in the lab.

Ultimately, the goal for your time working in the lab is to (1) learn new things, (2) apply your classroom knowledge, and (3) get an understanding of what it is like to conduct academic research.

In general, I would expect masters and undergraduates to work under the guidance of a PhD student with the possibility of coauthorship of a research publication. In this case we can have joint meetings of the whole team working on a research project. Alternatively, for highly-motivated RIs, it may be possible to lead a research effort and write a paper as a “first-author”. Leading a project is hard work and can be very time consuming, so if this is something that you intend to pursue, let’s discuss it early and plan out the steps.

SPARC Lab lists PhD students (RAs), RIs, RFs, and PIs as authors on academic papers according to the IEEE authorship guidelines.

For important communications, email is best. For everyday research and lab activities, slack is more efficient. Typically, I have 30 minute meetings with PhD students every week, although this may vary on a case-by-case basis. In general I expect this meeting to be relatively casual updates on technical, administrative, and general life. There is no need to prepare anything beforehand. Additionally, there will be occasional formal lab-wide meetings for practice presentations and bi-annual check-ins to discuss overall progress and career planning.

In general, I try to be responsive to digital messages. However, I prioritize in-person communication, so if you need to discuss something in-depth or urgently, it is better to discuss in-person. In general, I try to set aside time every business day (Monday through Friday excluding holidays) to clear out my inbox, so you can expect a response once every business day. If you are a member of the lab, I will not “ghost” you, so if you did not get a reply to an email when you were expecting one, I apologize and please follow up.

I have a few ideas that drive my advising philosophy. Firstly, I believe that a PhD education should be about producing researchers and not about producing research. The research results are a necessary result of that process, but not the main goal.

Secondly, in research, I like to think of the advisor as the TA and nature as the teacher. If research is a class that the PhD student is taking then the advisor is the TA and not the teacher. The laws of nature/science/logic are the teacher, and the advisor is just trying to help you understand the teacher’s really confusing lecture notes. I work on the same team as my student’s to try to understand the world and come up with the best solutions possible.

Ultimately, the PhD should be an educational experience for the student and I believe in centering my advisee’s learning and growth first and foremost.

I try to engage in research with my students as though I were a well-informed collaborator. I give students space to work independently and then meet with them periodically to help pull ideas together. My natural inclination is to be hands-on when collaborating in-person during scheduled meetings, but to give students time to work independently between meetings. That said, I find it best to work on a case-by-case-basis to establish the advising relationship that works best for each student.

I have twice-a-year career check-in meetings with students. Over the course of the PhD people’s career goals will likely change and I want to help you guide your PhD journey to best support your post-PhD goals.

I fully support my students going into academia, industry, or whatever alternative option (policy, startups, outreach, etc.) they want to pursue and am happy to discuss how to best shape their PhD towards those goals.

I think that every PhD student, and especially robotics PhD students, should do a 3-month internship during their PhD. It can give them great perspective on the problems that they are working on, introduce them to new ideas, significantly supplement the PhD stipend, and help them build their network of connections. I recommend the summer after the 3rd or 4th year of the PhD as an ideal time to do an internship because it can inspire the final push in your research and build useful industry connections that will help you get a job after the PhD.

A second internship can also be a great idea, especially if it is at a largely different place that will provide a new perspective (e.g., once at a research institute and once at a startup). A third internship is where the benefits may begin to offset to the costs for your PhD in terms of continuity and timeline, but I’m happy to discuss this on a case-by-case basis.

SPARC lab is brand new so we are still developing our lab culture! In addition to research activities, I intend to hold regular social events. Prof. Chris Rogers has also brought up some intra-departmental robot competitions, so we’ll see about what fun robotics-related events we can get going at Tufts.

Ultimately, we’ll be shaping the lab culture together! I want to hear from you about what *you* want for the lab. This will be through both informal suggestions and bi-annual anonymous reverse job reviews where I want to hear your suggestions for me and for the lab.

The goal is for SPARC Lab to grow into a bustling research space with 5+ PhD students, 2+ masters students, and 4+ undergraduate students that are passionate, self-motivated, and curious who are pushing the limits of what we can do in robotics and how we understand safety in autonomy. Additionally, I want to develop a collaborative, convivial environment where we learn from each other and enjoy working together.

To borrow a phrase from my (Ryan’s) grad school mentor:

Formality and respect are different things. I expect us to respect eachother, but there’s no need for formality. – Gunter Niemeyer

With that in mind, you’re welcome to call me “Ryan”, “Kazuo”, “Professor Cosner”, “Prof. Cos”, “Dr. Cosner”, or even my college nickname “RyCos”. I tend to prefer “Ryan”, but all are perfectly fine with me, especially to avoid confusion since “Ryan” is such a common name. Similarly, my pronouns are he/they and you should feel free to use either set.

Also, for reference,

• Ryan is the standard american pronunciation, e.g. Ryan Gosling.
• Kazuo is pronounced Ka (as in “cause”), zu (like “zoo”), and o (like “oh my gosh”); each syllable has equal length and stress.
• Cosner is pronounced with Cos like cause, and ner as in dinner.

As for you, I’ll call you by whatever name and pronouns you prefer and I’ll do my best to pronounce your name correctly. If I mispronounce it, I sincerely apologize and please please feel free to correct me!