Friday, Sept. 13
10:40 a.m. - Seminar in AERO 120
11:30 a.m. - Panel Discussion / Q&A in AERO 111

This seminar will recount the two-year proximity operations and remote sensing campaign at Bennu, including the dramatic sample collection event and the events leading to the landing of the sample capsule in Utah.

A panel discussion will follow, featuring members of the Navigation and Flight Operations Team from NASA Goddard, Lockheed Martin, and KinetX, who will each recount specific challenges faced during the mission and the innovations that were implemented to overcome them.

Featured Speakers:

Dr. Michael C Moreau (AeroEngr MS’97, PhD’01) has worked at NASA’s Goddard Space Flight Center since 2001, and for over 10 years has served in leadership roles on the OSIRIS-REx Mission, as the manager of the Navigation Team during development, launch, and Bennu encounter, then as deputy project manager and leader of the sample return capsule recovery team. Mike’s Ph.D. research at �鶹��Ѱ�����focused on applications of the Global Positioning System in high Earth orbits, and contributed to the adoption of GPS on NASA missions such as GOES and Magnetosphere Multiscale. Before attending CU, he earned a BS in Mechanical Engineering at the University of Vermont.

Over three decades, Dr. Peter Antreasian (AeroEngr PhD’92) has made contributions to the navigation of NASA missions, Galileo, NEAR, Mars Odyssey, MER, Cassini-Huygens, GRAIL, and OSIRIS-REx. He began his career at the Jet Propulsion Laboratory in 1992, then joined KinetX 20 years later to lead the OSIRIS-REx navigation team. His expertise in orbit determination and navigation has been crucial in the success of these missions, including the first-ever landing of a spacecraft on an asteroid and the return of an asteroid sample to Earth. Peter earned his BS, MS and PhD in Aerospace Engineering, respectively, from Purdue, University of Texas and University of Colorado.

Dr. Jason Leonard (AeroEngr MS’12, PhD’15) received his Ph.D. in Aerospace Engineering Sciences from the �鶹��Ѱ����� under the advisement of Dr. George Born. Currently, he is the Orbit Determination Group Supervisor at KinetX Aerospace and Deputy Navigation Team Chief for the NASA OSIRIS-REx and OSIRIS-APEX missions. He has been the Orbit Determination Team Lead for OSIRIS-REx since prior to Launch, during the duration of proximity operations and its successful acquisition of asteroid regolith, and through its return of the sample to Earth. For his contributions to the mission, Jason received the NASA Exceptional Engineering Achievement Medal and the PI Award of Distinction.

Dr. Daniel Wibben is the Maneuver Design Group Supervisor for the Space Navigation and Flight Dynamics practice at KinetX Aerospace, Inc. Since joining the company, he has held the role of Maneuver and Trajectory lead for the OSIRIS-REx asteroid sample return mission. He has also been involved with the planning and operations of the LUCY, LunaH-Map, and DAVINCI missions. He received his B.S. in Aerospace and Mechanical Engineering, and M.S. and Ph.D. in Systems Engineering from the University of Arizona where his research was focused on nonlinear guidance techniques for asteroid proximity operations and planetary landing.

Coralie D. Adam (AeroEngr MS’17) is the Optical Navigation Group Supervisor at KinetX. She holds a B.S. in aerospace engineering and astronomy from the University of Illinois, and an M.S. in aerospace engineering sciences from the University of Colorado at Boulder. During her 12 years at KinetX, Coralie has had lead roles on the navigation teams for NASA’s New Horizons, OSIRIS-REx, Lucy, and OSIRIS-APEX missions. In addition to leading the OSIRIS-REx optical navigation subsystem from development through sample collection, she co-convened the scientific investigation of Bennu’s active particle ejection phenomena. Coralie is currently the deputy Navigation Team Chief on NASA’s Lucy mission, and a navigation lead and science co-investigator on the OSIRIS-APEX extended mission to asteroid Apophis.

Ryan Olds (AeroEngr BS’04, MS’09) has 19 years of experience in Guidance Navigation and Controls at Lockheed Martin Space supporting NASA Deep Space Exploration Missions.  Ryan started his career working on the Pointing Control System for the Spitzer Space Telescope.  He developed the reaction wheel control system for the twin-spacecraft GRAIL mission and supported test, integration, launch, and operations at the Moon.  Ryan began working on OSIRIS-Rex in 2013 by developing control systems as well as the Natural Feature Tracking system which provided autonomous navigation for OSIRIS-REx during the mission’s sample acquisition phase.  Ryan is currently a Guidance, Navigation and Controls manager and continues to support Deep Space Exploration missions such as OSIRIS-REx and DAVINCI.

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Romit Maulik
Assistant Professor, Data Science, Pennsylvania State University
Wednesday, Mar. 12 | 10 a.m. | AERO 114

Abstract: Machine learning stands poised to revolutionize the process of scientific discovery across various disciplines. In this talk, we will introduce a state-of-the-art scientific machine learning paradigm - differentiable physics (DiffPhys). DiffPhys can be considered a system identification paradigm that can be applied to determine neural network approximations of governing laws given data. It can also be used to improve first-principles-based simulations of physical phenomena by learning corrections to governing laws (for instance for closure modeling in multiscale applications). Notably, optimizing these neural networks necessitates a differentiable programming paradigm where gradients of a loss function can be propagated through a numerical solver. In this talk, we will introduce DiffPhys algorithms that (1) can learn models for dynamical systems from sparse data, (2) efficiently compute sensitivities for systems exhibiting deterministic chaos, (3) leverage graph neural networks for geometry-invariant learning, and (4) provide physically meaningful interpretations for neural network behavior thereby engendering scientific discovery. We will demonstrate the capabilities of DiffPhys on canonical and realistic scientific computing problems and close with a discussion of the future possibilities of this approach.

Bio: Romit Maulik is an Assistant Professor of Data Science in the College of Information Sciences and Technology at Pennsylvania State University and a Joint Appointment Faculty at the Mathematics and Computer Sciences Division at Argonne National Laboratory (Argonne). He obtained his PhD in Mechanical and Aerospace Engineering at Oklahoma State University in 2019 and was the Margaret Butler Fellow and then a Staff Scientist at Argonne National Laboratory before joining Penn State in 2023. His research centers around machine learning for scientific computing with an emphasis on scalable, physically consistent, and robust algorithm construction for simulation-based scientific discovery of multiscale physics from multifidelity data. He has led research projects sponsored by multiple agencies and is an Early Career Awardee of the Army Research Office.

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Gary McGraw
Systems Engineering Consultant
Friday, Mar. 14 | 10:40 a.m. | AERO 114

Abstract: Global Navigation Satellite Systems (GNSS) like the US Global Positioning System (GPS) are critical to the operation of many aspects of our modern society and are used daily by billions of people. A major error source for GNSS is multipath propagation and mitigating these errors is a major concern in high-accuracy positioning applications like survey, aircraft landing, and precision construction and agriculture. This talk presents an introduction to what multipath is, how it affects GNSS measurements, and ways to mitigate its effects. The use of carrier smoothing of code pseudorange measurements is discussed as an effective multipath and noise mitigation technique that does not require access to the inner workings of the receiver like other mitigation techniques require. Recent developments in approaches to statistically model multipath errors as stochastic processes and how the multipath error statistics are affected by carrier-code smoothing will be introduced. These techniques are useful in safety of life applications, like aviation navigation, where it is necessary to have statistical bounds for error sources.  

Biography: Dr. Gary McGraw is a systems engineering consultant, specializing in Positioning, Navigation and Timing (PNT). He retired as a Technical Fellow at Collins Aerospace where he led the development of several high accuracy and high integrity navigation systems for civil aviation and military applications. His current research areas are focused on the use of communication data links for positioning and timing and GNSS-based aircraft landing system developments. He received the B.S. degree in Electrical Engineering and Mathematics from Iowa State University, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of California, Los Angeles. Dr. McGraw is a Fellow of the Institute of Navigation (ION) and is a Senior Member of the IEEE.  He is an Associate Editor of the ION NAVIGATION journal, was the recipient of the 2011 Johannes Kepler Award from the ION, and currently serves as the ION President.

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Bobby Hodgkinson
Associate Teaching Professor, Smead Aerospace
Friday, Mar. 14 | 12:45 - 2:15 P.M. | AERO N240

Abstract: Artificial intelligence is no longer a distant concept—it’s here, and can help shape the way we teach, learn, and assess. This talk will provide a foundational understanding of generative AI (GenAI) in education, focusing on what faculty, staff, and students at �鶹��Ѱ�����Boulder can do right now with available tools and resources. We will explore how chatbots, custom GPTs, and applications like NotebookLM and deep research are transforming studying, research, and classroom engagement.

Beyond these entry points, the talk will examine AI-augmented applications, including automated grading assistants, AI-enhanced concept exams, and AI-driven code interviews. Attendees will also gain insight into how generative AI is being used across campus, including the Leeds School of Business, the Office of Information Technology, and the Center for Teaching and Learning. We will then extend the discussion to the broader community, highlighting AI initiatives within the Boulder Valley School District (BVSD) and the Rocky Mountain AI Interest Group (RMAIIG).

This session is designed to provide a clear, practical roadmap for getting started with generative AI today—without requiring technical expertise or financial investment. By the end, participants will leave with an understanding of the immediate applications of AI in their professional and educational environments, as well as awareness of the growing AI ecosystem in Boulder and beyond.

Biography: Bobby is an Associate Teaching Professor in Aerospace Engineering at �鶹��Ѱ�����Boulder, exploring AI-driven education. He has developed AI-assisted grading tools, concept assessments, and code interviews to enhance student learning. He is actively involved in campus-wide AI initiatives and collaborates with colleagues across disciplines to explore AI’s role in education. 

Beyond CU, he engages with the broader AI and education community to advance AI literacy. His talks explore practical applications of generative AI in education, from foundational tools to emerging trends shaping the future of learning.

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Charbel Farhat
Professor, Aeronautics and Astronautics, Stanford University
Friday, Mar. 7 | 1 p.m. | AERO 111 |

Abstract: The AFOSR MURI effort, titled “A Robust Multi-Physics Design Analysis and Optimization Framework for Hypersonic Systems Grounded in Rigorous Model Reduction,” unites a multi-disciplinary team of engineering scientists from Stanford University, the University of Minnesota, Rice University, and the University of Notre Dame. This ambitious and collaborative initiative is dedicated to tackling the computational challenges inherent in the development of hypersonic systems. The team’s primary goal is to design a computationally efficient framework for multi-physics design analysis and optimization under uncertainty, harnessing the power of surrogate modeling techniques to achieve remarkable efficiency gains. The lecture will begin with a comprehensive yet succinct overview of the global research effort, offering insights into the project’s objectives, broader impact, and the computational obstacles it seeks to overcome. Among these challenges are the surrogate modeling of quantities of interest versus spatio-temporal fields, the complexities of low-dimensional modeling in the presence of shocks, shock-shock and shock-boundary layer interactions, turbulence, and the intricacies of training surrogate models in high-dimensional parameter spaces for coupled vehicle-trajectory problems and optimization. The discussion will then shift to recent contributions by the speaker, including advanced nonlinear projection-based reduced-order models that address the closure error in the latent space using a deep learning approach. These models have been validated for hypersonic benchmark flow problems and have led to the development of an efficient global optimization method. Finally, the lecture will explore the application of these methods to simulate hypersonic flight dynamics and optimize trajectory planning, demonstrating their potential for advancing the design and analysis of hypersonic systems. 

Bio: Charbel Farhat is the Vivian Church Hoff Professor of Aircraft Structures in the School of Engineering at Stanford University, where from 2008 to 2023, he chaired the Department of Aeronautics and Astronautics. He is also Professor in the Institute for Computational and Mathematical Engineering. He was designated by the US Navy recruiters as a Primary Key-Influencer and flew with the Blue Angels during Fleet Week 2014. He is a Member of the National Academy of Engineering (US); a Member of the Royal Academy of Engineering (UK); a member of the Lebanese Academy of Sciences; a Doctor Honoris Causa from Ecole Nationale Supérieure d’Arts et Métiers, Ecole Centrale de Nantes, and Ecole Normale Supérieure Paris-Saclay; a designated ISI Highly Cited Author in Engineering; and a Fellow of AIAA, ASME, IACM, SES, SIAM, USACM, and WIF. He has trained so far about 100 PhD and post-doctoral students. For his research on aeroelasticity, aeroacoustic scattering, CFD, dynamic data-driven systems, fluid-structure interaction, high performance computing, model reduction, and physics-based machine learning, he has received many professional and academic distinctions including: the Ashley Award for Aeroelasticity and the Structures, Structural Dynamics and Materials Award from AIAA; the Spirit of St Louis Medal and a Lifetime Achievement Award from ASME; the Gordon Bell Prize and the Sidney Fernbach Award from IEEE; the Gauss-Newton Medal from IACM; the Grand Prize from the Japan Society for Computational Engineering Science; the John von Neumann Medal from USACM; and the Olof B. Widlund Prize for Excellence in Domain Decomposition Methods from DDM.org.
 

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Adam Thorpe
Postdoctoral Researcher, University of Texas at Austin
Monday, Mar. 10 | 10 a.m. | AERO 114

Abstract: Autonomous systems must learn, adapt, and make decisions in novel, unpredictable environments. However, data-driven approaches often struggle to generalize and can be fragile in such environments. My research addresses this challenge by developing learning-based methods that explicitly incorporate mathematical structure, enabling autonomy to adapt, scale, and generalize. 

Central to this approach are structured representations of policies, operators, and distributions that leverage Hilbert space theory, statistical learning, and neural network models. In this talk, I will present results demonstrating how autonomous systems can adapt and transfer to new scenarios within seconds using minimal online data—without the need for additional retraining. 
I will highlight advances in neural operator learning, where efficient function-to-function mappings achieve orders-of-magnitude improvements in accuracy over state-of-the-art methods, with significant implications for autonomy. Finally, I will discuss efforts to design autonomous systems that operate safely around humans by tailoring responses to individual needs and preferences.

Bio: Adam Thorpe is a postdoctoral researcher at the Oden Institute for Computational Engineering and Sciences at The University of Texas at Austin. He received his PhD at the University of New Mexico in 2023. Adam's research interests are in the area of data-driven and learning-based control, with applications to humans and autonomy, space systems, and robotics.
 

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Sherman Lo
Senior Research Engineer, Stanford GPS Laboratory
Friday, Mar. 7 | 10:40 a.m. | AERO 114

Abstract: GPS and other global satellite navigation systems (GNSSs) has become integral to our society in both obvious and subtle ways. We knowingly rely on it every day for mapping or ride sharing. We unknowingly use it when we watch a video on our smartphone, we turn on the lights or make a stock purchase as it provides timing to important infrastructure. When we fly, GNSS is critical to many systems onboard our plane. It has profoundly changed our society. But it can do more and we are still developing ways to using it more effectively or for more applications. One place GNSS has not been used a lot is in the oceans - that is starting to change. We are developing technology using GNSS and satellite communications that will provide greater awareness of the oceans by tracking marine life and alerting us with their position should they be poached. We called this the Fin Alert Shark Tag (FAST).

Apex marine fish such as shark and tuna are key to the health of the ocean ecosystem.  However, they are also highly prized and valuable. So they are common targets for poaching and illegal fishing. The initial goal of FAST is to develop a tag for these protected marine species which will alerts us immediately with their positions should they be caught. As such, it must operate practically anywhere on the surface of the earth, survive the challenges of being on marine life and provide location information quickly. These three challenges means that we must have a 24/7 communications link anywhere on earth (Iridium is used), it must be able to survive pressures of up to 200 times that of atmospheric pressure at sea level (2000 m depth) and must provide GNSS positions with only a few seconds of observations. Furthermore, these tags need to be small, low profile, low power and low costs - in other words, low SWaPC.

This talk will discuss the marine poaching problem and cover the key pain points to a marine anti-poaching tag that FAST is solving. It will discuss the testing and prototyping conducted on the tag. It shows field test results of the tag on elephant seals – which ended up being the most precise long-term tracking that has been done on this species. We will cover how the technology is not only useful for marine anti-poaching but for many other maritime applications such as retrieval of lost equipment (AUV, fishing nets, lobster pots, etc.). These assets are costly to lose both economically and because they can harm marine life. FAST is essentially an airtag for protecting the ocean.

Bio: Sherman Lo is a senior research engineer at the Stanford GPS Laboratory. He also is executive director of the Stanford Center for Position Navigation and Time (SCPNT) and a Stanford instructor. His research work focuses on navigation safety, security and robustness. At Stanford, he was Associate Investigator for the FAA evaluation of enhanced Loran and alternative position navigation and timing (APNT) systems for aviation. He currently leads work examining GNSS resiliency, interference and spoofing detection and mitigation. He has over 170 conference, 30 journal, 19 magazine publications and 10 issued US patents. He is also a fellow of the Institute of Navigation (ION) and just finished his term as its president.

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Harsh Bhundiya
PhD candidate, MIT Aerospace Materials and Structures Laboratory
Wednesday, Mar. 5 | 9:30 a.m. | AERO 111

Abstract: Modern deployable space structures have enabled spectacular missions like the James Webb Space Telescope, but they are constrained by fairing size and by a tradeoff between deployed size and structural precision that limits their use for future communications and astronomy applications. In-space assembly and manufacturing (ISAM), or the robotic construction of structures in space, offers a promising solution to overcome these issues and enable novel spacecraft architectures on orbit and on planetary surfaces. Structures constructed in space can achieve higher packaging ratios and be optimized for loads on orbit; however, current ISAM concepts are hindered by inefficient manufacturing processes with high power requirements and challenges in the coupled design of spacecraft and fabricated structures. In this talk, I present my work addressing these issues to enable energy-efficient, rapid ISAM of large space structures. I first discuss designing manufacturing processes for space, using a quantitative framework for material and process selection that considers the important metrics of thermal stability, energy consumption, and accuracy. This analysis suggests deformation processes are well-suited for ISAM, due to an order of magnitude lower energy consumption than other candidate processes. Next, I discuss the design of ISAM spacecraft through an analysis of fabrication time, considering constraints such as power, attitude control authority, and avoidance of control-structure interactions. This analysis shows how attitude control authority is the most dominant constraint on fabrication time of large-diameter structures and the importance of using multiple spacecraft to decrease fabrication time. Finally, I synthesize my results with an exemplary ISAM process, termed Bend-Forming, which can efficiently form truss structures from metallic feedstock. I highlight its application for constructing large reflector antennas and ongoing efforts to understand attitude control during fabrication, paving the way for a future space demonstration. 

Bio: Harsh Bhundiya is a PhD candidate in the MIT Aerospace Materials and Structures Laboratory. His research interests lie in spacecraft structures, deployable structures, and space robotics. He currently researches the deformation processing of large truss support structures and the design of spacecraft for in-space assembly and manufacturing. Prior to his PhD, he completed a B.S. in Mechanical Engineering from Caltech in 2020 and a M.S. in Aeronautics and Astronautics from MIT in 2022. He received the 2022 AIAA Spacecraft Structures Best Paper Award and is a NASA Space Technology Graduate Research Fellow. 

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A Future Insight Seminar and Book Signing
Dan Caruso
Author:
Tuesday, March 11 | 4 P.M. | AERO N240

The first 50 attendees will receive a free signed Bandwidth book.

No one knows the birth of the Internet, and the establishment of mobile communications and fiber optic networks better than Dan Caruso, one of the changemakers who largely shaped the modern communications age. At the dawn of the digital revolution, he was about to embark on an adventure that would not only impact the course of his life for decades to come, but permanently change the very essence of how people relate to each other on a daily basis. For the first time, Caruso pulls back the curtain in Bandwidth, a no-holds-barred memoir that celebrates the pioneers who invented the Internet’s early backbones, the renegades who lit the fiber revolution, the antics that caused it to all come crashing down, and the new wave of entrepreneurs who rose the industry back from its ashes.  For more information on Bandwidth please go to: 

Dan Caruso, is a fierce business leader known for his extraordinary track record of success in the telecommunications, aerospace and technologies industries.  As founder and CEO of Zayo Group, Caruso led the company to a $14.3 billion exit, delivering exceptional returns for investors. His career highlights include co-founding executive roles at Level 3 Communications and leadership positions at Metropolitan Fiber Systems (MFS), both achieving multi-billion-dollar enterprise values. Caruso also spearheaded the 2004 take-private of ICG Communications, achieving a remarkable 25x return.  In 2020, he founded Caruso Ventures, a prominent investment firm focusing on quantum technology, space tech, and other Colorado scaleups. A three-time “Decacorn” entrepreneur, his expertise is shared with aspiring innovators through numerous organizations, including Endeavor Global, Elevate Quantum, and the University of Chicago’s Polsky Center for Entrepreneurship and Innovation.

Caruso holds an MBA from the University of Chicago and a BS in mechanical engineering from the University of Illinois. His numerous accolades include the Colorado Technology Association’s Lifetime Achievement Award, and the Colorado Governor’s Corporate Citizenship Medal. In 2015, Dan and his wife, Cindy, established The Caruso Foundation to Support Initiatives that Inspire Entrepreneurship, Innovation, and Inclusion. The Caruso Foundation supports a number of impactful organizations including the �鶹��Ѱ�����’s Startup Summer, Catalyze CU, and New Venture Challenge, Endeavor Colorado, Colorado Thrives and several others. Dan recently gave the commencement address for the �鶹��Ѱ����� MBA (2020).

Mark N. Sirangelo created and hosts the �鶹��Ѱ�����Future Insight Seminar Series as CU’s Entrepreneur-Scholar in Residence. He is a previous Chairman of the U.S. Dept. of Defense’s Defense Innovation Board and the DoD’s Space Advisory Committee. Mark was the founding executive and head of Sierra Nevada Corporation’s Space Systems and has served as the Chief Innovation Officer of Colorado.

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Zhiyu Zhang
Postdoctoral Fellow, Electrical Engineering, Harvard University
Wednesday, Feb. 26 | 10 a.m. | AERO 114

Abstract: Despite the advancements of autonomous systems from decades of engineering, there is always the need to make them even more efficient and reliable. Machine learning holds great potential to achieve this goal, as it can leverage computation and data on an unprecedented scale. An important challenge is thus synergizing these two separate areas, which requires fundamental algorithmic innovations due to the high stakes of interacting with the physical world.

In this talk, I will describe my unique approach to tackle this challenge, specifically from the perspective of adaptive online optimization. This is a major research topic within theoretical machine learning, but my talk will focus on its engineering implications tailored to autonomous systems. The central question is the following: given a “black box” machine learning module, how can we use principled insights from adaptive online learning to improve its efficiency and reliability?

My talk will answer this question in two concrete problems. First, based on [arxiv:2402.02720] (ICML’24) and [arxiv:2410.02561] (in submission), I will demonstrate how to sequentially build trustworthy confidence set predictions on top of an arbitrary point-predicting machine learning model, without explicit statistical assumptions on the nature. Next, based on [arXiv:2405.16642] (NeurIPS’24), I will show that in lifelong reinforcement learning, a theoretically-grounded regularizer can mitigate an intriguing collapse behavior called “loss of plasticity”. These results can be applied to various high-impact modalities of autonomous systems.

Bio: Zhiyu Zhang is currently a postdoctoral fellow in electrical engineering at Harvard University. He obtained his PhD in systems engineering from Boston University, and BEng in mechanical engineering from Tsinghua University. His research centers around the theory and practice of adaptive online learning, which concerns optimal sequential decision making with Bayesian-type prior knowledge. On the application side, he is also excited about various aspects of robotics and automation, especially algorithmic approaches that efficiently utilize large-scale pretraining. He has been recognized by the BU systems engineering outstanding PhD dissertation award, as well as outstanding reviewer awards from NeurIPS, ICML and AISTATS. He also serves as an action editor for the journal TMLR. 

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