Pedro Nardelli is a Full Professor (tenured) of IoT in Energy Systems at the LUT School of Energy Systems, Lappeenranta University of Technology (Finland). He holds a double doctoral degree in electrical engineering from the University of Campinas (Brazil) and communications engineering from the University of Oulu (Finland). As a Docent in Information Processing and Communications Strategies for Energy Systems, he leads research in cyber-physical systems, smart grids, and 6G-enabled energy networks. Research Interests : His work focuses on integrating IoT, AI, and communication technologies into energy systems. Key areas include cyber-physical systems, UAV-enabled networks, sustainable energy management, and cybersecurity in critical infrastructure. He emphasizes interdisciplinary approaches to address challenges in the green-digital transition. Projects & Leadership : He is Principal Investigator for projects such as 'Energy-Conscious Operation: Network Efficiency for Wireless Sustainability' (2024–2026) and coordinates the Finnish-Brazilian AI and 5G training program. Past roles include leadership in the 'Hydrogen and Carbon Value Chains in Green Electrification' initiative (2021–2024). Publications : Recent work explores topics like hybrid optimal power flow models, UAV-IRS NOMA systems, and energy-centric analysis. His research bridges theoretical frameworks with practical applications, emphasizing sustainability and resilience in energy networks. Labs & Teams : Leads the IoT Solutions group within LUT's MORE SIM research platform, focusing on simulation-driven innovation for energy systems.
Xiaonan Guo is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University. His research focuses on security and privacy in cyber-physical systems, mobile device security, IoT, mobile healthcare, and machine learning applications in mobile computing. He holds a PhD in Computer Science from the Hong Kong University of Science and Technology. His work spans innovative applications of mmWave technology for authentication, health monitoring, and activity recognition, alongside contributions to privacy-preserving systems and mobile deep learning optimization. Recent research emphasizes contactless human concentration monitoring, secure mobile DNN execution, and universal adversarial attacks against mmWave-based systems. Xiaonan’s publications reflect a strong focus on mobile and IoT-driven healthcare solutions, wearable device security, and cross-technology localization. His articles often bridge theoretical advancements with real-world applications in smart healthcare systems and pervasive computing environments.
Evava (Eva) Pietri is an Associate Professor in the Department of Psychology and Neuroscience at the University of Colorado Boulder. She previously held a position at IUPUI before moving to CU Boulder in 2021. Dr. Pietri earned her PhD in Social Psychology from The Ohio State University in 2013. Her research focuses on reducing biases and promoting diversity in STEM through interventions targeting identity-safety, intergroup relations, and allyship. Her work emphasizes understanding how marginalized individuals, particularly Black and Latina women, form connections with role models and perceive inclusive environments. Key projects include developing the Video Interventions for Diversity in STEM (VIDS) to address gender bias, exploring allyship frameworks, and examining organizational cues that foster belonging. She has secured grants from the National Science Foundation and SIOP to study identity-safety in STEM and anti-racism messaging strategies. Dr. Pietri’s lab, the PSIA Lab, employs experimental methods and collaborations to investigate real-world applications of social psychology. Current research includes expanding role model effectiveness, mitigating bias, and enhancing equity in education and workplaces. She advises PhD students Nadia Floyd and Sheba Aikawa, both from NYU, and collaborates widely on initiatives like the NSF-funded project on shared adversity and identity-safety.
Robert J. Doerksen is Professor of Medicinal Chemistry in the Department of BioMolecular Sciences at the University of Mississippi School of Pharmacy , Associate Dean of the Graduate School , and Research Professor in the Research Institute of Pharmaceutical Sciences . Since 2004 he has combined computational chemistry with experimental collaborations to advance drug discovery, particularly in glycoscience and cannabinoid research. Education: B.S. (Double First Class Honours) in Mathematics & Physics, University of New Brunswick, 1986 Graduate Diploma in Christian Studies, Regent College, Vancouver, 1996 Ph.D. in Chemistry, University of New Brunswick, 1998 (Advisor: Prof. Ajit Thakkar) Postdoctoral Fellow, UC Berkeley (with Prof. Martin Head-Gordon) Postdoctoral Fellow, University of Pennsylvania (with Prof. Michael Klein) Research Interests: Dr. Doerksen’s laboratory develops and applies computational medicinal chemistry approaches spanning chemoinformatics , molecular dynamics , virtual screening , and machine learning to understand how small molecules interact with proteins. Central themes include: Glycoscience : lectin–glycan interactions, glycosyltransferase regulation, glycomimetic design. Cannabinoids : CB1/CB2 receptor allosteric modulation, cannabidiol pharmacology, synthetic cannabinoid SAR. Neglected & Infectious Diseases : malaria, hepatitis B, tuberculosis, SARS-CoV-2, urinary-tract infections. Drug Delivery & Formulation : nanoparticle coatings, pharmacokinetic optimization, bioavailability enhancement. Publications Trend: Over 2023–2025 his 15 most recent papers reveal intense activity at the intersection of AI-driven discovery , glycobiology , and cannabinoid pharmacology , with emphasis on anti-infective, anticancer, and CNS-active agents. Key contributions include first-in-class MraY inhibitors for TB, cannabinoid-inspired antivirals against SARS-CoV-2, and glycomimetic antagonists of bacterial adhesins for UTI prevention. Scientific Awards & Honors: UM School of Pharmacy Faculty Service Award (2015–2016) UM School of Pharmacy Faculty Service Award (2010–2011) Editorial Boards: Molecules , AIMS Biophysics , Pharmaceutical Sciences , Perspectives in Medicinal Chemistry Repeated NIH, DoD, NSF, Wellcome Trust, and international grant-review panels (2010–present) Guest Editor for multiple special issues in Molecules and Frontiers journals Advising & Mentoring: As Associate Dean, Dr. Doerksen oversees University-wide graduate programs, chairs the Graduate Recruiting Fellowship and Scholarship Committee, and mentors students across disciplines. Faculty advisor for the UM chapters of the Christian Pharmacists Fellowship International (since 2005) and Taiwanese Student Association (2022–2025). He actively participates in PhD and MS thesis committees worldwide and has delivered NSF GRFP information sessions to support trainee funding. Laboratories & Teams: He directs research within the Computational Chemistry and Bioinformatics Research CORE (CCBRC) , fostering collaborative projects involving medicinal chemists, structural biologists, pharmacologists, and data scientists. The group leverages high-performance computing resources at the University of Mississippi to perform large-scale virtual screening, AI/ML model development, and integrative structural biology studies.
Yuan Liu is a faculty member affiliated with Guangzhou University's Cyberspace Institute of Advanced Technology. Their research focuses on cybersecurity, blockchain technology, federated learning, and IoT systems. They have held roles at multiple institutions, including Northeastern University (Software College) and Nanyang Technological University (PhD in Computer Engineering). Liu's work emphasizes secure communication, edge computing, and distributed systems, with contributions to protocols like blockchain-based redactable systems and quantum federated learning frameworks. They have collaborated extensively on projects addressing IoT security, smart healthcare, and privacy-preserving technologies. Key research trends include leveraging AI for enhanced security (e.g., watermarking frameworks, attack detection) and optimizing resource allocation in edge computing environments. Their publications span journals like IEEE Communications Surveys & Tutorials and conferences such as GLOBECOM.
Jordan J. Williams is an Assistant Professor and Director of the Neural Engineering & Modulation Laboratory (NEMo Lab) at the Joint Department of Biomedical Engineering, affiliated with Marquette University and the Medical College of Wisconsin. He holds MD and PhD degrees from Washington University in St. Louis and completed postdoctoral work at the University of Pittsburgh. Education: Postdoctoral Scholar (2013-2019), Systems Neuroscience Institute, University of Pittsburgh MD (2013), Washington University in St. Louis PhD (2013), Biomedical Engineering, Washington University in St. Louis B.S. (2005), Electrical Engineering and Engineering Physics, South Dakota State University His research focuses on neural engineering, optogenetics, and brain-machine interfaces. Key projects include peripheral optogenetic stimulation for motor recovery post-spinal cord injury, neuroprosthetics, and viral gene therapy applications. Recent publications highlight advancements in spatiotemporal nerve stimulation patterns, non-human primate studies, and neural interface biocompatibility. Dr. Williams teaches courses like Neural Engineering , Biomedical Signal Processing , and Control Systems for Biomedical Engineering . His funded grants include projects on functional motor recovery and improved spinal cord injury rehabilitation through optogenetics. Current research emphasizes the intersection of neuroscience and engineering to address paralysis, with a strong focus on translating preclinical findings into clinical applications. His work has been published in journals such as Journal of Neuroscience Methods , Science Robotics , and Frontiers in Neuroscience .
Dr. Paul Thevenon is an Assistant Professor at ENAC (National Civil Aviation School) in Toulouse, France, since July 2013. He holds a Habilitation à Diriger les Recherches (2023) and focuses on GNSS signal processing, integrity monitoring, and hybrid sensor systems. His research integrates advanced algorithms for cycle slip detection, multipath mitigation, and hybrid navigation filters using 5G/ LTE signals. Education: Bachelor's in Electronic Engineering from École Centrale de Lille (2004) Master's in Space Telecommunications from ISAE (2007) PhD in Signal Processing from ENAC (2010) Research Themes: GNSS robustness against malicious signals (e.g., evil waveforms) Multi-receiver systems for RTK positioning enhancement Integration of non-GNSS signals (5G, LTE) for hybrid navigation Factor graph approaches for carrier phase processing Awards: 2023: Habilitation à Diriger les Recherches Technical Contributions: Developed CNN-based multipath detection systems Advanced RTK ambiguity resolution via multi-rover arrays Validated TDCP measurements in low-cost PPP-IMU filters Labs/Teams: Core member of the SIGNAV lab, specializing in signal processing and navigation systems.
Andreas Güntner is an Assistant Professor at ETH Zürich's Department of Mechanical and Process Engineering and a research associate at the University Hospital Zürich's Endocrinology, Diabetes and Clinical Nutrition department. His interdisciplinary research integrates physics, chemistry, and medicine to develop micro/nanosystems for chemical sensing, with applications in healthcare and environmental monitoring. Research focuses on creating innovative sensor technologies from concept to validated devices, particularly chemoresistive gas sensors and handheld detectors. Recent breakthroughs include selective benzene and methanol detection systems, breath analyzers for metabolic monitoring, and nanoparticle-based sensors fabricated via flame synthesis. Publications demonstrate expertise in nanomaterials engineering for molecular sensing, with applications spanning medical diagnostics (breath analysis for diabetes) to environmental protection (air quality monitoring). His group develops complete sensing systems from fundamental material science to portable device implementation. Honors include the De Vigier Award (2022), ERC Starting Grant (2022), and ETH Medal for outstanding PhD (2017). Patents cover handheld detection devices for toxic compounds in consumer products.
Dr. Khoa Phan is a Senior Lecturer in the Department of Computer Science and Information Technology at La Trobe University, Australia. He holds an ARC DECRA Senior Research Fellowship (2020-2023) and has held prior positions at UCLA, Monash University, and others. His academic background includes a B.Eng. (UNSW), two M.Sc. degrees (University of Alberta and Caltech), and a Ph.D. in Electrical Engineering from McGill University. Dr. Phan's research focuses on optimizing next-generation communication networks, particularly in wireless communications, IoT, satellite systems, and machine learning applications. He has secured significant grants, including ARC Discovery Projects and industry partnerships. His work emphasizes secure cyber-physical systems, federated learning, and edge computing. He has received prestigious awards such as the ARC DECRA and Atwood Fellowship. His research spans over 100 publications, with contributions to areas like OTFS modulation, secure satellite communications, and graph-based anomaly detection. He actively supervises PhD students and collaborates internationally, including initiatives to strengthen ties between La Trobe University and Vietnamese institutions. Grants include ARC Discovery Projects (totaling $1.3M+), CRC SmartSAT, and industry scholarships. His work addresses energy efficiency, secure resource allocation, and AI-driven solutions for 5G/6G networks and the metaverse.
Professor Risteski Aleksandar holds a Ph.D. in Telecommunications and has been a faculty member at the Faculty of Electrical Engineering and Information Technologies, University Ss. Cyril and Methodius, since 1996. He currently serves as a Professor and has held roles including Vice-Dean for Science and International Cooperation (2008–2016). His research focuses on telecommunications, cybersecurity, blockchain applications in education, and IoT security. He has extensive industry experience, including internships at IBM T.J. Watson Research Center in the U.S. Education: Ph.D. in Telecommunications (2001–2004) M.Sc. in Telecommunications (1997–2000) Dipl. Ing. in Electronics and Telecommunications (1991–1996) Research Interests: Blockchain technology for academic credential verification Cybersecurity in tactical communication systems IoT energy efficiency and reliability Network intrusion detection using deep learning Anti-forgery systems using AI and blockchain Publications Trends: Over 50 publications since 2000, with recent focus on blockchain applications in education, cybersecurity frameworks for 5G networks, and IoT energy optimization. His work bridges theoretical advancements with practical implementations in telecommunication infrastructure. Awards: No specific awards listed, though his contributions to blockchain-based educational systems and cybersecurity research are notable. Advising & Grants: No listed students, but active in research projects funded by industry partnerships. Collaborations include IBM Research and initiatives in Macedonia's telecommunication sector. Labs/Teams: Affiliated with the Institute of Telecommunications at his faculty, focusing on network security and emerging technologies.
Michael Reimer is an Associate Professor at the University of Waterloo, affiliated with the Institute for Quantum Computing (IQC). His primary role involves advancing quantum photonic technologies through experimental and theoretical research. He is a full-time faculty member with expertise in semiconductor nanowire-based devices, quantum dot systems, and their integration with atomic ensembles for quantum communication applications. His research interests focus on developing high-efficiency single-photon sources and detectors, metamaterials for photonic applications, and leveraging cold atoms in hollow-core fibers to enhance quantum state manipulation. He also explores hybrid quantum-classical systems, particularly in optimizing signal-to-noise ratios (SNR) and mitigating nonlinear impairments in optical networks. His work bridges fundamental quantum optics with practical implementations in quantum computing and secure communication systems. Recent articles highlight trends toward achieving near-unity absorption in metamaterials, on-demand photon generation with quantum dots, and probabilistic constellation shaping for optical networks. While no scientific awards are explicitly mentioned, his contributions to quantum photonic devices are notable. No specific grants or advisees are detailed in the provided text, but his affiliation with IQC suggests involvement in collaborative research projects. He is affiliated with the Institute for Quantum Computing, a leading academic unit at the University of Waterloo.
Mujdat Cetin is a Professor of Electrical and Computer Engineering at the University of Rochester, serving as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science. Previously, he held faculty positions at Sabanci University, Istanbul, Turkey, and MIT. He earned his BS from Bogazici University (1993), MS from the University of Salford (1995), and PhD from Boston University (2001). His research focuses on computational imaging, signal processing, and machine learning applications in biomedical imaging, radar systems, and neuroscience. Key areas include uncertainty quantification in imaging, probabilistic methods for image analysis, and brain-computer interfaces. His work bridges electrical engineering, computer science, and biomedical applications. Notable awards include the IEEE Signal Processing Society Best Paper Award and the Turkish Academy of Sciences Distinguished Young Scientist Award. He is an IEEE Fellow and has held editorial roles for journals like IEEE Transactions on Computational Imaging and SIAM Journal on Imaging Sciences. His contributions span conference leadership, including Technical Program Co-chair roles for major signal processing and fusion events. Cetin’s research group has advanced computational sensing, medical imaging algorithms, and EEG-based emotion/motor imagery classification. Ongoing work emphasizes integrating physics-based models with deep learning for robust imaging and signal analysis.
Stephen Arrowsmith is a Professor and Hamilton Chair in Earth Sciences at Southern Methodist University (SMU). His research focuses on seismoacoustics, infrasound propagation, and geophysical monitoring of natural and anthropogenic phenomena. He holds a Ph.D. from the University of Leeds and has contributed significantly to nuclear test monitoring, atmospheric dynamics, and machine learning applications in geophysics. Key research areas include: Integration of seismic and acoustic data for event detection and discrimination Analysis of infrasound signals from earthquakes, explosions, and bolide impacts Development of automated detection algorithms using machine learning Urban seismology and distributed acoustic sensing Recent work emphasizes multi-modal data fusion, with studies on the Korean Peninsula, Tonga eruptions, and North Korean nuclear explosions. His contributions include advancing global infrasound networks and improving stratospheric atmospheric modeling through infrasound observations. He maintains an active research website at allthespheres.com .
Yasutoshi Makino is a researcher specializing in ultrasound-based haptics, tactile feedback systems, and human-computer interaction. He has collaborated extensively with Hiroyuki Shinoda and other colleagues, focusing on mid-air haptic displays, noncontact object manipulation, and sensory reproduction. Research Interests Makino's work explores the intersection of acoustics, neuroscience, and engineering to create immersive tactile experiences without physical contact. His innovations include ultrasound-driven actuation mechanisms, thermal sensation rendering, and real-time human motion prediction for robotic systems. Recent Publications The 15 most recent articles highlight advancements in airborne ultrasound tactile displays, texture synthesis via GANs, and applications in guide dog training analysis, virtual reality, and interactive robotics. Key themes include dynamic pressure control , multi-stimulus integration , and low-latency systems . Collaborations Co-authored with Masahiro Fujiwara (43 papers) Collaborated with Hiroyuki Shinoda (145 papers) Worked with Shun Suzuki, Takaaki Kamigaki, and Ryoya Onishi on thermal and mechanical haptic feedback systems.
Balu Santhanam is a Professor in the Department of Electrical and Computer Engineering at the University of New Mexico. He is affiliated with the Signal Processing and Communications research groups. His academic journey includes a PhD from Georgia Institute of Technology (1998), an MS from Georgia Tech (1994), and a BS from Saint Louis University (1992). Research interests span signal representations, ICA-SVM hybrid systems, discrete fractional Fourier analysis, and applications in SAR vibrometry. He has received notable awards, including the ECE Distinguished Teacher Award (2000, 2005) and the Lawton Ellis Award (2012). His work includes 3 U.S. patents related to signal processing and SAR imagery. Teaching focuses on digital signal processing, probability theory, and multirate systems. His research emphasizes chirp parameter estimation, SAR-based vibration detection, and robust communication systems. Key contributions include advancements in DFRFT applications and clutter suppression techniques. Education: PhD (Georgia Tech, 1998), MS (Georgia Tech, 1994), BS (Saint Louis University, 1992) Labs/Teams: Signal Processing Group, Communications Research Team
