جستجوی استاد راهنمای دانشگاهی

Saumen Mandalمشاهده پروفایل

استاد

• Optimal experimental design
• Biostatistics
• Data science
• +۷ مورد دیگر

Dr. Saumen Mandal is a Professor in the Department of Statistics at the University of Manitoba, Faculty of Science. He holds a PhD from the University of Glasgow, UK, and MSc/BSc (Gold Medal) from the University of Calcutta, India. His research focuses on optimal experimental design, biostatistics, data science, shrinkage estimation, and constrained optimization. He has received numerous teaching awards including the Dr. and Mrs. H.H. Saunderson Award for Excellence in Teaching, Students Choice Best Professor Award, and multiple Merit Awards. He is also a P.Stat. designee from the Statistical Society of Canada. Education: PhD (Statistics), University of Glasgow, UK MSc (Statistics), University of Calcutta, India (First Class First, Gold Medal) BSc Honours (Statistics), University of Calcutta, India Research Interests: Optimal design theory and applications Biostatistical methods for clinical trials and healthcare data Data science and machine learning techniques Shrinkage estimation and model selection Linear models and goodness-of-fit testing Publications span topics like optimal regression designs, response-adaptive clinical trial methods, and statistical models for healthcare data. His work emphasizes practical applications in medicine and data-driven decision making. Awards include: Teaching Excellence Awards (2005-2007) Merit Awards for Teaching and Research (2010-2019) Faculty of Science Innovation in Teaching Award (2020) He advises graduate students in statistics and contributes to research teams in biostatistics and data science. His office is temporarily located at 256 Parker Building during construction.

• Statistical Inference
• Measurement Error Models
• Boundary Crossing Problems
• +۳ مورد دیگر

Liqun Wang is a Professor of Statistics at the University of Manitoba, within the Faculty of Science. His research focuses on statistical inference in complex models, measurement error correction, boundary crossing problems in stochastic processes, and Monte Carlo simulation methods. He holds a prominent role in advancing methodologies for nonlinear time series analysis and Bayesian inference. His work integrates theoretical rigor with practical applications, addressing challenges in econometrics, environmental science, and public health. Notable contributions include advancements in instrumental variable estimation, second-order least squares methods, and high-dimensional covariance estimation. He actively mentors graduate students in these areas and has published extensively in top-tier statistical journals. Recent research highlights include Bayesian bias correction techniques, sparse covariance matrix estimation, and modeling SARS-CoV-2 dynamics via wastewater data. His methodologies often bridge computational efficiency with statistical accuracy, making them applicable to diverse fields such as finance, biostatistics, and environmental monitoring. Despite prolific output (over 70 publications since 1990), Dr. Wang has yet to be explicitly noted for formal scientific awards. His academic profile emphasizes methodological innovation, with a strong focus on real-world data challenges and interdisciplinary collaboration.

Dario Fioreمشاهده پروفایل

استاد پژوهش

• Cryptography
• Computer Security
• Cloud Computing Security
• +۳ مورد دیگر

Dario Fiore is an Associate Research Professor at the IMDEA Software Institute in Madrid, Spain. He holds a Ph.D. in Computer Science from the University of Catania, Italy (2010), and has held postdoctoral positions at institutions including Max Planck Institute for Software Systems (Germany), New York University (USA), and École Normale Supérieure (France). His research focuses on Cryptography and Security, particularly designing provably-secure cryptographic primitives for Cloud computing applications, including secure data delegation, homomorphic authenticators, zero-knowledge proof systems, functional encryption, and homomorphic encryption. He advises multiple PhD students and has led projects like the ERC Consolidator Grant PICOCRYPT (2021–2026). He has been recognized with awards such as the ACM SIGMM Test of Time Honourable Mention (2022) and the IET Information Security Premium Award (2020). Education: Ph.D. in Computer Science, University of Catania, Italy (2010) Visiting Student: IBM T.J. Watson Research Center and New York University (USA) Research Interests Fiore’s work bridges theoretical and practical aspects of Cryptography, emphasizing efficiency and real-world applications. Key areas include: Secure delegation of computation/data to Cloud systems Homomorphic encryption and authenticators Zero-knowledge proof systems (e.g., zkSNARKs) and functional encryption Cryptography for privacy-preserving machine learning and decentralized systems Grants & Projects ERC Consolidator Grant PICOCRYPT (2021–2026) NEC Laboratories Europe collaboration (2024) ESPADA and PRODIGY EU projects Awards ACM SIGMM Test of Time Honourable Mention (2022) IET Information Security Premium Award (2020) ACM CCS 2022 Top Reviewer Award Labs & Teams Fiore leads a dynamic research group with active collaborations in cryptography, including postdocs, PhD students, and industry partnerships with Protocol Labs, Nomadic Labs, and Tezos Foundation.

Tsung-Yi Hoمشاهده پروفایل

استاد

• Design Automation for Microfluidic Biochips
• Nanometer Integrated Circuits
• Embedded Systems
• +۳ مورد دیگر

Tsung-Yi Ho is a Professor in the Department of Computer Science at National Tsing Hua University, Taiwan. He holds the Hans Fischer Fellowship at the Technical University of Munich's Institute for Advanced Study (TUM-IAS). His primary research focuses on design automation for microfluidic biochips and nanometer integrated circuits, emphasizing reliability, optimization, and interdisciplinary applications in bioengineering. Ho received his Ph.D. in Electrical Engineering from National Taiwan University in 2005. He has held positions at National Cheng Kung University and National Chiao Tung University before joining National Tsing Hua University. His work bridges algorithmic design with practical biochip fabrication, addressing challenges like contamination control, routing optimization, and fault tolerance in microfluidic systems. His research interests span design automation for emerging technologies, including paper-based biochips and 3D microfluidic architectures. He has pioneered methods for integrating hardware-software co-design principles into biochip development, enhancing both functionality and reliability. His contributions include novel routing algorithms, contamination mitigation techniques, and reliability-aware synthesis frameworks. Ho has authored over 100 publications, including influential papers in IEEE Transactions on CAD and ACM journals. He serves on the editorial boards of multiple top-tier journals and chairs professional chapters for ACM and IEEE. His awards include the Humboldt Research Fellowship, Dr. Wu Ta-You Memorial Award, and Best Paper Awards at VLSI Test Symposium and IEEE Transactions on CAD. His current projects involve optimizing control-fluidic co-design for paper-based biochips and developing AI-driven frameworks for microfluidic functionality prediction. He collaborates widely, leading cross-disciplinary initiatives at TUM-IAS and Taiwan's academic institutions.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Li Wei is a distinguished academic affiliated with Tsinghua University, with a focus on interdisciplinary research spanning artificial intelligence, machine learning, and computer vision. His work often intersects with medical informatics, remote sensing, and signal processing, demonstrating a commitment to advancing technological solutions in healthcare, environmental monitoring, and engineering systems. Research interests include deep learning applications in clinical diagnostics, satellite data analysis for climate modeling, and optimization of energy storage systems. He has contributed to innovative solutions in areas such as UAV-enabled edge computing, privacy-preserving blockchain protocols, and thermal-based surveillance systems. His collaborative projects often involve multidisciplinary teams across institutions. Publications reflect a strong emphasis on practical applications, such as mobile health tools for tumor recognition, transformer-based super-resolution techniques for oceanography, and AI-driven risk classification models for respiratory diseases. While no specific awards or grants are listed, his prolific output across top-tier journals indicates sustained research impact. Professional activities include contributions to conferences like RecSys, MICCAI, and AAAI, and editorial roles are implied through his extensive publication record. Collaborations with industry partners (e.g., in energy systems and medical imaging) suggest engagement with real-world problem-solving.

• Evolutionary Computation
• Genetic Programming
• Robotics
• +۵ مورد دیگر

Eric Medvet is a professor specializing in evolutionary computation, genetic programming, and robotics. He is actively involved in research areas such as neuroevolution, soft robotics, and modular robotics. His work bridges theoretical advancements in evolutionary algorithms with practical applications in robotics and AI. Roles: Conference chair for EuroGP (2020-2022), co-chair of multiple workshops and sessions. Key Research: Focus on genetic programming, embodied intelligence, and the design of adaptive robotic systems. Research Interests: His work emphasizes the development of scalable and interpretable AI systems, particularly through evolutionary methods applied to robotics. He explores topics like neuroevolution for soft robots, quality diversity algorithms, and the integration of machine learning with evolutionary computation. Publications: His recent work highlights trends in interpretable AI, modular robotics control, and evolutionary algorithms for complex systems. Notable contributions include studies on MAP-Elites, graph-based genetic programming, and the application of LLMs in automated testing. Grants & Labs: Developed frameworks like JGEA for evolutionary computation experiments. Collaborates on projects integrating evolutionary methods with real-world robotics applications.

• Control Systems
• Machine Learning
• Robotics
• +۶ مورد دیگر

Marko Tanasković is a researcher at Singidunum University's Faculty of Informatics and Computing, specializing in control systems, robotics, and electrical engineering. He holds a PhD from ETH Zurich (2015) in Information Technology and Electrical Engineering, following degrees from University of Belgrade (BEng, 2009) and ETH Zurich (MEng, 2011). His research focuses on adaptive control systems, machine learning applications in engineering, and sensorless motor control. Key contributions include: Development of predictive algorithms for traffic systems and industrial automation Innovations in rotor orientation determination for PMSM motors Integration of AI in fraud detection and building climate control Recent work includes a 2024 study on wearable health monitoring devices and a 2022 paper on drone forensics. He has authored/co-authored over 15 peer-reviewed articles and holds patents in motor control technologies. Current affiliations include Singidunum University's Department of Electrical Engineering and Collaboration with ETH Zurich alumni networks. Active in international conferences such as Sinteza and IEEE events.

Luca Mottolaمشاهده پروفایل

استاد

• Battery-less IoT
• Mobile Space Robotics
• Smart Cities
• +۳ مورد دیگر

Luca Mottola is a Full Professor at Politecnico di Milano and Senior Researcher at RI.SE Sweden. He leads the Networked Embedded Software Lab (NESLab), focusing on next-generation IoT, battery-less systems, and mobile robotics. His work bridges embedded computing, wireless networks, and cyber-physical systems, with applications in smart cities and space robotics. Education: No explicit details provided in texts. Research interests include energy-harvesting IoT, drone communication, and real-time systems. Notable awards include the ACM SENSYS Test of Time Award (2022) and multiple Best Paper Awards at top conferences like MOBICOM and RTCSA. Grants/Projects: Overseen projects on battery-less IoT, drone localization, and energy-efficient systems. Editorial roles include IEEE Transactions on Mobile Computing and ACM Transactions on Sensor Networks. Labs/Teams: NESLab, FlyZone Testbed for aerial drones, and collaborations with NXP Technologies, TU Graz, and USC.

Mounir El Asmarمشاهده پروفایل

دانشیار

• Construction Management
• Sustainable Engineering
• Project Delivery Systems
• +۳ مورد دیگر

Mounir El Asmar is an Associate Professor at Arizona State University’s School of Sustainable Engineering and the Built Environment in the Del E. Webb School of Construction. He is also a Senior Sustainability Scientist at the Julie Ann Wrigley Global Institute of Sustainability and an Honors faculty member at Barrett Honors College. Additionally, he serves as co-director of the National Center of Excellence on SMART Innovations. His roles emphasize leadership in sustainable engineering, project delivery systems, and infrastructure development. El Asmar holds a Ph.D., M.S., and B.E. in Civil and Mechanical Engineering from the University of Wisconsin-Madison and the American University of Beirut. His research focuses on decision-making in construction, innovative project delivery systems like Design-Build and Integrated Project Delivery (IPD), sustainable performance metrics, and infrastructure resilience. He has conducted research for the U.S. Department of Transportation, the Construction Industry Institute, and global organizations. His work has been recognized with over 25 awards, including the CII Distinguished Professor Award, ASCE’s Thomas Fitch Rowland Prize, and the DBIA Distinguished Leadership Award. He has led funded projects on topics like EVMS reliability, public-private partnerships, and sustainable construction practices. El Asmar also chairs major conferences, such as the 2020 ASCE Construction Research Congress, and serves on editorial boards for journals like the ASCE Journal of Construction Engineering and Management. He advises Ph.D. students and teaches courses on construction engineering, sustainable practices, and project management. His research integrates technology like augmented reality (AR) to enhance communication and quality control in construction sites. He actively bridges academic research with industry needs through workshops, policy development, and community engagement. Key affiliations include the Global Futures Scientists and Scholars program, the Transportation Research Board, and the Design-Build Institute of America. El Asmar’s contributions span education, research, and service, reflecting ASU’s New American University mission of innovation and societal impact.

Ayman El-Hagمشاهده پروفایل

دانشیار

• Electrical Engineering
• High Voltage Engineering
• Power Systems
• +۵ مورد دیگر

Ayman El-Hag is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He is affiliated with the Outdoor Insulation and Condition Monitoring Research Group, focusing on advancing technologies for high-voltage insulation systems and smart grid infrastructure. His work integrates machine learning, signal processing, and materials science to improve condition monitoring and diagnostic methods for power equipment. Research interests include partial discharge detection using UHF and acoustic sensors, machine learning applications for defect classification in outdoor insulators, and the development of non-invasive sensing techniques for real-time monitoring. He also explores energy management systems leveraging fuzzy logic and smart meter data analysis for residential and grid-level applications. Recent publications highlight advancements in capsule networks for insulator discharge prediction, deep learning-based hydrophobicity classification, and novel antenna designs for partial discharge localization. His work emphasizes practical solutions for power system reliability and environmental resilience of insulation materials under extreme conditions. El-Hag is a Full-time faculty member and holds Adjunct faculty status, contributing to interdisciplinary research projects. He actively engages in promoting condition monitoring methodologies through educational initiatives and industry partnerships.

• Marine Renewable Energy
• Wind Energy Systems
• Drivetrain Design and Reliability
• +۷ مورد دیگر

Amir R. Nejad is a Professor in the Department of Marine Technology at NTNU, leading the Marine Energy Systems and Autonomics (MESA) research group. He holds roles such as Chair of the EAWE WindEurope Scientific Track Committee and co-chair of the Drivetrain Technical Committee at the European Academy of Wind Energy (EAWE). His research focuses on stochastic design, reliability-based operation, dynamic modeling, and fault detection in marine and offshore renewable systems. Nejad is an editorial board member for journals like Wind Energy Science and Ocean Engineering , and a member of ISO committees on drivetrain health monitoring. Education: PhD in Marine Technology, NTNU (2015) MSc Subsea Engineering, University of Aberdeen (2012) BSc Mechanical Engineering, Tehran University (2009) His research interests emphasize offshore wind drivetrain reliability, condition monitoring, and digital twin applications. Recent work includes studies on blade bearing fatigue estimation, SCADA-based lifetime extension of drivetrains, and wake steering techniques in floating wind farms. Key awards include multiple 'Best Lecturer' honors at NTNU and international conference recognitions. His research has been supported by grants such as the Nowitech Fellowship and DNV Education Fund. Key Projects: Marine System Dynamics and Vibration Lab (MD Lab) oversees projects on wind turbine drivetrains, floating offshore systems, and digital twin integration. Lab/Teams: MD Lab focuses on advancing marine energy systems through experimental and computational studies.

• Belt Conveyor Systems
• Bulk Solids Handling
• Energy Efficiency in Material Transportation
• +۵ مورد دیگر

Professor Craig Wheeler is a distinguished academic in the School of Engineering at the University of Newcastle, specializing in Mechanical Engineering with a focus on bulk solids handling and belt conveyor technology. As Associate Director of the Centre for Bulk Solids and Particulate Technologies and Deputy Chairman for the Australian Society for Bulk Solid Handling, he has established the university as a global leader in fundamental and applied research within this field. Wheeler's research interests primarily center on reducing the energy intensity and environmental impact of ore and mineral transportation globally. His work develops novel theoretical approaches to model and optimize belt conveyor and bulk handling systems, with significant contributions in energy-efficient transportation, dust emission control, and innovative conveying technologies like the Rail Conveyor system. His research bridges fundamental computational techniques with practical industrial applications, addressing real-world challenges in bulk material handling. His extensive publication record demonstrates trends toward increasingly sophisticated modeling techniques, combining continuum mechanics, discrete element methods, and computational fluid dynamics to solve complex problems in bulk material flow and energy consumption. Recent work shows particular emphasis on large-diameter idler rollers for energy savings, rail-running conveyor systems, and advanced dust control methodologies. 2023 Engineers Australia - Australian Society for Bulk Solids Handling 2017 Significant Contributions to Engineers Australia's Warman Design and Build Competition (Weir Minerals) 2017 Australian Council of Engineering Deans National Award for Engineering Education Excellence 2016 Innovative Technology Award (Australian Bulk Handling) 2010 Rising Star Award (Newcastle Innovation, The University of Newcastle) 2009 Pro-Vice Chancellor's Award for Research Excellence 2006 Best Research and Development Project (Australian Bulk Handling Review) 2000 A.W. Roberts Award (Australian Society for Bulk Solids Handling) Professor Wheeler has successfully led numerous Linkage Projects with major companies including Rio Tinto, Veyance Technologies, and Laing O'Rourke, securing significant cash and in-kind contributions for research projects. His industrial consulting experience, built on a 10-year engineering career with BHP, provides valuable insights that bridge fundamental research with practical applications. He actively supervises research students and contributes to professional development courses both within Australia and internationally. As a key member of the Centre for Bulk Solids and Particulate Technologies in association with TUNRA Bulk Solids, Wheeler leads research teams focused on developing eco-friendly conveying solutions. His work has resulted in new licensed technologies, internationally recognized testing methods, design guidelines, and Australian Standards that have transformed industry practices worldwide.

Irina Panovskaمشاهده پروفایل

دانشیار

• Macroeconomics
• Econometrics
• Time Series Analysis
• +۴ مورد دیگر

Irina Panovska is an Associate Professor of Economics at the University of Texas at Dallas (UT Dallas), affiliated with the School of Economic, Political, and Policy Sciences. Her research focuses on macroeconomic policy responses, business cycle dynamics, and economic recoveries. She teaches macroeconomics, forecasting, and business cycles at both undergraduate and graduate levels. Education includes a PhD (2013) and MA (2009) in Economics from Washington University in St. Louis, and a BS in Economics and Mathematics from Ohio University (2007). She holds visiting scholar positions at the University of Zagreb (2023–2026) and has collaborated with institutions in Australia and Croatia. Research interests emphasize modeling policy impacts on economies, labor market dynamics, and time series analysis. Recent work addresses business cycle synchronization in the EU, jobless recoveries, and the effects of the pandemic on financial markets. She has presented at conferences including the Society for Economic Measurement and the Southern Economic Association. Professional activities include serving as Treasurer of the Society for Nonlinear Dynamics and Econometrics (2019–2024) and organizing academic sessions. In 2023, she received the School’s Teaching Comet Award for excellence in instruction. Her work bridges theoretical frameworks with policy relevance, addressing issues like fiscal policy effectiveness, inflation dynamics, and maternal labor force participation. Current projects include keynote talks on commercial real estate and mentoring initiatives for junior economists.

• Control Theory
• Autonomous Systems
• Reinforcement Learning
• +۳ مورد دیگر

Dr. Cheng-Chew Lim is a Professor in the School of Electrical and Mechanical Engineering at the University of Adelaide. He specializes in control theory, autonomous systems, and multi-agent reinforcement learning. His research focuses on trusted autonomous systems, secure cyber-physical networks, and decentralized decision-making models. He has published over 300 articles and supervised 50+ PhD and master’s students. Dr. Lim teaches courses in control systems, autonomous systems, and engineering project management. He has held editorial roles, including Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics, and is actively involved in professional associations like the IEEE Control and Aerospace Electronic Systems Joint Chapter. His current projects include physics-informed neural networks for medical imaging, secure distributed autonomous systems, and resilient formation control under cyberattacks. Dr. Lim has secured research grants from ARC and industry partnerships, emphasizing practical applications in robotics, cybersecurity, and smart systems.

Yu Xiangمشاهده پروفایل

استادیار

• Robotics
• Computer Vision
• 3D Environment Perception
• +۵ مورد دیگر

Dr. Yu Xiang is an Assistant Professor of Computer Science at the University of Texas at Dallas (UT Dallas), leading the Intelligent Robotics and Vision Lab (IRVL) . He holds a Ph.D. in Electrical and Computer Engineering from the University of Michigan (2016) and prior roles include Senior Research Scientist at NVIDIA (2018–2021) and postdoctoral research at the University of Washington. Research Focus : His work centers on robotics and computer vision , particularly enabling robots to perceive 3D environments, plan actions, and interact autonomously in human-centric spaces. Key areas include unseen object segmentation, 6D pose estimation, manipulation trajectory optimization, and lifelong learning through robot-environment interaction. Key Contributions : Developed datasets like MultigripperGrasp and HO-Cap , and pioneered methods such as DeepIM for 6D pose estimation. His lab’s robot Ramp focuses on tasks like object manipulation and human-robot collaboration. Grants : NSF SMILE grant ($750K), DARPA Perceptually-enabled Task Guidance (co-PI), Sony Research Award (PI). Awards : NVIDIA Academic Grant (2024), Sony Research Award (2022), ECCV Best Paper (2018). Lab Activities : Engages in STEM outreach, including mentoring high school students in the 2024 Summer Bridge Camp. Current projects emphasize self-supervised learning and embodied AI for robotic systems.