Professor Shubhra Pasayat is affiliated with the Department of Electrical and Computer Engineering and Materials Science & Engineering at the University of Wisconsin-Madison. She holds a PhD (2021) and MS (2017) from University of California, Santa Barbara, and a B.Tech (2013) from Indian Institute of Technology Kharagpur. Research focus: MOCVD growth of III-nitrides/oxides for optoelectronics, power electronics, quantum materials, and bio-photonics Key applications: LEDs, LASERs, HEMTs, bio-photonics, and sanitization technologies Her research interests center on wide bandgap semiconductor materials, particularly group-III nitrides and oxides. She works on strain relaxation techniques using porous GaN substrates, quantum dot engineering for visible light emission, and high-voltage GaN HEMT development. Current projects include lattice engineering for improved crystal quality and thermal management in RF devices. Publications highlight advancements in ultraviolet lasers, high-electron-mobility transistors, and micro-LEDs. Her work spans fundamental material studies and device optimization for industrial applications like electric vehicles, 5G/6G communications, and horticultural lighting. 2024 Awards : NSF CAREER, ONR DEPSCoR, WARF Early Career Innovator 2022 Awards : UCSB ECE Outstanding Dissertation, JAP Best Paper She teaches ECE 235 Introduction to Solid State Electronics and mentors research at both graduate and undergraduate levels.
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.
Nonappa Nonappa is an Associate Professor (tenure track) in Nanochemistry at Tampere University's Faculty of Engineering and Natural Sciences since 2020. With a multidisciplinary background spanning organic chemistry, supramolecular systems, nanoparticle self-assembly, and advanced electron microscopy, he leads research at the intersection of materials science and biomedical applications. PhD in Organic Chemistry (IISc Bangalore, 2008) Docent in Soft Matter Microscopy (Aalto University, 2017) Executive MBA (Quantic School, 2020) Research focuses on bio-based optical materials using nanocellulose for sustainable photonics, breast cancer models via lab-on-a-chip systems, and precision nanomaterials through tailored self-assembly mechanisms. His team develops 3D extracellular matrices for cancer tissue culture and plasmonic nanodevices for photonic applications. Recent publications highlight gold/silver nanocluster assemblies (43+ citations in 2021-2025), electron tomography for structural analysis, and metastasis modeling systems. Key awards include Italy's Abilitazione Scientifica Nazionale (2018) and Aalto University's Docent title (2017).
Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.
Yayue Pan is a Professor at the Department of Mechanical and Industrial Engineering, University of Illinois Chicago (UIC) , and serves as the Director of NASA MIRO Center for In-Space Manufacturing: Recycling and Regolith Processing (CISM-R2) . Her research focuses on advancing Additive Manufacturing (AM) technologies for applications in biomedical engineering , energy storage , and smart structures . Ph.D., Industrial and Systems Engineering, University of Southern California (2014) M.S., Mechanical Manufacturing and Automation, Zhejiang University, China (2010) B.S., Industrial Engineering, Zhejiang University of Technology, China (2007) Her work addresses technical challenges in AM such as multi-material printing , multi-scale fabrication , and field-assisted processes . Notable projects include: Development of electrostatically-assisted direct ink writing (eDIW) for high-speed, high-resolution printing Continuous projection stereolithography for rapid solid object manufacturing Acoustic field-assisted particle patterning for smart composites Light-curable hydrogels for corneal repair applications Her 15 most recent publications (2022–2025) span topics in: Multi-material AM (conductive polymers, hierarchical composites) Biomedical applications (soft robotics, corneal repair) Energy components (battery electrolytes, supercapacitors) Field-assisted processes (acoustic, electrostatic, magnetic) Scientific Awards : 2024 ASME Chao and Trigger Young Manufacturing Engineer Award 2022 UIC Researcher of the Year Rising Star Award 2020 ASME CIE TC Leadership Award 2019 UIC Outstanding Teaching Award 2017 SME Outstanding Young Manufacturing Engineer Award NSF REU Supplements (2023–2024) Advising : Mentored 24+ graduate/undergraduate researchers, including 17 NASA/GPIP interns. Former advisees hold academic positions at University at Buffalo and University of North Carolina at Charlotte , and industry roles at Apple , GE Healthcare , and ANSYS . Grants : Recipient of a $4.65M NASA grant and multiple NSF awards. Collaborations include Northwestern University, University of Michigan, and NASA centers.
Dr. Christopher M. Wolverton is a Professor of Materials Science and Engineering at Northwestern University , where he leads the Wolverton Research Group . His work focuses on computational materials science with applications in energy sustainability , particularly in batteries , hydrogen storage , and thermoelectrics . PhD in Physics from University of California, Berkeley BS in Physics (summa cum laude) from University of Texas, Austin His research leverages first-principles quantum mechanical simulations and machine learning to enable virtual materials synthesis before laboratory testing. The group specializes in hybrid computational methods integrating Density Functional Theory (DFT) , Monte Carlo simulations , and phase-field microstructural models . The article portfolio shows leadership in energy storage materials , with recent work on data-driven nanoparticle facet control , mixed-anion semiconductors , and machine learning-accelerated discovery . Publications span top journals including Nature Energy , Nature Materials , and Science . 2006 Ford Motor Company Technical Achievement Award 2005 Ford Patent & Publication Awards 2003 Ford Environmental/Physical Sciences Recognition As advisor to PhD candidates Zhenpeng Yao , Shiqiang Hao , and Shane Patel , he fosters interdisciplinary research connecting materials informatics with experimental validation . The group maintains active collaborations with Argonne National Lab and MIT/Harvard teams.
Michael Murrell is an Associate Professor of Biomedical Engineering at Yale University, with additional appointments in the Physics Department and the Molecular, Cellular and Developmental Biology Track. He holds a B.S. from Johns Hopkins University and a Ph.D. from MIT. His research focuses on understanding cellular mechanics through biomimetic systems and soft matter physics, aiming to bridge biological principles with technological innovation. Key interests include the mechanical basis of cell division, migration, and energy dynamics in cytoskeletal networks. Selected honors include the Postdoctoral Fellowship from the Institute for Complex Adaptive Matter (2010–2012), an NIH Biotechnology Training Grant (2005–2008), and the MIT Presidential Fellowship (2004). His lab, the Laboratory of Living Matter, investigates how physical properties of cells drive life processes, using tools from synthetic biology and computational modeling. Recent work explores energy localization in cytoskeletal networks and mechanical memory in actin systems. Publications highlight advancements in actomyosin contractility, cortical flows, and biophysical energy conversion. The lab actively seeks PhD students and postdocs to join interdisciplinary projects at the Yale Systems Biology Institute and the Physical Engineering and Biology Program.
Charles Yang is a Professor of Linguistics and Computer Science at the University of Pennsylvania , where he also directs the Cognitive Science Program. His research integrates computational models with studies of language acquisition, processing, and evolution. Education: Ph.D. in Computer Science, MIT, 2000 Yang's work spans language acquisition , computational linguistics, and the evolution of cognition. He has authored The Price of Linguistic Productivity (2016), which received the Leonard Bloomfield Award from the LSA. Recent publications focus on large language models as cognitive models, the Chinese aspectual system , and statistical approaches to linguistic patterns. His 15 most recent articles (2025-2021) demonstrate a trajectory from computational models of language change to machine translation and multiword expression analysis . Yang has received significant funding from the National Science Foundation and the Guggenheim Foundation . He co-directs the Integrated Language Science and Technology group with John Trueswell and mentors students in linguistics, computer science, and psychology.
Qing Li is an Associate Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. He holds a B.E. in Electronics Engineering from Tsinghua University (2006) and a Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology (2013). Prior to CMU, he worked as a postdoctoral researcher at the National Institute of Standards and Technology (NIST), where he developed quantum frequency conversion and microresonator-based optical systems. His research focuses on light-matter interactions in integrated photonics, emphasizing nonlinear optics and quantum information processing. He has pioneered silicon carbide and aluminum nitride platforms for chip-scale quantum technologies and optical metrology. Dr. Li has been recognized with prestigious awards including the Darpa Young Faculty Award (2019), OSA Paul F. Forman Team Engineering Excellence Award (2020), and Sigma Xi Best Ph.D. Thesis Award (Georgia Tech). His work bridges classical and quantum information systems, with applications in secure communication, atomic systems interrogation, and high-precision frequency synthesis. He actively contributes to the Pittsburgh Quantum Institute (PQI), advancing regional quantum engineering initiatives. His research group’s key projects include developing compact optical frequency synthesizers, soliton microcombs for communication grids, and entangled photon pair sources for quantum networks. Grants and collaborations support his exploration of novel photonic materials and devices, targeting advancements in both fundamental science and applied technologies.
Andrea C. Ferrari is a Professor of Nanotechnology at the Department of Engineering, University of Cambridge, UK. He holds multiple leadership roles, including Director of the Cambridge Graphene Centre and EPSRC Centre for Doctoral Training in Graphene Technology, and serves as Science and Technology Officer for the EU Graphene Flagship. Education: PhD, University of Cambridge (2001) Laurea in Nuclear Engineering, Politecnico di Milano (1997) Ferrari’s research focuses on graphene, 2D materials, and their applications in nanotechnology, photonics, optoelectronics, and Raman spectroscopy. His work bridges fundamental studies and industrial translation, emphasizing scalable synthesis and characterization techniques. His publications span over 390 peer-reviewed articles, including groundbreaking studies on graphene photonics, quantum emitters, and advanced optoelectronic devices. These works are highly cited, with an H-index of 115 and over 116,000 total citations. Scientific Awards: IMR-Lee Hsun Lecture Award (2018) Nanosmat Award (2017) Knight Officer of the Order of the Star of Italy (2017) ERC Synergy Grant (2013) EU-40 Materials Prize (2011) Royal Society Wolfson Research Merit Award (2010) ERC Starting Grant (2008) Philip Leverhulme Prize (2005) Ferrari has secured over £60M in research funding, including leadership roles in the EU Graphene Flagship and the EU Quantum Technology Flagship. He is a Fellow of the Optical Society, Materials Research Society, Institute of Physics, and American Physical Society.
Minghao Qi is a Professor in the Department of Electrical and Computer Engineering at Purdue University's College of Engineering, West Lafayette. His research focuses on integrated photonics systems for optical communications, quantum information, and precision metrology applications. Professor Qi's work spans several critical photonics domains: Design and application of microresonator-based optical frequency combs (Kerr combs) Silicon and silicon nitride integrated photonic circuits Thin-film lithium niobate devices for nonlinear optics Quantum information processing using frequency-bin entangled photons Photonic neuromorphic computing with machine learning co-design Optical sensors and time-of-flight ranging systems Analysis of his 2022-2025 publications reveals three dominant research vectors: (1) Vernier microcombs for optical atomic clocks and RF stabilization, (2) Trident edge coupler architectures for octave-spanning nonlinear processes on lithium niobate, and (3) Physics-informed neural networks applied to photonic device design and signal processing. His recent work demonstrates strong convergence between integrated photonics, quantum technologies, and machine learning.
Dr. Benjamin C.K. Tee is an Associate Professor at the National University of Singapore (NUS), affiliated with the College of Engineering and the Department of Materials Science and Engineering. He leads the Sensors.AI Labs, focusing on transforming materials science, mechanics, and biology into cutting-edge technologies for robotics and healthcare in the AI era. His research explores novel materials and fabrication techniques to develop flexible, stretchable electronic sensors. These innovations enable applications in human-machine interfaces, biomedical devices, and AI-driven robotic systems. Key areas include self-healing materials, large-scale tactile sensing, and wireless health monitoring. His work has garnered international acclaim, including the James Dyson Foundation Prize (International Winner, 2021), MIT TR35 Innovator (2015), and recognition as a World Economic Forum Young Scientist (2019). His inventions have been commercialized through co-founded startups Privi Medical (acquired in 2021) and Hannah Life Technologies. Scientific Awards: National Research Foundation Fellowship (2017) MIT TR35 Innovator (Global) (2015) World Economic Forum Young Scientist (2019) James Dyson Foundation Prize - International Winner (2021) IES Prestigious Engineering Award (2020) Stanford University Top 2% Scientists (2021)
Pim de Vink is a doctoral researcher at Eindhoven University of Technology , affiliated with the Biomedical Engineering department and Chemical Biology group. Supervised by dr. L.-G. Milroy and prof. L. Brunsveld , his work bridges supramolecular chemistry and chemical biology , focusing on host/guest chemistry for protein complex modulation. Education: B.Sc. in Chemistry (2014) from University of Amsterdam M.Sc. in Biomedical Engineering (2016) from TU/e Internship at Max Planck Institute for Molecular Physiology (2016) on gold-catalyzed synthesis Research Themes: His research develops switchable cucurbituril-based systems for light-controlled enzyme activation and artificial signaling networks. Key areas include protein-protein interaction stabilization , thermodynamic modeling , and allosteric nuclear receptor modulation . Publication Trends: Across JACS , Chemical Science , and RSC Chemical Biology , his work from 2017–2023 emphasizes supramolecular tools for biochemical applications. Notable contributions include 100-fold affinity enhancement of 14-3-3 ligands and UV-responsive cucurbituril release mechanisms . Grants: Funded by Netherlands Organization for Scientific Research (NWO) through Gravity program 024.001.035 and VICI grant 016.150.366.
Nedim Pervan is a Full Professor at the Faculty of Mechanical Engineering, University of Sarajevo, Bosnia and Herzegovina. His academic position focuses on mechanical engineering with emphasis on product design, structural analysis, and biomechanical applications. He maintains an active research profile with numerous publications and collaborations across various engineering disciplines, with office hours every workday from 09:00 to 10:00 in room 314. Professor Pervan's research interests span multiple domains of mechanical engineering. He has made significant contributions to additive manufacturing, particularly in polymer gear production and analysis. His work explores mechanical properties, failure mechanisms, and service life of polymer gears manufactured through additive processes. Additionally, he has conducted extensive research on external fixation devices used in orthopedic treatments, analyzing their biomechanical characteristics and structural stability under various loading conditions. His expertise extends to finite element analysis, structural optimization, and the application of 3D scanning technologies within Industry 4.0 contexts. His research demonstrates a strong connection between theoretical engineering principles and practical applications across automotive, medical devices, and manufacturing industries. His recent publication record reveals a strong trend toward interdisciplinary research bridging mechanical engineering with biomedical applications and advanced manufacturing technologies. A significant portion of his work focuses on polymer gears and additive manufacturing, examining material properties and performance characteristics. Another substantial research stream involves biomechanical engineering, particularly the analysis of external fixation devices. His publications demonstrate a methodological approach combining experimental testing with finite element analysis. More recently, his research has expanded into 3D scanning applications in manufacturing and the electrification of transportation systems in Bosnia and Herzegovina. Professor Pervan has been involved in numerous research projects that have advanced the capabilities of the Faculty of Mechanical Engineering. These include the "Integrated Intelligent CAD System for Interactive Design, Analysis and Prototyping of Compression and Torsion Springs" (2022), "Opremanje Laboratorije za razvoj i dizajn proizvoda" (2020), and "Modernizacija Laboratorije za ispitivanje mašinskih konstrukcija" (2019-2020). These projects have focused on developing advanced laboratory facilities, intelligent CAD systems, and equipment for mechanical design and analysis, with several specifically targeting 3D scanning technology implementation. His collaborative work extends across multiple research teams within the Department of Mechanical Constructions at the University of Sarajevo. He frequently collaborates with researchers including Adis Muminović, Elmedin Mešić, and Muamer Delić on projects related to additive manufacturing, biomechanical engineering, and structural analysis. His research group appears actively involved in both theoretical and applied engineering research with practical industrial and medical applications, contributing significantly to Bosnia and Herzegovina's engineering research landscape.
Risto Miikkulainen is a Professor of Computer Science and Neuroscience at the University of Texas at Austin and VP of AI Research at Cognizant AI Lab. He directs the UTCS Neural Networks Research Group and is currently on leave from UT, working on Evolutionary Computation and Deep Learning at Sentient Technologies, Inc. Education: Ph.D. in Computer Science, UCLA, 1990 M.S. in Applied Mathematics, Helsinki University of Technology (now Aalto University), 1986 Risto Miikkulainen's research focuses on biologically-inspired computation such as neural networks and evolutionary computation. His work spans three main areas: (1) Neuroevolution, evolving complex deep learning architectures and recurrent neural networks for sequential decision tasks in robotics, games, and artificial life; (2) Cognitive Science, developing models of natural language processing, memory, and learning that shed light on disorders such as schizophrenia and aphasia; and (3) Computational Neuroscience, studying the development, structure, and function of the visual cortex, episodic memory, and language processing. His research combines theoretical understanding of biological information processing with practical applications for developing intelligent artificial systems. His recent publications (2025) show a strong focus on evolutionary approaches to AI development, particularly in neural architecture search, loss function optimization, and explainable AI. Many papers explore the intersection of evolutionary computation with deep learning, creating more efficient and transparent AI systems. His work spans theoretical foundations and practical applications in areas ranging from environmental control systems to cognitive modeling. Scientific Awards: College of Fellows, International Neural Network Society, 2024 Best Pathway to Impact Award, NeurIPS Climate Change workshop, 2024 AAAI Fellow, 2023 IEEE CIS Evolutionary Computation Pioneer Award, 2020 Gabor Award, International Neural Network Society, 2017 Outstanding Paper of the Decade Award, International Society for Artificial Life, 2017 IEEE Fellow, 2016 Multiple Best Paper Awards at GECCO, CIG, and CEC conferences Deployed Application Award, AAAI/IAAI-2013, AAAI/IAAI-2018 Miikkulainen has extensive experience mentoring students through undergraduate research courses like CS378 Computational Intelligence in Game Design I and II, where students develop independent research projects on the OpenNERO research platform. He has received multiple awards for deployed applications, demonstrating the practical impact of his research. His work has led to the development of the NERO game platform, which serves as both an educational tool and research platform for AI. He directs the UTCS Neural Networks Research Group, which focuses on neuroevolution, cognitive science models, and computational neuroscience. The group has developed the NERO (Neuro-Evolving Robotic Operatives) platform, a machine learning game that allows users to train intelligent agents through evolutionary computation. The group's work spans theoretical research and practical applications in AI, with connections to both academic and industry partners.
