جست‌وجوی استاد و پژوهشگر

Joseph Mooreمشاهده پروفایل

استادیار

• Aerial Robotics
• Computational Control
• Learning-based Control
• +۳ مورد دیگر

Dr. Joseph Moore is an Assistant Professor in the Department of Mechanical Engineering at Johns Hopkins University (JHU), serving as Director of the Agile and Intelligent Robotics (AIRO) Laboratory. He is affiliated with the Laboratory for Computational Sensing and Robotics (LCSR), the Institute for Assured Autonomy (IAA), and holds a Bridging Faculty appointment in the Research and Exploratory Development Department (REDD) at JHU/APL. His research focuses on computational control, machine learning, and robotics to enable agile systems operating in complex environments. Dr. Moore previously served as Robotics Group Chief Scientist at JHU/APL, leading projects on hybrid unmanned aerial-aquatic vehicles and aerobatic fixed-wing systems. He has secured funding as Principal Investigator (PI) for ONR, DARPA, and ARL programs, particularly in post-stall maneuvering control and multi-robot coordination. His work emphasizes robust control strategies for autonomous systems in constrained environments. Research interests include aerial robotics, optimization, and learning-based control. Notable contributions involve NMPC-based systems, UAV navigation, and adaptive control for uncertain environments. His recent articles highlight advancements in swarm coordination, morphing-wing UAVs, and PAC-NMPC frameworks. Dr. Moore advises students such as Mark Gonzales and Adam Polevoy. Key grants include ONR/DARPA-funded projects on post-stall flight control and Army-funded multi-robot coordination efforts. His lab (AIRO) and collaborations (LCSR, IAA) drive applied and theoretical robotics research.

Iris F.A. Visمشاهده پروفایل

استاد

• Operations Management
• Supply Chain Design
• Sustainable Logistics
• +۵ مورد دیگر

Prof. Iris F.A. Vis is a Professor of Industrial Engineering at the University of Groningen's Faculty of Economics and Business. She specializes in logistics and operations management, focusing on optimizing processes through quantitative and qualitative methods. Her work intersects logistics with sectors like healthcare, education, and energy. She leads major projects such as SMiLES (sustainable mobility-logistics integration) and designs logistics solutions for personalized learning systems in schools. She has advised over a dozen PhD students and collaborates with industry partners globally. Awards include Fellowship in the Netherlands Academy of Engineering. Education: M.Sc. Mathematics (Leiden University), PhD in Operations Management (Erasmus University Rotterdam) Roles: Captain of Science for Topsector Logistics, Member of multiple national advisory boards Research interests span sustainable transportation networks, port optimization, healthcare logistics, and educational logistics. Key projects include LNG supply chain design, offshore wind farm maintenance planning, and synchromodal transport networks. Over 45 peer-reviewed publications and 18 media engagements highlight her impactful contributions. Teaching includes courses on supply chain network design, technology-enabled innovation, and operations management at all academic levels. She advises on industrial partnerships and digital transformation initiatives in the Northern Netherlands region.

Mesut Baranمشاهده پروفایل

استاد

• Power Systems
• Smart Grid Technology
• Distributed Energy Resources
• +۳ مورد دیگر

Mesut Baran is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He focuses on applying computer, control, and system analysis techniques to power system operation and planning, particularly in smart power distribution systems and distributed energy resource integration. His research emphasizes grid resilience, renewable energy integration, and advanced grid technologies. Education: Ph.D. in Electrical Engineering, University of California, Berkeley (1988) Master's in Electrical Engineering, Middle East Technical University, Turkey (1981) Bachelor's in Electrical Engineering, Middle East Technical University, Turkey (1979) Research interests include power electronics, smart grid technologies, distributed energy resource management, and grid resilience. His recent work addresses challenges in EV charging impacts, fault tolerance, and microgrid coordination. Baran has contributed to IEEE standards and received prestigious awards, including the IEEE Fellow designation (2010) and the William F. Lane Outstanding Teaching Award (2017). Publications highlight advancements in distribution system state estimation, fault mitigation, and microgrid management. Collaborations with industry (e.g., Strata Solar) and federal grants ($3.1M DOE award) underscore his applied research impact. He teaches foundational courses like ECE200 and advises on FREEDM Systems Center projects.

Navid Izadyمشاهده پروفایل

مدرس ارشد

• Operations Research
• Healthcare Management
• Stochastic Modelling
• +۳ مورد دیگر

Dr. Navid Izady is a Reader in Operations & Supply Chain at Bayes Business School, part of City St George's, University of London. His academic career includes a PhD from Lancaster University Management School (2010), and prior roles at the University of Southampton. He specializes in stochastic modelling for healthcare and manufacturing operations, collaborating with hospitals and healthcare organizations on sponsored research and consultancy projects. Dr. Izady holds qualifications in Industrial Engineering from Sharif University of Technology (BSc and MSc) and a PhD in Management Science. He teaches operations management, stochastic modelling, healthcare modelling, and decision analysis across BSc, MSc, and MBA programs. His research focuses on optimizing healthcare logistics, patient flow management, and resource allocation in hospitals. He has developed frameworks for managing pandemic and non-pandemic demand, reconfiguring inpatient services, and optimizing staffing and patient admission/discharge processes. His work bridges theoretical stochastic models with practical healthcare challenges, emphasizing operational efficiency and resilience. Notable contributions include studies on inpatient bed pressure reduction, sample pooling techniques for pandemic testing, and queueing theory applications in emergency departments and specialty clinics. His publications highlight innovations in healthcare operations management and simulation methods. Dr. Izady's expertise includes operations research, simulation, statistics, and stochastic processes. He supports industry partnerships and has supervised numerous research students, contributing to both academic and applied knowledge in healthcare and manufacturing systems.

Zhaohai Liمشاهده پروفایل

استاد

• Genetic Epidemiology
• Statistical Methods for Meta-analysis
• Clinical Biostatistics
• +۳ مورد دیگر

Dr. Zhaohai Li Professor of Statistics at George Washington University, specializing in statistical methodologies for genetic epidemiology and clinical biostatistics. His research focuses on meta-analysis techniques, empirical Bayes methods, and population-based study designs. He has contributed extensively to improving statistical approaches in clinical trials and addressing challenges in genetic association studies. Education: Ph.D. in Statistics, Columbia University, 1989 Research Interests: His work addresses critical issues in modern biostatistics including: Population stratification in genetic studies Hardy-Weinberg equilibrium testing Optimal experimental design for case-control studies Handling missing data in genetic linkage analysis Development of robust statistical tests for complex survey data Publications Overview: Dr. Li's recent work emphasizes methodological advancements in: Bayesian approaches to population genetics Meta-analytic frameworks for combining study results Statistical solutions for multi-stage clinical trials Algorithmic improvements for genome-wide association analyses Professional Contributions: His articles consistently address practical challenges in biomedical research, bridging theoretical statistics with real-world genetic and clinical applications.

Kyle DeMarsمشاهده پروفایل

دانشیار

• Space situational awareness
• Navigation, guidance, and control
• Bayesian filtering
• +۶ مورد دیگر

Kyle DeMars is an Associate Professor and Associate Department Head for Theoretical and Computational Research in the Department of Aerospace Engineering at Texas A&M University. He holds a Ph.D. from The University of Texas at Austin (2010) and has expertise in space situational awareness, navigation systems, Bayesian filtering, and information theory. His work focuses on advanced estimation techniques for spacecraft autonomy and space surveillance. Dr. DeMars' research emphasizes robust nonlinear filtering, multitarget tracking, and information-theoretic approaches to orbital dynamics. He has developed innovative methods for spacecraft navigation, including terrain-relative systems and anonymous feature processing. His contributions address challenges in uncertainty quantification, sensor fusion, and cislunar space domain awareness. Education: Ph.D./M.S.E./B.S. in Aerospace Engineering (UT Austin, 2004–2010) Awards: AIAA Young Professional Award (2017), NASA Innovation Award (2014), and multiple teaching/research recognitions Labs/Teams: Active in space situational awareness, guidance & control, and probabilistic navigation systems Key trends in his publications include: Advances in particle flow and Gaussian mixture methods for nonlinear estimation Cislunar trajectory analysis and resonance-based surveillance strategies Development of fault-resistant and anonymous navigation frameworks Integration of information theory into sensor tasking and uncertainty management His work bridges theoretical developments with practical applications in planetary landing navigation, space traffic management, and autonomous spacecraft systems.

STEVEN CONSTABLEمشاهده پروفایل

استاد

• Geophysics
• Marine Geology and Geophysics
• Electromagnetism
• +۵ مورد دیگر

Steven Constable is a Professor of Geophysics at the Institute of Geophysics and Planetary Physics (IGPP) within the Scripps Institution of Oceanography at UC San Diego. He specializes in electrical conductivity studies of Earth’s crust and mantle, seafloor instrumentation development, and geophysical data analysis. His research focuses on understanding tectonic processes, subduction zone dynamics, and marine geohazards through electromagnetic methods. Education: B.S., University of Western Australia Ph.D., Australian National University Research Interests: Electrical conductivity of crust and mantle Seafloor instrumentation development Magnetotelluric and controlled-source electromagnetic (CSEM) methods Subduction zone fluid dynamics CO 2 sequestration monitoring Mid-ocean ridge magmatism Grants & Collaborations: NSF-NERC Collaborative Research: Magnetotelluric imaging of plume-ridge interactions (Galapagos) Magnetotelluric Investigation of the Salton Trough (MIST) Experiment PI-LAB Experiment at the Equatorial Mid-Atlantic Ridge Labs & Teams: He leads the Marine Electromagnetics Lab , developing cutting-edge instrumentation for marine geophysical surveys. His team collaborates globally on projects ranging from Arctic permafrost assessment to subduction zone imaging.

Gareth Robertsمشاهده پروفایل

استاد

• Computational Statistics
• Stochastic Processes
• Bayesian Statistics
• +۵ مورد دیگر

Professor Gareth Roberts is a Professor in the Department of Statistics at the University of Warwick. His research focuses on Computational Statistics, particularly MCMC methods, stochastic processes, Bayesian inference, statistical privacy, and applications in infectious disease modeling and sports analytics. He leads the OCEAN project with Eric Moulines, Michael Jordan, and Christian Robert, and teaches the ST923 lecture course on advanced statistical methods. His research interests include developing efficient sampling algorithms (e.g., MCMC, PDMP), statistical methodology for missing data, and privacy-preserving statistical techniques. Recent work emphasizes high-dimensional Bayesian models, quasi-stationary Monte Carlo, and scalability of computational methods. Publications span innovations in MCMC theory, applications to epidemiology, and sports probability modeling. His work on the Zig-Zag process and stereographic MCMC demonstrates contributions to PDMP-based sampling. Collaborations include interdisciplinary projects on bacterial transmission dynamics and statistical methods for big data. He actively participates in academic leadership, including organizing courses and contributing to the statistical community through projects like OCEAN. Contact: Gareth.O.Roberts@warwick.ac.uk .

Karl-Erik Årzénمشاهده پروفایل

استاد

• Control Engineering
• Cyber-Physical Systems
• Real-Time Systems
• +۹ مورد دیگر

Karl-Erik Årzén is Professor and Head of the Department of Control Engineering at Lund University's Faculty of Engineering. He is also Co-director of the Wallenberg AI, Autonomous Systems and Software Program (WASP) and a key member of ELLIIT, the excellence center in information technology. His roles include leadership in AI and digitalization profile areas at both LTH and Lund University. His research lies at the intersection of control engineering and computer science, with a focus on cyber-physical systems, real-time systems, embedded control, and resource management in cloud and edge computing environments. He has pioneered methods for predictable performance in cloud applications and dynamic resource allocation using control-theoretic approaches. The recent publications highlight a strong trend in control over the cloud and edge, real-time scheduling co-design, distributed camera systems, and reinforcement learning for auto-scaling. Key topics include model predictive control, LQG-based scheduling, bandwidth allocation, and robustness in cyber-physical systems. The work spans theoretical control design and practical implementation in distributed systems. His scientific awards include multiple Best Paper Awards from IEEE and ACM conferences in 2018, 2016, and 2004, recognizing excellence in autonomic computing, edge computing, and real-time systems. Best Paper Award, IEEE International Conference on Autonomic Computing, 2018 Best Paper Award, IEEE International Conference on Edge Computing (EDGE), July 2018 Best Paper Award - RTNS 2016 Best Paper Award - RTCSA 2004 Årzén has supervised over 20 PhD students, including Mikael Johansson, Anton Cervin, Yang Xu, and Per Skarin, and currently supervises Ahmed Al Bayati and Max Nyberg Carlsson. His grant portfolio includes major projects such as WASP, AORTA (VINNOVA), and ELLIIT's 'Robust and Secure Control over the Cloud'. He has also contributed to innovation through tools like TrueTime and Jitterbug. He leads the RobotLab LTH initiative and is involved in the Nordic University Hub on Industrial Internet of Things (HI2OT). His work bridges academia and industry, with collaborations on adaptive control, cloud-native systems, and autonomous robotics.

Roland Pailمشاهده پروفایل

استاد

• Physical Geodesy
• Satellite Geodesy
• Global and Regional Gravity Field Modeling
• +۴ مورد دیگر

Prof. Roland Pail is a full Professor of Astronomical and Physical Geodesy at the Technical University of Munich (TUM). He leads the Chair of Astronomical and Physical Geodesy, part of the TUM School of Engineering and Design. His research focuses on physical and numerical geodesy, global/regional gravity field modeling, and satellite gravity missions like GOCE, GRACE, and future initiatives like MAGIC. He has held leadership roles, including President of IAG Commission 2 (2015–2019) and Vice Dean of TUM's Department of Aerospace and Geodesy. Pail earned his doctorate (sub auspiciis praesidentis) from TU Graz (1999) and habilitation in 2002. He is a Fellow of the International Association of Geodesy and has received numerous awards for his contributions to geodesy. His work integrates satellite data with geophysical modeling to monitor mass transport processes (e.g., ocean circulation, ice melt) and Earth's interior dynamics. He collaborates internationally on missions such as the DFG Research Training Group UPLIFT and the MAGIC constellation. Key publications include gravity field models (e.g., XGM2016, GOCO06s) and studies on future mission design, stochastic modeling, and climate monitoring. Pail’s scientific awards include the IAG Fellowship (2011), Young Authors Award (2006), and the Allmer-Löschner Prize (2000). His research also addresses quantum sensor applications in satellite gravimetry and the development of next-generation gravity field retrieval techniques.

Aliasghar Arabmaldarمشاهده پروفایل

استادیار

• Operations Research
• Data Envelopment Analysis
• Robust Optimization
• +۴ مورد دیگر

Aliasghar Arabmaldar is an Assistant Professor in Business Analytics and Systems (BAS) at the Hertfordshire Business School, University of Hertfordshire. He holds a PhD in Operations Research from the University of Sistan and Baluchestan (2017), an MSc in Applied Mathematics-Operations Research from the University of Guilan (2011), and a BSc in Applied Mathematics from the same institution (2009). His research focuses on robust optimization, data envelopment analysis (DEA), decision-making under uncertainty, and performance evaluation in complex systems. Key research interests include: Developing robust DEA models to handle uncertainty in data Resilience-efficiency trade-offs in retail logistics Network production processes and efficiency measurement Integration of undesirable outputs in performance evaluation Recent work emphasizes application in retail management, oil industries, and higher education sectors. His 2025 project, 'Enhancing Research Publication Skills Through Writing Retreats,' highlights his commitment to academic development. Peer-reviewed contributions appear in top journals like European Journal of Operational Research and Expert Systems with Applications. He serves as a reviewer for 12+ journals including OMEGA and Operations Research Spectrum, demonstrating his influence in operational research and analytics.

Difan Zouمشاهده پروفایل

استادیار

• Machine Learning Theory
• Deep Learning Theory
• Optimization Algorithms
• +۵ مورد دیگر

Dr. Difan Zou is an Assistant Professor in the Department of Computer Science at the University of Hong Kong's School of Computing and Data Science. He holds a PhD in Computer Science from UCLA and degrees in Applied Physics and Electrical Engineering from the University of Science and Technology of China (USTC). His research focuses on machine learning theory, optimization, and learning structured data such as time-series and graph data, with an emphasis on understanding deep learning's theoretical underpinnings like optimization trajectories and generalization properties. Dr. Zou's academic background includes a B.S. from USTC's School of Gifted Young (Applied Physics) and a M.S. in Electrical Engineering from the same institution. His work bridges theoretical foundations and practical applications, addressing challenges in adversarial robustness, algorithm design for deep neural networks, and explainable machine learning systems in healthcare and finance. His research projects aim to establish rigorous frameworks for deep learning optimization, develop efficient training algorithms, and integrate conventional statistical models with machine learning for improved interpretability. He has received the Bloomberg Data Science Ph.D. Fellowship and has contributed to top-tier conferences like ICML, NeurIPS, and ICLR.

Yuan Zhongمشاهده پروفایل

دانشیار

• Applied Probability
• Stochastic Systems
• Operations Management
• +۷ مورد دیگر

Yuan Zhong is an Associate Professor of Operations Management at the University of Chicago Booth School of Business . He previously held positions as an Assistant Professor at Columbia University’s Department of Industrial Engineering and Operations Research and was a Postdoctoral Scholar at UC Berkeley’s Computer Science Department. Education: PhD in Operations Research, MIT (2012) MA in Mathematics, Caltech (2008) BA in Mathematics, University of Cambridge (2006) His research focuses on applied probability and stochastic system design , with applications in cloud computing , supply chain management , and e-commerce logistics . Recent work explores multi-period production systems and dynamic resource allocation in data centers and healthcare operations . Recent publications analyze cloud value chains , sparse graph design for delivery networks, and process flexibility in manufacturing. He has contributed to journals like Operations Research , Annals of Applied Probability , and Stochastic Systems . Scientific Awards: 2012 Kenneth C. Sevcik Outstanding Student Paper Award Best Student Paper Award at ACM Sigmetrics (2012) He teaches courses in business process fundamentals and queueing theory , with a future schedule including Operations Management: Business Process Fundamentals (2025–2026). No explicit student advising list was provided.

• Adaptive nonparametric statistics
• High-dimensional data analysis
• Financial time series modeling
• +۵ مورد دیگر

Prof. Vladimir Spokoiny is a leading figure in stochastic algorithms and nonparametric statistics at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) and Humboldt University of Berlin . His work bridges mathematical statistics with practical applications in finance, medicine, and machine learning. Born in 1959 in Moscow, USSR PhD from Lomonosov Moscow State University (1988) Habilitation from Humboldt University (1996) Head of WIAS research group since 2000 Professor at Humboldt University since 2002 Spokoiny's research focuses on adaptive nonparametric methods, high-dimensional data analysis, and statistical finance. His innovations in local homogeneity testing and propagation-separation methods have advanced volatility modeling, image analysis, and manifold learning. He employs Bayesian optimization frameworks and stochastic control techniques for financial instrument pricing. Recent scientific contributions include generalized bootstrap procedures for Bures-Wasserstein barycenters (2024), dimension-free Laplace approximation bounds (2023), and structure-adaptive manifold estimation (2022). His 19+ PhD students and editorial roles in top journals like The Annals of Statistics demonstrate sustained academic impact. International Statistical Institute member American Statistical Association fellow Institute of Mathematical Statistics member Bernoulli Society member

Lisa Willsمشاهده پروفایل

استادیار

• Computer Architecture
• Hardware Acceleration
• Hardware-Software Co-Design
• +۵ مورد دیگر

Lisa Wills serves as Assistant Professor of Computer Science at Duke University's Trinity College of Arts & Sciences and holds a joint appointment in Electrical and Computer Engineering at the Pratt School of Engineering since 2019. Her research bridges computer architecture and domain-specific applications, with a focus on hardware acceleration for computationally intensive fields. Dr. Wills earned her Ph.D. from Columbia University in 2014. Her academic journey reflects a deep commitment to advancing hardware-software co-design methodologies for real-world computational challenges. Her research centers on developing efficient hardware accelerators for big data analytics, particularly in genomics, graph processing, and database systems. She pioneers frameworks that simplify accelerator deployment while tackling critical bottlenecks in genomic data analysis, protein structure prediction, and privacy-preserving computing. Current work focuses on hardware-aware machine learning systems and energy-efficient architectures for emerging AI applications. Analysis of her publication record reveals a clear trajectory: from foundational work in database processing units (2014-2016) to specialized genomic accelerators (2019-2021), then evolving toward ML-enhanced design automation (2022-2023) and cutting-edge architectural abstractions (2024-2025). Her research consistently targets the intersection of hardware efficiency and domain-specific computational demands, with increasing emphasis on AI/ML workloads. Google ML and Systems Junior Faculty Award (2025) Dr. Wills actively mentors doctoral students including Chris Kjellqvist (lead architect of Beethoven accelerator framework), Mason Ma (PyTFHE FHE framework), and Mansi Choudhary (COCOSSim accelerator simulator). Her research is supported by significant grants including the NSF AI Institute: Athena ($20M, 2021-2027), Meta-funded ProSE accelerator project (2023-2026), and NSF CAREER award (2021-2026), totaling over $25M in active funding. She directs the APEX Lab (Application-driven Programmable Efficient Accelerated Systems), which develops open-source frameworks like Beethoven for FPGA/ASIC accelerator deployment and focuses on lowering barriers for non-hardware researchers to leverage custom acceleration in genomics, AI, and big data applications.