جست‌وجوی استاد و پژوهشگر

• Digital Communications
• Signal Processing
• Networking
• +۳ مورد دیگر

Mahsa Derakhshani is a Senior Lecturer (equivalent to Associate Professor) in Digital Communications at Loughborough University's Wolfson School of Mechanical, Electrical and Manufacturing Engineering. She leads research in the Signal Processing and Networks Research Group (SPNRG) and serves as an Associate Editor for the IET Signal Processing Journal. Her academic journey includes a PhD from McGill University (2013), followed by postdoctoral roles at the University of Toronto and Imperial College London. Key awards include the Royal Academy of Engineering/The Leverhulme Trust Research Fellowship (2020-21) and NSERC Postdoctoral Fellowships (2015-2017). Research interests focus on digital communications, machine learning for signal processing, wireless networks, and reinforcement learning applications. Recent work addresses challenges in satellite communications, OTFS modulation, and opto-physiological monitoring. She has authored over 70 publications spanning topics like NOMA systems, MIMO optimization, and edge-assisted live streaming. Education: PhD (McGill, 2013), MSc (Sharif University, 2008), BSc (Sharif University, 2006) Affiliations: IEEE Senior Member, IET Member, Fellow of the Higher Education Academy Grants & Funding: Leverhulme Trust, Royal Academy of Engineering, NSERC Labs/Teams: Active in SPNRG and collaborates on projects involving 5G/6G networks, satellite systems, and biomedical signal processing. Current research emphasizes AI-driven solutions for communication networks and wearable health monitoring technologies.

Joshua Garciaمشاهده پروفایل

استادیار

• Software Engineering
• Software Architecture
• Software Vulnerability Management
• +۵ مورد دیگر

Joshua Garcia is an Assistant Professor in the Informatics Department at the University of California, Irvine (UCI), within the Donald Bren School of Information and Computer Sciences. His research focuses on software architecture, automated testing, and cybersecurity, particularly in autonomous systems and mobile applications. He leads projects like DeltaDroid, Doppelgänger Test Generation, and Darcy, which address software vulnerability management, architectural consistency, and safety-critical systems. Key achievements include an NSF CAREER Award (2025), an NSF CRI Grant (2018), and a DARPA competition win (2024). His work is adopted by organizations like Boeing, Google, and NASA. Garcia collaborates internationally, involving institutions in Padova and researchers like Luca, Jessy Ayala, and Philipp. Research Interests: Software architecture evolution, automated exploit generation, autonomous vehicle testing, and accessibility in software development Grants: NSF CAREER ($500K+), NSF CRI ($1M+) Labs/Teams: HexHive Group, Autonomous Systems Testing Lab

Toby Daviesمشاهده پروفایل

دانشیار

• Crime Science
• Urban Analytics
• Spatial Analysis
• +۶ مورد دیگر

Toby Davies is an Associate Professor in Criminal Justice Data Analytics at the University of Leeds, School of Law. His work focuses on quantitative criminology, spatial analysis, and computational methods to inform crime prevention. He holds a Mathematics degree from the University of Oxford (2008) and a PhD from University College London (UCL), followed by postdoctoral research on the EPSRC-funded Crime, Policing and Citizenship project. Before joining Leeds in 2023, he was at UCL’s Department of Security & Crime Science. His research spans interdisciplinary topics including urban form and crime, crime modeling, social networks, and cybercrime. He has collaborated with police agencies (West Yorkshire, Thames Valley, West Midlands Police) and governmental bodies (UK Home Office, London Mayor’s Office). A strong advocate for Open Science, he co-founded JDI Open to promote open practices in crime science. His recent work emphasizes policy interventions like phasing out pointed kitchen knives to reduce knife crime, leveraging data-driven approaches. He publishes widely in criminology, physics, network science, and general science journals, and has guest-edited special issues. His applied research aims to bridge theory and practice, developing tools deployed operationally in policing. Current interests include financial crime dynamics, social contagion of crime, and street network configurations’ impact on crime patterns.

Alexander Rodríguezمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Time Series Analysis
• +۷ مورد دیگر

Alexander Rodríguez is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on advancing AI methods for modeling complex spatiotemporal dynamics, particularly in applications related to population health and community resilience. He specializes in machine learning, time series analysis, uncertainty quantification, and multi-agent systems, with an emphasis on scientific modeling and data-driven decision-making. Recent contributions include keynote talks at AAMAS 2025 (Autonomous Agents for Social Good workshop), presentations at the US National Academies Symposium, and invited talks at AAAI 2025 on topics like knowledge-guided machine learning and public health prediction. He co-organizes AAMAS 2025 as sponsorship co-chair and leads initiatives in AI for science and epidemic forecasting. His publications emphasize neural networks for time series forecasting, biomedical foundation models, and epidemic surveillance systems. Notable work includes 'Neural Conformal Control for Time Series Forecasting' (AAAI 2025) and 'Deepcovid: An operational deep learning-driven framework for explainable real-time forecasting' (2021). No scientific awards explicitly listed in available texts. His research group actively collaborates on grants related to AI applications in public health and infrastructure resilience, with a focus on data-centric methodologies and multi-agent systems.

Ben Buchlerمشاهده پروفایل

استاد

• Quantum Information
• Quantum Communication
• Optics
• +۳ مورد دیگر

Prof Ben Buchler is a Professor at The Australian National University (ANU), affiliated with the Physics Education Centre and the ARC Centre of Excellence for Quantum Computation and Communication Technology. His research focuses on quantum optics, atomic sensors, and optomechanics. He leads projects on quantum memory systems, gravitational wave detection, and exotic physics searches using global magnetometer networks. Research Interests: Quantum Communication and Information Cold Atom Physics Optical Sensors and Magnetometry Optomechanical Systems Gravitational Wave Detection Technologies Recent work highlights advancements in room-temperature quantum memory, cross-phase modulation in atomic systems, and applications of optomechanics for single-phonon control. Collaborations include global initiatives like the GNOME (Global Network of Optical Magnetometers) for dark matter and gravitational wave studies. Grants and Projects: ARC Centre of Excellence for Quantum Computation and Communication Technology (2018–2025) Projects on atomic sensors for dark matter, rotation, and magnetic field detection Labs/Teams: Active in the Physics Education Centre and collaborates with international teams on quantum optics and sensor technologies.

Brenda Rubensteinمشاهده پروفایل

دانشیار

• Electronic Structure Methods
• Quantum Chemistry
• Molecular Computing
• +۲ مورد دیگر

Brenda Rubenstein is an Associate Professor of Chemistry and Physics at Brown University and Director of the Data Science Institute. Her research bridges quantum chemistry and materials science, developing stochastic electronic structure methods for accurate large-scale simulations. She also explores molecular computing and computational biophysics. Her group emphasizes diversity, collaboration, and work-life balance. Recent work includes quantum Monte Carlo techniques for materials discovery, machine learning approaches for protein dynamics, and quantum computing applications. She received a named professorship in 2025 and leads the Levermore Global Scholars program. Her publications focus on computational methods across chemistry, physics, and materials science.

• Software Engineering
• Cyber-Physical Systems
• Digital Ecosystems
• +۳ مورد دیگر

Tero Päivärinta is a Professor at the University of Oulu, Faculty of Information Technology and Electrical Engineering. His work focuses on software engineering, digital ecosystems, and cybersecurity. He specializes in empirical studies of software systems, digital twins, and autonomous driving technologies. His research explores hybrid intelligence systems, data-centric decision-making, and governance of collective ambidexterity in digital initiatives. Education: PhD holder with extensive experience in academic and industry collaborations. Key research domains include cyber-physical systems, DevSecOps automation, and IT governance in public sectors. He co-leads projects such as the NUVE Lab’s vehicle testing frameworks and contributes to initiatives like the Software-Defined Vehicle project. Research highlights include advancing knowledge graphs for manufacturing, cybersecurity compliance in DevOps pipelines, and evaluating data-driven decisions. His articles emphasize interoperability challenges, adaptive systems design, and sustainable digital transformation in public utilities. Professional contributions include organizing the TKTP Annual Symposium and co-editing volumes celebrating academic peers like Markku Oivo. His work bridges theoretical software engineering with practical applications in mobility, governance, and industrial systems.

Alexander J. Stewartمشاهده پروفایل

دانشیار

• Mathematical Biology
• Computational Social Science
• Cultural Evolution
• +۷ مورد دیگر

Alexander J. Stewart is an Associate Professor of Informatics at Indiana University Bloomington and an Honorary Professor of Mathematics at the University of St Andrews. He holds a PhD in Mathematical Biology from University College London (2010), and earlier degrees in Physics (Imperial College London) and Mathematical Biology (University College London). His research focuses on mathematical and computational models of social learning in online ecosystems, addressing topics such as misinformation, polarization, and cooperative behavior through lenses of game theory and evolutionary dynamics. Key themes include cultural evolution's interaction with algorithms, social network structures' impact on collective decisions, and cognitive mechanisms shaping social interactions. His work has been published in high-impact journals like Nature , PNAS , and Science Advances , addressing topics ranging from information gerrymandering to the evolution of stereotyping. He leads projects funded by the John Templeton Foundation, exploring cultural evolution and online social contracts. Advising over 7 graduate students and postdocs, he has mentored researchers now in academic and industry roles globally. His teaching includes advanced courses on game theory and social media dynamics. Media contributions include analyses on polarization's societal impacts and the dangers of algorithmic misinformation. Current research initiatives include modeling behavioral motivations for cooperation and investigating eco-evolutionary trade-offs in pathogen competition. His labs focus on interdisciplinary approaches to understanding complex social and biological systems.

Karim ZKIKمشاهده پروفایل

دانشیار

• Cybersecurity
• Blockchain Technologies
• Artificial Intelligence
• +۳ مورد دیگر

Karim ZKIK is an Associate Professor of Cyber Security and Information Systems at ESAIP Graduate School of Engineering, Angers, France. Previously, he served as an Assistant Professor at the International University of Rabat (UIR), Morocco. His roles include Educational Manager of the Cyber Security track, Head of the Cybersecurity Innovation Hub, and committee member for ABET certification and curriculum design. He actively contributes to academic service, organizing conferences such as the International Conference on Cryptology, Coding Theory, and Cyber Security (I4CS 2022), and serves as a Guest Editor for Computers and Industrial Engineering . His research focuses on cybersecurity for connected systems, blockchain technologies, AI-driven security solutions, and cyber resilience in industrial control systems. Recent work explores integrating blockchain and machine learning for threat detection, secure IoT networks, and supply chain resilience. Key contributions include frameworks for cyber resilience in retail and airlines, blockchain-based crowdfunding security, and SDN-based attack mitigation. ZKIK holds a Habilitation (2024) and PhD in Cyber Security from Université d’Angers and Mohamed V University, Rabat. He holds over 20 certifications from EC-Council, IBM, and Cisco. His work bridges theoretical research and industry applications, addressing challenges in smart environments, industrial systems, and sustainable supply chains.

M. Hadi Aminiمشاهده پروفایل

استادیار

• Distributed Machine Learning
• Federated Learning
• Interdependent Networks
• +۷ مورد دیگر

M. Hadi Amini is an Assistant Professor at Florida International University's Knight Foundation School of Computing and Information Sciences. He founded and directs the Sustainability, Optimization, and Learning for InterDependent networks (SOLID) laboratory, focusing on cyber-physical-social systems and distributed AI applications. Ph.D., Electrical and Computer Engineering (2019), Carnegie Mellon University M.Sc., Electrical and Computer Engineering (2015), Carnegie Mellon University M.Sc. (2013), Tarbiat Modares University B.Sc. (2011), Sharif University of Technology His research spans federated learning, interdependent network optimization, and AI applications in smart cities , energy systems , and healthcare . Recent work emphasizes privacy-preserving techniques, quantum encryption, and blockchain integration for secure distributed learning. The 15 most recent publications highlight trends in large language models , edge computing , medical imaging security , and infrastructure resilience , with interdisciplinary emphasis across computer science, systems engineering, and urban planning. Best Paper Award, IEEE Conference on Computational Science & Computational Intelligence (2019) Best Journal Paper Award, Springer Nature Operations Research Forum (2021) Excellence in Teaching Award, FIU (2020) Multiple Best Reviewer Awards, IEEE Transactions NSF Travel Awards (2019) As Associate Editor for Frontiers in Communications and Networks and book series editor for Sustainable Interdependent Networks , he actively shapes research discourse. His lab has secured $3.6M in federal/state funding for AI-driven infrastructure projects.

• Artificial Intelligence Law
• Technology and Society
• Discrimination Law
• +۵ مورد دیگر

Andrew D. Selbst is a Professor of Law at UCLA School of Law and currently serves as the William J. Friedman and Alicia Townsend Friedman Visiting Professor of Law at Harvard Law School. He has been on the UCLA faculty since 2020 and previously held positions as a Postdoctoral Scholar at the Data & Society Research Institute and a Visiting Fellow at Yale Law School's Information Society Project. He has also taught as an Adjunct Professor at Fordham Law School. Professor Selbst received his educational training from prestigious institutions: S.B. in Physics and Electrical Science and Engineering from MIT (2004) M.Eng. in Electrical Engineering and Computer Science from MIT (2005) J.D. from the University of Michigan Law School (2011) Before entering academia, Professor Selbst worked as a design engineer at Cirrus Logic and Analog Devices. Following law school, he served as a Privacy Research Fellow at NYU School of Law's Information Law Institute, an Alan Morrison Supreme Court Assistance Fellow at Public Citizen Litigation Group, a Senior Associate in Hogan Lovells US LLP's Communications group, and clerked for federal judges including the Honorable Dolly M. Gee and the Honorable Jane R. Roth. Professor Selbst's research examines the complex relationship between law, technology, and society. Drawing on resources from computer science, sociology, and science and technology studies, he seeks to understand how technologies interfere with existing legal regimes and how legal actors can respond to the social effects of new technology. His recent work has focused specifically on the effects of machine learning and artificial intelligence on various legal domains, including discrimination law, policing practices, credit regulation, data protection frameworks, and tort law. His interdisciplinary approach combines technical understanding of AI systems with deep legal analysis to address emerging challenges in the digital age. Professor Selbst teaches courses in torts, information privacy and data protection, a seminar on law, technology and society, and beginning in 2025, a dedicated course on Artificial Intelligence Law. His publications have appeared in leading law journals including Boston University Law Review, California Law Review, Harvard Journal of Law and Technology, and University of Pennsylvania Law Review, as well as in the ACM Conference on Fairness, Accountability and Transparency. He is also the coauthor of a forthcoming casebook on Artificial Intelligence Law. His scholarly contributions demonstrate a consistent focus on the intersection of emerging technologies and legal frameworks, with particular attention to how AI systems create novel challenges for established legal doctrines. Professor Selbst's work has been influential in shaping academic and policy discussions around AI regulation, algorithmic accountability, and the adaptation of legal systems to technological change.

Yi-Chi Liaoمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction
• Human-in-the-Loop Optimization
• Reinforcement Learning
• +۴ مورد دیگر

Yi-Chi Liao is a postdoc researcher at ETH Zürich under the SIP Lab, funded by the ETH Zürich Postdoc Fellowship Programme. He holds a PhD from Aalto University (supervised by Prof. Antti Oulasvirta) and Master's/Bachelor's degrees from National Taiwan University. His research focuses on computational interaction, human-in-the-loop optimization, and biomechanical simulation. He has contributed to over 14 top-tier publications in venues like CHI, UIST, and TiiS. Education: PhD in Computer Science, Aalto University (Finland) Master's in Computer Science, National Taiwan University Bachelor's in Computer Science, National Taiwan University Research Interests: Advances in HILO frameworks for adaptive systems, human-AI co-design, and biomechanical motion simulation. His work integrates reinforcement learning, Bayesian optimization, and computational modeling to enhance interactive systems. Publications: Over 14 papers in top HCI venues, emphasizing optimization techniques, tactile interfaces, and 3D interaction. Recent work includes meta-Bayesian optimization for wrist-based interactions and real-time target inference via biomechanical simulation. Awards: CHI 2022 Honorable Mention, multiple outstanding review recognitions, and the ETH Postdoc Fellowship. He serves on program committees for CHI, UIST, and TEI. Teaching: Delivered lectures on Bayesian statistics, deep learning, and computational design at Aalto and NTU. Served as a teaching assistant for HCI and computer architecture courses. Labs: Active in the Human-Computer Interaction Lab (Saarland University), SIP Lab (ETH Zürich), and collaborations with Meta Reality Labs.

Alexander D Maloneyمشاهده پروفایل

استاد

• Theoretical Physics
• Quantum Information Theory
• Black Holes
• +۴ مورد دیگر

Alexander D Maloney serves as the Kathy and Stan Walters Endowed Professor of Quantum Science and Director of the Institute for Quantum and Information Science at Syracuse University's College of Arts and Sciences, Department of Physics. His leadership bridges theoretical physics and quantum information science through institutional and research initiatives. His academic foundation includes a Ph.D. in Physics from Harvard University (2003) with dissertation "Time-Dependent Backgrounds of String Theory," complemented by dual B.Sc. and M.Sc. degrees in Physics and Mathematics from Stanford University (1998). Maloney's research centers on quantum gravity and information theory, exploring black hole physics through string theory frameworks. His work connects quantum field theory, cosmology, and information paradox resolution, with particular focus on wormhole geometries, resurgence phenomena, and holographic dualities. This interdisciplinary approach examines how quantum information principles shape spacetime structure. Recent publications reveal evolving emphasis on Narain ensemble statistics, modular invariance applications, and quantum cosmology in reduced dimensions. His 2020-2025 output demonstrates methodological progression from semiclassical gravity to non-perturbative quantum gravity techniques, increasingly integrating machine learning concepts with theoretical frameworks. His scientific recognition includes: James McGill Professor at McGill University (2023) Sir William Macdonald Chair in Physics at McGill University (2020) Maloney secures major research funding including NSERC Discovery Grants (2015-2020, 2020-2025) and Simons Foundation's "It from Qubit" collaboration (2015-2022). He actively shapes academic discourse through search committee leadership, journal reviewing (Journal of High Energy Physics since 2007), and international conference organization at Aspen Center for Physics and Institute for Advanced Study. As Director of Syracuse's Institute for Quantum and Information Science, he cultivates collaborative research environments exploring quantum gravity applications to quantum computing and cosmological modeling.

• Complex Systems Modeling
• Computational Mathematics
• Applied Mathematics
• +۷ مورد دیگر

Yannis Kevrekidis is a Professor at Princeton University with a distinguished career in computational mathematics and chemical engineering. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich (TUM-IAS) and has held visiting positions at institutions like the Zuse Institute Berlin and Caltech. Education : National Technical University of Athens (Chemical Engineering) University of Minnesota (PhD in dynamical systems) Research Interests : Equation-Free and Variable-Free Modeling Complex Systems Dynamics Multiscale Computation Integration of Machine Learning with Scientific Computing Pattern Formation & Instability Analysis Key Article Trends : Advanced data-driven modeling of dynamical systems Manifold learning for reaction coordinates Projective integration methods Coarse-grained modeling across disciplines Applications in epidemiology, neuroscience, and fluid dynamics Scientific Awards : Guggenheim Fellowship Humboldt Research Award Computing in Chemical Engineering Award (AIChE) Bodossaki Academic Award Allan P. Colburn Award Collaborations : Extensive international collaborations with institutions in Germany, Austria, and the UK Key role in the Complex Systems Modeling and Computation focus group at TUM-IAS

• Quantum Computing Theory
• Computational Complexity
• Quantum Information Science
• +۳ مورد دیگر

Adam Bouland is an Assistant Professor of Computer Science at Stanford University, affiliated with the CS Theory Group. He holds a Ph.D. from MIT (advised by Scott Aaronson), followed by postdoctoral research at UC Berkeley and the Simons Institute for the Theory of Computing (advised by Umesh Vazirani). His research focuses on quantum computing theory, computational complexity, and their connections to physics. He teaches courses such as Quantum Complexity Theory (CS 359D) and Quantum Computing (CS 259Q), and has advised numerous doctoral, master’s, and postdoctoral researchers. His research group includes Tamara Kohler (postdoc), Shaun Datta, Jack Zhou, Jordan Docter, and Chenyi Zhang (doctoral students), among others. Bouland’s work bridges quantum algorithms, entanglement theory, and complexity theory, with contributions to quantum supremacy, pseudorandomness, and holographic principles. Recent highlights include studies on BosonSampling hardness, AdS/CFT duality constraints, and efficient quantum compilation. He has served on program committees for FOCS 2019, QIP 2020, STOC 2023, and ITCS 2025. His research is supported by grants including the NSF CAREER Award for exploring quantum pseudorandomness and complexity frontiers.