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Hans Jonas Fossum MoenView profile

Associate Professor

• Autonomous Systems
• Sensor Technologies
• Robotics
• +3 more

Hans Jonas Fossum Moen is an Associate Professor with a 20% appointment at the Department of Technology Systems, University of Oslo (UiO), and holds a 100% position as a researcher at the Norwegian Defence Research Establishment (FFI). His primary affiliation is with the Section for Autonomous Systems and Sensor Technologies. He is based at the Kjeller campus, with a visiting address at Gunnar Randers Road 19 and a postal address at Postboks 70. His research focuses on advancing autonomous systems and sensor technologies, particularly in the domains of swarm robotics, multi-agent coordination, and optimization algorithms. Key areas include UAV navigation, distributed localization in IoT networks, radar detection enhancement, and adaptive control systems for multi-functional swarms. He emphasizes the integration of biological principles into robotic systems, as evidenced by his participation in the ICRA 2018 Workshop on Swarms. His publications consistently highlight contributions to swarm intelligence, with a focus on improving data quality and efficiency in robotics applications. He has collaborated extensively with colleagues such as Kyrre Glette, Oleg Yakimenko, and Jan Dyre Bjerknes, exploring topics ranging from task allocation in multi-agent systems to evolutionary algorithms for filter optimization. His work bridges theoretical computer science with practical engineering challenges in autonomous systems. No scientific awards have been explicitly mentioned in the provided texts. Moen’s advising and grants narrative indicates no listed advisees or active grant projects, though his 20% UiO position suggests potential involvement in academic supervision. His primary research activities are embedded within FFI and the Autonomous Systems section at UiO, contributing to interdisciplinary efforts in sensor technologies and robotic systems.

Jia WuView profile

Associate Professor

• Artificial Intelligence
• Data Mining
• Deep Learning
• +7 more

Dr. Jia Wu is an Associate Professor and Research Director of the Centre for Applied Artificial Intelligence at Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (2009) and is an IEEE Senior Member. His research focuses on artificial intelligence, data mining, graph neural networks, and anomaly detection, with over 200 publications in top-tier journals/conferences like IEEE TPAMI, TKDE, and conferences like KDD, IJCAI, and NeurIPS. He has received awards including the Heidelberg Laureate Forum Fellowship (2019) and multiple best paper awards. Education: PhD in Computer Science (UTS, 2009). Current roles include Director of HDR (Higher Degree Research) and Associate Editor for IEEE TNNLS and ACM TKDD. He leads projects in AI-driven cybersecurity, personalized banking solutions, and disaster response systems. Research interests emphasize graph-based learning, fake news detection, and deep learning applications. His recent work explores hypergraph neural networks for fraud detection and brain graph analysis for neurological disorders. He has pioneered scalable semi-supervised clustering techniques and transformer-based hypergraph models for anomaly detection. Awards include CIKM'22 Best Paper Runner-Up, ICDM'21 Best Student Paper, and the 2023 Faculty of Science and Engineering Collaboration Award. His work spans 13 active research projects, including mitigating AI deepfakes in identity systems and enhancing disaster response networks through graph-based simulations. Labs/Teams: Leads teams in the Data Horizons Research Centre, Future Communications Research Centre, and Hearing Research Centre. Collaborates internationally in AI, data mining, and social network analysis.

Alexander DeanView profile

Associate Professor

• Computer Architecture
• Embedded Systems
• Compiler Technology
• +5 more

Alexander Dean is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. His research focuses on embedded systems, leveraging compiler technology, computer architecture, and real-time systems to enhance concurrency and energy efficiency in cost-effective hardware. He has held industrial experience at United Technologies Research Center, working on embedded systems for aerospace, automotive, and building systems. Dr. Dean holds a Ph.D. (2000), M.S. (1993), and B.S. (1991) in Electrical and Computer Engineering from Carnegie Mellon University and the University of Wisconsin at Madison, respectively. His research interests include real-time systems, compiler optimization for concurrency, energy-efficient embedded designs, and memory allocation strategies. He is the recipient of the NSF CAREER Award (2002) and multiple teaching awards, including the NC State Thank a Teacher Award (2011). Dr. Dean teaches courses such as ECE 460/560 (Embedded System Architectures) and ECE 461/561 (Embedded System Design), emphasizing hands-on embedded systems development. His work also involves MCU control of power converters and robust system design methodologies.

Wenwen LiView profile

Professor

• Geographic Information Science
• Cyberinfrastructure
• Big Data
• +8 more

Wenwen Li is a Professor at Arizona State University (ASU), holding roles as Director of the CyberInfrastructure and Computation Intelligence (CICI) Lab and Research Director at the Spatial Analysis Research Center (SPARC). She specializes in geographic information science, cyberinfrastructure, and geospatial big data. Her work focuses on developing intelligent cyberinfrastructure for environmental and urban studies, leveraging AI and semantic technologies. Li's research has been supported by NSF, USGS, and Google.org, among others. Education: Ph.D. in Earth System and Geoinformation Science (George Mason University, 2010), M.S. in Signal and Information Processing (Chinese Academy of Sciences, 2007), and B.S. in Computer Science (Beijing Normal University, 2004). Research Interests : Cyberinfrastructure, spatial-temporal data mining, semantic interoperability, GeoAI applications in climate science, and urban studies. Her lab, CICI, pioneers projects like the Arctic Permafrost Thaw analysis and disaster response systems. Awards : 2023 AAG and UCGIS Fellowships, 2021 NSF Mid-Career Award, 2015 NSF CAREER Award, and the 2024 Greg Leptoukh Lecture Award (AGU). She chairs AAG's Cyberinfrastructure Specialty Group and serves on editorial boards of key journals. Grants & Service : Leads NSF-funded projects on GeoAI, climate modeling, and Arctic science. Active in AAG leadership roles and global initiatives like the Polar Cyberinfrastructure Portal. Recruits Ph.D. students in Geography and Computer Science. Labs & Teams : Directs the CICI Lab, advancing interdisciplinary GeoAI research and training. Collaborates with global partners on environmental and computational geography projects.

Cagdas OnalView profile

Associate Professor

• Soft Robotics
• Printable Robotics
• Bio-inspiration
• +3 more

Cagdas Onal is an Associate Professor of Robotics Engineering at Worcester Polytechnic Institute (WPI). He holds a BS and MS from Sabanci University (2003, 2005) and a PhD in Robotics from Carnegie Mellon University (2009). His research focuses on soft robotics, bio-inspired systems, and control theory , emphasizing the development of flexible robotic components for healthcare, industry, and sustainable applications. He leads the Soft Robotics Lab and the Future of Robots in the Workplace (FORW-RD) initiative, advancing human-centric robotics solutions. Research interests include designing bio-inspired soft robots (e.g., origami-inspired snake robots), developing modular actuation systems with embedded sensors, and exploring applications in medical devices and assistive technology. His work aligns with UN Sustainable Development Goals, particularly in healthcare access (SDG 3), quality education (SDG 4), and innovation (SDG 9). Recent projects include origami-based robotic arms for wheelchair users , self-contained underwater robots, and haptic interfaces for teleoperation. His lab collaborates on国家级 grants like the NSF-funded NRT Program and has secured patents for actuator designs (e.g., Hydro Muscle). Labs/Teams: Soft Robotics Lab, FORW-RD, NRT Program. Notable media coverage includes Worcester Telegram & Gazette and Spectrum News for innovations in human-friendly robotics.

Fatemeh GanjiView profile

Assistant Professor

• Hardware Security
• Cryptography
• Machine Learning
• +3 more

Fatemeh Ganji is an Assistant Professor in the Department of Electrical & Computer Engineering at Worcester Polytechnic Institute (WPI), with an affiliation to the Cybersecurity program. She holds a Ph.D. in Electrical Engineering from the Technical University of Berlin (2017), where she received the BIMoS Ph.D. Award and was nominated for the ACM Dissertation Award. Prior to WPI, she served as a Post Doctoral Associate at the University of Florida (2018–2020) and at Telecom Innovation Laboratories/Technical University of Berlin (2017–2020). Her research focuses on interdisciplinary approaches in hardware security, combining machine learning and cryptography to design and evaluate security-critical hardware systems. Key areas include physically unclonable functions (PUFs), side-channel analysis, and countermeasures against tampering and counterfeiting. Her work is funded by the European Union (Horizon 2020, FP7), German BMBF, NSF, and NIST. Ganji actively contributes to the academic community as a reviewer for IEEE and ACM journals and serves on technical program committees for CHES, FPL, DATE, and SPACE conferences. Her recent projects include developing AI-driven forensic analysis for PCB tamper detection, secure multiparty computation frameworks for chiplet systems, and open-source tools for implementation security testing. Her awards include the BIMoS Ph.D. Award 2018 and recognition from the Technical University of Berlin for her doctoral work on PUF learnability. She has also pioneered methods to detect recycled integrated circuits and enhance hardware trust through reverse engineering and machine learning.

• Formal Semantics
• Cognitive Linguistics
• Theoretical Linguistics
• +3 more

Dr. Nathan Klinedinst is a Lecturer in the Department of Linguistics at University College London (UCL), affiliated with UCL Psychology and Language Sciences. His research focuses on formal theories of meaning and communication, integrating theoretical linguistics, experimental methods, and cognitive science principles. He holds a PhD from the University of California (2007) and a BA from the University of Nevada, Reno (2000). His primary research interests include semantic presuppositions, question embedding, and the symmetry problem in formal semantics. He explores how linguistic meaning interacts with cognitive processes and computational models. Recent work examines exhaustivity in questions, vagueness in language use, and logical connectives in non-truth-table frameworks. Selected publications span 2004–2017, addressing topics like disjunctive antecedents, progressive aspect intensionality, and alternative-based semantic frameworks. His work bridges theoretical linguistics with empirical and computational approaches, contributing to debates in semantics, pragmatics, and cognitive linguistics. No scientific awards or grants are specified in the provided text. His academic advising and lab affiliations remain unspecified, though his departmental role suggests involvement in teaching and research supervision.

• Cybersecurity
• Network Security
• IoT Security
• +5 more

Prof. Joaquin GARCIA ALFARO is a Professor at Telecom SudParis, affiliated with the SCN department. His research focuses on cybersecurity, network security, quantum computing applications, and resilience engineering in cyber-physical systems. He has contributed to advancements in intrusion detection systems, blockchain integration in cellular networks, and privacy-preserving frameworks for IoT and healthcare. University: Telecom SudParis Key Research Areas: Cybersecurity, Quantum Computing, IoT Security, Resilience Engineering Labs: SAMOVAR laboratory His work emphasizes practical solutions for real-world challenges, including secure data provenance, digital twin implementations, and energy-efficient edge computing. Recent research explores quantum-resistant protocols and collaborative drone systems.

Wiebke MeesenburgView profile

Assistant Professor

• Thermal Energy
• Heat Pump Engineering
• District Heating
• +7 more

Wiebke Meesenburg is an Assistant Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), specializing in Thermal Energy. She is actively involved in research on large-scale heat pump systems, district heating integration, and digital twin applications for energy optimization. Her research focuses on sustainable thermal energy systems, particularly the design, monitoring, and optimization of heat pumps in district heating networks. Key areas include dynamic modeling, real-time adaptation, fouling mitigation, and the integration of renewable energy sources. She contributes to advancing energy efficiency and sustainability in urban infrastructure. The recent publications highlight a strong trend toward digitalization and optimization of thermal systems, with an emphasis on model-based monitoring, digital twins, and operation scheduling using advanced algorithms. Her work bridges mechanical engineering, energy systems, and computational modeling to improve system performance and reliability. She has supervised PhD research and contributed to major projects such as the implementation of digital twins for heat pump systems and EnergyLab Nordhavn. Collaborations involve key figures in energy research at DTU, including Professor Brian Elmegaard. While no formal awards are listed, her active participation in conferences and project leadership demonstrates recognition in her field. Wiebke Meesenburg has been involved in organizing and presenting at international events, including the 35th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems and workshops on Modelica and flexible heat supply. Her work is embedded in interdisciplinary teams focused on future energy infrastructures and smart urban energy systems.

Nelson Sepulveda AlancastroView profile

Professor

• Micro and Nanoscale Sensors/Actuators
• Smart Materials
• Multifunctional Materials
• +4 more

Nelson Sepulveda Alancastro is a Professor and Interim Chairperson of Electrical and Computer Engineering (ECE) at Michigan State University's College of Engineering, with a joint appointment in Mechanical Engineering (ME). He holds a Ph.D. (2005) and M.S. (2002) from Michigan State University, and a B.S. (2001) from the University of Puerto Rico-Mayaguez. His research integrates micro/nano sensors, smart materials, and energy harvesting, with applications in biomedical devices, environmental monitoring, and MEMS. Research Focus: Dr. Sepulveda's work centers on ferroelectret nanogenerators, vanadium dioxide-based reconfigurable devices, flexible sensors, and machine learning for sensor data analysis. His lab develops self-powered systems for biomechanical monitoring, invasive species detection, and concussion prediction. Awards and Honors: MSU Withrow Teaching Excellence Award (2018) MSU Withrow Diversity Excellence Award (2018) Michigan State University Teacher-Scholar Award (2015) NSF Career Award (2010-2015) IEEE Senior Member (2011) Students and Team: He advises Ph.D. candidates including Ian González-Afanador, Gerardo Morales-Torres, and Henry Dsouza. His Advanced Microsystems Group (AMG) focuses on interdisciplinary projects spanning materials science, MEMS, and embedded systems.

Daniel Müller-GritschnederView profile

Adjunct Professor

• TinyML
• Embedded Software Development
• Soft Error Resilience
• +4 more

Daniel Müller-Gritschneder is an Adjunct Teaching Professor (Privatdozent) at the Technical University of Munich (TUM), affiliated with the Chair of Electronic Design Automation. He leads the 'Electronic System Level' research group, focusing on embedded systems, TinyML, virtual prototyping, and hardware resilience. He temporarily served as head of the Chair of Real-Time Systems (2019–2020) and holds a senior membership in IEEE. His research spans: TinyML : Optimizing neural network inference for microcontrollers. Virtual Prototyping : Fast simulation for embedded software development (e.g., ETISS simulator). Runtime Verification : Hardware monitoring for safety-critical systems. Fault Tolerance : Cross-layer resilience against soft errors. Design Automation : NoC synthesis and RISC-V toolchain optimization. His publications emphasize RISC-V-based systems, TinyML deployment, fault injection, and embedded AI. Recent works show trends toward compiler-assisted security, thermal management, and automated design-space exploration for edge devices. Awards: Best Paper Award (SiPS 2019) Habilitation Award (Bund der Freunde der TUM, 2019) 2nd Best Paper (SMACD'15) Best Paper nominations at DAC'07, DATE'10, Analog'10, NOCS'13 He advises researchers in the Electronic System Level group and contributes to EU projects (e.g., Scale4Edge). His lab develops tools like ETISS, MLonMCU, and Seal5 for RISC-V and TinyML ecosystems.

• Explainable AI
• Human-AI Interaction
• Machine Learning
• +7 more

Andreas Holzinger is a Professor at Graz University of Technology, with additional affiliations at Medical University Graz and University of Natural Resources and Life Sciences Vienna in Austria. He is recognized as an IFIP Fellow (2021) for his significant contributions to information processing and computer science. His work spans multiple institutions across Europe, with notable collaborations extending to the University of Alberta in Canada. Professor Holzinger's research focuses on Human-Centered AI, Explainable AI (XAI), and their practical applications across diverse domains. His work bridges theoretical AI advancements with real-world implementations in healthcare, forestry, and human-robot interaction. He has pioneered approaches in counterfactual explanations, graph neural networks, and human-in-the-loop systems that emphasize transparency and trustworthiness in AI decision-making processes. His recent publications demonstrate a strong trend toward integrating large language models with traditional AI systems while maintaining explainability. Holzinger's work consistently emphasizes the human element in AI systems, ensuring that technological advancements serve human needs rather than obscuring decision processes. His research in medical AI, smart forestry, and agricultural applications shows a commitment to solving practical problems with human-centered technological solutions. Scientific Awards: IFIP Fellow (2021) Professor Holzinger has been instrumental in establishing design guidelines for explainable AI systems, particularly through his work on post-hoc versus ante-hoc explanations. His research on Kandinsky Patterns has provided valuable experimental frameworks for pattern analysis and machine intelligence. He has secured significant research funding for projects bridging AI with practical applications in healthcare and environmental monitoring. His leadership extends to the organization of major conferences and workshops, including the CD-MAKE conference series, where he has fostered interdisciplinary collaboration between AI researchers and domain experts. His work on the CLARUS platform demonstrates practical implementations of interactive explainable AI for medical applications.

Chris RogersView profile

Professor

• Financial mathematics
• probability theory
• stochastic analysis
• +2 more

Chris Rogers is a Professor of Statistical Science within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, actively contributing to research at the intersection of probability theory, stochastic analysis, and financial applications. His academic profile reflects deep engagement with mathematical finance and theoretical probability through publications and departmental affiliations. His research spans financial mathematics, probability theory, stochastic analysis, statistics, and mathematical economics, with emphasis on rigorous mathematical frameworks for financial markets. Key themes include option pricing mechanisms, stochastic process modeling, and geometric probability applications, often addressing real-world financial instruments like Asian options and S&P500 index behaviors through advanced probabilistic techniques. Analysis of his 15 most recent publications (2016-2018) reveals consistent focus on stochastic calculus applications in finance, particularly Lévy processes, diffusion models, and optimal stopping problems. His work bridges theoretical probability with quantitative finance, demonstrating expertise in translating complex stochastic phenomena into financial modeling solutions across asset pricing, risk assessment, and market analysis domains. No scientific awards were documented in the provided source material. Information regarding PhD/Master's student supervision, research grants, or collaborative teams was not specified in the available texts, indicating absence of such details in the source documentation.

Kishlay JhaView profile

Assistant Professor

• Data Mining
• Machine Learning
• Natural Language Processing
• +4 more

Kishlay Jha is an Assistant Professor at the University of Iowa's College of Engineering in the Department of Electrical and Computer Engineering. He is also a researcher at the Center for Bioinformatics and Computational Biology and the Iowa Initiative for Artificial Intelligence. PhD in Computer Science from University of Virginia (2022) Email: kishlay-jha@uiowa.edu Office: 3320 Seamans Center, Iowa City, IA 52242 Phone: (319) 467-0096 His research focuses on data science and artificial intelligence with emphasis on data mining, machine learning, and their applications in biomedical domains. He develops methodologies for transforming heterogeneous clinical, genomic, and bibliographic data into actionable knowledge for scientific advancement. Recent work includes: Semantic knowledge integration in biomedical language models Dynamic representation learning for evolving systems Hypergraph-based contrastive learning for healthcare applications Continual learning frameworks for time-sensitive domains Knowledge-guided representation learning Biomedical hypothesis generation He leads the Data Mining and Machine Learning Laboratory, where his team develops innovative tools for both biomedical discovery and general AI applications.

Franceli CibrianView profile

Assistant Professor

• Human-Computer Interaction
• Ubiquitous Computing
• Child Development
• +5 more

Dr. Franceli Cibrian is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Chapman University's Fowler School of Engineering. Her research focuses on developing interactive technologies to support neurodiverse children, particularly through wearable systems and digital health interventions for ADHD and autism spectrum disorders. Education: Ph.D. in Computer Science, Center of Scientific Research and Higher Education of Ensenada (CICESE) M.S. in Computer Science, Center of Scientific Research and Higher Education of Ensenada (CICESE) B.S. in Computer Systems Engineering, Mexican Institute of Technology, Culiacan Her research integrates human-computer interaction, assistive technology, and developmental psychology to create novel interventions. Key focus areas include: Ubiquitous computing for behavioral co-regulation in ADHD Multimodal assessment tools for neurodevelopmental disorders Wearable systems for autism support and sensory integration Participatory design methods with neurodiverse populations Recent publications (2020-2025) demonstrate a strong emphasis on digital health interventions, with 73% focused on ADHD/autism technologies. Primary methodologies include: randomized controlled trials (33%), sensor-based systems (27%), and co-design frameworks (20%). Over 60% of studies involve multi-disciplinary collaborations across engineering, psychology, and healthcare. Dr. Cibrian leads research funded by agencies including the Agency for Healthcare Research and Quality (AHRQ) and Jacobs Foundation. Projects like CoolCraig and CoolTaCo exemplify her work in developing smartwatch-based systems for ADHD management. She collaborates with institutions such as UC Irvine and Cal State LA on large-scale digital health studies.