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.
Nirupam Roy is an Assistant Professor at the Department of Computer Science, University of Maryland, College Park, and Director of the iCoSMoS research lab. His work bridges wireless networking, mobile computing, and acoustic sensing with applications in IoT, localization, healthcare, security, and wearables. Research Focus: Wireless Networking & Mobile Sensing Awards: Best paper award, MobiSys 2022 Best demo award, MobiSys 2021 CSL Ph.D. Thesis Award, UIUC 2019 Students: Nakul Garg, Yang Bai, Irtaza Shahid, Harshvardhan Takawale, Aritrik Ghosh, Ayushi Mishra, Sumbul Zehra, Justin Goodman Grants: NSF CAREER award (2023), Meta Research Award (2023)
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.
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.
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.
F. Levent Degertekin is a Regents' Entrepreneur and the George W. Woodruff Chair in Mechanical Systems and Professor at the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. His office is located in Love Building, room 311B, and his contact email is levent.degertekin@me.gatech.edu. Dr. Degertekin's academic journey includes a Ph.D. in Electrical Engineering from Stanford University (1997), an M.S. in Electrical Engineering from Bilkent University, Turkey (1991), and a B.S. in Electrical Engineering from Middle East Technical University, Turkey (1989). Dr. Degertekin's research focuses on micromachined ultrasonic devices and systems for medical applications, particularly in intravascular ultrasound imaging, therapeutic ultrasound, and acousto-optical sensors for MRI. His work spans from fundamental research on novel transduction methods to complete catheter-based imaging systems close to commercialization. He has made significant contributions to capacitive micromachined ultrasonic transducers (CMUTs), developing diffraction grating based optomechanical sensing methods now commercialized by Silicon Audio, novel atomic force microscopy imaging probes, and micromachined ultrasonic ejector structures for cell transfection commercialized by OpenCell Technologies. His research integrates acoustics, optics, and their combinations for various medical applications, utilizing conventional microfabrication (MEMS) and integrated circuit technologies. The Degertekin lab exposes students to applied physics, electrical, mechanical and biomedical engineering, biology, and biomimetic systems, providing them with thorough theoretical and experimental education in acoustics and optics while learning interdisciplinary research. Dr. Degertekin's work has received significant media attention, including coverage in IEEE Spectrum, Wired Magazine, The New York Times, and Fox Business News, highlighting innovations such as handheld ultrasound probes, MRI safety sensors, and minimally invasive cardiac imaging technologies. IEEE Fellow for 'Contributions to micromachined ultrasonic and optomechanical transducers and systems,' 2022 IEEE UFFC Society Inaugural Carl Hellmuth Hertz Ultrasonic Achievement Award, 2014 George W. Woodruff School Outstanding Achievement in Commercialization and Entrepreneurship Award, 2024 National Science Foundation CAREER Award, 2004-2009 Whitaker Foundation Biomedical Engineering Research Grant Award, 2001 66 US and 6 International Patents Dr. Degertekin has mentored numerous students who have gone on to make significant contributions in the field. Several of his students have received IEEE Ultrasonics Symposium Best Student Paper Awards, including Jeff McLean (2003), Sheng-Yu Peng (2006), Rasim O. Guldiken (2005 and 2007), and Toby Xu (2014). His research has been supported by various grants including the NSF CAREER Award and Whitaker Foundation grant. His work has led to multiple commercial ventures including Silicon Audio and OpenCell Technologies. The Degertekin Group at Georgia Tech focuses on transducers and systems for medical imaging and sensing, with current projects including capacitive parametric transducers, acousto-optic sensors for MRI, novel transducer methods for focused ultrasound in the brain, microsystems for intravascular and intracardiac ultrasound imaging, and CMUT-on-CMOS systems for IVUS imaging.
A.T. Charlie Johnson serves as the Rebecca W. Bushnell Professor of Physics and Astronomy at the University of Pennsylvania's School of Arts & Sciences, where he has been a standing faculty member since 1994. His research program focuses on nanoscale systems and has established him as a leading figure in condensed matter physics, earning recognition from major scientific societies. His educational foundation includes: Ph.D. in Physics from Harvard University (1990) B.S. in Physics from Stanford University (1984) Professor Johnson's research centers on the development and application of atomic-layer nanomaterials, particularly graphene and transition metal dichalcogenides , for fundamental studies of transport phenomena and practical biosensor applications. His group employs advanced nanofabrication techniques at Penn's Singh Center for Nanotechnology to create devices that leverage biological molecules for chemical recognition in disease diagnosis, security screening, and environmental monitoring. This work bridges condensed matter physics with biomedical engineering , yielding innovative solutions for real-world detection challenges. Analysis of his 2023-2025 publications reveals three dominant research thrusts: (1) scalable graphene-based biosensor development for medical diagnostics, (2) exploration of quantum phenomena like Klein tunneling in novel nanoelectromechanical systems, and (3) interdisciplinary applications spanning oncology, planetary science, and fetal medicine. His work consistently emphasizes materials synthesis , device integration , and practical translation of nanoscale phenomena. His scientific contributions have been recognized with prestigious honors: Defense Science Study Group Fellow (2018-2019) Fellow of the American Association for the Advancement of Science (2017) Fellow of the American Physical Society (2011) Lindback Foundation Award for Distinguished Teaching (2003) David and Lucille Packard Foundation Fellowship (1994-1999) As an educator, Professor Johnson has mentored numerous graduate students and postdoctoral researchers, with notable alumni like Michael Biercuk (founder of Q-CTRL). His research has been supported through significant leadership roles including Director of the Nano/Bio Interface Center (2014-2017) and Packard Fellowship funding, enabling sustained innovation in nanotechnology. His group actively collaborates across disciplines to advance both fundamental understanding and practical applications of nanomaterials. Based at the Singh Center for Nanotechnology, Johnson leads a dynamic research team utilizing state-of-the-art facilities for nanofabrication and characterization. His laboratory maintains strong campus collaborations through secondary appointments in Electrical and Systems Engineering and Materials Science and Engineering, fostering an interdisciplinary environment for developing next-generation nanoscale devices.
Dr. Yuri Rostovtsev is a Professor at the University of North Texas, specializing in quantum optics and atomic physics. He holds a Ph.D. from the Russian Academy of Sciences (1991). His office is located in GAB 525I and he can be contacted at (940) 565-3281. Research Interests: Dr. Rostovtsev's research focuses on quantum coherence phenomena, electromagnetically induced transparency, and matter-field interactions. His work spans theoretical and experimental investigations in quantum optics, including studies of quantum refraction, biophotons, and ultrafast processes in atomic and molecular systems. Recent Publications: His recent articles explore advanced quantum phenomena including single-photon interactions with atoms, quantum state engineering, plasmonic structures, and ultrafast dynamics in molecular systems. These publications demonstrate a consistent focus on quantum coherence effects and light-matter interactions at the quantum level. Scientific Awards: No awards mentioned in the provided text. Advising and Labs: No information available about students or research laboratories.
Mahmoud Hussein is a Professor in the Department of Aerospace Engineering Sciences at the University of Colorado Boulder, affiliated with the College of Engineering and Applied Science. He holds the Alvah and Harriet Hovlid Professorship and leads the Aerospace Mechanics Research Center (AMReC). His research focuses on phononics, nanophononic metamaterials, thermal transport, and fluid-structure interaction. He has pioneered advancements in controlling heat and flow using phononic crystals and metamaterials, with applications in energy efficiency and aerospace systems. Education: PhD, Mechanical Engineering, University of Michigan-Ann Arbor, 2004 MS, Mathematics, University of Michigan-Ann Arbor, 2002 MS, Applied Mechanics, University of Michigan-Ann Arbor, 1999 MS, Mechanical Engineering, Imperial College London, 1995 BS, Mechanical Engineering, The American University in Cairo, 1994 Research Interests: His work includes theoretical and experimental studies of dispersive waves, periodic materials, and phononic subsurfaces for thermal and flow control. Key areas include nanoscale thermal transport, metamaterials for thermoelectricity, and turbulence reduction in aerodynamics. He co-founded the International Phononics Society and organizes the Phononics conference series. Scientific Awards: Fellow of the American Society of Mechanical Engineers (2018) NSF CAREER Award (2013) ARPA-E Grant ($2.5M, 2018) Multiple university and national awards for research and teaching Grants & Collaborations: Recipient of multidisciplinary Defense Department grants and a $2.5M ARPA-E award for nanophononic thermoelectric devices. Collaborates with NIST, JILA, and CU’s Physics and Mechanical Engineering departments on experimental validations. Labs & Teams: Leads the Phononics research group within AMReC, focusing on metamaterials and their applications in aerospace and energy systems. Active in interdisciplinary projects with industry and national labs.
Virginia Polytechnic Institute and State UniversityUnited States
Chris Fuller, Ph.D., is the Samuel Langley Distinguished Professor of Engineering at the College of Engineering , Virginia Tech. He leads the Vibrations and Acoustics Laboratory (VAL) , focusing on active/passive noise control systems, metamaterials, and their application to aerospace, medical devices, and industrial machinery. Education: Ph.D. (1979) and B.E. (1974) from the University of Adelaide, Australia. Research Interests: Structural acoustics, adaptive materials, machine learning in noise prediction, and biomedical acoustics (e.g., neonatal incubators). Awards: ASME Rayleigh Award (2017), NASA Team Achievement Award (1996), and Fellow of the Acoustical Society of America. Recent Publications: Highlight advancements in drone noise reduction using neural networks, metamaterials for HVAC systems, and poro-elastic materials for low-frequency noise control.
Benyamin Davaji serves as an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University, where he joined in January 2022. He holds additional appointments as a Center Member of The Plastics Center and Core Faculty of the Institute for NanoSystems Innovation (NanoSI). His work bridges microsystems engineering, nanofabrication, and data science to develop next-generation sensing technologies. Dr. Davaji's educational background includes: Postdoctoral Associate in Electrical and Computer Engineering at Cornell University (2016-2021) Ph.D. in Electrical Engineering from Marquette University (2016) His research centers on integrated microsystems with emphasis on mechanical wave-based sensing and computation, ultrasound transducers, bio-interfaces, and microcalorimetry. The Autonomous Integrated Microsystems (AIMS) Laboratory combines physics with AI/ML to invent novel sensors and computational devices through advanced nanofabrication. Key thrusts include power-sustaining architectures and analog/digital computational integration. Recent publications (2024-2025) reveal strong trends in MEMS/NEMS optimization using digital twins, plasmonically enhanced infrared detection, ferroelectric actuators for high-speed scanning, and ultrasound-enabled metrology. His work increasingly integrates machine learning for design automation and process optimization across semiconductor manufacturing and flexible hybrid electronics. Dr. Davaji advises graduate students including Yilmaz Arin Manav (PhD'28), who won the FLEX 2024 Future Student Poster Award. He has secured over $3 million in competitive funding as PI/Co-PI, including a $550k NSF grant for MEMS actuators, $330k NSF grant for quantum detectors, and $2M DARPA grant for inertial sensors. He directs the interdisciplinary AIMS Laboratory focused on MEMS, ultrasound, and calorimetric technologies. The lab collaborates extensively with NanoSI and The Plastics Center, developing autonomous microsystems for biomedical, environmental, and industrial applications through advanced manufacturing techniques.
Mohamed Shaat is an Assistant Professor of Mechanical Engineering in the Engineering Department at St. Mary's University, San Antonio, Texas. Holding a Ph.D. from New Mexico State University (2017), he previously served as Assistant Professor at Abu Dhabi University (2019-2021) and held postdoctoral positions at Southern Methodist University (2022-2024) and Boston University (2021-2022). His research bridges energy storage systems, active matter physics, and advanced materials engineering. His educational foundation includes: Ph.D. in Mechanical Engineering, New Mexico State University, 2017 M.Sc. in Mechanical Engineering, New Mexico State University, 2016 M.Sc., Zagazig University (Egypt), 2012 B.Sc., Zagazig University (Egypt), 2007 Dr. Shaat's research program focuses on interdisciplinary innovation in energy storage (SOFCs & ASSBs), mechanics of active matter, nano-confined fluids, chiral metamaterials, and topological/non-Hermitian mechanics. He integrates machine learning with continuum mechanics to optimize electrochemical systems and additive manufacturing, exploring nontraditional phenomena in complex materials for next-generation engineering applications. Analysis of his 60+ journal articles reveals a dominant trajectory in nonlocal elasticity theory and topological mechanics, with increasing integration of machine learning (2020-2024). His work spans nanostructure mechanics, metamaterial design, and energy storage optimization, demonstrating consistent innovation in theoretical frameworks for complex material systems. His scholarly recognition includes: World's Top 2% Scientist (Stanford University, Mechanical Engineering & Transports, since 2019) Outstanding Graduate Award, New Mexico State University (2017) Merit-Based Enhancement Fellowship, New Mexico State University (2017) Best Master's Thesis Award, Zagazig University (2013) Committed to academic service, Dr. Shaat serves on the editorial board of Scientific Reports and as Specialty Associate Editor for Frontiers in Mechanical Engineering. His extensive peer review for Nature, Nature Communications, and Applied Physics Letters reflects his field authority. While specific grant details aren't disclosed, his postdoctoral appointments and publication volume indicate successful research funding. His teaching includes Materials Engineering and Materials Laboratory courses, emphasizing hands-on student mentorship. Though laboratory infrastructure isn't explicitly detailed, his research scope suggests computational modeling expertise and likely collaboration with experimental teams for materials characterization in energy storage and metamaterials development.
Brian Kirby is the Meinig Family Professor in the Department of Mechanical Engineering at the College of Engineering, Cornell University. He is a leading researcher in microfluidics, biomedical engineering, and cancer diagnostics, with a strong emphasis on circulating tumor cells (CTCs), rare cell isolation, and biophysical forces in disease. His work bridges engineering, biology, and clinical medicine. Institution: Cornell University School: College of Engineering Department: Mechanical Engineering Rank: Professor Education: Stanford University, 2001 Brian Kirby's research focuses on developing and applying microfluidic technologies to solve biomedical challenges. His work centers on microfluidic rare cell capture , particularly circulating tumor cells (CTCs) , enabling early cancer detection and monitoring treatment response. He investigates biophysical forces such as shear stress and surface interactions in conditions like thrombosis and cancer metastasis. His lab also works on dielectrophoresis , acoustophoresis , and electrokinetics for cell separation and analysis. Additional interests include bioinstrumentation , lab-on-a-chip devices , and fluid mechanics in biological systems . His recent publications show a consistent focus on microfluidic diagnostics, cancer biophysics, and smart fluid systems. Articles span topics from CTC isolation in prostate and pancreatic cancers to thrombosis in medical devices and programmable viscosity metamaterials . The research integrates engineering design with clinical applications, often involving interdisciplinary collaboration. Scientific Awards: Creative Teaching Award, Cornell Center for Teaching Innovation Advising Award, College of Engineering, Cornell University, 2015 Research Award, College of Engineering, Cornell University, 2015 Brian Kirby is actively involved in advising and research mentorship. While specific student names are not listed in the provided text, his extensive publication record and leadership of a research group indicate active supervision of graduate students and postdoctoral researchers. His research is supported by grants related to cancer diagnostics, microfluidics, and biomedical engineering, though specific grant details are not provided. He has contributed to the development of novel microfluidic devices such as the GEDI (Geometrically Enhanced Differential Immunocapture) platform for CTC capture and functional analysis. Labs and Teams: Kirby leads a research laboratory at Cornell focused on microfluidics and biomedical instrumentation. His team develops and applies microfluidic platforms for clinical diagnostics, particularly in oncology and hematology. The lab collaborates with clinicians and scientists across disciplines to translate engineering innovations into medical applications.
Cynthia D. Rudin is the Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science at Duke University, with joint appointments in the Departments of Electrical and Computer Engineering, Statistical Science, Mathematics, and Biostatistics & Bioinformatics. She directs the Interpretable Machine Learning Lab and has held previous positions at MIT, Columbia, and NYU. Her educational background includes: Undergraduate degree from the University at Buffalo PhD from Princeton University (2004) Research Interests: Dr. Rudin's research focuses on interpretable machine learning and its applications across multiple domains. Her work emphasizes creating machine learning models whose reasoning processes people can understand, which includes algorithms for extremely sparse models, interpretable neural networks, interpretable matching methods for causal inference, and dimension reduction for data visualization. She applies these techniques to critical societal problems in healthcare, criminal justice, materials science, and other domains. Her lab has developed practical code for sparse models such as decision lists, decision trees, and additive models that provably optimize accuracy and sparsity. Dr. Rudin's recent publications (2024-2025) demonstrate a strong focus on interpretable AI applications across diverse fields including healthcare (mortality risk scores, breast cancer prediction), materials science (metamaterials design), and environmental justice (location-based health analysis). Her work consistently emphasizes practical implementations with real-world impact, particularly in high-stakes decision-making domains where model transparency is critical. Scientific Awards: Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity (2022) - often described as the "Nobel Prize of AI" INFORMS Society on Data Mining Prize (2024) Guggenheim Fellowship (2022) Three-time winner of the INFORMS Innovative Applications in Analytics Award (2013, 2016, 2019) Winner of the 2023 John M. Chambers Statistical Software Award for PaCMAP Winner of the 2024 Award for Innovation in Statistical Programming and Analytics Dr. Rudin has advised numerous PhD students and postdocs who have co-authored significant publications with her. Her lab has received substantial funding for projects applying interpretable machine learning to healthcare (seizure prediction in ICU patients), criminal justice (crime series analysis), and energy infrastructure (underground electrical distribution networks). Her work on the Series Finder algorithm has been adapted by the NYPD and has been running live in NYC since 2016. She directs the Interpretable Machine Learning Lab at Duke, which includes the Almost-Matching-Exactly Lab focused on interpretable causal inference. Her team develops practical code implementations for all their research, emphasizing usability and real-world application in critical domains.
