University of Illinois Urbana-ChampaignUnited States
Hadi Meidani is a Clinical Associate Professor at the Carle Illinois College of Medicine , specifically within the Department of Biomedical and Translational Sciences at the University of Illinois at Urbana-Champaign . He teaches courses in Civil and Environmental Engineering, including topics like Systems Engineering & Economics , Machine Learning in CEE , and Uncertainty Quantification . Ph.D., Civil Engineering, University of Southern California (2012) M.S., Electrical Engineering, University of Southern California (2012) M.S., Structural Engineering, Sharif University of Technology (2005) B.S., Civil Engineering, K.N. Toosi University of Technology (2002) Dr. Meidani's research focuses on uncertainty quantification , scientific machine learning , and optimization under uncertainty for engineering systems. His work spans stochastic multiscale analysis , physics-informed machine learning , and model reduction techniques. His recent publications emphasize machine learning for infrastructure systems , graph neural networks , physics-informed models , and traffic assignment . Key trends include deep learning , multi-fidelity modeling , and neural operator transformers applied to metamaterial design , seismic reliability , and autonomous freight delivery .
Robert MacCurdy is an Assistant Professor at the Department of Mechanical Engineering, University of Colorado Boulder . He leads the Matter Assembly Computation Lab (MACLab) focused on automating robot design and fabrication. His research bridges computational design and advanced manufacturing to create "robots that walk out of the printer." The lab develops tools like OpenVCAD , an open-source volumetric multi-material geometry compiler.
R. Edwin García is a Professor at the School of Materials Engineering at Purdue University, where he has been faculty since 2005. He holds appointments in the Materials Engineering department within Purdue's College of Engineering, specifically in the School of Materials Engineering located in the Neil Armstrong Hall of Engineering at Purdue's West Lafayette campus. His educational background includes: B.S. in Physics from the National University of Mexico (1996) M.S. in Materials Science and Engineering from Massachusetts Institute of Technology (2000) Ph.D. in Materials Science and Engineering with a minor in Applied Mathematics from Massachusetts Institute of Technology (2003) Professor García's research focuses on the design of materials and devices through the development of a fundamental understanding of the solid state physics of individual phases, their short and long range interactions, and associated microstructural properties and time evolution. His current research emphasizes establishing relationships between material properties and resultant performance and degradation in electrochemical systems. He integrates computational approaches ranging from kinetic Monte Carlo, phase field and level set methods, to finite elements, finite volumes, and symbolic computing. His work particularly addresses microstructure design, crystallographic texture, and grain boundary science and engineering to control the topology of underlying phases and establish practical relations between processing, microstructure, and material properties. His recent publications demonstrate a strong focus on lithium-ion battery technology, ferroelectric materials, and computational modeling of material behaviors. The research trends show increasing integration of machine learning with traditional computational methods, exploration of novel sintering techniques like flash sintering, and deeper investigation into the fundamental mechanisms of material degradation in energy storage systems. His work spans multiple length scales from atomistic to continuum modeling, reflecting a comprehensive approach to materials design and analysis. Professor García teaches several courses including MSE 230 (Structure and Properties of Materials), MSE 350 (Thermodynamics of Materials), MSE 597G (Modeling and Simulation of Materials), MSE 597I (Introduction to Computational Materials), and MSE 597N (Physical Properties of Crystals). He mentors graduate students in areas related to computational materials science, battery technology, and microstructural evolution. His research group, the Laboratory of Computational Microstructures, focuses on developing home-grown analytical theories and algorithms to resolve relevant time and length scales in materials systems. The group's work has significant implications for portable power sources, including rechargeable batteries and fuel cells, as well as for ferroelectric ceramic applications.
Dr. Carolyn Conner Seepersad is a Woodruff Professor in the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. She leads the Digital Design and Manufacturing research group and previously founded the Center for Additive Manufacturing and Design Innovation at The University of Texas at Austin. Her research focuses on additive manufacturing, materials design, and process innovation. She holds editorial roles, including Editor-in-Chief of the ASME Journal of Mechanical Design, and has received numerous awards for research and teaching. Education: PhD, Mechanical Engineering, Georgia Tech, 2004 MS, Mechanical Engineering, Georgia Tech, 2001 BA, Philosophy, Politics, and Economics, Oxford University, 1998 BS, Mechanical Engineering, West Virginia University, 1996 Her research interests span design for additive manufacturing, simulation-based materials and structures, and metamaterials. She emphasizes manufacturing-aware design and sustainability. Key contributions include lattice structure optimization, negative stiffness composites, and process-aware manufacturing techniques. Her publications reflect advancements in additive manufacturing processes, materials characterization, and design methodologies. Awards include the ASME Design Automation Award and recognition as a University of Texas System Academy of Distinguished Teachers. Seepersad has advised on grants such as the LEAP-HI GOALI project and contributed to initiatives like the Solid Freeform Fabrication Symposium. Her work bridges academia and industry, emphasizing practical applications and innovation. Labs/Teams: Leads the Digital Design and Manufacturing group at Georgia Tech, previously directed the UT Austin Additive Manufacturing Center.
Yongmin Liu is a Professor in Mechanical and Industrial Engineering and Electrical & Computer Engineering at Northeastern University, and a member of the Cross-College Magnetics Center. He holds a PhD in Applied Science and Technology from UC Berkeley (2009), with earlier degrees from Nanjing University. His research focuses on nano-optics, metamaterials, plasmonics, and their applications in optical devices and systems. His interdisciplinary work bridges engineering, physics, and AI, with notable contributions to metasurface design and optical neural networks. Education: PhD, Applied Science and Technology, UC Berkeley (2009) M.S. and B.S., Physics, Nanjing University (2003, 2000) Research Interests: Nano-optics, nanoscale materials engineering, metamaterials, plasmonics, and applied physics. His group develops novel optical materials and devices for applications like super-resolution imaging, efficient light harvesting, and biomedical detection. Recent projects include AI-driven photonic materials design and meta-optical neural networks. Key Achievements: Recipient of the Søren Buus Outstanding Research Award (2024) NSF CAREER Award (2017) and ONR Young Investigator Award (2016) Elected SPIE Fellow (2023) and Optica Fellow (2023) Lab & Collaborations: Head of the Yongmin Liu Research Group, collaborating with institutions like Georgia Tech and Purdue University. Recent grants include a $1.5M NSF DMREF grant for AI-driven photonic materials and a $468K NSF grant for meta-optical neural networks.
Andres F. Arrieta is an Associate Professor of Mechanical Engineering at Purdue University's School of Mechanical Engineering in West Lafayette, Indiana. He leads the Programmable Structures Lab and holds affiliations with multiple research areas including dynamics, advanced materials, and robotics. His research focuses on adaptive structures, mechanical metamaterials, and programmable systems. Education: Mechanical Engineer, Universidad de los Andes, Bogotá, 2006 Ph.D., University of Bristol, United Kingdom, 2010 Postdoctoral Research Fellow, ETH Zurich, 2012 Research Interests: His work emphasizes multistable structures, structural nonlinearity, and elastic instabilities. He explores applications in energy harvesting, morphing wings, and mechanical metamaterials. Recent trends include bio-inspired designs and smart materials for adaptive systems. Awards: 2019 ASME Best Paper Award (Bioinspired Materials) 2018 Gary Anderson Early Achievement Award 2017 Journal Cover Feature (Chiral Metastructure) 2012 ETH Postdoctoral Fellowship Labs/Teams: Leads the Programmable Structures Lab, focusing on innovative metastructures and adaptive robotics systems.
University of Illinois Urbana-ChampaignUnited States
Ahmed Elbanna is an Associate Professor at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering and the Department of Civil and Environmental Engineering. He holds a Ph.D. from Caltech (2011) and has been on UIUC faculty since 2013. His research focuses on mechanics of complex systems, including earthquake dynamics, metamaterials, and biomaterials. He has received prestigious awards such as the NSF CAREER Award (2018) and the Donald Biggar Willett Faculty Fellowship (2020). Education: Ph.D. Civil Engineering, California Institute of Technology (2011) M.S. Applied Mechanics, California Institute of Technology (2006) M.S. Structural Engineering, Cairo University (2005) B.S. Civil Engineering, Cairo University (2003) Research Interests: Elbanna’s work spans theoretical and applied mechanics, with emphasis on fracture, wave propagation, and critical phenomena in geophysical and biological systems. Key areas include: Earthquake mechanics and granular matter dynamics Mechanical metamaterials for wave control Networked biological materials (e.g., bone, hydrogels) Modeling epidemic dynamics during the COVID-19 pandemic Awards & Recognition: National Science Foundation CAREER Award (2018) Donald Biggar Willett Faculty Fellow (2020) Journal of Applied Mechanics Award (2019) UIUC Teaching Excellence Awards (2018–2024) Service & Leadership: Member of the UIUC Faculty Senate (2020–present) Co-Chair, Computational Mechanics Committee (ASCE EMI, 2024–present) Leader, Computational Science Group at Southern California Earthquake Center (2019–present) Labs & Groups: Leads the Mechanics of Complex Systems Lab at UIUC, focusing on interdisciplinary research in geophysics, materials science, and computational modeling.
Andres Arrieta is an Associate Professor in the School of Mechanical Engineering at Purdue University. His research focuses on adaptive structures, mechanical metamaterials, and programmable systems. He holds a PhD from the University of Bristol and conducted postdoctoral research at ETH Zurich. Education: Mechanical Engineer, Universidad de los Andes, 2006 PhD in Mechanical Engineering, University of Bristol, 2010 Postdoctoral Research Fellow, ETH Zurich, 2012 Research Interests: Adaptive Structures Multistable Systems Structural Nonlinearity Robotics & Mechanosensing Origami Engineering Awards: 2019 ASME Best Paper Award 2018 Gary Anderson Early Achievement Award 2012 ETH Postdoctoral Fellowship Labs: Directs the Programmable Structures Lab , exploring smart materials and morphing systems.
Junfei Li is an Assistant Professor in the School of Mechanical Engineering at Purdue University. His research focuses on advanced acoustic technologies, including acoustic tweezers, acoustofluidics, metamaterials, and underwater communication systems. He specializes in multiphysics wave propagation, noise control, and energy harvesting. Li's work bridges fundamental science and engineering applications in biomedical devices, sustainable energy, and advanced materials. Research Interests: Acoustic tweezers for microscale manipulation Design of metamaterials for acoustic control Ultrasound and underwater communication systems Energy-efficient noise mitigation strategies His recent publications emphasize innovations in acoustic metasurfaces, nonreciprocal sound propagation, and biomedical acoustic applications. Li’s research has implications for improving medical imaging, energy sustainability, and next-generation acoustic devices. Awards & Recognition: None explicitly listed in the provided materials. Advising & Grants: No student advisees or grant information specified in the text.
Susanna Thon is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University (JHU), affiliated with the Whiting School of Engineering. She serves as Associate Director of the Ralph O’Connor Sustainable Energy Institute (ROSEI) and a member of the Data Science and AI Institute. Her research focuses on nanomaterials engineering for optoelectronic devices, emphasizing solar energy conversion and sensing. Notable areas include plasmonic-photocatalytic systems using aluminum nanoparticles and nanostructured materials like colloidal quantum dots for next-generation devices. Thon holds a BSc from MIT (2005) and MSc/PhD in Physics from UC Santa Barbara (2008/2010). She joined JHU in 2013 after postdoctoral work at the University of Toronto. Her work is funded by agencies such as the NSF, U.S. Army, and Maryland Energy Innovation Institute. She has published over 50 peer-reviewed papers and received JHU’s Catalyst and Discovery awards. Key research projects include developing plasmonic systems to enhance light absorption in titanium dioxide and creating scalable fabrication techniques for optoelectronic materials. Thon’s team also advances quantum dot solar cells and novel characterization methods for energy materials. She actively participates in professional societies, including the Optical Society of America and IEEE. Her grants and collaborations aim to train the next generation in sustainable energy research, with recent initiatives funded through NSF and Space@Hopkins seed grants. Thon’s lab integrates nanophotonics, materials science, and machine learning to address global energy challenges.
State University of New York at BuffaloUnited States
Jun Liu is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the School of Engineering and Applied Sciences, University at Buffalo. His research focuses on advanced energy materials, nano/micro-mechanics, and self-powered systems, with applications in triboelectric energy harvesting and scanning probe microscopy. Education: PhD, Materials Engineering, University of Alberta (2018) MS, Materials Science, Shanghai University (2015) BE, Materials Science and Engineering, Nanchang University (2012) Research Interests: Development of tribovoltaic and triboelectric systems for self-powered electronics Mechanical energy harvesting via dynamic heterojunctions and Schottky contacts 3D-printed hydrogel structures for energy absorption and flexible electronics Nanoscale characterization using atomic force microscopy Design of nanocomposite sensors and catalytic materials Publication Trends: His work emphasizes triboelectricity, nanoscale energy conversion, and sustainable materials. Recent articles explore bionic tactile sensing, tunable hydrogels, and quantum dynamics in sliding interfaces. Awards: SONY Faculty Innovation Award (2021) Nature Springer MINE Young Scientist Award (2020) International Contest of Applications in Nano/Micro Technology Prize (2013) Laboratory: Advanced Energy Materials and Nanomechanics Lab at University at Buffalo.
Mohsen Habibi is an Assistant Professor at the University of California, Davis, leading the Advanced Manufacturing Lab (AML). His research focuses on Additive Manufacturing (AM), particularly pioneering Direct Sound Printing (DSP), an ultrasound-based technique for 3D printing via sonochemistry and thermochemistry. His work has been recognized with the David Dornfeld Manufacturing Vision Award (2024), NSF Blue Sky Competition win, and inclusion in Quebec Science magazine's top 10 discoveries of 2022. Before academia, Dr. Habibi worked as a senior manufacturing engineer at General Motors and mechanical designer at MDA (a space technology firm). He held roles as a research associate at Concordia University and a postdoctoral fellow at the University of British Columbia, collaborating with industries like Pratt & Whitney and MAL Inc. His research bridges acoustic physics, materials science, and biomedical engineering, emphasizing non-invasive applications such as in-situ tissue printing and remote polymerization. Research Highlights: His lab explores holographic DSP for complex patterns, minimally invasive medical applications, and sustainable 3D printing systems for underserved regions. Key areas include metamaterials, acoustic holography, and energy-efficient manufacturing. Awards & Recognition: David Dornfeld Manufacturing Vision Award (2024) NSF Blue Sky Competition Winner Quebec Science Magazine's Top 10 Discoveries (2022) Altmetric 99th percentile for DSP publication impact Lab Activities: The AML develops technologies like Remote Distance Printing (RDP) for inaccessible locations and Holographic DSP (HDSP) for multi-pattern fabrication. Projects include acoustic-matter interaction studies and CAD/CAM process optimization.
Jonathan Fan is an Associate Professor at Stanford University in the Department of Electrical Engineering. His teaching portfolio includes graduate and undergraduate courses in electromagnetics, integrated circuit fabrication, and specialized studies across all quarters. EE 242: Electromagnetic Waves (Autumn) EE 312: Integrated Circuit Fabrication Laboratory (Winter) ENGR 42/EE 42: Electromagnetics and Applications (Spring) 11 independent studies and thesis courses (EE 190, EE 191, EE 300, etc.) His research focuses on nanophotonics and metasurface engineering , with particular emphasis on inverse design methodologies, machine learning -driven photonic optimization, and machine learning in electromagnetic simulation. His recent publications demonstrate a strong trend toward deep learning-enabled photonic design and high-speed optimization of complex optical systems. His work spans metamaterial fabrication , nonlocal effects in metasurfaces, and multi-functional optical devices such as spaceplates for aberration correction. Key technical contributions include physics-augmented neural networks , reparameterization techniques for design constraints, and topology-optimized metasurfaces .
Wan Shou is an Assistant Professor in the Department of Mechanical Engineering at the University of Arkansas. His research focuses on multiscale manufacturing, advanced materials, and functional devices, with applications in wearables, robotics, and sustainable technologies. Ph.D., Mechanical Engineering, Missouri University of Science and Technology M.S., Mechanical Engineering, University of Louisiana at Lafayette B.E., Textile Engineering, Tianjin Polytechnic University, China Dr. Shou’s research spans laser-based manufacturing , nanomanufacturing , machine learning-assisted processes , and bioresorbable electronics . He explores 3D printing of polymer and metal composites, energy materials , and functional textiles for wearable sensors and environmental applications. Recent publications highlight his work in additive manufacturing , computational design of composites, and self-powered sensing systems . His team integrates machine learning with materials discovery to optimize performance. Editor’s pick of Science Magazine US Patent 11,752,700: Data-driven material formulation US Patent 11,993,850: Laser-assisted nanoparticle printing Dr. Shou’s patents and publications reflect a commitment to innovative manufacturing and environmentally conscious design . His work bridges materials science , robotics , and smart systems , advancing energy and water technologies.
University of Illinois Urbana-ChampaignUnited States
Kai A. James is an Associate Professor in the Department of Aerospace Engineering at the University of Illinois at Urbana-Champaign (UIUC), with affiliations in Computational Science and Engineering. He holds a Ph.D. (2012) and M.A.Sc. (2006) from University of Toronto, and B.A.Sc. (2004) in Engineering Science. His research focuses on multidisciplinary design optimization, topology optimization, aeroelasticity, and nonlinear mechanics, with applications to aerospace structures, additive manufacturing, and smart materials. He teaches courses such as Structural Design Optimization (AE 498), Nonlinear Solid Mechanics (AE 598), and Finite Element Analysis (ME 471). His honors include the NSF CAREER Award (2018), Scott White Aerospace Engineering Fellow (2020), and UIUC Teacher of the Year (2017). His work spans academic publications (over 50 journal/conference articles listed) and innovations in topology optimization frameworks for complex systems. Recent research emphasizes bi-stable structures (e.g., cardiovascular stents, morphing airfoils), thermomechanical design of shape-memory alloys, and spatial packing optimization for engineering systems. His lab, located in Talbot Laboratory, develops computational tools for multiphysics and multiscale design optimization.
