جست‌وجوی استاد و پژوهشگر

Stephanie Forrestمشاهده پروفایل

استاد

• Computational Immunology
• Computer Security
• Automated Software Repair
• +۱۲ مورد دیگر

Stephanie Forrest is a Professor in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU), where she also serves as Director of the Biodesign Center for Biocomputation, Security and Society. She is an External Faculty member of the Santa Fe Institute (SFI), where she previously held leadership roles including Co-Chair of the Science Board (2010–2013) and Interim Vice President for Academic Affairs (1999–2000). Prior to joining ASU, she was a Regents Distinguished Professor and Department Chair of Computer Science at the University of New Mexico (2006–2011). B.A., St. John's College M.S., Ph.D., University of Michigan, Computer Science Her research lies at the intersection of biology and computation, focusing on computational immunology, automated software repair, evolutionary computation, and modeling complex systems such as cancer and immune responses. She pioneers bio-inspired algorithms for cybersecurity and develops evolutionary techniques for software optimization and repair. Her work integrates principles from immunology, genetics, and complex adaptive systems to solve problems in computer science. The trends in her recent publications reveal a sustained focus on automated program repair using evolutionary computation, GPU code optimization, and modeling biological systems. Her work increasingly combines machine learning with mutation-based search, explores semantic representations in code repair, and applies bio-inspired models to real-world software and security challenges. ACM/AAAI Allen Newell Award (2011) Presidential Young Investigator Award (1991) Stanislaw Ulam Memorial Lectures, SFI (2013) IEEE Fellow UNM Annual Research Lecture (2012) IEEE S&P Test of Time Award (2020) ICSE Most Influential Paper Award (2019) ACM/SIGEVO Impact Award (2019) SEAMS Best Paper Award (2019) WEIS Best Paper Award (2015) Stephanie Forrest has advised numerous students and researchers, often in collaboration with W. Weimer, C. Le Goues, and M. Moses. Her research is supported by major funding agencies including the National Science Foundation (NSF), Defense Advanced Research Projects Agency (DARPA), Air Force Research Laboratory (AFRL), and the Santa Fe Institute. She has also contributed to public policy as a Jefferson Science Fellow at the U.S. Department of State (2013–2014), advising on cyber-policy. She currently chairs the Government Affairs Committee of the Computing Research Association (CRA). She leads the Biodesign Center for Biocomputation, Security and Society, which brings together interdisciplinary teams to study the co-evolution of technology and society. Her research group has developed tools for intrusion detection (e.g., STIDE, pH, RISE), automated software repair (e.g., GenProg), and biological modeling (e.g., CancerSim). Current projects include engineering diversity for enhanced cybersecurity, measuring internet censorship, and modeling immune and cancer systems.

Scott Koziolمشاهده پروفایل

دانشیار

• Robotics
• Neuromorphic Engineering
• Embedded Systems
• +۹ مورد دیگر

Dr. Scott Koziol is an Associate Professor and Associate Chair in the Department of Electrical & Computer Engineering at Baylor University, College of Engineering. He is actively leading research and teaching in robotics, embedded systems, and neuromorphic engineering. PhD in Robotics, Georgia Institute of Technology (2013) MS in Mechanical Engineering, Georgia Institute of Technology (2011) MS in Electrical Engineering, Iowa State University (2000) BS in Electrical Engineering, Cedarville University (1998) His research focuses on robotics, neuromorphic engineering, embedded systems, and analog/digital signal processing . He develops bio-inspired algorithms for autonomous navigation and implements them using reconfigurable analog VLSI and Field Programmable Analog Arrays (FPAA). His work integrates machine learning with stochastic computing and explores event-based vision, quantum random number generation, and assistive technologies such as The Reminding Walker device. His recent publications span topics including autonomous path planning, millimeter-wave radar for biomechanics, stochastic neural networks, and quantum workforce development. These works appear in journals like IEEE Access , Entropy , and Quantum Science and Technology , reflecting a multidisciplinary approach that bridges robotics, computing, and biomedical applications. Best Live Demo Award, ISCAS 2010 Outstanding Poster Presentation Award, URSA Scholars Week 2016 Dr. Koziol advises numerous graduate and undergraduate students, including thesis authors in robotics and neuromorphic computing. His research is supported by sponsors such as DARPA, ONR, DOE, and Baylor’s internal programs. He leads the Neuromorphic & Robotic Systems Lab located at the Baylor Research and Innovation Collaborative (BRIC), where his team conducts cutting-edge work in hardware-based neural computation and autonomous systems.

Josep Maria Porta Pleiteمشاهده پروفایل

پژوهشگر

• Motion Planning
• Computational Robotics
• Robot Kinematics
• +۷ مورد دیگر

Josep Maria Porta Pleite is an Associate Researcher at the Institut de Robòtica i Informàtica Industrial (IRI), a joint center of the Spanish National Research Council (CSIC) and Universitat Politècnica de Catalunya (UPC). He leads the Kinematics and Robot Design (KRD) research group and has been actively contributing to robotics and computational kinematics since 2007. His work bridges theoretical algorithm development and practical applications in robotics, molecular biology, and environmental toxicology. Porta’s research spans motion planning , robot kinematics , SLAM , and planning under uncertainty . He has made significant contributions to solving complex kinematic problems in closed-chain systems and molecular conformational spaces. His work often involves developing efficient algorithms and open-source software tools such as the CuikSuite , Cuik-KDtree , and Pose SLAM , which are widely used in robotics research. His recent publications (2019–2025) reveal a strong trend toward interdisciplinary applications, particularly in zebrafish behavioral analysis and neurotoxicology , where computational methods are applied to assess environmental contaminants. He also continues to advance core robotics problems, including trajectory optimization, hand-eye calibration, and closed-form solutions in rotation geometry. His work appears in top-tier journals such as IEEE Transactions on Robotics , Mechanism and Machine Theory , and Science of the Total Environment . Porta has served as an associate editor for IEEE Transactions on Robotics (2015–2018) and has supervised numerous students and collaborators. He has led long-term software development efforts and secured research funding through national and European projects. Scientific Contributions: Lead developer of the CuikSuite for motion analysis of closed-chain systems. Coordinator of the KRD research group since 2011. Contributor to ambient intelligence and robot localization during his postdoc at the University of Amsterdam. He advises multiple students in robotics, computer vision, and biomedical applications, and his team develops tools for path planning, singularity analysis, grasp optimization, and molecular modeling. There is no indication of part-time status, retirement, or former affiliation.

Markus Linderمشاهده پروفایل

استاد

• Biosynthetic Materials
• Biomolecular Materials
• Protein Engineering
• +۱۲ مورد دیگر

Markus Linder is a Professor in the Department of Bioproducts and Biosystems at Aalto University, specializing in Biomolecular Materials. He leads cutting-edge research in biosynthetic materials, with a focus on genetically engineering proteins from natural sources such as silk and barnacle cement to develop sustainable, high-performance materials. His work is closely tied to the LIBER Centre of Excellence and several major research projects, including NextSkins and FIRI BioFoundry, which aim to pioneer living and hybrid materials. Education: Doctoral degree in Engineering and Technology, Helsinki University of Technology (1997) Master's degree in Engineering and Technology, Helsinki University of Technology (1993) His research interests center on biosynthetic materials , where he uses genetic engineering to design proteins that self-assemble into functional materials. These materials are produced in microbes and can exhibit properties like self-healing, adaptability, and high toughness. His vision includes replacing petrochemical-based materials with bio-derived alternatives, contributing to sustainability and the UN Sustainable Development Goals. A key focus is on spider silk proteins, hydrophobins, and phase separation phenomena in protein solutions. The recent research articles (2019–2025) reflect a strong trend in protein engineering , self-assembly , and sustainable material fabrication . Topics include artificial spider silk spinning, hydrogel design, protein quantification methods, and microbial-based living materials. These works span disciplines such as material science, biophysics, analytical chemistry, and synthetic biology, often published in high-impact journals like Nature Materials and Advanced Functional Materials . Scientific Awards: VTT Prize for Scientific Excellence (1998) Leading edge-lecturer (2015) Linder actively supervises students and postdoctoral researchers, with over 15 theses supervised. He has secured significant research grants and leads multiple projects funded by national and EU sources. His academic service includes membership on editorial boards, evaluation of funding applications, and participation in tenure track committees. He also engages in public outreach, such as presenting on life-inspired materials to non-academic audiences. Linder is a key figure in the LIBER Centre of Excellence , where interdisciplinary teams from bioscience, physics, chemistry, and computational modeling collaborate to develop hybrid materials inspired by biological systems. The center focuses on life-like properties such as growth, adaptation, and signal transmission, aiming to create next-generation interactive materials.

• Machine Learning
• Deep Learning
• Network Security
• +۵ مورد دیگر

Ayman Yafoz is an active Associate Professor in the Department of Computer Science at Alexandria University's College of Engineering. His research bridges computer science and engineering with a focus on practical security and optimization applications. His scholarly output includes 60 publications with an h-index of 12, demonstrating consistent contributions to his fields of expertise. Dr. Yafoz's research interests center on the intersection of optimization algorithms and machine learning/deep learning techniques. His work spans network security, intrusion detection systems, medical image analysis, Arabic language processing, and IoT security applications. He frequently develops novel hybrid models that combine metaheuristic optimization approaches with neural network architectures to solve complex real-world problems. His publication portfolio reveals strong trends in security applications, with approximately 60% of his recent work focused on various aspects of cybersecurity including intrusion detection, botnet detection, and encryption. Another significant portion addresses medical imaging and accessibility technology, demonstrating his commitment to applying computational methods to healthcare challenges. Dr. Yafoz maintains active research collaborations with scholars across the Middle East, particularly with co-authors Raed Alsini, Omar Alghushairy, and Khaled Tarmissi. His work appears primarily in engineering journals with notable publications in Alexandria Engineering Journal, IEEE Access, and Sustainability.

• Cognitive Capitalism
• Digital Technology and Democracy
• Philosophy of Techniques
• +۳ مورد دیگر

Fabien Granjon is a Professor of Sociology at Université Paris 8. His research focuses on cognitive capitalism, digital technology's societal impacts, and the philosophy of technological mediation. He actively contributes to interdisciplinary studies on sustainable innovation, education technology, and the socio-political dimensions of digital practices. Granjon co-edits sections on 'Cognitive Capitalism' and 'Democracy and Digital Technology' in Cahiers Costech, a journal he collaborates with through its editorial committee. His work bridges sociology, cognitive science, and technological design, emphasizing how tools shape perception and cognition. Research Interests: Cognitive Capitalism, Digital Democracy, Enactive Design, Sustainable Innovation in Engineering Editorial Roles: Section Editor for 'Cognitive Capitalism' and 'Democracy and Digital Technology' (Cahiers Costech) Key Contributions: Exploring code as a semiotic tool, digital literacy frameworks, and the societal implications of AI Granjon’s recent work analyzes how technical models from diverse disciplines (e.g., neurophysiology, computer vision) are reframed into computational tools, influencing both developer perception and end-user experiences. He emphasizes the role of code as a mediating artifact in collaborative innovation processes.

Stephan Günnemannمشاهده پروفایل

استاد

• Machine Learning
• Data Analysis
• Graph Neural Networks
• +۴ مورد دیگر

Prof. Stephan Günnemann is a Professor of Data Analytics and Machine Learning at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He leads the Munich Data Science Institute as Executive Director and directs the Konrad Zuse School of Excellence in Reliable AI. His research focuses on enhancing the reliability of machine learning systems, particularly in graph-based and temporal data analysis. Prof. Günnemann holds a PhD from RWTH Aachen University (2012) and has held postdoctoral and senior research positions at Carnegie Mellon University (USA), Simon Fraser University (Canada), and Siemens AG. He founded the Emmy Noether Research Group at TUM in 2015 and has been recognized with prestigious awards including the Heinz Maier-Leibnitz Medal (2022) and the ACM SIGKDD Best Paper Award (2018). His research interests span adversarial robustness, graph neural networks, and molecular data analysis. Recent work emphasizes certifiable AI safety, efficient data pruning, and uncertainty estimation in heterogeneous systems. He has contributed to over 150 peer-reviewed publications, with a focus on foundational ML challenges and real-world applications. Key Awards: Heinz Maier-Leibnitz Medal (2022), Google Faculty Award (2020), DFG Emmy Noether Programme (2015) Leadership Roles: Executive Director of Munich Data Science Institute, Director of Konrad Zuse School Past Roles: Postdoctoral Fellow at CMU, Researcher at Siemens His lab actively explores cutting-edge AI topics such as graph representation learning, quantum chemistry simulations, and trustworthy ML systems. Current projects include developing certifiable defense mechanisms against adversarial attacks and scalable molecular generation frameworks.

• Machine Learning
• Robotics
• Human-Robot Interaction
• +۴ مورد دیگر

Joseph Camp is a Professor of Comparative Pathobiology and Secretary of Faculties at Purdue University. His work spans interdisciplinary areas including machine learning, robotics, and human-robot interaction. He leads the Global Engineering Program and holds affiliations across technical and administrative roles. His research focuses on reinforcement learning, multi-agent systems, and applying large language models to enhance decision-making and action anticipation in robotics and healthcare contexts. Key research interests include neuro-symbolic systems for short-context action prediction, robust reinforcement learning under noisy feedback, and geometric reasoning for zero-shot robotic grasping. His work also explores disentanglement in concept-residual models and transfer learning in multi-agent environments. He has published extensively on topics ranging from probabilistic imitation learning to bio-inspired robot locomotion design. While no specific awards or grants are listed, his contributions to human-robot interaction (HRI) and AI-driven medical devices reflect a strong focus on practical applications. His lab’s recent work integrates large language models with traditional robotics algorithms to improve adaptability in dynamic environments. No advising relationships or student names are explicitly mentioned in the provided texts.

Frederike Dümbgenمشاهده پروفایل

استادیار

• Robotics
• Optimization
• Machine Learning
• +۱۲ مورد دیگر

Frederike Dümbgen is an incoming Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering, starting in Spring 2026. She is currently a researcher with the Willow team at Inria Paris, focusing on optimization for robotics, and previously served as a postdoctoral fellow at the University of Toronto's Robotics Institute. Education: Ph.D. in Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland (2021) M.Sc. in Mechanical Engineering, EPFL (2016) B.Sc. in Mechanical Engineering, EPFL (2013) Her research centers on improving the efficiency and safety of robots operating in the physical world through principled optimization and machine learning methods. She emphasizes certifiable and globally optimal algorithms to build reliable foundations for next-generation robotics in domains such as autonomous vehicles, assistive technology, and manufacturing. Her work bridges robotics, control systems, and artificial intelligence, with a strong focus on mathematical rigor and scalability. The 15 most recent publications highlight a consistent trajectory in robotics-focused optimization, particularly in state estimation, SLAM, pose estimation, and data-driven methods. These works frequently employ semidefinite programming, convex relaxations, and Koopman-based linearization, demonstrating a deep integration of theoretical optimization with practical robotic applications. Keywords across these papers include robotics, optimization, machine learning, and estimation, with subfields like certifiable algorithms, global optimality, and sensor fusion recurring throughout. Dr. Dümbgen has not yet had scientific awards listed in the provided text. She has not yet advised any named students in the provided materials, and there is no mention of grants or funding sources. However, her research trajectory and publication record suggest active involvement in competitive research environments. Her experience includes internships at Disney Research and ABB, and her master’s thesis was completed at ETH Zürich’s Autonomous Systems Lab. She is currently affiliated with the Willow research team at Inria Paris, a group known for foundational work in computer vision, machine learning, and robotics. This team emphasizes mathematical rigor in algorithm design, aligning closely with her focus on certifiable and globally optimal methods.

• Bio-Inspired Computing
• Neuromorphic Systems
• Artificial Intelligence
• +۳ مورد دیگر

Oliver Rhodes is a Lecturer in Bio-Inspired Computing, specializing in neuromorphic systems, artificial intelligence, and hardware acceleration. His work bridges computational neuroscience and computer engineering to advance adaptive edge AI and 3D-integrated chips. Active in UN Sustainable Development Goals for digital futures Key collaborator in EU-funded NimbleAI and SpiNNaker projects Research focuses on: Neuromorphic vision modalities Spiking neural network optimization Energy-efficient hardware accelerators 3D sensing-processing integration Parallelization techniques Recent publications highlight trends in adaptive edge AI, neuromorphic hardware, and computer vision algorithms. His work has technological and economic impacts through brain-inspired computing advancements. He supervises research datasets and contributes to media discussions on neuromorphic computing, including real-time neural simulation and AI completeness concepts.

Fulvio Pintoمشاهده پروفایل

مدرس ارشد

• Smart Materials and Structures
• Multiscale Composites
• Hybrid Laminates
• +۷ مورد دیگر

Fulvio Pinto is a Senior Lecturer in the Department of Mechanical Engineering at the University of Bath, actively contributing to multiple research centers including the Centre for Regenerative Design & Engineering for a Net Positive World (RENEW) and the IAAPS Centre for Integrated Materials, Processes & Structures (IMPS). His work bridges academic research with industrial applications across aerospace, automotive, energy, and sports industries while maintaining a strong commitment to sustainability principles aligned with UN Sustainable Development Goals. Dr. Pinto's research focuses on developing intelligent materials and structures through innovative approaches. His primary interests include bio-inspired composites for enhanced impact resistance, hybrid materials with integrated non-destructive evaluation and structural health monitoring capabilities, natural fiber composites for sustainable structural applications, active polymers for shape-morphing aerospace structures, and advanced nanomaterial-reinforced composites. His work consistently addresses the critical balance between high performance, reduced weight, and environmental sustainability. His recent publications demonstrate significant contributions to hydrogel technology, sustainable composite manufacturing, damage detection systems, auxetic materials for impact protection, and bistable composite structures. These works reflect his commitment to pushing the boundaries of materials science while addressing real-world engineering challenges. His research output shows strong emphasis on practical applications with multiple industry collaborations. Sustainability and Climate Action Awards 2024 Dr. Pinto actively supervises doctoral students and participates in numerous research projects including the Multi-Application BONDing (MABOND) initiative (2024-2027), GKN Prosperity Partnership (2023-2028), and KTP with Active Research Ltd (2023-2025). His work extends beyond traditional academic boundaries through public engagement activities including workshops, public talks, and community demonstrations focused on sustainable materials. He maintains strong connections with industry partners through Innovate UK and EPSRC-funded projects, ensuring his research has tangible real-world impact.

• Artificial Intelligence
• Machine Learning
• Automotive Production
• +۸ مورد دیگر

Prof. Dr. Alexander Schiendorfer is a faculty member at Technische Hochschule Ingolstadt within the Faculty of Industrial Engineering , focusing on AI-based Optimization in Automotive Production . His research bridges Artificial Intelligence with manufacturing and industrial engineering , particularly in constraint programming, self-organizing systems, and machine learning applications for composite materials. His work spans pedagogical innovation in machine learning education, real-time manufacturing analytics , and AI for energy systems . Recent publications highlight applications in gas grid management, synthetic data frameworks, and defect analysis in autonomous driving sensors. He leads teams at AImotion Bavaria , collaborating on projects like SmartManPy for synthetic manufacturing data and MORL agents for multi-objective energy optimization. His methodological contributions include certainty groups for neural network confidence estimation and hierarchical resource allocation algorithms.

• Computer Vision
• Sensor Fusion for Mobile Robotics
• Low Power Computing
• +۴ مورد دیگر

Jorge Lobo is an Assistant Professor at the Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Coimbra. His research focuses on computer vision, sensor fusion for mobile robotics, and low power computing, with recent emphasis on quantum computing and bio-inspired computational approaches. B.Sc/M.Sc in Electrical Engineering (University of Coimbra) Ph.D. in Electrical and Computer Engineering (2007, thesis: 'Integration of Vision and Inertial Sensing') Research Interests: Specializes in artificial perception systems combining computer vision and inertial sensing for robotics. Develops probabilistic models for sensor fusion and explores unconventional computing architectures like stochastic circuits and quantum computing for efficient Bayesian inference. Key applications include autonomous robotic grasping and disaster response systems. Scientific Leadership: Principal Investigator for Q-Bet: Bridging classical/quantum computing via HPC and bio-inspired methods Founding member of Quantum@UC interest group Coordinator of quantum computing specialization courses (collaboration with Physics & Computer Science departments) Key Projects: Contributed to European initiatives BACS (Bayesian Cognitive Systems), HANDLE (robotic dexterity), BAMBI (Bayesian inference hardware), and ECOBOTICS.SEA (marine ecosystem robotics). Academic Recognition: IEEE Senior Member and former president of the Portuguese IEEE RAS chapter. Developed innovative remote labs for stochastic computing education.

• Artificial Intelligence
• Biophysics
• Sensor Technology
• +۳ مورد دیگر

Dr. Sietse van Netten is an Associate Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Artificial Intelligence department within the Bernoulli Institute. His research focuses on biophysics, artificial intelligence, and sensor technology, particularly in bio-inspired systems for hydrodynamic sensing and flow analysis. He holds a prominent position in the field of artificial lateral line systems, developing algorithms and sensor platforms for underwater object localization and environmental monitoring. Key research themes include neural network applications for hydrodynamic imaging, multiplexed fiber optic sensors (FBG-based), and biomechanical studies of fish lateral line systems. His work bridges biological principles with engineering solutions, advancing robotics, environmental science, and biomedical applications. Van Netten collaborates internationally on projects involving fluid dynamics, sensor fusion, and machine learning. His contributions span over five decades of publications, emphasizing interdisciplinary approaches to solve complex hydrodynamic and sensory challenges. He currently leads research in sensor array design and bio-inspired systems at the University of Groningen.

Pavlos Antoniouمشاهده پروفایل

استاد آموزشی

• Networked Systems Design
• Nature-Inspired Algorithms
• Swarm Intelligence
• +۵ مورد دیگر

Pavlos Antoniou is a Teaching Professor at the Department of Computer Science, University of Cyprus since 2009 and a member of the Networks Research Laboratory (NetRL). He holds a Ph.D. (2012) and a Dip.Ing. (2005) in Electrical and Computer Engineering from the National Technical University of Athens. His research focuses on nature-inspired algorithms for networked systems, including swarm intelligence, bio-inspired congestion control, and machine learning applications in complex systems. He has contributed to projects like the EU-funded GINSENG and the SEnDIng IoT initiative, and co-founded the energy-intelligent startup Viridom. Education: Ph.D. in Computer Science, University of Cyprus (2012) Dip.Ing. in Electrical and Computer Engineering, National Technical University of Athens (2005) Research Interests: Design/analysis of networked systems using bio-inspired models (e.g., Lotka-Volterra, bird flocking) Machine learning for complex system optimization Energy-efficient IoT and smart home technologies Self-organizing wireless sensor networks Service Roles: Departmental Webmaster for Computer Science Logipaignion Competition Webmaster (annual computer game competition for students) Awards: Top 3% graduate student at NTUA (2000s) IKY Scholarship for academic excellence Special awards for academic excellence in high school Labs/Teams: Networks Research Laboratory (NetRL) at University of Cyprus.