Professor Timothy Sands

Timothy Sands

Professor of Practice in Space Systems
Sibley School of Mechanical and Aerospace Engineering
449 Upson Hall
Systems Engineering
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Biography

Dr. Timothy Sands is on the engineering faculty at Cornell University since 2020, where his research and teaching focus on astronautical engineering and systems engineering. From 2010 through 2020, he was an executive and senior leader of both military postgraduate universities (the Air Force Institute of Technology and the Naval Postgraduate School respectively), serving sequentially as Chief Academic Officer, Associate Provost, Dean, Associate Dean, and research center Director in addition to serving as a Fellow of the Defense Advanced Research Projects Agency (DARPA). He executed the DARPA Challenge for digital manufacturing analysis, correlation, and estimation (DMACE), investigating the science behind the burgeoning field of digital manufacturing.

During nearly three decades of active duty service in the U.S. Air Force, he performed space mission design and space experimentation for the Department of Defense (DoD) Space Test Program (STP) including the middle atmosphere high resolution spectrograph investigation (MAHRSI) flown in the pallet system on space shuttle mission STS-66; as well as the polar ozone and aerosol measurement (POAM) geophysical research mission flown on the French SPOT-4 satellite; and also the beryllium induced radiation experiment flown on Russian RESURS satellite. His other interesting space experiment missions include the polar orbiting geomagnetic survey flown on the defense meteorological satellite program; the remote atmospheric and ionospheric detection system on TIROS-J; and the solar wind interplanetary measurement flown on the NASA WIND satellite. He was the propulsion engineer of the Atlas space launch vehicle, the reliability engineer of the Centaur upper stage, and an electronic warfare engineer and operator, having flown over six-hundred hours in combat in four countries, being thrice decorated for single acts of combat gallantry and bravery in addition to other decorations for achievement and meritorious service.

His areas of academic expertise include space mission design; guidance, navigation, and control; estimation; adaption and learning; and nonlinear systems; and his minor fields include electrical engineering topics of electronic warfare and autonomous systems. His background represents a breadth of leadership experience in space experimentation across academia, the aerospace industry in general, and particularly the defense department. His research has been funded by DARPA, ONR, AFOSR, AFGSC, and AETC and has been awarded one shared patent in spacecraft attitude control.

Recognized for his teaching and mentorship at the Naval Postgraduate School and Air Force Institute of Technology, Dr. Sands remains broadly interested in social sciences disciplines of deterrence, command and control communications, and international relations as well as technical translation, particularly of engineering developments written in Chinese.

Research Interests

Autonomous robotics (particularly in CIS-lunar space): My goal is to apply a broad background to the problems faced by the nation and the world, particularly emphasizing student growth and expanded critical thinking abilities. A broad background has coalesced into the initial expression of a method of deterministic artificial intelligence, where physics-based mathematics are used to establish self-awareness augmented by both simple and optimal learning methods, and the results enhance system autonomous robustness in the face of significant damage applied to electrical systems, mechanical systems, and complex systems such as global temperatures and sales of electric vehicles.

Teaching Interests

Formal responsibilities include a suite of courses teaching complimentary skills necessary for robotic space systems, while two of the courses (analytic optimization and adaptive & learning systems) have broader application to a very large set of technical problems:

1) The course called Space Robotics teaches basic dynamics, materials and structural properties, and automatic control including structural interaction.  The course is offered at the senior undergraduate level and also the professional level, tailored to working professionals in the space industry. The course is cross listed as mechanical and aerospace engineering (MAE 4816/5816) and systems engineering (SYSEN 5816). Students may also pursue this course by enrolling in MAE 6910 Independent Study and an MEng Project is also available by enrolling in MAE 6900 Special Investigations in Mechanical and Aerospace Engineering. 

2) The course called Astronautic Optimization teaches students how space robots can maneuver with minimal fuel usage to grasp & grapple; and repair/refuel/resupply operational spacecraft. The course is offered at the professional level tailored to working professionals in the space industry and also at the doctoral level. The course is cross listed as mechanical and aerospace engineering (MAE 5830/6830) and systems engineering (SYSEN 5830/6830) and an MEng Project is also available by enrolling in MAE 6900 Special Investigations in Mechanical and Aerospace Engineering. 

3) The course called Adaptive and Learning Systems teaches students how to design any kind of system (including but not necessarily space robots) that autonomously adapts to dynamically changing situations by either “adapting” or alternatively “learning”. Such skills are essential for autonomous space robots to interact with systems of unknown properties that are sometimes uncooperative. This broad, professional writing course allows students to bring any nature of system of their choice to the class (including from their thesis/dissertation/MEng project).  During the four-month semester, the students write four professional manuscripts, rather than do homework and take tests. The course is offered at the professional level tailored to working professionals in the space industry and also at the doctoral level. The course is cross listed as mechanical and aerospace engineering (MAE 5280/6280) and systems engineering (SYSEN 5280/6280) and an MEng Project is also available by enrolling in MAE 6900 Special Investigations in Mechanical and Aerospace Engineering.  

4) Students pursuing the professional master’s degree, the Master of Engineering (MEng) will notice graduate research projects complimenting the three courses listed above, permitting students to spend 1-2 semesters deeply dive into the topics of those courses with matching names: Space Robotics, Astronautic Optimization, and Adaptive and Learning Systems. A fourth available project emphases defensive maneuvers in space without using propellant.

All these courses have been designed to accommodate students of disparate backgrounds to contribute to the state of knowledge, and they are taught with my unique teaching style that blends the learning methods of the military with those of traditional academia. You will be challenged, but you will do well (see the list below of student-authored publications).

In service to the university and my colleagues, I remain interested in developing a new course in Defense Space Systems Engineering, while other courses of interest include MAE 4730/5730 Intermediate Dynamics, MAE 4780/5870 Feedback Control Systems, MAE 6060 Spacecraft Attitude Dynamics, Estimation, and Control, MAE 6760 Model Based Estimation, MAE 6780 Multivariable Control Theory, and MAE 6850 Hamiltonian Dynamics.

Service Interests

My goal is to make Cornell astronautics known particularly throughout the defense department as well as the commercial space enterprise including Varda Space Industries, SpaceX, Blue Origin, and others. I seek to align my personal research interests with student learning, actual spaceflight, and especially job placement visibility for students. Having already performed years of university service at very senior levels, I henceforth focus solely on student successes as Professor of the Practice.

Selected Publications

Student authored

  • Kuck, E.; Sands, T. Space robot sensor noise amelioration using trajectory shaping. Sensors 2024, 24(2), 666. (IF 3.9)
  • Hall, D.; Sands, T. Vehicle Directional Cosine Calculation Method. Vehicles 2023, 5(1), 114-132.
  • Koo, S.; Sands, T. Bilinear interpolation of three-dimensional gain-scheduled autopilots. Sensors 2023, 24(1),13. (IF 3.9)
  • Pittella, A.; Sands, T. Proposals for surmounting sensor noises. Sensors 2023 (IF 3.847), 26(6), 3169.
  • Menezes, J.; Sands, T. Discerning Discretization for Unmanned Underwater Vehicles DC Motor Control. J. Mar. Sci. Eng. 2023 (IF 2.744), 11(2), 436. (IF 2.9). Highly Cited Paper award, 2023.
  • Ribordy, L.; Sands, T. Chaotic van der Pol oscillator control algorithm comparison. Dynamics 2023, 3(1), 202-213. Title Story Award, 2023, #4 of top 6 articles.
  • Wang, Z.; Sands, T. Artificial Intelligence-Enhanced UUV Actuator Control. AI 2023, 4(1), 270-288.
  • Wilt, E.; Sands, T. Microsatellite Uncertainty Control Using Deterministic Artificial Intelligence. Sensors 2022 (IF 3.847), 22(22), 8723.
  • Raigoza, K., Sands, T. Autonomous Trajectory Generation Comparison for De-Orbiting with Multiple Collision Avoidance. Sensors (IF 3.847) 2022, 22(16). Highly cited paper award, 2023.
  • Koo, S., Travis, H., & Sands, T. Impacts of discretization and numerical propagation on the ability to follow challenging square wave commands. Journal of Marine Science and Engineering (IF 2.744) 2022, 10(3), 419.
  • Zhai, H., Sands, T. Comparison of controlling nonlinear van der Pol systems. Sensors (IF 3.847) 2022, 22(16).
  • Osler, S. N., Sands, T. A. Controlling remotely operated vehicles with deterministic artificial intelligence. Applied Sciences (IF 2.838) 2022, 12(6), 2810.
  • Sandberg, A.; Sands, T. Autonomous trajectory generation algorithms for spacecraft slew maneuvers (#1 top cited in 2022). Aerospace (IF 2.660) 2022, 9(3), 135.  #1 top-cited in 2022; Highly Cited Paper Award, 2023.
  • Zhai, H., Sands, T. Controlling chaos in Van Der Pol dynamics using signal-encoded deep learning (Highly Viewed Paper) Mathematics (IF 2.592) 2022, 10(3), 453.
  • Zhai, H.; Sands, T. Comparison of Deep Learning and Deterministic Algorithms for Control Modeling. Sensors 2022, 22, 6362. Highly cited paper award, 2023.
  • Banginwar, P.; Sands, T. Autonomous Vehicle Control Comparison. Vehicles 2022, 4(4), 1109-1121.
  • Yao, P.; Sands, T. Micro satellite orbital boost by electrodynamic tethers. Micromachines (IF 3.523) 2021, 12(8), 916.
  • Shah, R.; Sands, T. Comparing Methods of DC Motor Control for UUVs (Highly Cited Paper Award, #5 of top 5). Applied Sciences (IF 2.838) 2021, 11(11), 4972. Top Cited Paper Award, 2021; Highly Cited Paper Award, 2022; Highly Cited Paper Award, 2023.
  • Smeresky, B., Rizzo, A., & Sands, T. A. Optimal Learning and Self-Awareness Versus PDI (Editor's Choice Article Award) Algorithms (IF=2.267) 2020, 13(1), 23. Highly Cited Article, 2022.
  • Smeresky, B., Rizzo, A., & Sands, T. A. Kinematics in the Information Age. Mathematics (IF 1.105) 2018, 9(6), 149.
  • Cooper, M., Heidlauf, P., & Sands, T. A, (2017). Controlling Chaos - Forced van der Pol Equation. Mathematics (IF=1.154) 2017, 5(4), 70.

Published to aid learning in courses

  • Sands, T. Bio-Inspired Space Robotic Control Compared to Alternatives. Biomimetics 2024, 9(2). (Impact Factor 4.5)
  • Sands, T. Inducing Performance of Commercial Surgical Robots in Space. Sensors 2023 (IF 3.847), 23(3), 1510.
  • Sands, T. Flattening the curve of flexible space robotics. Applied Sciences (IF 2.838), 2022, 12(6), 2992. Highly Cited Paper Award, 2023.
  • Sands, T. Treatise on analytic optimal spacecraft guidance and control. Frontiers in Robotics & AI (IF 4.331) 2022, (9).
  • Sands, T. A. Countering the deleterious effects of electromagnetic pulse. Frontiers in Electronics, 2021, 10(2), 727994.
  • Sands, T. Virtual sensoring of motion using Pontryagin’s treatment of Hamiltonian systems. Sensors (IF 3.567) 2021, 21(13), 4603.  Highly Cited Paper Award, 2022, Highly Cited Paper Award, 2024.
  • Sands, T. Control of DC Motors to Guide Unmanned Underwater Vehicles. Applied Sciences (IF 2.838) 2021, 11(5), 2144. Top Cited Paper Award, 2021, Highly Cited Paper Award, 2022, Highly Cited Paper Award, 2023
  • Sands, T. Development of deterministic artificial intelligence for unmanned underwater vehicles. Journal of Marine Science and Engineering (IF 2.458) 2020, 8(8), 578. Best Paper Award, 2020, Best Paper Award in Engineering, 2021,  Highly Cited Paper Award, 2022, Best Paper Award in Engineering, 2022
  • Sands, T. A, (2019). Comparison and Interpretation Methods for Predictive Control of Mechanics. Algorithms (IF=2.175) 2019, 12(11), 232.
  • Sands, T. A, Optimization Provenance of Whiplash Compensation for Flexible Space Robotics. Aerospace (IF 1.659) 2019, 9(6), 93.

Selected Awards and Honors

Education

  • Ph.D., Astronautical Engineering, Naval Postgraduate School
  • Graduate Certificate, Astronautical Engineering, University of California – Extension, Los Angeles
  • Graduate Certificate, Aeronautics and Astronautics, Stanford University (distance learning)
  • M.S.,Space Studies, University of North Dakota (distance learning)
  • M.Eng.,Space Operations, University of Colorado, Colorado Springs (distance learning)
  • Degree of Engineer, Mechanical Engineering, Columbia University (distance learning)
  • M.S., Mechanical Engineering, Stanford University (distance learning)
  • B.S., Mechanical Engineering, North Carolina State University

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