We live in an exciting moment in aerospace’s history. Funding for space research is booming after years of stagnation, and the academic community is poised to fundamentally shape the future of aerospace. Our goal at the MLDS lab is to explore the most rapidly evolving facets of astrodynamics and machine learning research to help define the next generation of spaceflight.

Today, well-known machine learning tools, including ChatGPT, DALLE, and others, are making headlines, but many people do not realize there are other branches of machine learning that are making equally high-impact developments for science and engineering at-large. Researchers are designing machine learning models which are redefining fields like nuclear fusion, protein folding, and artificial general intelligence. Industry is racing to build next generation of autonomous vehicles and complex-decision making agents. Others still are even exploring how nation-states can be more productive in their diplomacy using AI models. It is clear that the early adopters of these technologies are making waves in their respective fields, but despite this, machine learning remains largely underutilized by many academic disciplines… including aerospace.

I argue this is because aerospace is a notoriously risk averse community, and rightfully so. There is no room for error when flying satellite worth hundreds of millions of dollars or carrying human payloads. Consequently, the community has be slow to adopt or even explore cutting-edge advances in machine learning. The MLDS lab is working to change this. Specifically, the MLDS lab’s core objective is to design trustworthy and physics-based machine learning models that can be flown on-board spacecraft. We believe physics-based models are the crucial link that can unite machine learning with aerospace, producing models that are rooted in first-principles without sacrificing performance. At present, we are among the few research groups in the country exploring this intersection of work, and we’d love to have you join our team.

Leveraging top-tier facilities like the IDEA Factory and the Iribe Center, renowned institutes like the Center for Robotics and UMIACS, and state-of-the-art high-performance computing capabilities, our students are equipped with the exact resources to excel at this research. Moreover, the graduates of the University of Maryland are instructed by faculty members and lecturers who actively work on flagship NASA missions, both on science and flight dynamics teams. UMD’s proximity to some of the largest funded aerospace research centers in the country, government agencies, and a wealth of internship opportunities uniquely positions our students for successful careers.

My goal as an advisor will be to help you grow into a prominent researcher operating at this intersection of physics-based machine learning models and space systems research. I will strive to give you the resources and mentorship to thrive at UMD, working closely together on research projects, papers, and presentations. If you want to learn more about my advising philosophy and expectations, I’ll be posting additional documents in our on-ramping handbook in the near future. I look forward to collaborating with you soon.