جست‌وجوی استاد و پژوهشگر

Arpan Gujaratiمشاهده پروفایل

عضو هیئت علمی

• Real-Time Systems
• Distributed Systems
• Cyber-Physical Systems
• +۷ مورد دیگر

Arpan Gujarati is a Sessional Lecturer in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Systopia Lab. He teaches graduate and undergraduate courses such as CPSC 538G (Distributed Systems), CPSC 416 (Operating Systems), and CPEN 432 (Real-Time System Design). He holds a PhD from the Max Planck Institute for Software Systems and TU Kaiserslautern, where he was supervised by Björn B. Brandenburg. PhD: Max Planck Institute for Software Systems & TU Kaiserslautern (2020) Undergraduate: Birla Institute of Technology and Science (BITS Pilani) Postdoctoral Researcher: MPI-SWS Research Associate: UBC Software Development Engineer: Citrix R&D, India His research focuses on real-time and distributed systems, with applications in cyber-physical systems, fault tolerance, and machine learning reliability. He investigates scheduling algorithms, reliability analysis, and the integration of learning-enabled components into safety-critical systems. His work combines theoretical analysis with practical system implementations, often involving real-world testbeds and open-source tools. His recent publications span top-tier venues including RTSS, OSDI, ECRTS, DSN, and Middleware, with a strong emphasis on performance predictability, resilience of ML systems, and real-time communication. His work frequently addresses challenges in timing guarantees, fault tolerance, and system reliability in both cloud and embedded environments. SIGBED Paul Caspi Memorial Dissertation Award Best Paper Award at RTSS 2022 Distinguished Artifact Award at OSDI 2020 Best Student Paper Award at Middleware 2017 Outstanding Paper Award at RTCSA 2025 He advises several PhD students and undergraduate researchers at UBC, including Heng Zhao, Aida Aminian, Zainab Saeed Wattoo, and Philip Schowitz. He has led multiple research projects involving robotic arms, NVIDIA Holoscan, FreeRTOS, and distributed key-value stores. His lab work emphasizes reproducibility, open datasets, and practical system building. He has served on program committees for RTSS, RTAS, ECRTS, and Middleware, and contributes to journals such as Real-Time Systems and JSys.

Cristiana Bolchiniمشاهده پروفایل

استاد

• Dependability
• Fault Tolerance
• Reliability Analysis
• +۸ مورد دیگر

Cristiana Bolchini is a Professor at the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano. She holds a PhD in Automation and Computer Science Engineering (1997) and a Laurea in Electronic Engineering (1993), both from Politecnico di Milano. Her research focuses on dependable systems, fault tolerance, and embedded systems design, with recent work on ICT solutions for smart buildings and energy efficiency. She coordinates projects such as the FP7 SAVE initiative and serves on technical committees for conferences like DATE and DAC. Education: PhD in Automation & Computer Science (1997), Laurea in Electronic Engineering (1993), both from Politecnico di Milano. Research interests span dependability (fault modeling, diagnosis), heterogeneous architectures, and sustainable smart environments. She collaborates with Prof. Giuliana Iannaccone on foresight for sustainable built environments and has led EU-funded projects like the SAVE initiative. Publications include over 150 refereed papers on dependability and context-awareness. She holds editorial roles for journals such as IEEE Transactions on Computer-Aided Design and ACM Transactions on Embedded Computing Systems. Awards include IEEE Senior Member status and two Cisco University Research Program Fund gifts (2012, 2014). Academic roles include Rector’s delegate for Southeast Asia relations and leadership of Technology Foresight workgroups. She teaches courses on computer science fundamentals and dependable systems, emphasizing problem-solving and programming in Python and C.

Eunsuk Kangمشاهده پروفایل

دانشیار

• Software Engineering
• Formal Methods
• Software Design
• +۶ مورد دیگر

Eunsuk Kang is an Associate Professor in the Software and Societal Systems Department at Carnegie Mellon University's School of Computer Science. Their research focuses on the intersection of software engineering and formal methods, emphasizing rigorous modeling and analysis techniques to create safe, secure, and reliable systems. PhD in Computer Science from MIT Postdoctoral scholar at NSF ExCAPE program Former connected vehicles researcher at Toyota Their research interests span software design, requirements engineering, modeling, specification and verification, system safety, security, and cyber-physical systems (CPS). Recent projects explore robustness in evolving environments, specification engineering, automated reasoning for complex systems, and safety/resilience mechanisms in ML-based CPS. Publications highlight advancements in Signal Temporal Logic decomposition, LTL specification learning, and requirement-driven adaptation frameworks. Selected scientific contributions include: tl;dr: Chill, y’all – AI will not devour SE (Onward! Essays 2024): Critical perspective on AI integration in software engineering FairSense (ICSE 2025): Long-term fairness analysis for ML-enabled systems AlloyMax (ESEC/FSE 2021): Relational specification satisfaction techniques As an educator, Kang teaches graduate courses in software design and formal methods, including: 17-423/723: Designing Large-Scale Software Systems 17-614 & 624: Formal Methods 17-445/645: Software Engineering for AI-enabled Systems 17-651: Models of Software Systems Service activities include: Program co-chair for SEAMS 2026 Co-organizer of Dagstuhl Seminar on Specification Engineering Co-organizer of International Workshop on Designing Software Program committee member for ICSE, OOPSLA, ASE, and specialized conferences Notable research collaborations include work with: Ben-hau Chia (PhD student) Parv Kapoor (PhD student) Yiliang (Leo) Liang (PhD student) Sumon Biswas (Postdoc) Rômulo Meira-Góes (Postdoc)

• Software Engineering
• Model Driven Engineering
• Software Product Lines
• +۷ مورد دیگر

Jean-Marc Jezequel is a Professor of Software Engineering at University of Rennes , affiliated with CNRS , Inria , IRISA , and Institut Universitaire de France (IUF) . His research focuses on Model-Driven Engineering , Software Product Lines , Dynamic Adaptation , and Executable Meta-languages . Key Contributions : Pioneering work in aspect-oriented and model-driven approaches for software evolution Foundational research on model transformations (e.g., UMLAUT framework) Advances in testing and validation of distributed systems Research Trends from his recent publications include: Intelligent modeling assistance integrating machine learning Contextual variability modeling for complex systems Runtime model execution for self-adaptive systems Formal methods and constraint resolution for UML validation Collaborations include researchers from Luxembourg, Montreal, Colorado State University, and INRIA.

Qiang Zhuمشاهده پروفایل

استاد

• Data Science
• Data Management
• Machine Learning
• +۳ مورد دیگر

Qiang Zhu is a Professor in the Department of Computer and Information Science at the University of Michigan-Dearborn, holding the William E. Stirton Professorship (2017–2024). He founded the Data Science/Management Research Laboratory and is affiliated with the Michigan Institute for Data Science (MIDAS). His research spans data science, data management, and machine learning. Ph.D., University of Waterloo M.S., McMaster University M.Eng., Southeast University B.S., Southeast University Research focuses on advanced data indexing, query optimization, and AI-driven data management, with applications in genomics, network systems, and education. His work integrates machine learning with database systems for scalable solutions. Recent publications include topics in federated learning fairness, digital twin middleware, project-based CS education, and genome data indexing. Scientific contributions recognized through awards like the Wilkes Award (2008), ACM Distinguished Scientist (2013), and Springer Nature Editor of Distinction (2025). 2013–2018: Department Chair NSF, IBM, and Ford grants Over 250 conference committee roles He directs the Data Science/Management Research Lab, focusing on collaborative projects in genome analytics and smart computing infrastructures.

Stefan Schulteمشاهده پروفایل

استاد

• Data Engineering
• Blockchain Technologies
• Internet of Things (IoT)
• +۴ مورد دیگر

Prof. Dr.-Ing. Stefan Schulte is a Full Professor at Hamburg University of Technology, leading the Institute for Data Engineering and the Christian Doppler Laboratory Blockchain Technologies for the Internet of Things (CDL-BOT). He holds a diploma in Economics and a Bachelor's in Computer Science from the University of Oldenburg, followed by a Master's in Information Technology (with Merit) from the University of Newcastle. After completing his PhD at TU Darmstadt in 2010, he held roles as Postdoctoral Researcher at TU Wien, Assistant Professor (tenure-track), and eventually Associate Professor before joining TU Hamburg in 2021. His research focuses on data engineering, blockchain technologies applied to IoT, elastic computing, and quality-of-service (QoS) aspects in smart systems. Notable contributions include work on fog computing, federated learning, and cross-blockchain interoperability. He has published over 140 papers in top-tier venues like IEEE Transactions on Services Computing and ACM Computing Surveys. Key awards include Best Paper Awards at the IEEE International Conference on Blockchain (2020) and the European Conference on Service-Oriented and Cloud Computing (2023). Prof. Schulte chairs major conferences such as the IEEE International Conference on Fog and Edge Computing (ICFEC 2025) and serves on editorial boards for journals like IEEE Transactions on Services Computing. He leads CDL-BOT, a lab exploring blockchain applications in IoT and manufacturing. His industrial collaborations include projects like SIMPLI-CITY (smart mobility) and CREMA (cloud-based manufacturing). Current research emphasizes blockchain interoperability, federated learning frameworks, and edge-AI systems. He actively reviews proposals for the German Research Foundation, EU programs, and industry initiatives.

• Industrial Automation
• Robotics
• Flexible Manufacturing Systems
• +۴ مورد دیگر

Mikel Bueno Viso is a Researcher at Cranfield University's School of Aerospace, Transport and Manufacturing, affiliated with the Centre for Robotics and Assembly. His work focuses on Robotics , Industrial Automation , and Flexible Manufacturing Systems . He holds a BSc and MSc in Industrial Engineering from the University of the Basque Country and a Robotics MSc from Cranfield University. His research centers on ROS 2-based frameworks , robot perception , and modular middleware for reconfigurable manufacturing. Key projects include developing software architectures for object detection , pose estimation , and seamless robot integration . Mikel is currently a part-time PhD candidate investigating Flexible and Reconfigurable Manufacturing . His 2024-2025 publications demonstrate applications in Reconfigurable robotic cells Modular software frameworks Industry 4.0 automation His work leverages technologies like YOLOv8 for object detection and OpenCV for pose estimation, aiming to transform traditional manufacturing through software abstraction and interoperability .

Raúl Suárez Feijóoمشاهده پروفایل

پژوهشگر

• Robotized Assembly
• Fine Motion Planning
• Robotized Grasping and Manipulation
• +۴ مورد دیگر

Summary Raúl Suárez Feijóo is a Researcher at the Universitat Politècnica de Catalunya (UPC) in the Institut d'Organització i Control de Sistemes Industrials (IOC). Born in Asturias, Spain (1959), he holds an Electronic Engineering degree from the National University of San Juan, Argentina (1984), and a Doctorate in Engineering from UPC (1993). His research focuses on robotized assembly, fine motion planning, grasping, and multi-robot systems. He has led the IOC as Deputy Director (2003–2009) and Director (2009–2016). He coordinates doctoral programs in Automation and Robotics at UPC. Suárez has authored numerous publications and received awards including the Best Paper Award at IEEE ISATP (1995) and the Best Reviewer Award at ICRA (2015). Education: Electronic Engineer (1984, Argentina), Doctor Engineer (1993, UPC) Leadership Roles: Director of IOC (2009–2016), Coordinator of Doctoral Programs Research: Over 100 publications in robotics, emphasizing grasping, manipulation, and industrial automation. His work includes contributions to robotic hands, motion planning, and collaborative robotics. Notable projects involve 5G-enabled robotic systems and multi-AGV traffic management. Awards highlight his impact in both international and national robotics communities.

• Sensor Networks
• Pervasive Computing
• Middleware
• +۵ مورد دیگر

Sanem Kabadayı is an Assistant Professor at the Department of Computer Engineering, Istanbul Technical University. Her research focuses on sensor networks, middleware frameworks, and pervasive computing. Research Interests Sensor Networks (Wireless, Ad-hoc, Heterogeneous) Pervasive and Ubiquitous Computing Middleware Design for Smart Environments Data Abstraction and Communication Paradigms Advisory Role Advisor for Yakup Kayatas' thesis on AI-assisted IoT agents for smart spaces (2025). Recent Research Trends Her 15 most recent publications (2006–2024) emphasize sensor network architectures, middleware innovations for programmable environments, and abstractions for heterogeneous sensor integration. Key themes include AI-driven IoT agents, positioning techniques in wireless networks, and virtual sensor frameworks.

Gian Pietro Piccoمشاهده پروفایل

استاد

• Building Automation
• Pervasive Computing
• Software Engineering
• +۱۲ مورد دیگر

Gian Pietro Picco is a Full Professor at the Department of Information Engineering and Computer Science (DISI) at the University of Trento, Italy. His research focuses on wireless networks, particularly ultra-wideband (UWB) technology, wireless sensor networks, and cyber-physical systems. He teaches courses including Distributed Systems, Low-power wireless networking for the Internet of Things, and Programming 2. Professor Picco's research interests span several interconnected domains in pervasive computing and networking: Ultra-wideband (UWB) technology for precise localization and communication Wireless sensor networks and Internet of Things (IoT) systems Cyber-physical systems and networked control Energy-efficient networking protocols Distributed systems and middleware Software engineering approaches for networked embedded systems His recent publications demonstrate a strong focus on ultra-wideband technology applications, particularly in localization, ranging, and concurrent transmissions. His work bridges theoretical foundations with practical implementations, often addressing real-world challenges in human-robot interaction, contact tracing, and infrastructure monitoring. A notable trend is the increasing application of UWB technology for precise positioning in complex environments, with significant contributions to understanding and mitigating human occlusion effects on ranging accuracy. Professor Picco has received numerous prestigious awards for his research contributions: Best Paper Award at IPSN'23 for "Network On or Off? Instant Global Binary Decisions over UWB with Flick" Best Paper Award at IPIN 2019 for "TALLA: Large-scale TDoA Localization with Ultra-wideband Radios" Best Paper Award at EWSN 2018 for "Concurrent Ranging in Ultra-wideband Radios" Best Paper Award at IPSN 2015 for "Geo-referenced Proximity Detection of Wildlife" Best Paper Award at IPSN 2011 for "Wireless Sensor Networks for Adaptive Lighting in Road Tunnels" Best Paper Award at IPSN 2009 for "Monitoring Heritage Buildings with Wireless Sensor Networks" Mark Weiser Best Paper Award at PerCom 2012 Professor Picco actively advises numerous PhD and Master's students, with several of his advisees becoming prominent researchers in wireless networking. His laboratory has secured significant funding for research projects in wireless sensor networks, IoT systems, and cyber-physical systems. His work has practical applications in heritage building monitoring, wildlife tracking, road tunnel lighting systems, and pandemic contact tracing. His research group maintains the Cloves large-scale ultra-wideband testbed and has developed several middleware systems for wireless sensor networks, including Lime and TeenyLIME. The group collaborates extensively with international research institutions and has made significant contributions to standardization efforts in IoT networking protocols.

Manos Kapritsosمشاهده پروفایل

دانشیار

• Distributed Systems
• Formal Verification
• Fault-Tolerant Replication
• +۳ مورد دیگر

Manos Kapritsos is an Associate Professor in the Department of Computer Science & Engineering at the University of Michigan, Ann Arbor. His research focuses on increasing the reliability of distributed systems through formal verification, fault-tolerant replication, and automated proof techniques. He has contributed to projects such as Aegean (replication beyond client-server models), I4 (automated inductive invariant inference), and Armada (concurrent code verification). His work emphasizes practical applications of formal methods to ensure system correctness and performance. Education details are not explicitly provided in the text, but his career trajectory suggests advanced academic training in computer science. His research interests include distributed systems, formal verification of protocols, and high-performance systems. Notable awards include the NSF CAREER Award (2021), Google Faculty Award (2017), and Distinguished Paper Awards at PLDI 2020 and USENIX Security 2017. Teaching includes courses on formal verification, operating systems, and distributed systems, reflecting his expertise in systems software. He has advised students through his GLaDOS research group, though specific advisee names are not listed. Key grants include NSF FMitF and Large grants to advance verification techniques for distributed systems. Recent articles focus on automating proofs for undecidable protocols (Basilisk), efficient replication communication (Scrooge), and formal latency analysis (Performal). His work bridges theory and practice, aiming to simplify end-to-end verification of complex systems.

Yongfeng Zhangمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Information Retrieval
• +۸ مورد دیگر

Yongfeng Zhang is an Associate Professor in the Department of Computer Science at Rutgers University. He is also the Director of the AIOS Foundation . His research focuses on Machine Learning, Data Mining, Recommender Systems, and Explainable AI , with notable contributions to fair and personalized AI, AI for science, and social good. Education and Experience: PhD in CS (2011–2016) from Tsinghua University Postdoc at UMass Amherst (2016–2017) Joined Rutgers as Assistant Professor (2018), promoted to Tenured Associate Professor (2024) Research Interests: Machine Learning, Data Mining, Information Retrieval, Recommender Systems, Natural Language Processing, ML Systems, AI Agents, Explainable AI, Fairness, and AI for Science. Recent Achievements: Received the 2024 ACM SIGIR Test of Time Award, 2024 Presidential Teaching Excellence Award, and multiple NSF grants. He has advised over 15 PhD students and led projects on trustworthy AI, generative recommendation, and causal inference. Awards: 2024 ACM SIGIR Test of Time Award 2021 NSF CAREER Award 2015 Microsoft PhD Fellowship Grants & Labs: Led NSF grants on explainable AI, conversational recommendation, and neural-symbolic AI. Active in the AIOS platform development and multiple research labs.

Lokesh Dasمشاهده پروفایل

استادیار

• Autonomous Vehicles
• Traffic Control Systems
• Deep Learning
• +۴ مورد دیگر

Dr. Lokesh Das is an Assistant Professor in the School of Computing at Wichita State University’s College of Engineering. His research focuses on developing advanced algorithms for autonomous vehicle traffic control systems using deep learning and V2X communication technologies. Key interests include reinforcement learning applications in real-world traffic scenarios, IoT security frameworks, and smart infrastructure integration. Research Interests: Autonomous Vehicle Coordination Reinforcement Learning for Traffic Systems IoT Security and Edge Computing Dynamic Wireless Charging Solutions Pedestrian Safety Prediction His recent work emphasizes multi-agent systems for traffic optimization and safety-aware adaptive control mechanisms. No specific awards are listed, but his publications reflect impactful contributions to intelligent transportation systems and machine learning applications. Advising and grants information is not explicitly provided in the text. Dr. Das’s research lab likely focuses on real-world implementation of AI-driven traffic solutions.

Christian Haubeltمشاهده پروفایل

استاد

• Embedded Systems
• Cyber-Physical Systems
• Smart Sensors
• +۹ مورد دیگر

Christian Haubelt is a Professor at the Institute of Computer and Network Engineering, School of Engineering, University of Rostock, Germany. He is actively engaged in research and teaching in the areas of embedded and cyber-physical systems, smart implants, and IoT. His work is supported by multiple national and international projects including ELAINE (SFB 1270), SmILE (EU), 6G-Health (BMBF), and GenerIoT (BMBF). His research interests include: Embedded and Cyber-Physical Systems Smart Sensors and Smart Implants System-Level Design Methodologies SystemC-based Modeling and Verification Design Space Exploration and Multi-Objective Optimization Industrial Internet of Things and 6G for Healthcare His recent publications focus on real-time communication protocols, 5G/6G localization, smart implants, and secure IoT systems. Trends show a strong emphasis on integrating embedded systems with medical and industrial applications, particularly leveraging TSN, MQTT-SN, and OPC UA for reliable and secure communication. His work bridges theoretical modeling with practical implementation in safety-critical domains. Christian Haubelt has supervised multiple researchers including Michael Nast, Benjamin Rother, Nico Kalis, and Nico Graumüller. He leads several funded research projects such as ELAINE, SmILE, 6G-Health, and SUSTAIN, which focus on smart implants, secure IoT, and next-generation medical systems. These projects involve collaboration with DFG, EU, and BMBF. He is involved in the following research labs and teams: Embedded Systems and Cyber-Physical Systems Group Smart Implants Research Team (SmILE, ELAINE) 6G-Health Localization Team Industrial IoT Security (SUSTAIN, CargoAssist)

Pedro M. B. Silva Girãoمشاهده پروفایل

استاد

• Instrumentation
• Transducers
• Wireless Sensor Networks
• +۶ مورد دیگر

Pedro M. B. Silva Girão is a Full Professor in the Department of Electrical Engineering at Instituto Superior Técnico (IST), University of Lisbon (UL), and a Senior Researcher at Instituto de Telecomunicações where he heads the Instrumentation and Measurements Group and coordinates the Basic Sciences and Enabling Technologies area. His dual institutional roles position him at the forefront of academic research and technological innovation in Portugal. His research program focuses on instrumentation, transducers, and measurement techniques with specialized applications in biomedical and environmental domains. Key interests include wireless sensor networks for health monitoring, metrology standards, and digital data processing methodologies. This work bridges engineering principles with real-world healthcare and ecological challenges, emphasizing practical implementations in diagnostic systems and environmental sensing. Analysis of his 2019-2024 publications reveals a strong thematic trajectory in IoT-enabled healthcare solutions and precision environmental monitoring. Recurring motifs include gait rehabilitation through mixed reality systems, advanced dosimetry for liver cancer radioembolization, microvascular reactivity assessment, and water quality sensor networks. His output demonstrates consistent interdisciplinary collaboration between engineering, medical, and environmental science communities. Dr. Girão's scientific recognition includes: IEEE Senior Member status IEEE IMS Distinguished Lecturer appointment Honorary Chairmanship of IMEKO TC19—Environmental Measurements As leader of the Instrumentation and Measurements Group at Instituto de Telecomunicações, he directs a multidisciplinary team developing next-generation measurement systems. Current initiatives integrate microwave Doppler radar, wearable biopotential sensors, and wireless networks for unobtrusive health monitoring and environmental assessment, with active partnerships across medical institutions and ecological agencies.