Tony G. Chen is an Assistant Professor at Georgia Tech. His research focuses on designing mechanical intelligent mechanisms for various robotics applications. Mechanical intelligence focuses on developing mechanisms and robotic platforms that make challenges for perception, control, and autonomy easier or more robust for natural, unstructured, and often unpredictable environments by virtue of their physical designs. To achieve this design approach, two main and common methods are often deployed: rapid prototyping and bio-inspiration. Chen is especially interested in three major research application areas: (i) field robotics where designing new robotic mobility platforms or equipping existing platforms with capabilities of manipulation, (ii) manipulation tasks in home and industrial environments, and (iii) biomimetic robotics for biological study.

Allannah is a Ph.D. candidate in the George W. Woodruff School of Mechanical Engineering. Allannah’s research focuses on waste heat recovery and water conservation. She is currently developing a system for seasonal thermal energy storage using absorption with upgraded heat delivery for residential and industrial end uses. The system aims to offset the imbalance that exists between energy supply and demand. She is also developing techniques to reuse and reduce energy and water consumption in the highly energy-intensive Aluminum casting industry. Allannah has worked as a summer research intern for the Electricity Supply Board in Ireland to design a district heating system for citizens in fuel poverty.

Allannah earned a Bachelor of Engineering in Mechanical Engineering from University College Dublin, Ireland in 2022 where she graduated first in her class.

Advisor: Srinivas Garimella

Mohsen Moghaddam is the Gary C. Butler Family Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering and the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology. He directs the Symbiotic and Augmented Intelligence Lab (SAIL), where his research focuses on developing human-centered computational models, algorithms, and tools at the intersection of AI and spatial computing to enhance learning and creativity in various cognitive and psychomotor tasks within industrial settings. Previously, Dr. Moghaddam was an Assistant Professor in the Department of Mechanical and Industrial Engineering and an Affiliated Faculty with the Khoury College of Computer Sciences at Northeastern University in Boston. He has also served as a Visiting Professor with the HumanTech project at Politecnico di Milano and as a Visiting Scholar at the Next Level Lab, Harvard University. Dr. Moghaddam earned his PhD in Industrial Engineering from Purdue University and completed a Postdoctoral Associate position at the GE-Purdue Partnership in Research and Innovation in Advanced Manufacturing. His research has been supported by the U.S. National Science Foundation, the U.S. Defense Advanced Research Projects Agency, the U.S. Navy, and industry partners.

Zhu's research focuses on the modeling and simulation of mechanical behavior of materials at the nano- to macroscale. Some of the scientific questions he is working to answer include understanding how materials fail due to the combined mechanical and chemical effects, what are the atomistic mechanisms governing the brittle to ductile transition in crystals, why the introduction of nano-sized twins can significantly increase the rate sensitivity of nano-crystals, and how domain structures affect the reliability of ferroelectric ceramics and thin films. To address these problems, which involve multiple length and time scales, he has used a variety of modeling techniques, such as molecular dynamics simulation, reaction pathway sampling, and the inter-atomic potential finite-element method. The goal of his research is to make materials modeling predictive enough to help design new materials with improved performance and reliability.

Zhou's research interests concern material behavior over a wide range of length scales. His research emphasizes finite element and molecular dynamics simulations as well as experimental characterization with digital diagnostics. The objective is to provide guidance for the enhancement of performance through material design and synthesis. Zhou maintains a high-performance computer cluster with 384 parallel processors and an intermediate-to-high strain rate material research facility which includes a split Hopkinson pressure bar apparatus, a tension bar apparatus, and a combined torsion-tension/torsion-compression bar apparatus.

Recent research focuses on the characterization of the dynamic shear failure resistance of structural metals and the role of microscopic damage in influencing failure processes through shear banding and fracture. Micromechanical models are developed to outline microstructural adjustments that can improve the performance of materials such as metal matrix composites, ceramic composites, composite laminates and soft composites. These models explicitly account for random microstructures as well as random crack and microcrack development. At the nanoscale, ongoing research focuses on the novel shape memory and pseudoelasticity that were recently discovered in metal (e.g., Cu, Au and Ni) nanowires. The coupling between the thermal and mechanical responses of semiconducting oxide (e.g., ZnO and GaN) nanowires is another active research direction which uses molecular dynamics simulations and continuum modeling. Dr. Zhou's group is also actively engaged in research on the equivalent continuum (EC) representation of atomistic deformation at different length scales. Related research projects are sponsored by the National Science Foundation (NSF), NASA, the Air Force Office of Scientific Research (AFOSR), the Air Force Research Lab (AFRL), the Office of Naval Research (ONR), the Army Research Office (ARO), industry, and the Center for Computational Materials Design (CCMD).

Dr. Ye Zhao started as an Assistant Professor at the George W. Woodruff School of Mechanical Engineering in January 2019. Previously he was a Postdoctoral Fellow at Harvard University and obtained his Ph.D. from UT Austin, where he worked on robust motion planning and decision-making for robot manipulation and locomotion problems with frictional contact behaviors. At Georgia Tech, he directs the Laboratory for Intelligent Decision and Autonomous Robots. His research interests lie broadly in planning, control, decision-making, and learning algorithms of highly agile, contact-rich, and human-cooperative robots. Dr. Zhao is especially interested in computationally efficient optimization algorithms and formal methods for challenging robotics problems with formal guarantees on robustness, safety, autonomy, and real-time performance. The LIDAR group aims at pushing the boundary of robot autonomy, intelligent decision, robust motion planning, and symbolic planning. The long-term goal is to devise theoretical and algorithmic underpinnings for collaborative humanoid and mobile robots operating in unstructured and unpredictable environments while working alongside humans. Robotic applications primarily focus on agile bipedal and quadrupedal locomotion, manipulation, heterogeneous robot teaming, and mobile platforms for extreme environment maneuvering.

Dr. Fan Zhang received her Ph.D. in Nuclear Engineering and M.S. in Statistics from UTK in 2019. She is the recipient of the 2021 Ted Quinn Early Career Award from the American Nuclear Society and joined the Woodruff School in July, 2021. She is actively involved with multiple international collaborations on improving nuclear cybersecurity through the International Atomic Energy Agency (IAEA) and the DOE Office of International Nuclear Security (INS). Dr. Zhang’s research primarily focuses on the cybersecurity of nuclear facilities, online monitoring & fault detection using data analytics methods, instrumentation & control, and nuclear systems modeling & simulation. She has developed multiple testbeds using both simulators and physical components to investigate different aspects of cybersecurity as well as process health management.