جست‌وجوی استاد و پژوهشگر

Joe Geunesمشاهده پروفایل

استاد

• Production Planning
• Supply Chain Management
• Logistics
• +۳ مورد دیگر

Joe Geunes is a Professor and Associate Department Head for Graduate Affairs in the Department of Industrial & Systems Engineering at Texas A&M University, holding the Mike and Sugar Barnes Professorship. His research focuses on production planning, supply chain management, logistics, and operations optimization. He earned his Ph.D. in Business Administration (Management Science & Operations Research) and M.B.A. from The Pennsylvania State University in 1999 and 1993, respectively. Dr. Geunes has received notable accolades including Fellow of the Institute of Industrial Engineers (2015), Marilyn and L. David Black Faculty Fellow (2022), and Best Reviewer Award from Omega (2022). His work spans infrastructure network restoration, supply chain resilience, and optimization algorithms for logistics systems. Recent projects address railcar operations, distribution network fortification, and disaster response strategies. Education: Ph.D., Business Administration (Management Science & Operations Research), The Pennsylvania State University – 1999 M.B.A., The Pennsylvania State University – 1993 Awards: Fellow, Institute of Industrial Engineers – 2015 Marilyn and L. David Black Faculty Fellow – 2022 Best Reviewer Award, Omega – 2022 Best Application Paper, IISE – 2018 His research integrates mathematical modeling and computational methods to address real-world challenges in supply chain design, inventory management, and infrastructure resilience. Recent publications emphasize multi-modal logistics, robust optimization under uncertainty, and post-disaster network recovery strategies.

Monticelli Silviaمشاهده پروفایل

استاد پژوهشی

• Molecular Immunology
• T lymphocyte biology
• Mast cell biology
• +۴ مورد دیگر

Silvia Monticelli is a Group Leader in Molecular Immunology at the Institute for Research in Biomedicine (IRB) in Bellinzona, Switzerland. She earned her Ph.D. from the University of Milan and has held research positions at San Raffaele Scientific Institute, King’s College London, and Harvard Medical School. Her research focuses on transcriptional and post-transcriptional regulation of immune responses, particularly in T lymphocytes and mast cells. Education: Ph.D. – University of Milan, Italy Postdoctoral training – San Raffaele Scientific Institute, Milan Research training – Randall Institute, King’s College London Research training – Center for Blood Research, Harvard Medical School Research Interests: Silvia Monticelli’s lab investigates the molecular mechanisms that control gene expression in immune cells. Her work spans epigenetic regulation , microRNA function , transcription factor networks , and RNA methylation in T cells and mast cells. A central theme is understanding how these mechanisms shape immune cell identity and function in health and disease. Publication Trends: Her recent publications (2020–2024) reflect a strong emphasis on RNA biology , epigenetic control , and immune regulation . She has contributed to high-impact journals such as Nature , Nature Immunology , and Science Advances , with work ranging from microRNA-mediated control of T cell activation to epigenetic regulation in mast cells and cancer immunity. Scientific Recognition: While no formal awards are listed, her work has been highlighted by editorial commentary and co-corresponding authorships in high-impact journals, indicating recognition within the immunology community. Labs & Affiliations: She leads a research group at the IRB in Bellinzona, focusing on immune cell regulation at the molecular level. Her lab is part of a broader European research network and collaborates with institutions like Harvard and the University of Milan.

Ida Scheelمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Data Science
• Machine Learning
• +۷ مورد دیگر

Ida Scheel is an Associate Professor in Statistics and Data Science at the University of Oslo , Department of Mathematics. She specializes in Bayesian hierarchical modeling, recommendation systems, and stochastic processes on networks. Her research interests include: Bayesian statistics and model diagnostics Data science applications in environmental and health domains Network-based machine learning Uncertainty quantification in predictive modeling Recent publication trends show a focus on Bayesian model validation, machine learning for product adoption prediction, and real-estate analytics. She contributes to interdisciplinary projects like BigInsight and CELS . Scientific awards : Sverdrup Prize for Young Researchers (2011) Advising : Supervised 8 PhD students (main/co-supervisor) in areas spanning Bayesian causal effects, neural network survival analysis, and model conflict detection. Key grants include participation in the Data Science@UiO and Integreat projects. Labs/teams : Active member of the Center for Computational Inference in Evolutionary Life Science (CELS) and the BigInsight center.

Chew Lock Yueمشاهده پروفایل

استاد

• Complex Systems
• Nonlinear Dynamics
• Quantum Physics
• +۵ مورد دیگر

Professor Chew Lock Yue is an Associate Dean (Students) in the College of Science and a Full Professor in the School of Physical & Mathematical Sciences at Nanyang Technological University (NTU). He holds a B.Eng (Hons) in Electrical Engineering from the National University of Singapore (1991), an M.Sc in Electrical Engineering from the University of Southern California (1997), and a Ph.D. in Theoretical Physics from NUS (2004). His research focuses on complex systems, nonlinear dynamics, quantum thermodynamics, and urban systems modeling. Current projects include thermodynamics of information processing, machine learning integration with complex systems, and statistical physics of sea-level rise. Professional roles span technical leadership at DSO National Laboratories (1992-2005), academic appointments since 2005 (Assistant Professor to Full Professor), and administrative roles including Cluster Deputy Director at NTU’s Data Science & Artificial Intelligence Research Centre (2018-2021). He has received multiple teaching awards, including the Nanyang Award for Excellence in Teaching (2007) and the Best Faculty Mentor Award (2013). Research interests also encompass social-ecological systems, quantum heat engines, and spatial agglomeration patterns in urban contexts. His work bridges physics with interdisciplinary challenges like climate modeling and machine learning, with over 150 publications in peer-reviewed journals. Active in education, he teaches courses on quantum mechanics, nonlinear dynamics, and statistical physics.

Nicola Calabrettaمشاهده پروفایل

استاد

• Optical Communication Systems
• Photonics
• Telecommunications Engineering
• +۶ مورد دیگر

Nicola Calabretta is a Full Professor in Electro-Optical Communication Systems and Senior Research Fellow at Eindhoven University of Technology (TU/e). His work focuses on smart optical networks, high-speed electronics, FPGA implementations for scheduling algorithms, and photonic integrated circuits. He holds a PhD from TU/e (2004) and previously conducted research at DTU Fotonik and the Sant'Anna School of Advanced Studies. His expertise spans optical signal processing, multi-level modulation formats, and applications in data center and metro networks. Key research areas include optical switching architectures (e.g., SOA-based switches), WDM systems, and low-latency interconnect networks. He has led projects like ADAPTOR (resource optimization), SmartTWO (future telecom technologies), and 5G-MOBIX (cross-border mobility). His courses include 'Optical Fibre Communication Technology' and 'Optical Interconnection Networks.' Collaborations involve institutions globally, with recent work emphasizing photonic integration for neural networks, ultra-fast switching, and edge computing. His contributions align with UN SDGs through sustainable telecom infrastructure advancements.

Oscar Mendez Maldonadoمشاهده پروفایل

مدرس

• Robotics
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Oscar Mendez Maldonado is a Lecturer in Robotics and Artificial Intelligence at the University of Surrey's School of Computer Science and Electronic Engineering, affiliated with the Robotics Department and CVSSP Centre. He holds a PhD (2018) and BEng (2013) from the University of Surrey. His research focuses on Machine Learning, Computer Vision, and Robotics, with emphasis on autonomous systems, localisation, and SLAM applications. Key projects include the Autonomous Valet Parking (AVP) system for indoor navigation and the SMILE project for sign language assessment using AI. He has supervised students like James Ross (Autonomous Vehicles), Xihan Bian (Reinforcement Learning), and Nimet Kaygusuz (Visual Odometry). Notable achievements include the Sullivan Thesis Prize (2018) and impactful publications in IEEE conferences (e.g., ICRA, CVPR, IROS). Research spans topics like 3D hand pose estimation via diffusion models, graph-based visual odometry fusion, and Raman spectroscopy for localisation. He contributes to open-source tools (e.g., RaSpectLoc GitHub) and collaborates with industry partners like Parkopedia. His work bridges theoretical advances with real-world applications in autonomous systems and healthcare.

Dong Liمشاهده پروفایل

دانشیار

• High performance computing
• Non-volatile memory
• Memory heterogeneity
• +۲ مورد دیگر

Dong Li is an Associate Professor at the University of California, Merced , where he directs the Parallel Architecture, System, and Algorithm Lab (PASA) and co-directs the High Performance Computing Systems and Architecture Group . He co-founded Yotta Labs Inc. and previously held research roles at Oak Ridge National Laboratory (2011-2014) and a PhD from Virginia Tech. Research Interests: Dong's work focuses on High performance computing (HPC) Memory heterogeneity and non-volatile memory Systems for machine learning and AI Fault tolerance in large-scale systems His innovations include heterogeneous memory optimization for recommendation models and GNNs, CXL memory integration, and persistent memory debugging tools. Recent Publications highlight advancements in CXL-based inter-node communication Memory tiering for laminography reconstruction ML-guided memory optimization for DLRM and GNN Fault tolerance benchmarks and error analysis Awards & Recognition: NSF CAREER Award (2016) Oracle Research Award (2022) ASPLOS Distinguished Artifact Award (2021) Virginia Tech Early Career Alumni Award (2023) Advising & Funding: Dong has mentored 22 students (8 PhD, 6 Master’s, 8 undergraduates) and secured grants from NSF, NVIDIA, Meta, and national labs (Argonne, Lawrence Berkeley, Lawrence Livermore). Collaborations include Microsoft (DeepSpeed, Intel PMDK), AMD, SK Hynix, and Intel/MICRON hardware donations.

• Dependable and Real-Time Systems
• Fault-Tolerant Distributed Systems
• Adaptive Systems
• +۲ مورد دیگر

Dr. Julián Proenza Arenas is an Associate Professor at the University of the Balearic Islands (UIB) within the Mathematics and Informatics Department of the Higher Polytechnic School. As Secretary of the Politechnical School, he contributes to the management of all engineering bachelor degrees and serves as Academic Coordinator for the EU EMJMD Programme on Engineering of Data-Intensive Intelligent Software Systems (EDISS) since 2021. Ph.D. in Informatics (2007), University of the Balearic Islands Licenciatura in Physics (1989), University of the Balearic Islands His research focuses on Dependable and Real-Time Systems , particularly fault tolerance in distributed environments, Clock Synchronization , and Fieldbus Networks . Recent work emphasizes Time-Sensitive Networking (TSN) and adaptive mechanisms for industrial automation. He has co-authored over 100 publications and three patents, with multiple best paper awards at IEEE conferences (2004–2023). IEEE Workshop on Factory Communication Systems Best Paper (2004) IEEE ETFA Best Work-in-Progress Paper (2012, 2014, 2016) IEEE ETFA Factory Automation Best Paper (2023) Dr. Proenza supervises 6 completed and 2 ongoing Ph.D. theses and leads national research projects like FT4TSNgrid (2022–2025) and DFT4FTT (2016–2019). He is Principal Investigator of the Sistemas Empotrados Inteligentes y Robótica (SRV-INTER) R&D group and actively collaborates with international institutions including Mälardalen University and Universidade do Porto.

• Data Science and Data Analytics
• Machine Learning
• Recommender Systems
• +۷ مورد دیگر

Guandong Xu is a Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he has been employed since 2012. He also serves as the Director of the UTS-Providence Smart Future Research Centre, which focuses on disruptive technology for sustainability, and leads the Data Science and Machine Intelligence Lab dedicated to research excellence and industry innovation in data science and artificial intelligence. Dr. Xu holds a PhD in Computer Science from Victoria University, Australia, along with MSc and BSc degrees in Computer Science and Engineering. After holding various research positions at European and Australian universities, he joined UTS in 2012 and was promoted to Associate Professor in January 2017, then to Professor in January 2019. His research spans data mining, machine learning, social computing, recommender systems, text mining, predictive analytics, and user behavior modeling. He has published over 240 papers in these areas with increasing citations from academia. His recent work demonstrates a strong focus on integrating large language models with recommendation systems, causal inference in recommendation, multimodal learning, and fairness in AI systems. His publications reveal sophisticated graph-based approaches and addressing challenges in dynamic recommendation scenarios, particularly through temporal modeling and hypergraph structures. Dr. Xu has received numerous prestigious awards including the Digital Disruptors Winner for ICT Research Project of the Year (2021), eBay's Leaders' Choice Award (2021), and was elected Fellow of Institution of Engineering and Technology (IET), UK (2021) and Fellow of Australian Computer Society (ACS) (2022). He has shown strong academic leadership as founding Editor-in-Chief of Human-centric Intelligent Systems Journal, Assistant Editor-in-Chief of World Wide Web Journal, and founding Steering Committee Chair of the International Conference of Behavioural and Social Computing Conference. He has supervised over 25 high degree research students and secured over $8 million in research funding from ARC, government, and industry sources, including projects like 'Smart Personalized Privacy Preserved Information Sharing in Social Networks' and 'A Secured Smart Sensing and Industry Analytics Facility for Industry 4.0.' Dr. Xu directs the Data Science and Machine Intelligence Lab at UTS, which aligns with UTS research priority areas in data science and artificial intelligence. The lab focuses on research excellence and industry innovation across academia and industry, with particular emphasis on developing advanced techniques for recommendation systems, knowledge graphs, and multimodal learning applications.

Beyza EKENمشاهده پروفایل

استادیار

• Software Defect Prediction
• Natural Language Processing
• Machine Learning in Software Engineering
• +۲ مورد دیگر

Beyza Eken serves as Assistant Professor in the Department of Software Engineering at Sakarya University's Faculty of Computer and Information Sciences, teaching core courses including Software Project Management, Natural Language Processing, and Graduation Projects while maintaining active research in software engineering and AI applications. Her academic credentials include: Doctorate in Computer Engineering from Istanbul Technical University (2015), thesis: "Software Defect Prediction" Master's in Computer Engineering from Istanbul Technical University (2011-2015), thesis: "Entity Name Recognition in Short Texts" Bachelor's in Computer Engineering from Sakarya University (2007-2011) Dr. Eken's research integrates machine learning with software engineering, specializing in defect prediction models that incorporate personalized developer factors and industrial deployment challenges. Her work bridges natural language processing for Turkish social media analysis with software quality assurance, demonstrating expertise in both theoretical modeling and practical implementation in industrial settings. Recent expansions include neuro-symbolic AI for test oracle generation and MLOps frameworks. Publication trends reveal consistent focus on empirical software engineering from 2018-2021 (defect prediction, community smells, industrial deployment), evolving into cutting-edge domains by 2023-2025 (neuro-symbolic testing, employee feedback analysis, MLOps). Her work shows strong industry-academia collaboration patterns with increasing methodological sophistication. Dr. Eken actively contributes to academic service as reviewer for ACM Transactions on Software Engineering and Methodology (2024) and IEEE Transactions on Software Engineering (2023). She leads research projects including "Developer-specific error prediction modeling" (2020) and the Mevlana exchange project with Ryerson University on data mining for defect prediction (2018), while supervising graduation projects and research area courses that develop student expertise in software engineering practices. Her international research engagement includes participation in the ASTERIx project at Università della Svizzera Italiana's Software Testing and Analysis Research Group (2023), demonstrating ongoing commitment to global collaboration in software engineering advancements.

Renaud Pacaletمشاهده پروفایل

پژوهشگر

• Hardware Security
• Side-Channel Attacks
• Embedded Systems
• +۷ مورد دیگر

Renaud Pacalet is a Researcher at Institut Mines-Télécom – Télécom Paris , affiliated with the Communications and Electronics (Comelec) Department and the System on Chip (LabSoc) research team under the Information Processing and Communication Laboratory (LTCI). His work spans hardware security, embedded systems, and software-defined radio (SDR) architectures. Current Research: Hardware security, side-channel attacks (power, timing, fault injection), RISC-V security analysis using gem5, FPGA scheduling for cloud data centers, and model-driven design methodologies. Past Research: Hardware acceleration for ray tracing, SDR front-end processing, SoC security, and memory bus protection (SecBus project). Teaching: Courses on Digital Systems, Computer Architecture, and Hardware Security at EURECOM, including lab sessions on side-channel attacks and fault analysis. Email: renaud.pacalet@telecom-paris.fr Contact: Télécom ParisTech, Campus SophiaTech, 450 route des Chappes 06410 Biot, France

Indranil Guptaمشاهده پروفایل

استاد

• Distributed Systems
• Cloud Computing
• IoT/Edge Systems
• +۴ مورد دیگر

Prof. Indranil Gupta (Indy) is a Professor of Computer Science at the University of Illinois at Urbana-Champaign, affiliated with the Beckman Institute and ECE department. His research focuses on distributed systems, including cloud computing, IoT, and machine learning systems. He leads the Distributed Protocols Research Group (DPRG) and collaborates with industry to improve production systems. Indy is an IEEE Fellow, ACM Distinguished Scientist, and recipient of the NSF CAREER Award and multiple Best Paper Awards. Education: PhD in Computer Science from Cornell University (2004), B.Tech from IIT Madras (1998). Industry experience includes roles at Google, Microsoft Research, and IBM Research. Teaching: Teaches CS 425 (Distributed Systems), CS 525 (Advanced Distributed Systems), and a Coursera MOOC with 250K+ enrollments. Known for innovative teaching methods, including music-based CS education. Awards: Over a dozen awards including Best Paper recognitions at IC2E, CCGrid, and ICAC. His students have won NSF Fellowships, Rising Stars in EECS, and Microsoft Dissertation Grants. Service: Served as General Chair of ACM PODC 2007, PC co-chair for multiple conferences, and editorial board member for IEEE TCC and ACM TAAS. Hosts the podcast 'Immigrant Computer Scientists.' Research Impact: Contributions include fault-tolerant protocols (e.g., Zeno, SWIM), distributed ML systems, and cloud resource management techniques used by major tech companies.

Wanja Hoferمشاهده پروفایل

پژوهشگر

• Embedded Operating Systems
• Real-Time Systems
• Aspect-Oriented Programming
• +۳ مورد دیگر

Dr. Wanja Hofer is a former research staff member at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). She specialized in embedded systems, real-time operating systems, and aspect-oriented programming. Her research focused on optimizing hardware-centric systems like Sloth and CiAO, addressing challenges in interrupt handling, scheduling, and software product line variability. Her academic journey includes a PhD in 2014 titled Sloth: The Virtue and Vice of Latency Hiding in Hardware-Centric Operating Systems . She contributed to key projects such as Sloth (time-triggered RTOS) and CiAO (aspect-oriented OS family), emphasizing scalability and configurability for automotive and embedded domains. Hofer also held roles like Web chair for EuroSys 2009 and co-maintained the EuroSys Research Directory. Her teaching involved Basics of Systems Programming in C and OS-related seminars. She advised over 15 graduate students on topics ranging from MPU-based task isolation to filesystem-level variability management. Notable contributions include hardware-accelerated interrupt handling, aspect-oriented OS design, and embedded system energy optimization. Hofer currently works at Brose Fahrzeugteile, applying her expertise in embedded systems and real-time computing to automotive technologies. Her work bridges academic innovation with industrial applications, particularly in safety-critical and resource-constrained environments.

• Edge Computing
• Fog Computing
• Cloud Computing
• +۷ مورد دیگر

Riccardo Lancellotti is an Associate Professor at the Department of Engineering 'Enzo Ferrari' of the University of Modena and Reggio Emilia. His research focuses on Edge/Fog/Cloud Computing, Cyber Security, and Resource Management in distributed systems. He has extensive contributions in optimizing infrastructure performance, load balancing, and energy efficiency in cloud and fog environments. His work often combines theoretical models with practical simulations, addressing challenges like stale information in edge systems and heterogeneous resource allocation in smart cities. Key research areas include: Fog/Edge computing infrastructure design and optimization Cloud resource provisioning and SLA compliance Security for Industry 4.0 and automotive systems Genetic algorithms for service placement Scalable VM clustering and resource allocation Publications highlight trends in cloud/fog integration, robust game theory for microservices, and distributed load balancing under dynamic conditions. His work emphasizes practical applications, such as pharmaceutical distribution routing and smart city sensor management. No awards are explicitly listed, but his extensive publication record reflects recognition in the field.

Debabrota Basuمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +۶ مورد دیگر

Debabrota Basu is a tenured faculty member (Inria Starting Faculty Position - ISFP) at the Scool team (previously called SequeL) of Inria Centre at University of Lille in France. He teaches postgraduate-level courses on privacy, responsible machine learning, and research methods in AI at École normale supérieure-PSL University, Université de Lille, and Centrale Lille. He is also a member of the ELLIS Society (European Laboratory for Learning and Intelligent Systems) and the Paris unit of ELLIS. Dr. Basu earned his PhD in Computer Science from the Department of Computer Science, School of Computing, National University of Singapore, advised by Stéphane Bressan and Pierre Senellart. Prior to that, he obtained a B.E. degree with Honours in Electronics and Telecommunication Engineering from Jadavpur University. Before joining Inria, he was a postdoctoral researcher at Chalmers University of Technology's Data Science and AI Division. Dr. Basu's research focuses on constructing algorithms for developing efficient, robust, private, and ethical learning machines that solve real-world problems. His methodological approach blends statistics, machine learning, and optimization. His application interests span sustainable agro-ecology, medical and pharmaceutical applications, energy-efficient autonomous systems, and algorithmic audits. Recent collaborations include the Inria-Indian Statistical Institute associate team SeRAI for developing Sequential Testing and Learning Algorithms for Verifiably Robust and Responsible AI, and the Inria-INRAE collaboration on Resilient Agricultural Decision Making under Environmental Risks. His publication record demonstrates expertise in bandit algorithms, reinforcement learning, and privacy-preserving machine learning. Recent work shows a strong theoretical foundation with practical applications, particularly in pure exploration bandits, differential privacy mechanisms, constrained optimization, and fairness verification. His research bridges the gap between theoretical guarantees and real-world deployment challenges across multiple domains. Dr. Basu has received notable recognition for his contributions: Best Student Paper Award at ACM EAAMO 2022 for 'On Meritocracy in Optimal Set Selection' Young researcher (JCJC) grant from the French National Research Agency (ANR) in 2022 in 'Artificial Intelligence and Data Science' He leads the project 'RL under Real-life Constraints: Regrets and Algorithms' and supervises PhD students and postdoctoral researchers. His research is supported by multiple projects including REPUBLIC ('Vers l'IA responsable avec l'apprentissage par renforcement sous contraintes') and 'Foundations of robustness and reliability in artificial intelligence.' Dr. Basu actively collaborates with institutions worldwide, including establishing the RELIANT associate team with Kyoto University for investigating structured multi-armed bandit problems.