Frances Zhu has a degree in Mechanical and Aerospace Engineering and a Ph.D. in Aerospace Engineering from Cornell University. She was previously a NASA Space Technology Research Fellow and an Assistant Research Professor in the Hawaii Institute of Geophysics and Planetology at the University of Hawaii, specialising in machine learning, dynamics, systems, and controls engineering. Since 2025, she has been an Assistant Professor at the Colorado School of Mines in the Department of Mechanical Engineering, affiliated with the Robotics program and Space Resources Program. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines.

Bayesian optimization (BO) is one of the most powerful strategies to solve computationally expensive-to-evaluate blackbox optimization problems. However, BO methods are conventionally used for optimization problems of small dimension because of the curse of dimensionality. In this paper, a high-dimensionnal optimization method incorporating linear embedding subspaces of small dimension is proposed to efficiently perform the optimization. An adaptive learning strategy for these linear embeddings is carried out in conjunction with the optimization. The resulting BO method, named efficient global optimization coupled with random and supervised embedding (EGORSE), combines in an adaptive way both random and supervised linear embeddings. EGORSE has been compared to state-of-the-art algorithms and tested on academic examples with a number of design variables ranging from 10 to 600. The obtained results show the high potential of EGORSE to solve high-dimensional blackbox optimization problems, in terms of both CPU time and the limited number of calls to the expensive blackbox simulation.

It didn't take long to find community on campus. To my surprise, out of the dozen students at a welcome event for the Indigenous community, three grad students and an undergrad were in the aero-astro department. As a prospective Course 16 major and a FIRST Robotics alum, I was excited to discover that they planned to start a new team for the First Nations Launch (FNL) rocketry competition, a NASA Artemis Student Challenge. It was the perfect opportunity to merge my technical passion with my cultural roots. That first year, many people questioned the need for our team.

Finally, there is no better read than recent research papers on the topic of severe researchers looking to make a career in the field. However, that requires working on a problem for some time, identifying the specific problem that interests you and finding relevant papers.

Entering that world felt like taking a step into the mysteries and secrets of the future. I was mind-blown by the promises of intelligent machines, capable of solving tasks forever reserved to us. I was deep-diving into the amazement of the mind through the familiar passages of technology. I had just finished my bachelor's in aerospace engineering and wanted to leap towards AI. It was late 2017 when I met the great Geoffrey Hinton and Andrew Ng.

Robotics is a multidisciplinary science that deals with the design, manufacture and use of robots. It is the joint study area of mechanical engineering, aerospace engineering, aerospace engineering, electronic engineering, computer engineering, mechatronics engineering and control engineering. Robots are complex machines that are managed through software and generate work and value for a useful purpose. Today I am going to write about Kalman Filters. If you're not familiar with the topic, you may be asking yourself, "What is a Kalman filter?"

NASA inaugurated its 23rd class of new astronauts on Monday, which includes 10 individuals who are set to walk on the moon and maybe even Mars. Deemed the'Artemis Generation,' this group consists of several former US military, an ex-SpaceX medical director and a bioengineer who also participated in the 2020 Tokyo Olympics as a track cyclist. The name is a reference to NASA's Artemis program, which aims to send the first woman and the first person of color to moon as early as 2025. The astronaut candidates for 2021 are: Nichole Ayers, Marcos Berríos, Guaynabo, Christina Birch, Deniz Burnham, Luke Delaney, Andre Douglas, Jack Hathaway, Anil Menon, Christopher Williams and Jessica Wittner. This is NASA first new class in four years and the group is set to begin the two-year training process in January 2022.

The system uses cameras that help lifeguards keep swimmers away from a hazardous situation near the shoreline. Every year, rip currents, undertows, and rip tides kill around 100 beachgoers in the United States. These bodies of water seep away from the shore through deep channels and are very common on nearly any worldwide beach. They are often indistinguishable in the eyes of a swimmer and even an inexperienced water sports enthusiast. And most people don't know what to do to avoid and survive a rip current.

This paper proposed a new estimation and control strategy to control the satellite attitude. As the attitude control strategy plays an essential role in the different kinds of space missions, scientists try to improve the performance of the satellite attitude system, regardless of the expense. In this study, we proposed an adaptive neuro-fuzzy integrated (ANFIS) satellite attitude estimation and control system. A pulse modulator is used to generate the right ON/OFF commands of the thruster actuator. To evaluate the performance of the ANFIS controller in closed-loop simulation, an ANFIS observer is used to estimate the attitude and angular velocities of the satellite using a magnetometer, sun sensor, and rate gyro data. Besides, a new ANFIS system will be proposed and evaluated that can simultaneously control and estimate the system. The performance of the ANFIS controller is compared with the optimal PID controller in a Monte Carlo simulation using different initial conditions, disturbance, and noise. The simulations are performed to verify the ANFIS controller's ability to decrease settling time and fuel consumption in comparison with the optimal PID controller. Also, examine the ANFIS estimator, and the results demonstrate the high skill of these designated observers. Moreover, we proposed an integrated ANFIS estimator and controller for satellite attitude control and estimation in the presence of noise and uncertainty, which can reduce the computational effort and offer smooth actuator actions.

This summer, four mechanical engineering graduate students had the opportunity to gain hands-on experience working in industry. Through the recently launched Industry Immersion Project Program (I2P), students were paired with a company and tasked with tackling a short-term project. Projects in this inaugural year for the program came from a diverse range of industries, including manufacturing, robotics, and aerospace engineering. A flagship program of the MechE Alliance, the I2P Program matches students with a company and project that best fits within their own academic experience at MIT. Projects are designed to be short term, lasting three to six months. Building upon programs such as the Master of Engineering in Advanced Manufacturing and Design and Leaders for Global Operations, which foster collaborations between students and the manufacturing industry, the I2P Program offers graduate students real-world experiences across industries.