WANG, Yushi is currently a Junior Researcher (Assistant Professor) at the Future Robotics Organization of Waseda University , with prior roles in the Faculty of Science and Engineering (2018–2021). His research focuses on robotics, tactile sensing, and actuator design. Education: Ph.D. in Science and Engineering from Waseda University (2015–2018). Research interests include Humanoid Robotics , Force/Torque Control , and Soft Robotics , particularly for applications in tactile sensing and safety mechanisms . Recent work explores Permanent Magnet Elastomer (PME)-based sensors and Series Clutch Actuators , enabling safer human-robot interactions and adaptive compliance. His publications span conferences like IROS , AIM , and SII , addressing challenges in 3-axis force measurement , collision safety , and material testing . He has taught courses such as 理工学基礎実験 and メカニカルエンジニアリングラボA (2018–2021). Professional memberships include IEEE , IEEE WIE , and the Japan Robotics Society . His work also involves patents for haptic interfaces and torque limiters , with grants like the 若手研究 (Young Researcher Grant) (2021–2023).
Sheng Sang is an Assistant Professor in the Department of Engineering Sciences at Bethany Lutheran College. His research lies at the intersection of Mechanical Engineering and Biomedical Engineering, with a strong emphasis on machine learning applications in composite materials and elastic metamaterials. His research interests include: Mechanical & Biomedical Engineering Machine Learning on Composites Elastic Metamaterials and Composites Optimization of Medical Devices Finite Element Modeling and Simulation Dr. Sang's recent publications demonstrate a consistent focus on integrating deep learning techniques with mechanical systems, particularly in predicting composite microstructures, tracking particles in complex systems, and optimizing wave propagation in metamaterials. His work frequently employs 3D CNNs and other neural architectures to solve inverse problems in material science. Scientific awards and recognition include: Dr. Lehtola Fellowship Research Grant ($9,000, PI), 2021–2023 Graco Engineering Lab Development Grant ($60,000), 2020–2022 He has been actively involved in teaching a wide range of engineering courses such as Fluid Mechanics, Solid Mechanics, Thermodynamics, and Computer-Aided Design. His research is supported by external grants, indicating active supervision and project leadership. Dr. Sang has collaborated with researchers across disciplines, including neuroscience and medical imaging, particularly in studies involving deep brain stimulation and fMRI. He is affiliated with research teams working on: Active elastic metamaterials design Machine learning for material characterization Optimization of biomedical devices using swarm intelligence Development of advanced simulation tools for composite systems
Ed Grant is a Professor in the Department of Chemistry at the University of British Columbia (UBC), Faculty of Science. He leads research in chemical physics, focusing on laser spectroscopy, ultracold plasmas, and Raman spectroscopy. B.A., 1969, Occidental College Ph.D., 1974, University of California, Davis Research Interests: Grant's work spans fundamental and applied domains. His team investigates ultracold plasmas using molecular beam techniques, revealing Coulombic interactions and strong correlations. In Raman spectroscopy, they develop instruments for microscale biological sample analysis and employ multivariate classification. Recent projects integrate quantum computing, machine learning, and environmental science (e.g., microplastics' atmospheric impact). Scientific Awards: R&D 100 Award (1998) Fellow of the American Physical Society (1992) Humboldt Research Award (1992, 2012) Kelly Award for Excellence in Undergraduate Teaching (1990) Fulbright Senior Scholar (1988)
Zheng Yang is a Professor at Tsinghua University's School of Software, with significant research contributions in cryptography, cybersecurity, and privacy-preserving systems. His work spans multiple institutions including collaborations with University of Helsinki's Secure System Group and Chongqing University of Technology. He maintains active research in both theoretical and applied security domains, with particular focus on industrial applications. Professor Yang's research interests center on cryptographic protocols, authentication mechanisms, and security for emerging technologies. His work addresses critical challenges in Cyber-Physical Systems security, Industrial Internet of Things protection, and privacy-preserving computation. He has made significant contributions to secure key exchange protocols, authentication systems, and defenses against sophisticated network attacks including DDoS mitigation strategies. His research bridges theoretical cryptography with practical implementations for resource-constrained environments. Analysis of Professor Yang's recent publications reveals a strong trend toward practical security solutions for industrial and embedded systems. His work increasingly focuses on balancing security with performance constraints in Cyber-Physical Systems and Industrial IoT environments. Key research themes include lightweight cryptography for resource-constrained devices, privacy-preserving location services, and novel authentication mechanisms that maintain security while minimizing computational overhead. His publications demonstrate consistent innovation in adapting cryptographic techniques to real-world security challenges. Professor Yang has established himself as a leading researcher through his extensive publication record in top security venues including IEEE Security & Privacy, USENIX Security, and ACM conferences. His work has been published consistently in high-impact journals and conferences, demonstrating sustained research productivity and influence in the security community. Professor Yang maintains active research collaborations with numerous institutions globally, evidenced by his extensive co-authorship network. His research has attracted significant funding for projects addressing critical security challenges in emerging technologies. His work on secure authentication protocols and privacy-preserving systems has practical applications across multiple industry sectors. Professor Yang leads research initiatives focused on secure Cyber-Physical Systems and Industrial IoT security. His laboratory work emphasizes practical implementations of cryptographic protocols for real-world systems, with particular attention to performance constraints in embedded environments. Current research directions include secure communication for programmable logic controllers, privacy-preserving location services, and adaptive defenses against sophisticated network attacks.
Glenn H. Fredrickson is the Mitsubishi Chemical Professor of Functional Materials in the Department of Chemical Engineering at the University of California, Santa Barbara, with additional appointments in the Materials Department. He directs the Mitsubishi Chemical Center for Advanced Materials (MC-CAM) and the Complex Fluids Design Consortium (CFDC), and previously chaired the Chemical Engineering department (1998-2001). He is an elected member of both the National Academy of Engineering and the National Academy of Sciences. Education Ph.D. Chemical Engineering, Stanford University (1984) M.S. Chemical Engineering, Stanford University (1981) B.S. Chemical Engineering, University of Florida (1980) Research Interests Fredrickson leads an internationally recognized program in theoretical and computational polymer science , focusing on self-assembly of block copolymers , complex fluids , and field-theoretic simulation methods . His group pioneered field-theoretic simulations (FTS) —numerical techniques to solve statistical field theories of polymers—enabling predictive design of advanced materials including high-performance plastics, ion-conducting electrolytes, and nanostructured membranes. Current themes include quantum-fluid analogs, machine-learning-accelerated discovery, and sustainable polymer formulations. Recent Publication Trends Between 2022 and 2025, Fredrickson’s group published extensively on block copolymer morphology control , polymer electrolytes for energy storage , phase-separation kinetics , and quantum many-body analogs in soft matter . Notable advances include machine-learning-enhanced self-consistent field theory, molecularly informed models for surfactant and polyelectrolyte systems, and the application of polymer field theory to spin-orbit-coupled Bose-Einstein condensates. Scientific Awards & Honors Election to National Academy of Sciences (2021) Materials Theory Award, Materials Research Society (2017) William H. Walker Award, AIChE (2016) Polymer Physics Prize, American Physical Society (2007) Election to National Academy of Engineering (2003) Alfred P. Sloan Fellow (1992) Camille and Henry Dreyfus Teacher-Scholar Award (1991) Presidential Young Investigator Award, NSF (1991) Research Group & Funding The Fredrickson Research Group comprises ~15 graduate students and several post-doctoral researchers. The group is supported by multi-agency grants including NSF, DOE, and industry partnerships through the Mitsubishi Chemical Center for Advanced Materials and the Complex Fluids Design Consortium. Laboratory & Collaborative Networks State-of-the-art computational facilities are housed in the Materials Research Laboratory (MRL) at UCSB. Collaborative projects extend to leading experimental groups worldwide, integrating theory with synthesis, characterization, and device testing to accelerate materials innovation.
Fraser King is an incoming Assistant Professor in the Department of Atmospheric and Oceanic Sciences (AOS) at the University of Wisconsin–Madison, starting in Winter 2026. He holds a PhD in Machine Learning and Remote Sensing of Precipitation from the University of Waterloo (2022) and is currently a postdoctoral research associate at NASA Goddard Space Flight Center. His research integrates machine learning with atmospheric physics to advance precipitation and snowfall retrieval, cloud microphysics, and climate modeling. He has held research positions at the University of Michigan and NASA Jet Propulsion Laboratory. His research interests include: Climate and Climate Change Radiation and Remote Sensing Synoptic Meteorology Atmospheric and Cloud Physics Large Scale Dynamics Machine Learning and Model Interpretability Arctic Snowfall Prediction His recent publications reflect a strong trend in applying deep learning (e.g., U-Net, CNNs) and unsupervised methods (PCA, t-SNE, UMAP) to radar and satellite data for precipitation and snow microphysics. Key themes include radar gap inpainting, melting layer detection, and dimensionality reduction for physical interpretation. His work bridges geoscience and AI, aiming for interpretable models that enhance physical understanding. Scientific awards and professional service include: Finalist for the 2023 Governor General's Gold Medal, University of Waterloo Associate Editor, Journal of Atmospheric and Oceanic Technology (AMS) Member, AMS Committee on Artificial Intelligence Applications to Environmental Science Executive Council Member, AGU Precipitation Technical Committee Executive Member, Eastern Snow Conference Research Board Fraser King has mentored students through research projects and led educational initiatives such as a 12-week course on machine learning for land cover classification. He has secured research experience through internships at Aquanty Inc. and multiple NASA-affiliated institutions. He founded MapsByFraser, a company combining cartography and satellite data, and has collaborated with Google's Quantum AI team. His technical skills span Python, deep learning frameworks, and high-performance computing platforms. He leads several major research projects: Towards Interpretable Physical Models : Using sparse autoencoders and nonlinear dimensionality reduction to interpret geoscience models. Microphysical Dimensionality Reduction : Applying PCA, t-SNE, and UMAP to identify physical modes in precipitation data. BlindPaint : A U-Net for radar gap inpainting in spaceborne systems. DeepPrecip : A deep learning model for surface precipitation retrieval. iPhone LiDAR : Using consumer smartphones for snow depth measurement via drones. NRCan Machine Learning Land Cover Classifier : Training ML models on Sentinel-2 data. Climate Model Calibration : Using ML to correct biases in snow-related climate variables. CloudSat Snowfall Validation : Validating high-latitude snowfall estimates. Snow Modelling : A Rust-based physical/temperature-index snow model.
Katarzyna Wac is a researcher at the University of Geneva affiliated with the Faculty of Economics and Management and the Information Science Institute . Her work bridges Digital Health , Mobile Computing , and Human-Computer Interaction , focusing on leveraging wearable devices, smartphones, and AI for health and quality of life (QoL) quantification. Research Themes: Digital biomarkers for Alzheimer's and migraines, QoL assessment via ubiquitous computing, peer- and self-reported behavioral data, and QoE of mobile applications. Labs: Leads the mQoL Lab , a platform for interactive, mobile, and wearable-based studies. Her recent publications explore Transformer models for health data analysis, social robots in homecare, and ethical frameworks for digital mental health. She has contributed to standards for proxy-reported QoL measures and personalized drug delivery systems in digital health. The multimodal integration of emotional signals and context-aware QoS/QoE provisioning for m-health services are recurring technical themes. Key collaborations include the MobiHealth project and COPD24 , translating future internet technologies into telemonitoring solutions. Her work spans from foundational studies on mobile cognition to applied ambulatory assessment of affect and health risks.
Shamik Sengupta is the Ralph E. and Rose A. Hoeper Professor at the University of Nevada, Reno (UNR) , where he serves as Professor in the Department of Computer Science & Engineering and Executive Director of the Cybersecurity Center . He holds a PhD in Computer Science from the University of Central Florida (2007) and a BE in Computer Science from Jadavpur University (2002). IEEE Senior Member Director, UNR Cybersecurity Center NSF CAREER Award Recipient
Per-Erik Hellström is a Professor at KTH Royal Institute of Technology, affiliated with the Department of Electronics and Embedded Systems. His research focuses on semiconductor process technology, particularly the heterogeneous integration of materials like SiGe, Ge, high-κ dielectrics, and metal gates with Si CMOS to advance integrated circuits. He leads KTH's FDSOI CMOS process and circuit technology, emphasizing sequential 3D integration for future CMOS developments. Additionally, he manages the Si and SiC process line at Electrum Laboratory, overseeing tool maintenance, process control, and upgrades. Researcher ID: ORCID Location: Kistagangen 16 Email: pereh@kth.se His work involves developing nanometer-sized transistors through double patterning techniques and studying material integration for enhanced device performance. He teaches courses in electrical circuits, semiconductor devices, and nanotechnology at both Bachelor's and Master's levels, including Electrical Engineering (IF1330) , Embedded Electronics (IE1206) , and Introduction to Integrated Circuits (IL2241) . He also supervises degree projects and exams. Scientific achievements include the 2020 G03 Best Paper Award for gate stack research. His recent publications highlight advancements in Type-II superlattices, 3D integration, and high-temperature sensors. Key collaborators include PhD students working on nanotechnology and process engineering.
John Dolbow is a Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University, with secondary appointments in Civil and Environmental Engineering and Mathematics. He is a Bass Fellow and holds leadership roles as Associate Vice President for Research & Innovation since 2024. Education: B.S.M.E. (University of New Hampshire, 1995), M.S. (Northwestern, 1998), Ph.D. (Northwestern, 1999) Research Focus: Computational fracture mechanics, phase-field modeling, hydrogels, and multiphysics problems in geomechanics and biomedical engineering His recent work advances phase-field methods for fracture nucleation, hydraulic fracturing in geothermal systems, and laser lithotripsy simulations. Dolbow leads Duke's Computational Mechanics Laboratory, integrating civil, mechanical, and materials science approaches. Key contributions include: Erratum corrections for computational mechanics frameworks Nitsche-stabilized methods for interface constraints Phase-field models for surfactant-driven particle raft fracture Multi-resolution approaches for hydraulic fracture simulation Embedded FEM techniques for moving boundary problems Scientific Recognition: R. H. Gallagher Young Investigator Award (2005) Robert J. Melosh Medal for Finite Element Analysis (1999) DOE Computational Science Graduate Fellowship (1997) DOE CSGF Steering Committee Chair
Jonathan Freund is Professor of Mechanical Science and Engineering and Aerospace Engineering at the University of Illinois at Urbana-Champaign, holding the Donald Biggar Willett Professorship since 2016. He serves as Head of Aerospace Engineering (2020-present) and is Co-Director of the Center for Exascale-enabled Scramjet Design (CEESD). His academic journey began with all three degrees in Mechanical Engineering from Stanford University (B.S. 1991, M.S. 1992, Ph.D. 1998), followed by faculty positions at UCLA (1997-2001) before joining UIUC. Freund's research spans fluid mechanics with applications in biomedical systems, aeroacoustics, and materials science. His work focuses on computational modeling of cellular blood flow, jet noise control, plasma-coupled combustion, uncertainty quantification, and nanoscale material processing. He develops advanced simulation tools to investigate phenomena ranging from atomically thin liquid films to spacecraft propulsion systems. His laboratory leverages high-performance computing to solve complex multiphysics problems requiring exascale capabilities. Analysis of his recent publications reveals a strong emphasis on computational fluid dynamics applied to biological systems (35%), aeroacoustics and jet noise (25%), materials processing at nanoscale (20%), and uncertainty quantification methods (20%). His work consistently bridges fundamental fluid mechanics with practical engineering applications, particularly in medical technologies and advanced propulsion systems. Donald Biggar Willett Professor (2016-present) Kritzer Faculty Scholar (2011-2016) Fellow of the American Physical Society (2011) Campus Excellence in Faculty Mentoring Award (2017) APS DFD Gallery of Fluid Motion Winner (2000) Associate Fellow of AIAA (2012) Freund has advised numerous graduate students and received multiple teaching honors including the Engineering Council Award for Excellence in Advising (2008, 2012) and repeated recognition on the List of Excellent Teachers. His research has been supported by agencies including the Department of Energy's National Nuclear Security Administration. He leads the CEESD center which develops physics-faithful predictive simulations for scramjet design using advanced high-temperature composite materials.
Muhammad Hussain is a Professor at the Elmore Family School of Electrical and Computer Engineering, Purdue University. His research focuses on futuristic electronics spanning healthcare, environment, energy, robotics, and defense applications, utilizing state-of-the-art CMOS technology for mass production of IoT and IoE devices. These systems range from rigid to flexible/stretchable electronics, emphasizing manufacturability and sustainability. Campus: West Lafayette Office: BRK 2042 Email: mmhece@purdue.edu Labs: DREAM (Device Research Engineering Applications and Manufacturing) Research Interests: His work in microelectronics and nanotechnology drives innovations like: Flexible hybrid electronics for extreme environments and defense applications Brain organoid electrophysiology tools Dissolvable chip packaging for sustainable e-waste reduction 3D heterogeneous integration of CMOS systems Wearable sensors for marine environments and robotics
Pyry Kivisaari is a Postdoctoral Researcher at the Department of Neuroscience and Biomedical Engineering, Aalto University, specializing in computational modeling of optoelectronic semiconductor devices. His work bridges theoretical physics and engineering applications with significant contributions to light-emitting diode and solar cell technologies. His primary research domains include: Optoelectronics and photonics Semiconductor device physics Nanoscale optoelectronic structures Numerical simulation frameworks Light-matter interaction in resonant cavities Efficiency enhancement mechanisms Analysis of his 15 most recent publications (2019-2024) reveals a consistent focus on thin-film and nanoscale optoelectronic devices, particularly examining carrier dynamics, light emission characteristics, and efficiency limitations in LED and solar cell architectures. His work frequently employs advanced simulation techniques to investigate novel device concepts like nanotree LEDs, double diode structures, and thermophotonic systems, with strong emphasis on practical engineering solutions for performance optimization. Dr. Kivisaari maintains active collaboration with leading researchers in semiconductor physics and has published in high-impact journals including Applied Physics Letters, Physical Review Applied, and Nano Letters, demonstrating sustained scholarly productivity in optoelectronic device research.
Vir V. Phoha is a distinguished Professor in the Department of Electrical Engineering and Computer Science at Syracuse University's College of Engineering and Computer Science. He holds multiple prestigious fellowships including AAAS, AAIA, IEEE, NAI, and SDPS, and was named an ACM Distinguished Scientist in 2008. Dr. Phoha's research spans across cybersecurity, machine learning, and biometrics. His work focuses on cutting across conventional disciplines to unify basic and common concepts, particularly in security (malignant systems, active authentication), machine learning (decision trees, statistical, and evolutionary methods), and computer networks (anomalies, optimization). He develops field-realizable defensive and offensive cyber-based systems using these methodologies. His recent publications reveal a strong focus on continuous authentication, biometric security, fake news detection, and adversarial challenges in cybersecurity. The research shows an evolution from traditional network security to more specialized areas like wearable device security, keystroke dynamics, and gait authentication. Scientific Awards: Fellow of AAAS, AAIA, IEEE, NAI, SDPS ACM Distinguished Scientist (2008) IEEE Computer Society Distinguished Visitor (2024-2026) ACM Distinguished Speaker (2012-2015) IEEE Region 1 Technological Innovation Award (2017) "Highest Impact Award" IEEE CVPR 2018 Workshop on Biometrics Dr. Phoha serves as an associate editor for the ACM journal, ACM Digital Threats: Research and Practice (DTRAP) , and as an associate editor of IEEE Transactions on Computational Social Systems (TCSS) . He has advised numerous students who have gone on to publish significant research in cybersecurity and biometrics. His work has been supported by grants from DARPA and NSF, including the development of the BB-MAS dataset which became one of IEEE DataPort's most popular datasets.
Professor Natalie Wheeler is a Professorial Fellow-Research at the University of Southampton, affiliated with the Optoelectronics Research Centre (ORC). Her research focuses on advanced optical fiber technologies, particularly hollow-core fibers and their applications in photonics, gas sensing, and mid-infrared light transmission. She leads or co-leads multiple projects funded by the Royal Society and EPSRC, including initiatives like FASTNET and EVacuAted Optical Fibres. Key research projects include developing low-loss hollow-core photonic crystal fibers for mid-IR applications and exploring gas-induced optical properties in fibers. Her work integrates laser machining, gas dynamics modeling, and distributed sensing techniques. Recent publications highlight breakthroughs in gas-filled fiber fabrication, pressure dynamics, and Raman spectroscopy probes. Education: Details not explicitly stated in provided texts. Affiliations: Member of Hollow Core Fibre, Gas Photonics, Fibres and Communications, and Advanced Fibre Applications research groups. Publications span journals like Optics Express , ACS Photonics , and Journal of Lightwave Technology , emphasizing fiber design, gas dynamics, and optical transmission. Current grants include EPSRC funding for next-generation optical networks and UV-to-infrared fiber systems. Her work bridges fundamental fiber physics and practical applications in sensing, communications, and biomedical imaging, with a focus on hollow-core fiber innovations.
