جست‌وجوی استاد و پژوهشگر

Spiridon Ivanov Penevمشاهده پروفایل

استاد

• Wavelet Methods in nonparametric curve estimation
• Edgeworth expansions and Saddlepoint Approximation Methods
• Structural Equation Models
• +۸ مورد دیگر

Professor Spiridon Ivanov Penev is a leading academic in the School of Mathematics and Statistics at the University of New South Wales. He holds a PhD in Mathematical Statistics from Humboldt University (Berlin, Germany) and has been affiliated with UNSW since 1992, progressing from Lecturer to Professor in 2019. His research spans wavelet methods, saddlepoint approximations, structural equation models, and stochastic risk analysis. Education: PhD in Mathematical Statistics, Humboldt University Current Affiliation: Department of Statistics, School of Mathematics and Statistics, UNSW His work focuses on advanced nonparametric techniques, including wavelet-based signal recovery with adaptive sampling rates, and robust inference in structural equation models. He has developed bias-corrected reliability measures for psychometric applications and contributed to stochastic optimization problems in finance and engineering. Recent publications highlight his expertise in semiparametric regression, robust portfolio optimization, and marine engineering applications using machine learning. Key trends include the use of Bregman divergence for shape-preserving estimation and Markov chain methods for climate model weighting. Scientific Awards: DAAD award Elected member of the International Statistical Institute (ISI) He has supervised numerous grants as Chief Investigator, including Australian Research Council projects and industry collaborations. Administrative roles include membership in the School of Mathematics and Statistics Executive Committee. Teaching duties span advanced statistical inference, multivariate analysis, and data science applications.

Pasquale Davide Schiavoneمشاهده پروفایل

مدرس

• Embedded Systems
• RISC-V Architecture
• Ultra-low-power Computing
• +۵ مورد دیگر

Pasquale Davide Schiavone holds multiple research and teaching positions at École Polytechnique Fédérale de Lausanne (EPFL), serving as a Lecturer at the School of Computer and Communication Sciences (IC) and as a Scientist at both the Embedded Systems Laboratory (ESL) within the School of Engineering (STI) and PAT Administration. His interdisciplinary work bridges computer architecture, embedded systems design, and biomedical applications, with office located at ELG 136 in Lausanne, Switzerland. Dr. Schiavone's research focuses on ultra-low-power computing systems, particularly RISC-V architectures and TinyML applications for edge devices. His work develops open-source hardware platforms like X-HEEP and HEEPOCRATES that enable energy-efficient AI at the edge, with applications spanning biomedical monitoring, neural interfaces, and wearable computing. He explores innovative hardware-software co-design approaches to overcome energy constraints in resource-limited environments. His recent publications reveal a consistent research trajectory centered on open, configurable computing platforms for specialized applications. The work spans from fundamental RISC-V architecture improvements (ARCANE, e-GPU) to application-specific implementations for biomedical contexts (BiomedBench, neural interfaces). A strong emphasis on energy efficiency permeates all his research, whether through novel arithmetic approaches (Posit), system architecture (near-memory computing), or specialized accelerators (Strela, Quadrilatero). Lecturer, School of Computer and Communication Sciences (IC) Scientist, Embedded Systems Laboratory (ESL), School of Engineering (STI) Scientist, PAT Administration, School of Engineering (STI) Dr. Schiavone teaches courses on hardware compilation, presenting algorithms and methods for transforming hardware description languages into optimized circuit implementations. His Embedded Systems Laboratory work places him at the forefront of developing practical, open-source solutions for next-generation computing challenges in energy-constrained environments.

Ilia Polianمشاهده پروفایل

استاد

• Circuit Architecture
• System Design
• Hardware Reliability
• +۶ مورد دیگر

Prof. Ilia Polian serves as Head of the Institute of Computer Engineering and Chair of the Hardware-Oriented Computer Science (HOCOS) department at the University of Stuttgart. His leadership spans research, teaching, and institutional coordination across multiple high-impact projects. Prof. Polian's research focuses on developing circuit and system architectures based on both traditional and novel principles, including neuromorphic, stochastic, and approximate architectures. His second major research focus is systematic design methodology and design automation, with particular emphasis on safety and reliability properties of developed systems. Current research directions include quantum computing engineering, secure mixed-signal neural networks, and resource-efficient stochastic circuits for near-sensor computing applications. His recent publications demonstrate strong trends in quantum computing (particularly circuit partitioning and compilation for multi-QPU architectures), hardware security (including memristive cryptographic implementations), and AI-driven approaches to hardware testing and reliability. These works bridge fundamental computer architecture research with practical industrial applications. University of Stuttgart's Publication Prize for Paper on Partitioning of Quantum Circuits Prof. Polian actively supervises doctoral students including Devanshi Upadhyaya, and leads significant research grants such as the DFG Priority Program Nano Security which he coordinates. His department offers numerous thesis and research opportunities for students interested in cutting-edge hardware research. The Hardware-Oriented Computer Science department maintains strong collaborations with industry partners including IBM, Infineon Technologies, and Advantest, as well as academic institutions through the IQST Graduate School and QuantumBW initiatives.

Manolis Chatzisمشاهده پروفایل

دانشیار

• Dynamic Systems
• Earthquake Engineering
• System Identification
• +۳ مورد دیگر

Manolis Chatzis is an Associate Professor in the Department of Engineering Science at the University of Oxford and a Tutorial Fellow at Hertford College. His research focuses on dynamic systems and earthquake engineering, particularly modeling risks for unanchored structural and non-structural components subjected to ground motions. University of Oxford - Department of Engineering Science Hertford College - Tutorial Fellow His work on system identification and observability of nonlinear systems aims to optimize sensor setups for infrastructure reliability. Recent publications address discontinuous Kalman filters for non-smooth dynamics, energy loss in rocking bodies, and experimental validation of seismic response models. Applications span seismically isolated buildings, museum artifacts, hospital equipment, and supercomputers. Key research trends include: Nonlinear dynamics of rocking/sliding systems Bayesian identification methods Energy dissipation mechanisms 3D motion tracking algorithms Sensor fusion and data-driven modeling His publications since 2010 demonstrate interdisciplinary collaboration across civil, mechanical, and computational engineering domains.

• Pneumonia
• Clinical Decision-Making
• Medical Informatics
• +۳ مورد دیگر

Dr. Barbara E. Jones serves as an Associate Professor in the Department of Internal Medicine at the University of Utah School of Medicine, with dual appointments in Pulmonary and Critical Care Medicine. Her clinical practice spans diverse healthcare settings within the Veterans Affairs system and academic medical centers, focusing on evidence-based adaptation of care to varied patient populations. Her educational background includes: M.D. from University of Washington School of Medicine B.A. in Philosophy from Dartmouth College Master of Science in Clinical Investigation (M.S.C.I) from University of Utah Postdoctoral Fellowship in Pulmonary and Critical Care Medicine at University of Utah Residency in Internal Medicine at University of Utah Dr. Jones' research centers on decision-making processes in pneumonia diagnosis and treatment, employing a tripartite informatics approach combining population analytics, cognitive behavior analysis, and clinical decision support systems. Her work specifically targets reducing diagnostic uncertainty and treatment variation across healthcare systems, with emphasis on equitable care delivery for diverse patient populations. Current projects investigate diagnostic discordance in community-acquired pneumonia, electronic surveillance for hospital-acquired infections, and machine learning applications for diagnostic error detection. Analysis of her 15 most recent publications reveals consistent focus on pneumonia management systems, with emerging emphasis on pandemic impacts on diagnostic practices and AI-driven quality improvement. Her work predominantly utilizes large VA healthcare datasets spanning 100+ medical centers, featuring mixed-methods approaches that integrate quantitative analytics with qualitative clinician experience assessment. Dr. Jones actively contributes to clinical guideline development and medical education through editorial work in major journals including Chest and Annals of Internal Medicine , where she frequently addresses controversies in pneumonia diagnosis and antibiotic stewardship. Her research program operates at the intersection of the University of Utah Health system and the Veterans Affairs national healthcare network, leveraging electronic clinical decision support implementations across diverse hospital settings including rural and critical access facilities. Current initiatives focus on real-time feedback systems for diagnostic performance improvement and automated surveillance for healthcare-associated infections.

Eleni Staiمشاهده پروفایل

استادیار

• Optimization
• Communications Networks
• Data Analytics
• +۴ مورد دیگر

Eleni Stai is an Assistant Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), affiliated with the Division of Communication, Electronic and Information Engineering. She holds advanced degrees in Electrical Engineering, Mathematics, and Applied Mathematical Sciences from NTUA and the National and Kapodistrian University of Athens. Her academic credentials include: Diploma in Electrical and Computer Engineering, NTUA (2009) B.Sc. in Mathematics, National and Kapodistrian University of Athens (2013) M.Sc. in Applied Mathematical Sciences, NTUA (2014) Ph.D. in Electrical Engineering, NTUA (2015) Dr. Stai's research integrates advanced optimization techniques with communications networks and energy systems. She develops stochastic and deterministic optimization frameworks for network resource allocation, data analytics on complex topologies, and smart-grid control applications. Her work bridges theoretical foundations with practical implementations in energy-harvesting networks, network slicing, and reinforcement learning for distributed systems. Analysis of her recent publications reveals dominant research thrusts in AI-driven network management (particularly O-RAN and network slicing), energy-integrated communications, and optimization of energy communities. A significant portion of her work addresses the convergence of 5G/6G networking with power systems, emphasizing real-time control and sustainability. Her scientific contributions have been recognized through prestigious awards: Chorafas Foundation Best Ph.D. Thesis award Thomaidis Foundation Best M.Sc. Thesis award Best Paper Award at ICT 2016 Best Presenter Award at IEEE ENERGYCON 2022 Dr. Stai serves on technical program committees for major international conferences and has co-authored the book "Evolutionary Dynamics of Complex Communications Networks". She teaches undergraduate courses in Queuing Systems, Computer Networks, and Social Network Analysis, reflecting her expertise in network theory and applications. Her research trajectory demonstrates continuous evolution from fundamental network optimization to AI-enhanced solutions for next-generation communication-energy systems. Her work builds upon her postdoctoral experience at EPFL (2016-2020) and ETH Zurich (2020-2023), where she developed advanced frameworks for communications networks and energy systems.

Gururaj Mirle Vishwanathمشاهده پروفایل

استادیار

• Renewable energy integration
• Machine learning for power systems
• Power electronics
• +۵ مورد دیگر

Gururaj Mirle Vishwanath is an Assistant Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur. His research focuses on the integration of renewable energy sources into power systems, with particular emphasis on grid stability and control mechanisms. His educational background includes a PhD from IIT Roorkee (2020), an M.Tech from NITK Surathkal (2015), and a B.Tech from Visveswariah Technological University Belgaum (2009). Dr. Vishwanath's research interests span renewable energy integration challenges, machine learning applications to power systems, power converters for electric vehicles, power electronics applications to power systems, and microgrid control including black out scenarios, islanded operation, and energy management systems. His work addresses critical challenges in modern power systems as they transition toward greater renewable penetration. His recent publications demonstrate a strong focus on microgrid control architectures, voltage regulation schemes, and integration of wind energy systems with the grid. The research spans both theoretical control algorithms and practical implementation challenges for power system stability and reliability in the presence of renewable energy sources. Dr. Vishwanath works within the Advanced Centre for Electronic Systems (ACES) at IIT Kanpur, where his research contributes to advancing power system technologies for the evolving energy landscape.

Sani Nassifمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Energy Analysis
• Integrated Circuits
• +۴ مورد دیگر

Sani Nassif is a Research Fellow at the Technical University of Munich (TUM) under the Rudolf Diesel Industry Fellowship, hosted by Professor Ulf Schlichtmann. With 28 years of experience at Bell Labs and IBM Research, he has led teams in integrated circuit modeling, simulation, statistical analysis, and optimization. Research Interests: His work bridges integrated circuit technology with cross-disciplinary applications in medicine. Key areas include variability analysis in semiconductor manufacturing, low-power circuit design, and reliability engineering for nano-scale systems. He focuses on applying machine learning and statistical methods to solve challenges in energy-efficient computing and biomedical systems. Selected Publications: His research spans circuit variability trends, leakage current modeling, and reliability frameworks for nano-era systems. Work includes foundational studies on SRAM failure analysis and CMOS scaling limitations. Scientific Awards: He is recognized as an IEEE Fellow IBM Master Inventor (75 patents) Rudolf Diesel Industry Fellow

• Manufacturing Management
• Production Planning
• Production Control
• +۱۴ مورد دیگر

Maria Leonilde Rocha Varela is an Associate Professor with Habilitation at the School of Engineering, University of Minho, Portugal, where she also serves as a Senior Researcher at the Algoritmi Research Centre. She has been an integrated member of the Algoritmi Research Centre since 2012 and works in the Department of Production and Systems. Dr. Varela earned her degree in Production Engineering from the University of Minho in 1994, completed a Master's in Computer Integrated Production at DPS-UMinho in 1999, and received her Ph.D. in Production and Systems from the University of Minho in 2007. Her primary research focuses on Manufacturing Management, particularly Production Planning, Control and Optimization, and Collaborative Paradigms, Networks and Decision Making Models. She maintains extensive international collaborations with institutions worldwide including the National Institute of Industrial Engineering, VSB-Technick Univerzita Ostrava, University of Belgrade, and others. Her research spans Web Applications and Services for supporting Engineering and Production Management, with increasing emphasis on Artificial Intelligence, Robotic Process Automation, and Industry 4.0/5.0 applications. She has made significant contributions to scheduling algorithms, optimization techniques, and decision support systems for manufacturing environments. Analysis of her recent publications reveals a strong trend toward integrating Artificial Intelligence with traditional manufacturing processes, particularly in Robotic Process Automation applications. Her research increasingly focuses on sustainable manufacturing practices, with numerous publications addressing energy efficiency, environmental sustainability, and resource optimization. There is a clear emphasis on multi-objective optimization approaches to solve complex manufacturing problems, particularly in distributed job shop scheduling. Her work demonstrates an evolution from traditional production planning methods to more advanced AI-driven approaches for Industry 4.0 and 5.0 environments. Dr. Varela has held significant academic leadership roles, currently serving as the director of the master's course in Engineering and Quality Management at DPS-UMinho. She previously coordinated the industrial management and systems subgroup from 2012 to 2021 and was part of the steering committee for the master's course in systems engineering between 2016 and 2019. She has successfully supervised more than 70 MSc projects, with over 15 currently ongoing, focusing on Production and Systems Engineering. Her supervision encompasses collaborative management models, traditional decision approaches, and web-based platforms incorporating AI techniques. She coordinates research projects including 2 concluded Ph.D. projects and 6 ongoing ones. She collaborates as a research member in several R&D projects with national and international industrial enterprises and institutions, and in international Erasmus projects. Dr. Varela is an active participant in the academic community, serving on editorial boards of several international journals and as a member of organizing and scientific committees for numerous international conferences. She is a member of several prestigious research networks including the Euro Working Group of Decision Support Systems (EWG-DSS), Institute of Electrical and Electronics Engineers (IEEE), Industrial Engineering Network, and the Institute of Industrial and Systems Engineers (IISE).

• Information Access
• Information Retrieval
• Natural Language Processing
• +۳ مورد دیگر

Tetsuya Sakai is a Professor at the School of Fundamental Science and Engineering within Waseda University's Faculty of Science and Engineering. His work focuses on information access, retrieval, and natural language processing, with a particular emphasis on evaluation frameworks for search systems. Affiliations: Waseda University (Faculty of Science and Engineering, School of Fundamental Science and Engineering) Academic Rank: Professor Research Interests : Dr. Sakai's research spans four key areas: (1) Information Access —designing systems for direct and immediate information delivery, (2) Search Evaluation —developing metrics like Height-Biased Gain and hierarchical intent-based diversity measures, (3) Fairness in IR —pioneering frameworks for group fairness in conversational search, and (4) Statistical Reform —advocating Bayesian methods and robust experimental design. His work also addresses privacy inconsistencies in mobile apps and cognitive biases in LLMs. Scientific Awards : Notable recognitions include induction into the SIGIR Academy (2023) , ACM Distinguished Member (2018) , ACM Senior Member (2016) , and multiple DEIM/FIT/CSS Best Paper Awards . He has received teaching honors like the Waseda Presidential Teaching Award (2016) and WASEDA e-Teaching Award (2018) . Article Trends : Recent publications highlight: Advancements in LLM-assisted relevance assessments and hallucination diagnostics for tool-augmented models Conversational search fairness through multi-level evaluation frameworks and group diversity metrics Innovations in 3D medical reconstruction from clinical data and multimodal uncertainty modeling Statistical rigor via randomization tests , credible intervals , and topic set design Privacy analysis in mobile app descriptions and cognitive bias studies in search interaction

• Distributed Systems
• Embedded Systems
• Ubiquitous Computing
• +۳ مورد دیگر

NAKAJIMA, Tatsuo serves as a Professor at Waseda University's School of Fundamental Science and Engineering, Department of Computer Network Engineering. Holding a Doctor of Engineering from Keio University, he has been affiliated with Waseda since 1999 after positions at Japan Advanced Institute of Science and Technology (1993-1999), Cambridge University, and Carnegie Mellon University. His academic profile shows substantial research output with 433 papers and 3,338 citations on Scopus, and 7,604 citations with an h-index of 42 on Google Scholar. Dr. Nakajima's research focuses on Distributed Systems, Embedded Systems, and Ubiquitous Computing, with particular emphasis on virtualization architectures for embedded environments. His work bridges theoretical computer science with practical applications in information appliances, operating systems, and persuasive computing technologies. He has developed innovative systems including SPUMONE (a composition kernel for multi-OS environments), SIGMA System, and SPLiT (a performance optimization library for multicore processors). Analysis of his 15 most recent publications reveals a consistent research trajectory centered on enhancing reliability, security, and performance of embedded and pervasive computing systems. His work shows increasing integration of human factors, particularly in sustainable behavior applications through persuasive technology. The research spans from low-level system architecture to user-centered applications, demonstrating both technical depth and practical relevance. Nokia Research Center, Visiting Research Fellow (2005.04) Dr. Nakajima's research has produced numerous practical frameworks including SPUMONE for multi-OS environments, SPLiT for performance optimization, and persuasive applications like EcoIsland for sustainable behavior. His work on kernel monitoring, anomaly detection, and self-healing systems demonstrates strong focus on system dependability. Current research appears directed toward integrating human factors with embedded systems, particularly in environmental sustainability applications. His laboratory work centers around the SPUMONE project, a virtualization layer for multi-core embedded systems that enables multiple operating systems to coexist with minimal engineering cost. This research environment supports exploration of resource management, security monitoring, and performance optimization in embedded contexts. The work has practical applications in information appliances, smart homes, and pervasive computing environments.

• Compilers
• Computer Architecture
• High-level Synthesis
• +۵ مورد دیگر

Scott Mahlke is a Professor and Associate Chair in the Electrical Engineering and Computer Science Department at the University of Michigan. He is affiliated with the Advanced Computer Architecture Laboratory and the Software Systems Laboratory, where he leads the Compilers Creating Custom Processors (CCCP) research group. His research focuses on compilers, computer architecture, and high-level synthesis, with particular emphasis on designing next-generation computer systems that overcome challenges in performance, power consumption, and reliability. His work bridges the gap between hardware and software through innovative compiler technology that enables customized processors and accelerators. Mahlke's publications demonstrate a strong focus on compiler techniques for exploiting instruction-level parallelism, memory system optimization, and application-specific processor design. His research spans from fundamental compiler algorithms to practical implementations in both general-purpose and embedded systems. National Science Foundation CAREER Award (2003) Morris Wellman Faculty Development Assistant Professor (2004) 2006 ISCA Most Influential Paper Award Multiple best paper awards at major architecture conferences As an advisor, Mahlke has chaired numerous Ph.D. dissertations and actively mentors graduate students in the CCCP group. His research is generously funded by the National Science Foundation, Gigascale Systems Research Center, ARM Ltd., Samsung Advanced Institute of Technology, and other major organizations. The CCCP group maintains strong industry partnerships that facilitate the transfer of research innovations to practical applications.

Todd Millsteinمشاهده پروفایل

استاد

• Network Verification
• Programming Languages
• Probabilistic Programming
• +۳ مورد دیگر

Todd Millstein is a Professor in the Computer Science Department at the University of California, Los Angeles (UCLA). He served as the Computer Science Department Chair from 2022-2025 and is also an Amazon Scholar. His research focuses on making software systems more reliable through programming languages techniques, with significant contributions to network verification and probabilistic programming. Millstein received his Ph.D. from the University of Washington Department of Computer Science, where he was a member of the Cecil group led by Craig Chambers. Prior to that, he completed his undergraduate studies at Brown University under the guidance of Paris Kanellakis and Pascal Van Hentenryck. Millstein's research spans several areas of programming languages and systems with a focus on reliability. He has made significant contributions to network verification, developing the Batfish network configuration analyzer which is now managed by Amazon Web Services and forms the basis of Oracle Cloud's Network Path Analyzer. His work has been recognized with the ACM SIGCOMM Networking Systems Award in 2025. He also works on interactive program verification through lemma synthesis and scalable reasoning methods for probabilistic programming languages. His research bridges programming languages theory with practical systems challenges, as highlighted in his SPLASH/OOPSLA 2024 keynote "Everything is a Program (even if it's not)". Millstein's recent publications demonstrate a consistent focus on verification and reliability across multiple domains. His work shows a progression from foundational programming language techniques to practical applications in networking and probabilistic systems. Key themes include data-driven approaches to program analysis, synthesis of verification artifacts, and applying programming languages techniques to non-traditional domains like network configuration. Millstein's scientific achievements have been recognized with numerous prestigious awards including an NSF CAREER Award, an ACM SIGPLAN Most Influential PLDI Paper Award, an ACM SIGCOMM Networking Systems Award, IEEE Micro Top Picks selection, best-paper awards from PLDI, OOPSLA, and SIGCOMM, a Microsoft Research Outstanding Collaborator Award, an Okawa Foundation Research Grant, an IBM Faculty Award, and a Facebook Research Award. He has also received both the Northrop Grumman Excellence in Teaching Award (for junior faculty) and the Eon Instrumentation Inc. Excellence in Teaching Award (for senior faculty) from UCLA Engineering. Millstein advises several Ph.D. students including Ana Brendel, Poorva Garg (co-advised with Guy Van den Broeck), Rajdeep Mondal (co-advised with George Varghese), and Rathin Singha (co-advised with George Varghese). His research has been supported by various grants including an NSF CAREER Award, Okawa Foundation Research Grant, IBM Faculty Award, and Facebook Research Award. He has also been a Co-Founder and Chief Scientist of Intentionet, which was later acquired by Amazon Web Services. Millstein is actively involved in the Batfish project, an open-source network configuration analyzer that has had significant practical impact. Batfish is now managed by AWS, powers Oracle Cloud's Network Path Analyzer, and is used by dozens of companies. His research group continues to work on network reliability, developing techniques for scalable BGP policy verification and behavioral testing of protocol implementations.

• Process Technology
• Device Design
• Silicon Carbide (SiC) Electronics
• +۳ مورد دیگر

Carl-Mikael Zetterling is a Professor and Head of Department at Kungliga Tekniska Högskolan (KTH) in Stockholm, Sweden, affiliated with the School of Electrical Engineering and Computer Science (ICT) and the Electronics and Embedded Systems department. His research focuses on process technology and device design for high-temperature, high-power silicon carbide (SiC) electronics, expanding into SiC-based analog and integrated circuits. He has authored over 300 publications, including books on SiC process technology and plagiarism prevention. Dr. Zetterling has held leadership roles such as Vice Dean of the School of ICT (2013–2017) and teacher representative on KTH's faculty board. He has collaborated internationally at Stanford University, Kyoto University, and Kyoto Institute of Technology. His work addresses applications in extreme environments, including Venus exploration and fusion reactor monitoring, with a focus on radiation tolerance and thermal resilience. The 15 most recent publications highlight trends in wide bandgap semiconductors, gamma irradiation effects on SiC devices, and high-temperature integrated circuits. His articles span structural health monitoring with machine learning, novel SiC diode designs, and radiation-hardened electronics. Key contributions include advancements in self-aligned contacts, trench MOSFETs, and compact modeling for extreme conditions. While no formal awards are listed, his roles in technical program committees (TMS Electronic Materials Conference, IEEE SISC Conference) and editorial work demonstrate significant academic service. He teaches courses ranging from digital design to high-temperature electronics, overseeing degree projects in embedded systems, communication, and nanotechnology.

Zhibin Chenمشاهده پروفایل

استادیار

• Transportation Network Modeling and Optimization
• Intelligent Transportation Systems
• Discrete Optimization
• +۴ مورد دیگر

Zhibin Chen is an Assistant Professor of Engineering at NYU Shanghai and concurrently a Global Network Assistant Professor within the broader New York University system. Since January 2019 he has led research and teaching activities at the Division of Engineering and Computer Science in Shanghai, while maintaining university-wide collaborations through his Global Network appointment. Education Ph.D. in Transportation Engineering, University of Florida (2017) Research Interests Dr. Chen’s scholarship centres on Transportation Network Modeling and Optimization , Intelligent Transportation Systems , and Discrete Optimization . He integrates operations research, data science, and engineering to address emerging challenges in electric mobility, autonomous vehicles, and large-scale urban networks. Recent thrusts include: Data-driven analytics of electric-vehicle charging behaviour under usage heterogeneity. Optimization of charging and swapping infrastructure for electric buses and trucks. Network-level deployment and control strategies for connected and automated vehicles. Day-to-day traffic dynamics and equilibrium models with elastic demand. Pricing, policy, and incentive design for sustainable transportation systems. Scientific Awards Stella Dafermos Best Paper Award – awarded at the 95th Transportation Research Board Annual Meeting. Ryuichi Kitamura Paper Award – also conferred at the 95th TRB Annual Meeting. Editorial & Professional Service Dr. Chen currently serves on the Editorial Advisory Board of Transportation Research Part C: Emerging Technologies , shaping the editorial direction of the leading journal in his field. Grants & Collaborations While specific grant identifiers are not disclosed in the provided text, Dr. Chen’s extensive publication record in top-tier journals ( Transportation Science , Transportation Research Parts B, C, D , IEEE ITS , Applied Energy ) and his editorial role indicate sustained research funding and active collaboration with international partners across North America and China. Laboratories & Teams Operating within the Division of Engineering and Computer Science at NYU Shanghai , Dr. Chen leads a research group focused on next-generation mobility analytics, leveraging the university’s interdisciplinary ecosystem and NYU’s Global Network resources to advance smart and sustainable transportation.