جست‌وجوی استاد و پژوهشگر

Do Young Eunمشاهده پروفایل

استاد

• Distributed Optimization
• Machine Learning
• Network Modeling
• +۵ مورد دیگر

Do Young Eun is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University (NC State), with affiliations in Computer Science and Operations Research. He holds a Ph.D. from Purdue University and M.S./B.S. degrees from KAIST, Korea. His research focuses on distributed optimization for machine learning, network modeling, and algorithms for social/wireless networks, with applications in epidemic analysis and graph analytics. Education: Ph.D. in Electrical and Computer Engineering, Purdue University (2003) M.S. in Electrical Engineering, KAIST (1997) B.S. in Electrical Engineering, KAIST (1995) Research Interests: Distributed optimization and machine learning Network modeling and performance analysis Epidemic modeling and control Graph analytics and social network analysis Stochastic processes and algorithms Highlighted Awards: NSF CAREER Award (2006) Outstanding Paper Award, ICML 2023 Best Paper Awards at IEEE ICCCN (2005), IPCCC (2006), NetSciCom (2015) Best Student Paper Award, ACM MobiCom 2007 Advising & Grants: Current advisees include Jie Hu, Yi-Ting Ma, and Feiya Xiang NSF Grant (2024–2027): 'Toward Maximally Efficient Sampling and Optimization for Decentralized Learning' Supervised over 10 Ph.D. students, many in academic or industry leadership roles Labs/Teams: His research group focuses on interdisciplinary projects at the intersection of networking, machine learning, and stochastic systems, with collaborations in academia and industry.

Aritra Mitraمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Control Theory
• +۶ مورد دیگر

Aritra Mitra is an Assistant Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds a Ph.D. from Purdue University (2020), an M.Tech. from IIT Kanpur (2015), and a B.E. from Jadavpur University (2013). Before joining NC State, he was a postdoctoral researcher at the University of Pennsylvania. His research focuses on enabling reliable, efficient learning and decision-making in large-scale distributed systems, addressing challenges like computation, communication constraints, and adversarial robustness. Key areas include control theory, machine learning, signal processing, and network science. Education: Ph.D., Electrical and Computer Engineering, Purdue University (2020) M.Tech., Electrical Engineering, Indian Institute of Technology Kanpur (2015) B.E., Electrical Engineering, Jadavpur University (2013) Research Interests: Dr. Mitra’s work bridges theoretical foundations with practical applications in distributed systems. He designs algorithms for federated learning, reinforcement learning, and adversarial robustness, with applications in control systems and networked environments. Recent efforts emphasize finite-time analysis of TD learning, heterogeneous federated systems, and resilient control under communication constraints. His contributions often integrate tools from stochastic approximation, optimization, and signal processing. Publications: His articles explore cutting-edge topics like federated TD learning, robust system identification under heavy-tailed noise, and distributed multi-agent optimization. Recent trends highlight advancements in asynchronous algorithms, delay-adaptive systems, and model-free control under communication bottlenecks. Grants & Labs: While specific grants are not detailed, his research aligns with themes in distributed computing and control, suggesting potential involvement in NSF or industry-funded projects. No lab-specific details are provided in the text.

Svetlana Yanushkevichمشاهده پروفایل

استاد

• Biometric Technologies
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

Dr. Svetlana Yanushkevich is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary. She is also a Full Member of the Hotchkiss Brain Institute and the Mathison Centre for Mental Health Research and Education. Her research focuses on biometric technologies, decision support systems, biomedical applications, and computational intelligence. She leads the Biometric Technologies Laboratory, developing strategies for risk assessment in biometric systems and healthcare monitoring through machine reasoning and signal processing. Education : BSc/MSc in Electrical Engineering (1989), State University of Informatics and Radioelectronics, Minsk PhD in Electrical Engineering (1992), same institution Dr. Habilitated in Technical Sciences (1999), Warsaw University of Technology Research Interests : Dr. Yanushkevich’s work spans biometric system design (e.g., gait analysis, facial attributes), decision support via probabilistic models (Bayesian networks, causal inference), biomedical applications (stroke rehabilitation, wearable sensors), and computational intelligence for data science. She emphasizes fairness, bias mitigation, and trustworthiness in AI systems, particularly in healthcare and accessibility contexts. Recent Research Trends : Her recent publications address causal modeling for accessibility barriers, UAV operator cognitive workload, and medical device optimization in radiation therapy. She explores AI ethics, stress contagion in human-robot teams, and cross-spectral biometric systems. Awards & Recognition : 2024 FEIC Fellow (Engineering Institute of Canada) 2019 Research Excellence Award (Schulich School of Engineering) 2001 Senior IEEE Membership Advising & Grants : She coordinates courses like ENCM 509 (Biometric Systems Design) and ENEL 610 (Biometric Technologies). Her research is supported by grants focusing on healthcare AI, accessibility technologies, and computational epidemiology. Labs & Collaborations : Her Biometric Technologies Lab collaborates with institutions like Hokkaido University and the IEEE Computational Intelligence Society. Projects include wearable health monitoring, decision support platforms, and AI-driven epidemiological modeling.

Peter Chinمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Differential Geometry
• +۸ مورد دیگر

Professor Peter Chin is a Professor of Engineering at Dartmouth College and Director of the Learning, Intelligence + Signal Processing (LISP) Lab. He holds affiliations with the Thayer School of Engineering and serves as Associate Editor of IEEE Transactions on Computational Social Systems. His research bridges signal processing, machine learning, game theory, and differential geometry, with applications in cybersecurity, healthcare, and network analysis. Education: Bachelor of Science in Electrical Engineering, Computer Science, and Mathematics from Duke University (1993) Doctor of Philosophy in Mathematics from MIT (1998) Research Interests: Chin’s work focuses on fundamental questions at the intersection of machine learning, game theory, and signal processing. His lab explores topics like adversarial defense mechanisms, topological machine learning, and computational neuroscience. Recent projects include cybersecurity resilience modeling, medical imaging enhancements via GANs, and multi-agent reinforcement learning frameworks. Publications Trends: His most recent articles address cutting-edge challenges in cybersecurity (e.g., autonomous defense systems), medical AI (e.g., Alzheimer’s classification), and adversarial robustness. A notable 2025 focus is on quantitative resilience modeling for cyber defense, reflecting growing demand for AI-driven security solutions. Awards: Faculty Scholar Award, Duke University George Sherred III Award, Duke University Julia Dale Memorial Award, Duke University Grants & Leadership: Recipient of DARPA cybersecurity research grants Co-chair for SPIE/DSS Cyber Sensing Conference (2013–2020) Developed novel compressive sensing microscope for biological imaging LISP Lab: This interdisciplinary lab pioneers projects like nFlip (multiplayer security game models) and topological machine learning frameworks, emphasizing practical applications of theoretical advancements.

Johannes Zeiherمشاهده پروفایل

استاد

• Quantum Computing
• Atomic Physics
• Quantum Matter Interfaces
• +۳ مورد دیگر

Prof. Johannes Zeiher is a Professor at Ludwig Maximilian University (LMU) and leads the independent research group Quantum Matter Interfaces . His work focuses on studying quantum systems of laser-cooled atoms coupled to optical resonators, aiming to advance quantum error correction and quantum many-body physics. He secured €3.3 million from Germany's BMBF for the SNAQC project on scalable neutral atom quantum computing. Research interests include quantum interfaces between atoms and photons, Rydberg arrays in optical tweezers, and hybrid architectures for quantum technologies. His group explores non-destructive measurements, feedback mechanisms, and entanglement generation in quantum systems. Key experimental tools include high-resolution microscopy and resonator-coupled systems. Prof. Zeiher's lab is located at the Max Planck Institute of Quantum Optics, collaborating on cutting-edge quantum technologies. He holds dual affiliations with LMU and the MPQ, advancing both theoretical and experimental frontiers in quantum computing and quantum simulation. His work bridges atomic physics, quantum optics, and condensed matter systems to realize practical quantum devices.

• Materials Science
• Nanotechnology
• Physics
• +۳ مورد دیگر

Ville Jantunen is a Researcher and Supervisor in the Doctoral Programme in Materials Research and Nanosciences. His research focuses on atomistic simulations of materials under extreme conditions, including ion irradiation effects, defect evolution in fusion materials, and nanoparticle dynamics. He is actively involved in the SPATEC project (2022–2026), funded by the Academy of Finland, which explores time and spatial dependence of cascade damage in materials under pulsed ion beams. Key research areas include computational materials science, radiation effects in nanomaterials, and predictive modeling of electronic/atomic phenomena. His work spans both fundamental and applied aspects, with contributions to quantum technology through spin-qubit array studies and fusion energy via tungsten defect analysis. Collaborations include international teams on nanoparticle shape transformation mechanisms and kinetic Monte Carlo simulations. He has organized educational outreach activities like the LEGO lab workshop at Helsinki Natural Science Lyceum (2018), demonstrating engagement in science communication. Publications emphasize interdisciplinary approaches, combining computational methods with experimental insights to address challenges in nanotechnology, fusion materials, and radiation physics.

Stavros Vougioukasمشاهده پروفایل

استاد

• Agricultural Robotics
• Mechanization of Specialty Crops
• Automation in Agriculture
• +۷ مورد دیگر

Stavros Vougioukas is a Professor and Vice Chair in the Department of Biological and Agricultural Engineering at the University of California, Davis, within the College of Engineering. He is actively involved in research and graduate mentorship, focusing on agricultural robotics and automation for specialty crops. His work integrates engineering solutions to improve efficiency and sustainability in farming systems. His research interests include agricultural robotics , automation of harvesting processes , sensors and control systems , precision agriculture , and wireless sensor networks for orchard environments . He develops technologies for robotic and robot-aided harvesting, particularly in strawberries and orchard crops, emphasizing optimal management of inputs and yield monitoring. The recent publications reflect a strong trend in robotics integration , real-time sensing , and data-driven decision-making in agriculture. His work spans mechanical design, signal processing, path planning, and structural durability, indicating a multidisciplinary approach to solving agricultural challenges through engineering innovation. Scientific Awards and Recognition: $1.6M grant (2021) to develop innovative fruit-picking machines CITRIS Seed Award (2023) for engineering solutions in agriculture Professor Vougioukas mentors graduate students and leads funded research projects focused on automation and robotics in agriculture. He has secured significant grants, including a $1.6M award for fruit-picking robotics, demonstrating strong research leadership. His collaborations span institutions and disciplines, particularly in agricultural machinery design and sensor network deployment. He leads research efforts in agricultural automation, particularly through projects involving robot-aided harvesting , orchard navigation systems , and wearable worker tracking devices . His lab contributes to the development of intelligent systems for sustainable farming, integrating mechanical, electronic, and computational components.

• Machine Learning
• Statistical Computing
• Cluster Analysis
• +۲ مورد دیگر

Theresa Scharl-Hirsch is a Senior Scientist and Deputy Scientific Director at the Core Facility Bioinformatics, University of Natural Resources and Life Sciences, Vienna (BOKU). She holds concurrent appointments at the Institute of Statistics, BOKU, and has extensive experience in bioprocess modeling, machine learning, and statistical computing. Her work bridges biochemical engineering with advanced data science methodologies. Her research focuses on real-time monitoring of biopharmaceutical processes, clustering of high-dimensional data (particularly RNA sequencing), and application of explainable machine learning techniques. She has developed statistical models for process optimization and quality prediction in antibody capture and protein purification, with a strong emphasis on industrial implementations using R programming. Key trends in her publications include three-way data analysis, matrix-variate Gaussian mixture models, and permutation-based variable importance methods for deep learning architectures. Her work spans bioprocess engineering, bioinformatics, and industrial data science applications.

Frank Kellyمشاهده پروفایل

استاد

• Random processes
• Networks
• Optimization
• +۳ مورد دیگر

Professor Frank Kelly holds the Professor of the Mathematics of Systems at the University of Cambridge , affiliated with the Department of Pure Mathematics and Mathematical Statistics (DPMMS). His work bridges theoretical insights with practical applications in random processes , networks , and optimization . Academic Rank: Professor Department: DPMMS Research Themes: Stochastic systems, network resource allocation, mathematical modeling Kelly’s research focuses on stochastic networks , including congestion control in communication systems, fair bandwidth sharing , and electricity grid optimization . His work often incorporates proportional fairness principles and Markov models to analyze complex systems. Recent publications reveal a trajectory in network dynamics and resource allocation , with applications to electric vehicles, satellite routing, and virtualized networks. His work shows interdisciplinary impact across computer science , telecommunications , and energy systems . Kelly has contributed foundational works like Stochastic Networks (2014) and pioneered models for loss networks and congestion control . He maintains active collaborations and has advised on policy matters, including spectrum auctions and network regulation.

Angelika Krehlمشاهده پروفایل

استاد

• Urban Spatial Structure Analysis
• Regional Economics
• Economic Geography
• +۵ مورد دیگر

Professor Angelika Krehl serves as Professor for Economics, regional and sectoral structural policy and Director of the NIERS Institute (Lower Rhine Institute for Regional and Structural Research) at Niederrhein University of Applied Sciences since 2020. Her academic leadership spans regional economics, urban spatial structure analysis, and sustainability research within the Department of Economics. Her research focuses on theoretical foundations and empirical analysis methods of urban spatial structures, regional economics, economic geography, and GIS-based regional research with emphasis on sustainability aspects. Key methodologies include polycentricity measurement, density pattern analysis, and integrated spatial modeling using remote sensing data. Her work bridges urban morphology with economic development processes. Recent publications reveal strong trends in polycentric urban systems analysis, corporate regional responsibility frameworks, and sustainable land use economics. Over 70% of her recent work examines German metropolitan regions through morphological polycentricity lenses, with increasing focus on sustainability transitions and social innovation mechanisms since 2020. Methodological innovation in GIS integration and spatial metrics development characterizes her publication trajectory. Professor Krehl leads multiple major research initiatives including RediStar (diversity-sensitive startup funding), AUFBRUCH (sustainable bioeconomy transformation), R(h)einTex (textile recycling innovation), and R³ (corporate regional responsibility). Her institutional leadership extends to directing NIERS Institute and serving on the university's Advisory Board for Research and Transfer. As Director of NIERS Institute, she oversees collaborative research teams focused on regional structural policy, with particular emphasis on lignite phase-out governance, sustainable learning projects, and regional innovation ecosystems. Her work integrates academic research with practical policy implementation through close partnerships with regional stakeholders.

• Soil Science
• Measurement Error Analysis
• Soil Chemistry
• +۶ مورد دیگر

Cynthia van Leeuwen is a Postdoctoral Research Fellow at Wageningen University & Research , specializing in Soil Geography and Landscape . Her work focuses on quantifying uncertainties in soil data and improving spectral modeling techniques. PhD in Soil Science (completed Aug 2024) as part of a project on soil databases with quantified uncertainties Active collaborations with researchers like Gerard Heuvelink, V. L. Mulder, and N. J. Kuhn Dr. van Leeuwen's research interests span: Measurement error analysis in soil data Development of pedotransfer functions Soil chemistry and spectral modeling Environmental applications of uncertainty quantification Her publications demonstrate expertise in: Statistical modeling of soil measurement errors Calibration of spectral data Wind erosion and PM10 emissions analysis Dr. van Leeuwen has contributed to significant projects like The development and use of a soil database with quantified uncertainties (2019-2024), and her work has been cited over 80 times in academic literature.

Leila Moslemi Naeniمشاهده پروفایل

مدرس ارشد

• Project Management
• Fuzzy Methods
• Mathematical Modelling
• +۵ مورد دیگر

Dr. Leila Moslemi Naeni is a Senior Lecturer at the University of Technology Sydney (UTS), School of Built Environment, with a dual appointment in the Faculty of Design, Architecture and Building. She previously served as a Lecturer at UTS (2016-2018) and Sessional Lecturer at Curtin University (2015-2016). PhD in Computer Science (University of Newcastle, 2017) MSc in Industrial Engineering (Sharif University of Technology, 2007) BSc in Industrial Engineering (Iran University of Science and Technology, 2004) Her research focuses on project management under uncertainty, integrating fuzzy systems and mathematical modeling with applications in construction, ESG reporting, and disruptive technologies. She developed innovative methods for statistical control charts in project monitoring and leads research on leveraging blockchain and digital tools for sustainable infrastructure. Recent publications highlight her work on resource-constrained scheduling algorithms, ESG integration in megaprojects, and gamification in project management education. She serves as Review Editor for Frontiers in Environmental Science and on the Editorial Board of Smart and Sustainable Built Environment . 2016 PMI NSW Research Award 2022 Walt Lipke Award 2013 FEBE Postgraduate Research Prize As an active research mentor, she supervises PhD students in machine learning applications, disaster management technologies, and social infrastructure investments. Her teaching emphasizes simulation-based learning, collaborating with Oulo University (Finland) to quantify educational value.

James Cragunمشاهده پروفایل

مدرس

• Political Psychology
• Quantitative Methods
• Political Behavior
• +۴ مورد دیگر

James Cragun is a lecturer and researcher in Political Science at Stony Brook University, specializing in political psychology, quantitative methods, and the intersection of religious faith with political behavior. He earned his Ph.D. in 2020, with a dissertation on individual differences in selective exposure to political information. His research combines computational models and empirical analysis to explore how cognitive biases and religious conviction shape information-seeking behaviors and decision-making. Education: B.S. in Psychology (University of Oregon, 2013), M.A. (Stony Brook, 2015), Ph.D. in Political Science (Stony Brook, 2020). Teaching: Instructed courses like POL 201: Statistical Methods in Political Science and POL 348: Political Beliefs and Judgment , emphasizing critical thinking, causal inference, and statistical literacy. Designed interactive teaching tools for quantitative concepts. Research: Investigates how rigid religious faith amplifies selective exposure to attitude-congruent political information, using experiments and agent-based models to study cooperative behavior and methodological innovations like interactive control variables. Scientific Awards 2018 Departmental Teaching Award, Stony Brook University. His work bridges political psychology, behavioral economics, and computational modeling, with a focus on understanding irrationality in political judgment and the role of religious cognition in information processing. Current projects include modeling vaccine anticipation effects on compliance.

Emese Lazarمشاهده پروفایل

استاد

• Risk Measurement
• Model Risk
• Financial Econometrics
• +۴ مورد دیگر

Professor Emese Lazar serves as Professor of Finance and Deputy School Director of Teaching and Learning at the ICMA Centre, Henley Business School, University of Reading. She joined the institution in 2005 and is an active member of the Econometrics with Data Science research cluster, focusing on quantitative finance applications. Her educational background includes: PhD in Finance from the University of Reading BSc in Finance and Banking from the University of Economic Studies, Bucharest BSc in Computer Science from the University of Bucharest Research interests span risk measurement and management , model risk , financial econometrics , derivatives pricing , green finance , climate risk in finance , and machine learning applications . Her work bridges theoretical finance with practical risk management challenges, particularly in climate-related financial risks and algorithmic risk modeling. Recent publications reveal a pronounced shift toward integrating climate risk metrics with traditional financial models and developing neural network-based forecasting for tail risk measures. Her research demonstrates consistent innovation in volatility modeling, model risk quantification, and climate finance applications across top-tier journals. Professor Lazar teaches postgraduate modules in Market Risk and Climate Change and Risk Management, supervising PhD students in her specialized research areas. She actively contributes to the Econometrics with Data Science research cluster, fostering interdisciplinary collaboration between finance, data science, and climate risk modeling.

Wai Kiong Oswald Chongمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Engineering Systems
• Optimization
• +۱۰ مورد دیگر

Dr. Wai Kiong Oswald Chong is an Associate Professor at Arizona State University's School of Sustainable Engineering and the Built Environment, with a dual affiliation as Senior Global Futures Scientist at the Global Futures Scientists and Scholars program. He holds a PhD in Civil Engineering from the University of Texas-Austin, MSc and BSc in Building from the National University of Singapore, and focuses on integrating artificial intelligence with sustainable engineering systems. PhD (2005): Civil Engineering, University of Texas-Austin MSc (1999) & BSc (1997): National University of Singapore His research bridges lunar construction with Earth-bound sustainable systems, covering topics like: Space habitat modularization Resource circularity systems AI-enhanced building codes Climate-resilient infrastructure Advanced energy modeling Construction supply chain optimization Publications demonstrate consistent focus on: Semiconductor facility HVAC optimization Building energy consumption anomalies Life cycle assessment frameworks Construction risk management Deconstruction and material reuse AI-driven system modeling Current research projects include: Lunar MVI (Moon Village Initiative) Semiconductor fab design optimization Human-AI knowledge interfaces Thermal insulation systems for extreme environments Smart grid energy modeling