جست‌وجوی استاد و پژوهشگر

Caroline Lemieuxمشاهده پروفایل

استادیار

• Fuzz testing
• Program synthesis
• Property-based testing
• +۴ مورد دیگر

Caroline Lemieux is an Assistant Professor at the University of British Columbia, specializing in automated software testing and reliability. Her research develops methods for testing, debugging, and improving software correctness through techniques like fuzz testing and program synthesis. She holds a Ph.D. from UC Berkeley advised by Koushik Sen and a B.Sc. in Computer Science and Mathematics from UBC. Research Interests: Dr. Lemieux's work focuses on: Fuzz testing for vulnerability detection Program synthesis using AI/ML approaches Property-based testing frameworks Automated debugging and specification mining Applications of reinforcement learning in test generation Publication Trends: Her recent papers explore large language models for test generation, fuzzing strategies in CI/CD environments, and neural-backed program synthesis. Work consistently bridges theoretical computer science with practical software engineering challenges. Awards: ACM SIGSOFT Distinguished Paper Award Google PhD Fellowship Best Paper Award (Industry Track) Berkeley Fellowship for Graduate Study

Shuting Hanمشاهده پروفایل

پژوهشگر ارشد

• Neuroscience
• Cortical Dynamics
• Sensory Processing
• +۳ مورد دیگر

Dr. Shuting Han leads a Junior Research Group at the University of Zurich under the Helmchen Lab, funded by the SNSF Ambizione Fellowship since 2024. She holds a Research Fellow position focusing on cortical dynamics underlying sensory processing and memory. Her research examines how distributed cortical areas interact during sensory processing and memory formation, utilizing multi-area two-photon calcium imaging, virtual reality behavior paradigms, electrophysiology, and advanced data analysis techniques. Key projects include investigating sensory representation in cortical areas, predictive processing in neural circuits, cortico-cortical interactions, memory consolidation across the neocortex, and developing high-throughput imaging methodologies. Her recent publications demonstrate expertise in cross-modal predictions, cortical microstates during consciousness alterations, and neural ensemble dynamics. She directs research on top-down predictive signals in neocortex and develops tools for volumetric neural imaging. SNSF Ambizione Fellowship Dr. Han mentors PhD students Maï Ly Leclair and Saidong Ma in the Helmchen Lab. Her group develops custom multi-area two-photon microscopes and applies machine learning for neural data analysis, bridging experimental neuroscience with computational approaches to decode cortical information processing.

Christopher Hojnyمشاهده پروفایل

استادیار

• Mixed-Integer Programming
• Symmetry Handling in Optimization
• Combinatorial Optimization
• +۳ مورد دیگر

Christopher Hojny is an Assistant Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology , specializing in combinatorial optimization. He contributes to the EAISI Foundational group and co-develops the academic solver SCIP . His research focuses on symmetry handling in mixed-integer programming , theoretical properties of integer programs, and algorithm development for combinatorial optimization. Recent work explores applications in graph neural network verification , clustering problems, and network coding through mixed-integer programming frameworks. Key publication trends show expertise in Symmetry detection and mitigation techniques Relaxation complexity theory Applications to machine learning robustness Decision diagram-based scheduling Scientific contributions include Proof systems for symmetry certification Topological bounds tightening in GNNs Stable set problem symmetry handling SCIP solver extensions He supervises PhD students Cédric Roy (NWO project on Local Symmetries) and Sten Wessel (co-supervised with Frits Spieksma), while Jasper van Doornmalen (2019-2023) investigated symmetry propagation algorithms.

• Biophysics
• Gene Regulatory Networks
• Immune System Dynamics
• +۶ مورد دیگر

Aleksandra Walczak is a Professor of Biophysics at the École normale supérieure , leading research at the Laboratoire de Physique Théorique (LPENS). Her work bridges statistical physics and biology, focusing on understanding complex living systems through gene regulatory networks, immune system dynamics, and population genetics. Research Interests : Non-equilibrium biological systems, T-cell/B-cell receptor repertoires, stochastic molecular systems, and evolutionary dynamics. Funding & Collaborations : Supported by the Fondation Bettencourt Schueller, she co-organizes interdisciplinary workshops like the Paris Workshop on Immunology and contributes to GDRI Evolution, Regulation and Signaling network. Recent Publications explore gene regulatory principles, immune repertoire modeling, viral-immune coevolution, and collective behavior. She has secured competitive grants and mentors students in theoretical biophysics. Awards : Habilitation à diriger des recherches (2012), Princeton Center for Theoretical Physics Postdoctoral Fellowship (2007-2010). Students & Positions : Actively supervises PhD/Master projects; offers internships in biophysics and quantum engineering.

Amy J. Koمشاهده پروفایل

استاد

• Computing Education
• Human-Computer Interaction (HCI)
• Equity & Justice in Computing
• +۶ مورد دیگر

Amy J. Ko is a Professor and Associate Dean for Academics at the Paul G. Allen School of Computer Science & Engineering and The Information School at the University of Washington, Seattle . She serves as Editor-in-Chief of ACM TOCE , co-directs Reciprocal Reviews and CS for All Washington , and leads the CENSOR Research Lab . Her work prioritizes equitable, liberatory computing education , bridging human-computer interaction and social justice . Research Focus: Equity in CS education, AI literacy, inclusive design, developer productivity, and ethical technology use Awards: Best Paper Award (2024, 2022) Diversity + Inclusion Award (2024) Most Influential Paper Honorable Mention (2014) Honorable Mention (2024, 2023) Publications: Over 184 works spanning program comprehension, assessment design, ethical pedagogy, and democratizing computing education Books: Foundations of Information , Critically Conscious Computing , and Teaching Accessible Computing Her Google Scholar profile reflects citations under her deadname, which she advocates to correct. She emphasizes student agency and community collaboration , particularly with marginalized groups in computing spaces.

Stefan Rassمشاهده پروفایل

استاد

• Security and Risk Management
• Decision and Game Theory for Security
• Security Infrastructures
• +۹ مورد دیگر

Stefan Rass is a full Professor at Alpen-Adria-Universität Klagenfurt (AAU), with additional affiliation at Johannes Kepler University Linz (JKU). He holds the academic title Univ.-Prof. (Universitätsprofessor) and possesses advanced degrees including PD (Privatdozent), Dipl.-Ing. (Diplom-Ingenieur), and Dr. (Doctor). His research spans multiple institutions and projects, with a focus on security and risk management through game theory applications. Professor Rass's research interests center around Security and Risk Management, Decision and Game Theory for Security, Security Infrastructures (including Key Distribution and Management, PKI, and Authentication), Unconditional and network security, Applied Quantum Cryptography, and Complexity Theory and Statistics in Security. His work bridges theoretical computer science with practical security applications, particularly in quantum networks and critical infrastructure protection. His recent publications demonstrate a strong trend toward interdisciplinary security research, combining game theory with quantum cryptography, robotics security, and AI-powered penetration testing. The articles reveal increasing focus on practical applications of theoretical security concepts, with notable work in quantum networks, deniable encryption techniques, robotics security benchmarking, and AI-assisted security testing. His research shows consistent evolution from theoretical foundations toward real-world implementation challenges. Professor Rass leads multiple ongoing research projects including Machine Learning for Risk Management, Safe and Secure Robotic Systems Engineering (SEEROSE), Simulation and analysis of critical network infrastructures in cities (ODYSSEUS), and security for cyber-physical value networks Exploiting smaRt Grid systems (synERGY). These projects, primarily funded by FFG (Austrian Research Promotion Agency), demonstrate his leadership in securing critical infrastructure and developing next-generation security frameworks.

Camille Casteraمشاهده پروفایل

دانشیار

• Optimization Algorithms
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

Camille Castera is an Associate Professor in optimization and machine learning at the Mathematical Institute of the University of Bordeaux (IMB). Her academic journey includes a postdoctoral position at the University of Tübingen (2022-2024) working on the TRINOM-DS project under Peter Ochs and Jalal Fadili, and a PhD at the IRIT laboratory in Toulouse supervised by Cédric Févotte, Edouard Pauwels, and Jérôme Bolte as part of the FACTORY project. Her research focuses on optimization algorithms for machine learning, particularly second-order methods with applications to deep learning, and the emerging field of learning optimization algorithms. Her work spans continuous optimization (convex & non-convex, non-smooth, stochastic), learning to optimize, and optimization for deep learning with automatic hyper-parameter tuning. Her recent publications demonstrate significant contributions to the field of optimization algorithms, particularly in developing novel approaches that bridge classical optimization techniques with learning-based methods. Her research shows a clear trajectory toward creating more adaptive and versatile optimization algorithms that can generalize beyond their training settings. At the University of Bordeaux, she teaches advanced courses including Large-scale optimization (Master 2), Introduction to deep learning (Master 2), Convex optimization (Master 1), and Image processing (Licence 3), while also supervising student projects on image processing and machine learning. Her technical work is supported by substantial implementation efforts, with publicly available code repositories demonstrating practical applications of her theoretical contributions to optimization algorithms.

Xin Fangمشاهده پروفایل

استادیار

• Power System Optimization
• Cyber-Physical Systems
• Renewable Energy Integration
• +۳ مورد دیگر

Xin Fang is an Assistant Professor in the Electrical & Computer Engineering Department at Mississippi State University, affiliated with the Bagley College of Engineering. His research focuses on power system optimization with uncertainty, cyber-physical dynamic modeling, and renewable energy integration. He holds a Ph.D. from the University of Tennessee, Knoxville, an M.S. from China Electric Power Research Institute, and a B.S. from Huazhong University of Science and Technology. Education: Ph.D., Electrical and Computer Engineering, University of Tennessee, Knoxville, 2016 M.S., Power System Automation, China Electric Power Research Institute, 2012 B.S., Power System Automation, Huazhong University of Science and Technology, 2009 His research interests emphasize cyber-physical systems integration, grid resiliency through multi-timescale modeling, and equitable energy distribution. Recent work addresses challenges in high-renewable grid operations, including dynamic co-simulation frameworks for electricity-transportation networks. He explores innovative solutions for frequency stability, voltage control, and market mechanisms for emerging energy systems. Publications highlight advancements in hybrid power plant optimization, distributed energy resource coordination, and equitable load-shedding methodologies. His work bridges theoretical modeling with practical applications in grid security and renewable integration.

Vera Miloslavskayaمشاهده پروفایل

مدرس

• Error-Correction Coding
• Information Theory
• Artificial Intelligence
• +۲ مورد دیگر

Dr. Vera Miloslavskaya is a Lecturer in Information Technology at the University of New England's School of Science and Technology. She holds a PhD from Peter the Great St. Petersburg Polytechnic University and specializes in error-correction coding and AI applications for telecommunications. Research Focus: Miloslavskaya develops machine learning-enhanced coding schemes for next-generation wireless systems. Her work on neural network-based polar coding and graph neural network applications in MIMO detection aims to improve reliability and efficiency in 6G networks. She maintains collaborations with the University of Sydney where she previously worked as a Postdoctoral Research Associate. Recognition: Awarded the IEEE Transactions on Communications Exemplary Reviewer (2022) and multiple Russian academic awards including the Gold Medal of the Russian Academy of Science (2012).

Murat Kantarciogluمشاهده پروفایل

استاد

• Data Privacy
• Computer Security
• Machine Learning
• +۵ مورد دیگر

Murat Kantarcioglu is an Ashbel Smith Professor of Computer Science at the University of Texas at Dallas within the Erik Jonsson School of Engineering and Computer Science. He holds visiting appointments at UC Berkeley and Harvard University, focusing on data privacy and security. With a Ph.D. in Computer Science from Purdue University (2005), he has made significant contributions to privacy-preserving data mining, blockchain analytics, and secure machine learning. Education: Ph.D. in Computer Science (Purdue, 2005) Current Roles: Ashbel Smith Professor (2021-present), Visiting Scholar at UC Berkeley (2020-present), Affiliate at Harvard (2013-present) Past Roles: Assistant (2005-2011), Associate (2011-2015), and Full Professor (2015-2021) at UTD His research focuses on data privacy , computer security , and machine learning , particularly addressing challenges in privacy-preserving distributed data mining , blockchain analytics , and adversarial machine learning . He has pioneered techniques for secure federated learning , topological analysis of blockchain networks , and privacy-utility tradeoffs in health data systems. Recent publications reveal a strong emphasis on IoT security , graph neural network vulnerabilities , and blockchain data structures . His work combines theoretical rigor with practical implementations using technologies like Intel SGX and homomorphic encryption. Notable Awards NSF CAREER Award (2009) IEEE Technical Achievement Award (2017) AMIA Homer Warner Best Paper Award (2014) Fellow of IEEE (2022), AAAS (2020), and ACM (2016) Key Projects Privacy-Preserving Genomics Data Sharing Adversarial Learning Frameworks Smart Contract Security Medical Data Protection Systems Labs Director of Data Security and Privacy Lab Collaborations with Vanderbilt, UC Berkeley (RISE Lab), and Harvard (Data Privacy Lab)

Marco Grangettoمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Artificial Intelligence
• +۵ مورد دیگر

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

Simone Brugiapagliaمشاهده پروفایل

دانشیار

• Mathematics of Data Science
• Computational Mathematics
• Numerical Analysis
• +۵ مورد دیگر

Simone Brugiapaglia is an Associate Professor in the Department of Mathematics and Statistics at Concordia University in Montréal, Canada. His academic journey includes a PhD in Mathematical Models and Methods from Polytechnic University of Milan (2016), an MSc in Mathematics from University of Pisa (2012), and a BSc in Mathematics from University of Pisa (2010), all earned cum laude . Prior to his current role, he held postdoctoral positions at École polytechnique fédérale de Lausanne (2016) and Simon Fraser University (2016-2019). Dr. Brugiapaglia's research bridges mathematics, data science, and computational methods. Key interests include: Foundations of deep learning and neural networks Compressed sensing and sparse recovery algorithms High-dimensional approximation theory Numerical methods for PDEs and diffusion equations Physics-informed machine learning Optimization techniques for large-scale problems His work develops rigorous mathematical frameworks for data-driven algorithms. His publications (30+ including two books) consistently focus on high-dimensional computation , featuring recent advances in neural network theory (e.g., generalization bounds, rank collapse), compressed sensing techniques (e.g., greedy algorithms, unrolled networks), and physics-informed learning. A strong trend involves combining traditional numerical methods with deep learning for PDE solutions. Awards & Fellowships: Concordia Research Fellow (2023) Leslie Fox Prize for Numerical Analysis (2nd place, 2019) PIMS Postdoctoral Fellowship (2016-2018) Multiple INdAM scholarships during graduate/undergraduate studies He has supervised over 20 trainees across postdoctoral, graduate, and undergraduate levels. While specific grants aren't detailed, his fellowship history indicates sustained research funding. No explicit research labs or teams are mentioned, but his supervision record and collaborative publications suggest active leadership in research groups focused on computational mathematics.

Yuan Hongمشاهده پروفایل

دانشیار

• Security
• Privacy
• Trustworthy Machine Learning
• +۸ مورد دیگر

Dr. Yuan Hong is an Associate Professor and Collins Aerospace Professor at the University of Connecticut's School of Computing, affiliated with the Connecticut Advanced Computing Center (CACC). He previously held an Assistant Professor position at Illinois Institute of Technology. His expertise spans Security, Privacy, and Trustworthy Machine Learning, with a focus on differential privacy, secure computation, adversarial attacks, and robustness in AI systems. He has received notable awards including the NSF CAREER Award and Cisco Research Awards, and his work has been published in top-tier venues like S&P, CCS, NeurIPS, and CVPR. Education: Ph.D. (Rutgers University), M.Sc. (Concordia University), B.Sc. (Beijing Institute of Technology). Research interests include adversarial machine learning, federated learning security, and privacy-preserving techniques. His recent work addresses challenges in machine unlearning, certified defenses, and robust watermarking for graph learning systems. He actively advises students (Ph.D., M.Sc., undergraduate) and leads the DataSec Lab. Current teaching includes Cybersecurity Lab, Computer Security, and Database Organization. Awards include the CCS Distinguished Paper Award (2024) and the Taylor L. Booth Graduate Fellowship for a student. He organizes conferences such as WPES'22 and DBSEC'22, and serves on editorial boards for IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Information Forensics and Security, and others. Key contributions include frameworks for certified adversarial robustness (e.g., UniCR), differential privacy mechanisms for federated learning, and defenses against poisoning attacks in video and code generation models.

• Software System Quality
• Software Architecture Design and Evaluation
• Software Visualization and Reverse Engineering
• +۸ مورد دیگر

Wilhelm Hasselbring is Professor of Software Engineering at the School of Electronics and Computer Science, University of Southampton. His research focuses on software system quality, architecture design, and distributed systems with emphasis on fault-tolerance and monitoring. Software System Quality Architecture Design and Evaluation Microservices and DevOps Digital Twins and Prototyping Open Science Practices Current research explores digital twin prototypes for smart farming applications, metamorphic testing methodologies, and scalable microservice architectures. His work bridges theoretical frameworks with industrial applications in middleware and cloud systems. Recent awards include the Ernst Denert Award for Software Engineering (2019-2020). Publications span topics from JavaBERT language models to MQTT bridge evaluations, emphasizing software visualization and reverse engineering techniques. Contact: W.Hasselbring@soton.ac.uk

Adel Abusittaمشاهده پروفایل

استادیار

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Dr. Adel Abusitta is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he conducts research at the intersection of artificial intelligence and cybersecurity. With expertise in secure and resilient AI systems, IoT security, and malware analysis, Dr. Abusitta has established himself as a significant contributor to the field of AI-powered cybersecurity solutions. Education: PhD in Computer Engineering from Polytechnique Montréal Postdoctoral Fellow at University of Montréal Postdoctoral Fellow at McGill University Dr. Abusitta's research focuses on developing secure and trustworthy artificial intelligence systems with applications in cybersecurity. His work spans several critical areas including explainable AI for security applications, AI-powered malware analysis, intrusion detection systems, and IoT security. He has made significant contributions to the understanding of how AI can be both secured against attacks and used to enhance security systems. His research addresses the dual challenge of making AI systems resilient to adversarial manipulation while leveraging AI's capabilities to detect and prevent cyber threats in complex environments like cloud computing and IoT networks. Analysis of Dr. Abusitta's recent publications reveals a strong focus on the intersection of AI and cybersecurity, particularly in developing robust anomaly detection systems, explainable security solutions, and resilient architectures for IoT environments. His work demonstrates a consistent trajectory toward making AI systems both more secure and more useful for security applications, with increasing emphasis on practical implementations that can withstand real-world challenges. Dr. Abusitta has collaborated extensively with Defence Research and Development Canada (DRDC) on projects related to AI-powered data analytics for discerning malware intent. He has also worked with industrial partners through the Institute for Data Valorization (IVADO) to develop privacy-preserving machine learning techniques that maintain accuracy while protecting sensitive information. His research has practical applications in critical infrastructure protection and secure AI deployment.