جست‌وجوی استاد و پژوهشگر

Luca Carloniمشاهده پروفایل

استاد

• Computer Science
• Embedded Systems
• Hardware Design
• +۶ مورد دیگر

Luca Carloni is a Professor of Computer Science and Department Chair at Columbia University's Columbia Engineering. He leads the System-Level Design Group, focusing on heterogeneous system-on-chip (SoC) architectures, networks-on-chip (NoC), and embedded systems. Carloni holds a Laurea Summa Cum Laude in Electronics Engineering from the University of Bologna and a PhD in Electrical Engineering and Computer Sciences from UC Berkeley. His work emphasizes specialized hardware design, energy-efficient computing, and FPGA-based prototyping. Research interests include system-level design methodologies for SoCs, embedded accelerators, and quantum computing hardware. He has pioneered frameworks like Embedded Scalable Platforms (ESP) and tools like MosaicSim for rapid SoC prototyping. Carloni has received numerous awards, including the NSF CAREER Award (2006), IEEE Fellow (2017), and multiple best paper awards at DATE and CloudCom conferences. He has served on editorial boards of IEEE Transactions on CAD and ACM Transactions on Embedded Computing , and chaired key conferences like EMSOFT and ESWeek. His research addresses challenges in heterogeneous architectures, power management, and the intersection of machine learning with embedded systems. Current projects explore quantum control systems, brain-computer interfaces, and energy-efficient datacenter computing.

Joyoung Leeمشاهده پروفایل

دانشیار

• Connected Vehicle (CV) Systems
• Traffic Management
• Autonomous Vehicle Control
• +۵ مورد دیگر

Dr. Joyoung Lee is an Associate Professor in the Department of Civil and Environmental Engineering at New Jersey Institute of Technology (NJIT). He previously served as Laboratory Manager at the Federal Highway Administration's Saxton Transportation Operations Laboratory. His research focuses on Connected Vehicle (CV) systems, including applications in traffic management, signal control optimization, and autonomous vehicle infrastructure integration. Dr. Lee holds a Ph.D. (2010) and M.S. (2007) in Transportation Engineering from the University of Virginia, and a B.S. (2000) in Transportation Engineering from Hanyang University. His work emphasizes CV-based solutions for real-time traffic systems, cooperative vehicle-infrastructure systems (CVIS), and autonomous vehicle integration. Notable achievements include the 2019 IEEE CAVS Best Paper Award and multiple best paper recognitions from PTV User Group Meetings. His research also addresses traffic safety through innovations like the Virtual Guide Dog system for visually impaired pedestrians and advanced traffic monitoring frameworks using LiDAR and computer vision. Education: Ph.D., Transportation Engineering, University of Virginia (2010) M.S., Transportation Engineering, University of Virginia (2007) B.S., Transportation Engineering, Hanyang University (2000) Dr. Lee's research interests span smart city infrastructure, edge computing for traffic systems, and sustainable transportation solutions. He has pioneered algorithms for cooperative intersection management, automated platooning systems, and federated learning-based traffic optimization. His work bridges theoretical models with real-world implementation through partnerships with FHWA and industry stakeholders. Key contributions include development of the Cumulative Travel-Time Responsive (CTR) traffic signal control system, smart arrival notification systems for paratransit services, and advanced microsimulation calibration techniques. His lab focuses on translating CV data into actionable strategies for safer, more efficient transportation networks. Awards: IEEE CAVS Best Paper Award (2019) ASCE Grand Challenge Innovation Contest Honorable Mention (2017) PTV VISSIM Best Paper Awards (2012, 2008) Excellence in Research Award (University of Virginia, 2011) Ongoing projects include semi-decentralized graph neural networks for traffic forecasting and low-cost LiDAR-based traffic monitoring systems. His work addresses critical challenges in autonomous vehicle integration, incident management, and infrastructure resilience through interdisciplinary collaborations.

Carolina Osorioمشاهده پروفایل

استاد

• Transportation Science
• Operations Research
• Artificial Intelligence
• +۴ مورد دیگر

Carolina Osorio is a Professor at HEC Montréal, holding the Scale AI Research Chair in Artificial Intelligence for Urban Mobility and Logistics. She is affiliated with the Department of Decision Sciences and is a member of the Group for Research in Decision Analysis (GERAD) and the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT). Her research focuses on transportation optimization, urban mobility, and data-driven simulation-based methods. She has been recognized among the world’s most influential researchers in 2023 and 2024. Education: Ph.D. in Mathematics, École Polytechnique Fédérale de Lausanne (EPFL) M.Sc. in Statistics, University College London (UCL) Bachelor’s in Engineering, École nationale supérieure d'informatique et de mathématiques appliquées de Grenoble (ENSIMAG) Research Interests: Her work emphasizes scalable transportation modeling, simulation-based optimization, and AI applications for urban logistics. She develops methods for large-scale network analysis, traffic demand estimation, and sustainable urban mobility solutions. Key areas include traffic signal optimization, car-sharing service design, and high-dimensional stochastic systems. Publications: Recent articles highlight advancements in scalable traffic demand estimation, Bayesian optimization for transportation systems, and simulation-based toll optimization. Her work addresses challenges in global highway networks, urban congestion dynamics, and multi-city calibration. Awards: Scale AI Research Chair (Artificial Intelligence for Urban Mobility and Logistics) Recognition as a world-leading researcher in transportation science Advising & Grants: Osorio collaborates on projects funded by Scale AI and leads research initiatives through GERAD and CIRRELT. Her supervision activities include teaching courses such as Decision Analysis and Sample Efficient Optimization at HEC Montréal. Labs & Teams: She contributes to interdisciplinary teams at GERAD and CIRRELT, focusing on integrating advanced analytics into urban transportation systems.

Yar Muhammadمشاهده پروفایل

مدرس ارشد

• Brain-Computer Interfaces
• Artificial Intelligence
• Machine Learning
• +۱ مورد دیگر

Dr. Yar Muhammad is a Principal Lecturer in Computer Science at the University of Hertfordshire's School of Physics, Engineering & Computer Science. His research develops Brain-Computer Interface applications using AI/ML techniques for healthcare. He holds a PhD in ICT (Tallinn University of Technology) and dual master's degrees. Research Leadership: Supervised PhD students: Nimra Memon (fault-tolerance in web services), Dmytro Zabolotnii (agent behavior prediction), Mahir Gulzar (context-aware modeling) Accepts self-funded PhD candidates in BCI/AI applications Awards: Young Investigator Award (Springer/IFMBE, 2014) Best Paper Award Runner-up (26th ISSC 2015) Professional Recognition: Fellow of Higher Education Academy IEEE Senior Member Editorial board member for multiple journals

Abraham Silberschatzمشاهده پروفایل

استاد

• Database Systems
• Operating Systems
• Storage Systems
• +۳ مورد دیگر

Abraham Silberschatz is the Sidney J. Weinberg Professor of Computer Science at Yale University. He previously served as Vice President of the Information Sciences Research Center at Bell Laboratories and held a chaired professorship at the University of Texas at Austin. His research focuses on database systems, operating systems, and network management. Silberschatz has advised over a dozen PhD students, many now in academia and industry. Education: Ph.D., Computer Science, Stony Brook University (SUNY) Research Interests: His work spans database systems, operating systems, storage systems, and network management. Notable contributions include foundational textbooks like Operating System Concepts and Database System Concepts , which have become industry standards. He has also developed innovative systems like DataPlay and contributed to projects such as NetInventory. Publications: His 15+ years of research include influential papers on database architecture, network routing, and distributed systems. Recent work explores leveraging non-volatile memory technologies in systems design. Awards: ACM Karl V. Karlstrom Outstanding Educator Award (1998) IEEE Taylor L. Booth Education Award (2002) VLDB Test of Time Award (2019) Multiple Bell Laboratories President's Awards for innovation Grants & Patents: Recipient of over two dozen grants and over four dozen patents, including foundational IP in multimedia storage and distributed systems. His team's HadoopDB project merged MapReduce and DBMS technologies. Labs/Teams: Collaborates with Prof. Robert Soulé on projects in database systems and networking, focusing on next-gen memory technologies. Active in mentoring graduate students and postdocs in their research group.

Konstantinos Spiliopoulosمشاهده پروفایل

استاد

• Stochastic Processes
• Applied Mathematics
• Probability Theory
• +۷ مورد دیگر

Konstantinos Spiliopoulos is a Professor and Director of Statistics at Boston University's Department of Mathematics and Statistics, part of the College of Arts & Sciences. He leads research in Applied Mathematics and Probability and Statistics groups, focusing on stochastic processes, machine learning, and mathematical finance. His research interests include stochastic analysis of complex systems, multiscale phenomena, and their applications to neural networks, PDEs, and financial modeling. Notable areas of study involve mean-field limits, rare event simulation, and asymptotic methods in stochastic differential equations. He has received grants such as DMS-EPSRC funding for analyzing online training algorithms in recurrent and deep neural networks. His work bridges theoretical advancements with practical applications in data science and computational methods. Spiliopoulos maintains an active presence in interdisciplinary research, addressing challenges in systemic risk, network dynamics, and optimization. His contributions span from fundamental probability theory to applied problems in engineering and finance.

• 3D Printing
• Ceramic Materials
• Sustainable Materials
• +۳ مورد دیگر

Julian Jauk is a Researcher at the Institute for Architecture and Media, TU Graz. His work focuses on innovative material systems, digital fabrication, and sustainable architectural design. He explores the integration of clay composites, mycelium-based materials, and knitted structures with advanced manufacturing techniques like 3D printing. Key research themes include lightweight ceramic structures, biocomposite materials, and computational design methodologies. His research emphasizes material-driven innovation, structural optimization, and environmental sustainability. Notable projects include MyCera (clay-mycelium composites) and ClayKnit (3D-printed clay-knitted hybrids). He also investigates mixed reality tools for architectural sketching and kinetic architectural prototypes. Publications from 2021–2024 highlight trends in bio-based materials, additive manufacturing, and material-property analysis. His work bridges traditional craftsmanship with cutting-edge digital fabrication, aiming to redefine sustainable building practices.

Lei Leiمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Reinforcement Learning
• Internet of Things (IoT)
• +۵ مورد دیگر

Lei Lei is an Associate Professor at the University of Guelph, specializing in Computer Engineering. Her research focuses on Machine Learning/Deep Reinforcement Learning, Internet of Things (IoT)/Internet of Vehicles (IoV), Mobile Edge Computing, and Smart Grid Optimization. She explores cutting-edge applications in energy-efficient systems, autonomous vehicles, and intelligent transportation networks. Her work integrates advanced AI techniques with real-world challenges in communication and control systems. Key research areas include optimizing electric vehicle charging schedules using hierarchical deep reinforcement learning and enhancing vehicular networks through 6G communication protocols. She has pioneered methods for joint communication-control systems, securing federated learning models, and developing robust resource allocation strategies in IoT and edge computing environments. Lei Lei’s publications emphasize interdisciplinary solutions, bridging computer science, electrical engineering, and transportation systems. Her recent work addresses challenges in smart grid security, multitimescale control systems, and the application of AI tools like ChatGPT in connected vehicles. She is affiliated with the AI Affiliated Faculty at the University of Guelph, reflecting her contributions to artificial intelligence research.

• Energy Efficiency in Software
• Multicore Memory Systems
• Hardware/Software Co-Design
• +۳ مورد دیگر

Arrvindh Shriraman is an Associate Professor and Program Director of Software Systems at Simon Fraser University's School of Computing Science in Surrey. His research focuses on energy-efficient software, multicore memory systems, and optimizing hardware/software interfaces for parallel programming. He holds a Ph.D. (2010) and M.S. (2006) in Computer Science from the University of Rochester, and a B.Eng. (2004) from the University of Madras. He teaches courses on parallel programming and energy-conscious software design. His research interests include synchronization mechanisms for domain-specific architectures, cache optimization, and FPGA-based acceleration. He has contributed to frameworks like Mu-grind for HLS-generated RTL instrumentation and TAPAS for parallel accelerator generation. Notable projects include RANGE-BLOCKS for synchronization in domain-specific systems and TapeFlow for gradient computation in neural networks. His work emphasizes real-time verification of autonomous systems and safety-critical trajectory planning for underwater vehicles. Shriraman collaborates closely with the Tangent Lab, exploring cutting-edge solutions in hardware-software co-design and embedded systems. His teaching and research bridge theoretical computer science with applied engineering challenges, addressing scalability, efficiency, and safety in modern computing systems.

• Control Systems
• Robotics
• Machine Learning
• +۳ مورد دیگر

Professor Bing Chu is an academic at the University of Southampton, actively contributing to research in control systems, robotics, and machine learning. They are a member of the Vision, Learning and Control Centre for Internet of Things and Pervasive Systems and the Centre for Robotics, focusing on interdisciplinary approaches that combine control theory with data-driven methodologies. Current research interests include: Iterative learning control Human-robot interaction Wind farm power optimization Robot behavior modeling Control system architectures Collaborative learning systems Recent publications highlight trends in data-driven control systems, human-robot interaction datasets, and optimization techniques for both continuous-time systems and wind energy applications. Professor Chu supervises multiple PhD students across robotics and electronic engineering, including Balint Gucsi, Haonan Shen, and Aleksander Wolski, while leading projects funded by Zhengzhou University and the Royal Society.

Christiane Barzمشاهده پروفایل

استاد

• Operations Research
• Revenue Management
• Stochastic Dynamic Optimization
• +۳ مورد دیگر

Christiane Barz is a Professor of Mathematics at the University of Zurich's Institute for Business Administration since 2016. Previously, she held academic roles at the UCLA Anderson School of Management, the Chicago Booth School of Business, and the Technical University (TU) Berlin. Her research focuses on stochastic dynamic systems, Markov decision processes, and their applications in revenue management. She emphasizes making mathematical tools accessible and practical for real-world problem-solving, particularly in optimizing decision-making under uncertainty. Education includes a degree in industrial engineering and a doctorate from the University of Karlsruhe (TH), Germany. Her career path includes postdoctoral research at the University of Chicago's Booth School of Business and roles as an Assistant Professor at UCLA. She combines academic excellence with balancing family life, advocating for gender equity in STEM fields. Her research explores risk-sensitive decision-making frameworks, dynamic pricing models for transportation and healthcare, and optimizing resource allocation in complex systems. Recent work includes applications in FlixBus, air cargo networks, and improving patient admission scheduling in hospitals. Barz's teaching philosophy prioritizes demystifying mathematics for students, encouraging critical engagement rather than fear of complexity. She collaborates with industry partners to apply operations research methods to real-world challenges, emphasizing both theoretical rigor and practical relevance.

• Database Technology
• Database Management Systems
• Distributed Systems
• +۴ مورد دیگر

Michael Stonebraker is a renowned computer scientist and Adjunct Professor of Computer Science at MIT's CSAIL. He is a pioneer in database technology, having developed foundational systems like INGRES and POSTGRES at UC Berkeley. His work spans database management, distributed systems, and data integration. He has founded multiple startups to commercialize his research and holds numerous awards, including the ACM Turing Award (2014) and IEEE John von Neumann Medal (2005). He earned his Ph.D. from the University of Michigan and undergraduate degrees from Princeton and Michigan. His research focuses on advancing database systems, operating systems, and big data analytics. Recent work includes contributions to video data management, cloud computing optimization, and data discovery systems. Education: Ph.D., Computer, Information and Control Engineering, University of Michigan (1971) M.S.E., Electrical Engineering, University of Michigan (1966) B.S.E., Electrical Engineering, Princeton University (1965) Research Interests: Database Technology, Distributed Systems, Data Integration, and Big Data Analytics. His work bridges theory and practice, emphasizing scalable architectures and real-world applications. Awards & Recognition: ACM Turing Award (2014) ACM SIGMOD Systems Award (2015) MIT Tech Review TR7 (2016) C&C Prize (2020) Grants & Labs: His research is supported by grants from NSF, DARPA, and industry collaborations. He leads MIT's efforts in database systems and is affiliated with CSAIL labs focused on data management and high-performance computing.

• Machine Learning
• Reinforcement Learning
• Online Learning
• +۴ مورد دیگر

Karan Singh serves as an Assistant Professor of Operations Research at Carnegie Mellon University's Tepper School of Business, where he develops theoretically rigorous algorithms for machine learning systems with emphasis on reinforcement learning and control theory. His work synthesizes techniques from online learning, optimization, and statistics to address complex interactive learning challenges. His academic journey includes: PhD in Computer Science from Princeton University under Elad Hazan Postdoctoral research at Microsoft Research (Redmond) Bachelor's degree in Computer Science from Indian Institute of Technology (IIT) Kanpur Singh's research program centers on three interconnected pillars: Algorithmic Reductions : Creating efficient methods to solve complex learning problems (e.g., reinforcement learning) using solvers for simpler tasks, yielding breakthroughs in online boosting and RL with concave rewards Nonstochastic Control : Establishing an algorithmic foundation for control theory through provably efficient instance-optimal algorithms that extend online learning to stateful systems Privacy-Preserving Online Learning : Investigating fundamental limits of regret minimization under differential privacy constraints while maintaining performance His approach consistently bridges theoretical computer science and practical control applications. Analysis of his 15 most recent publications reveals a clear evolution toward integrating algorithmic reductions with nonstochastic control frameworks. Recent work (2023-2025) demonstrates increasing focus on sample efficiency in agnostic boosting, privacy-aware optimization without smoothness assumptions, and competitive ratio analysis in online control. A unifying thread is the development of regret-optimal algorithms for linear dynamical systems under adversarial disturbances. His contributions have earned significant recognition: Best Paper Award at OptRL workshop (NeurIPS 2019) Spotlight Prize from New York Academy of Sciences' ML Symposium (2018) Multiple oral presentations at NeurIPS/ICML (acceptance rate Though specific student advisees aren't listed, Singh's extensive publication record with junior co-authors indicates active mentorship. His research has secured substantial support including a US patent (11,138,513 B2) for dynamic learning systems and collaborations through CMU's Machine Learning and Optimization group. Current projects involve interdisciplinary work on differentiable control libraries (Deluca) and medical applications like mechanical ventilation control. Singh leads research within CMU's Machine Learning and Optimization ecosystem, collaborating across computer science and engineering departments. His team develops foundational tools like the Deluca differentiable control library while pursuing real-world applications in healthcare systems, demonstrating strong cross-disciplinary integration.

Dahlia Malkhiمشاهده پروفایل

استاد

• Distributed Systems
• Blockchain Technology
• Financial Technology
• +۳ مورد دیگر

Professor Dahlia Malkhi is a leading academic and researcher in distributed systems and blockchain technology. She currently holds a faculty position at the University of California, Santa Barbara (UCSB), where she heads the Foundations of Financial Technology (FfTech) research lab. Her work focuses on reliability, security, and consensus mechanisms in distributed systems, with a recent emphasis on blockchain innovations like HotStuff, which underpins Diem, Aptos, and other blockchains. She has held influential roles at industry leaders such as Chainlink Labs, Diem Association, VMware, and Microsoft Research. Education: Ph.D. in Computer Science from The Hebrew University of Jerusalem. Past roles include CTO of Diem Association (2019–2022), Principal Researcher at VMware (2014–2019), and Partner Principal Researcher at Microsoft Research (2004–2014). Research Interests: Blockchain consensus algorithms (e.g., HotStuff, Flexible Paxos), Byzantine Fault Tolerance (BFT), secure multi-party computation (FairPlay), and distributed database systems (CorfuDB). Her work bridges academic theory with industrial applications, emphasizing practical scalability and security. Awards: ACM Fellow (2011), IEEE TCDP Outstanding Technical Achievement Award (2021), IBM Faculty Award (2003/2004). She has also held leadership roles in conferences like Usenix ATC and program chairs for multiple distributed systems events. Advising & Grants: Advises projects at Space Computer, Lyquor Labs, and Chainlink Labs. Her research labs and collaborations include work on BBCA-Chain, Lumiere, and BFTBrain, advancing consensus mechanisms in decentralized systems. Labs/Teams: Leads UCSB’s FfTech lab, co-founded VMware Research, and contributed to foundational blockchain projects like DiemBFT and Espresso Systems. Her work impacts technologies such as NSX-T control planes and distributed financial infrastructure.

Chun-Hung Chenمشاهده پروفایل

استاد

• Stochastic Simulation Optimization
• Optimal Computing Budget Allocation (OCBA)
• Systems Engineering
• +۳ مورد دیگر

Professor Chun-Hung Chen is a distinguished academic at George Mason University ’s Volgenau School of Engineering , where he holds the rank of Professor in the Department of Systems Engineering and Operations Research . He has also held professorships at National Taiwan University and visiting roles at institutions like University of Pennsylvania and Microsoft Research Asia . Education: PhD in Decision and Control, Harvard University (1994) MS in Electrical Engineering, National Taiwan University (1989) BS in Control Engineering, National Chiao-Tung University (1987) Research Interests focus on Stochastic Simulation Optimization , particularly his pioneering Optimal Computing Budget Allocation (OCBA) methodology. OCBA enhances simulation efficiency by dynamically allocating computational resources to critical design alternatives, reducing computation time by orders of magnitude. Applications span air transportation , healthcare , power grids , and semiconductor manufacturing . His 15 most recent articles (2022–2025) explore intersections of simulation optimization , artificial intelligence , reinforcement learning , and personalized medicine , emphasizing computational efficiency and stochastic systems in domains like microgrids and organ transplant logistics . Scientific Awards include: IEEE Fellow (2015) K.D. Tocher Medal (2017) Best Paper Awards at IEEE CASE (2019), LOGMS (2019), and IEEE ICC (2021) Harvard’s Eliahu I. Jury Award (1994) Advisory roles include editorial leadership in IIE Transactions , Journal of Simulation , and IEEE Transactions series. He has coordinated graduate programs at George Mason (2006–11, 2015–19) and led conferences like INFORMS International Meeting (2025) and Harvard Control Workshop (2024). His work is funded by organizations such as the National Science Foundation , National Institutes of Health , and Department of Energy , with applications in healthcare logistics and microgrid control .