جستجوی استاد راهنمای دانشگاهی

Bruce A. Maxwellمشاهده پروفایل

استاد آموزشی

• Computer Vision
• Robotics
• Computer Graphics
• +۳ مورد دیگر

Bruce A. Maxwell is a Teaching Professor and Assistant Director of Computing Programs at Northeastern University’s Seattle Campus, following roles as Chair of the Computer Science (CS) Department at Colby College (2013–2020) and leadership in establishing the Khoury College MS CS Align Program at the Roux Institute (2020–2022). His academic journey includes affiliations with Northeastern’s Seattle Campus and ongoing collaboration with Colby CS as a research scientist. He specializes in Computer Vision, Robotics, Computer Graphics, Game Design, and Data Analysis, with notable contributions to concussion management research through the Maine Concussion Management Initiative (MCMI), focusing on sports-related injury analysis and symptom monitoring. His research spans over two decades, with significant work in human-robot interaction, autonomous systems, and educational technology. Notable projects include developing tools for real-time shadow removal in autonomous driving contexts and analyzing cognitive outcomes in student-athletes post-concussion. Maxwell has authored over 50 peer-reviewed publications, emphasizing interdisciplinary approaches bridging computer science, sports medicine, and educational policy. Teaching innovations include integrating thematic elements (e.g., Lord of the Rings) into CS1 coursework and advocating for writing in computer science curricula. He maintains active roles in academic service, including SIGCSE conference contributions and panel discussions on gender equity in tech education. Education: Ph.D. in Robotics from Carnegie Mellon University (1996), M.Phil. in Engineering from Cambridge University (1993). Awards: Recognized for pedagogical contributions but no named awards listed in provided materials. Labs/Teams: Collaborates with the Maine Concussion Management Initiative and Khoury College’s Align Program team.

Yanbo Wangمشاهده پروفایل

دانشیار

• Power Systems
• Renewable Energy
• Microgrid Control
• +۵ مورد دیگر

Yanbo Wang is an Associate Professor and Vice Head of the Wind Power Research Programme at AAU Energy, part of Aalborg University's Faculty of Engineering and Science. He leads research in Electric Power Systems and Microgrids, focusing on intelligent energy systems and flexible markets. His work emphasizes stability analysis, control strategies for hybrid AC/DC systems, and renewable energy integration. He supervises multiple PhD projects on topics like multi-port energy routers and wind power converter optimization. Research interests include power electronics, microgrid stability, and energy storage systems. Key projects include the EU-funded 'S3SF: Smart Energy Solutions for Sustainable Future' and machine learning-based control strategies for multi-energy systems. He has authored over 176 publications, with recent work on DC microgrid reliability, converter design, and offshore wind energy systems. His team collaborates internationally to advance grid-friendly technologies and smart energy solutions. Notable contributions include advanced control schemes for retired batteries in DC microgrids and stability assessment methodologies for offshore wind inverters. He advises on six PhD projects and maintains a lab focused on renewable energy systems and power electronics innovation.

Lokukaluge Prasad Pereraمشاهده پروفایل

استاد

• Maritime Technology
• Offshore Systems
• Advanced Data Analytics
• +۶ مورد دیگر

Lokukaluge Prasad Perera is a Professor in Maritime Technology at UiT The Arctic University of Norway and a Senior Research Scientist in Smart Data at SINTEF Digital . He holds a BSc in Mechanical Engineering from Oklahoma State University (1999), MSc in Systems & Controls from the same institution (2001), and a PhD in Naval Architecture and Marine Engineering from Technical University of Lisbon (2012). His research focuses on Maritime and Offshore Systems , Advanced Data Analytics , Autonomous Navigation , Energy Efficiency , and Digital Twin Applications . He has published over 100 peer-reviewed papers and was recognized in the World's Top 2% Scientists (2021-2022) by Stanford University. Key professional experiences include roles at SINTEF Ocean (2014–2017), Center for Marine Technology and Engineering in Portugal (2008–2012), and Wärtsilä Finland (2012–2014). He has also held academic positions at Naval & Maritime Academy and Ocean University of Sri Lanka . His work addresses challenges in emission reduction , renewable energy integration , and safety-critical systems for maritime operations. Current projects emphasize trustworthiness of autonomous ships and data-driven decision frameworks for energy efficiency.

David Wanikمشاهده پروفایل

استادیار

• Data Science
• Natural Hazards
• Remote Sensing
• +۳ مورد دیگر

David Wanik is an Assistant Professor in the Department of Operations and Information Management and Associated Faculty in the Department of Civil and Environmental Engineering at the University of Connecticut. He serves as Academic Director for Business Data Analytics at the Stamford campus and conducts research in the Eversource Energy Center, focusing on data science, natural hazards, remote sensing, and IoT applications in utility systems. PhD, MS, and BS in Environmental Engineering from University of Connecticut His research bridges natural hazard prediction, power grid resilience, and environmental data science. Key themes include: Machine learning for power outage prediction Climate change impact on energy demand Remote sensing for population and environmental monitoring IoT-enabled infrastructure hardening Recent publications emphasize deep learning for nighttime light imagery analysis, hybrid physics-data-driven models for grid resilience, and climate-integrated demand forecasting. His work integrates satellite data, LiDAR, and utility infrastructure records for predictive analytics. Teaching includes courses in business analytics, Python-based data science, and deep learning for the MS Business Analytics and Project Management program.

Zoe Liمشاهده پروفایل

دانشیار

• Uncertainty Analysis
• Risk Management
• Stochastic Modelling
• +۷ مورد دیگر

Dr. Zoe Li is an Associate Professor in the Department of Civil Engineering at McMaster University, with an additional role as an Associate Member in the Department of Computing and Software. She specializes in developing modeling and decision-support tools to address challenges in water resources management, environmental systems analysis, and climate change impacts. Her research focuses on hydrological modeling, probabilistic forecasting, risk analysis, and environmental systems optimization. She holds a B.Eng. from China and M.A.Sc./Ph.D. degrees from the University of Regina. Her work spans interdisciplinary collaborations, including studies on dam safety under climate change, wastewater treatment plant optimization, and AI-driven environmental monitoring. She has been actively involved in projects such as CityDNA, a digital tool to model urban environments for decision-making during crises like the pandemic. Dr. Li teaches courses in environmental systems engineering and principles of environmental engineering, emphasizing uncertainty quantification and sustainable practices. Her research publications (15+ recent articles) address topics like machine learning applications in environmental systems, climate change impacts on water resources, and infrastructure resilience. She advises graduate students in environmental engineering and systems optimization. Notable grants include funding from Roche Canada for pandemic-related urban modeling. Her work is frequently published in high-impact journals like Journal of Environmental Management and Water Resources Research .

Priya Narasimhanمشاهده پروفایل

استاد

• Dependable Distributed Systems
• Fault-Tolerance
• Embedded Systems
• +۵ مورد دیگر

Priya Narasimhan is a Professor of Electrical & Computer Engineering at Carnegie Mellon University (CMU), affiliated with the College of Engineering. Her research focuses on dependable distributed systems, fault-tolerance, embedded systems, mobile systems, and sports technology. She leads the Intel Science and Technology Center in Embedded Computing (ISTC-EC) and founded YinzCam, a CMU spin-off providing mobile live streaming to sports venues. She holds multiple awards, including the Sloan Fellowship and NSF CAREER Award. Education: Ph.D. and M.S. in Electrical & Computer Engineering from UC Santa Barbara. Notable roles include former CTO of Eternal Systems, Director of Intel Labs Pittsburgh, and Director of CMU's CyLab Mobility Research Center. Research spans failure diagnosis in distributed systems, live upgrades, mobile cloud computing, football technology, assistive tech for the blind (Trinetra), and civic tech (iBurgh). Over 30+ students advised across Ph.D., M.S., and undergraduate programs. Active in entrepreneurship, teaching (courses like 18-349 Embedded Systems), and industry collaborations.

Fiona Sloothaakمشاهده پروفایل

استادیار

• Stochastic Operations Research
• Network Theory
• Cascading Failures
• +۶ مورد دیگر

Fiona Sloothaak is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science, specializing in Stochastic Operations Research. She holds the Eurandom Assistant Professor title and contributes to the Stochastic Operations Research group. Her research focuses on cascading failures in complex systems, power grids, battery swapping networks, and stochastic processes. Education: Fiona earned a PhD in Stochastic Operations Research from TU/e in 2020 and completed a Master's in Mathematics (2015). Her doctoral thesis, Criticality in power networks: a probabilistic approach , explores cascading failures in power systems. Research Interests: Fiona’s work addresses systemic risk in infrastructure systems, including power grids and transportation networks. She investigates cascading failure dynamics, load balancing in battery swapping stations, and resource pooling strategies. Her studies often employ probabilistic modeling and queueing theory. Key Projects: She co-authored the MARCONI project (2019–2022), focusing on integrated planning for maritime logistics and service systems. This involved optimizing supply chain resilience and remote control tower operations. Scientific Award: Applied Probability Trust Prize (2020) Teaching: Courses include Stochastic Networks, Insurance and Credit Risk, and Probability and Stochastics 1. Grants: MARCONI project funding for maritime logistics optimization. Labs/Teams: Active in TU/e’s Stochastic Operations Research group and Eurandom institute. Collaborations span network topology analysis and interdisciplinary systems research.

Brian Kulisمشاهده پروفایل

دانشیار

• Machine Learning
• Statistics
• Large-scale Data Analysis
• +۵ مورد دیگر

Brian Kulis is an Associate Professor at Boston University with appointments in the Department of Electrical and Computer Engineering, Computer Science, Systems Engineering, and the Faculty of Computing and Data Sciences. He holds the Peter J. Levine Career Development Professorship and has previously been an Amazon Scholar at Alexa AI (2019–2023) and an assistant professor at Ohio State University (2012–2015). His research focuses on machine learning, including large-scale optimization, metric learning, deep learning, Bayesian methods, and applications in audio and visual data analysis. He earned his PhD in Computer Science from the University of Texas at Austin (2008) and a BS in Computer Science and Mathematics from Cornell University. Key awards include the NSF CAREER Award (2015), CVPR Best Student Paper (2008), and ICML Best Student Paper (2007, 2005). His work spans publications in top venues like CVPR, NeurIPS, ICML, and ECCV, emphasizing scalable algorithms and domain adaptation. Current research explores metric learning, adversarial audio augmentation, and HPC anomaly detection. He advises multiple PhD students and collaborates on grants such as the NSF Traineeship for Sustainable Energy Solutions (2024). He teaches advanced courses in machine learning, deep learning, and data structures. His lab focuses on foundational and applied ML challenges, with affiliations in the Intelligent, Autonomous & Secure Systems group. Recent service includes senior area chair roles at AAAI, NeurIPS, and ICML.

Pooya Davariمشاهده پروفایل

استاد

• Harmonic and EMI analysis and mitigation in grid-tied converters
• Electromagnetic interference and compatibility
• High power density power electronic systems
• +۲ مورد دیگر

Pooya Davari is a Professor and Head of the Section for Applied Power Electronic Systems at Aalborg University , Denmark. He leads the EMI/EMC in Power Electronics Research Group and serves as Vice Chair of the Energy Efficiency Mission. His research focuses on electromagnetic interference (EMI) and harmonic mitigation in power electronic systems, with over 200 publications and significant contributions to renewable energy integration. Education: B.Sc. and M.Sc. in Electronic Engineering (2004, 2008), Ph.D. in Power Electronics from Queensland University of Technology (2013) Prior Roles: Lecturer at QUT (2013–2014), Postdoc at AAU (2014) Research Interests: Harmonic and EMI analysis in grid-tied converters High power density converter design Signal processing for converter modeling Reliability of power electronic systems Article Trends: Recent work emphasizes EMI/EMC in renewable energy systems, wide bandgap semiconductors (SiC/GaN), and reliability modeling for EVs and hydrogen production via electrolysis. Sub-fields include converter topologies, grid integration challenges, and AI-driven diagnostics. Scientific Awards: Equinor 2022 Prize (Denmark’s oldest engineering award) IEEE EMC Society Young Professional Award (2020) World’s Top 2% Highly Cited Scientist (Stanford, 2021–2025) Multiple best paper awards (IEEE, Applied Sciences, etc.) Grants & Editorial Roles: Recipient of grants from Innovation Fund Denmark (Supra-EMC project), Horizon Europe (SOLARIS), and industry partnerships. Serves as Area Editor for IEEE Transactions on Transportation Electrification , Associate Editor for IEEE Transactions on Power Electronics , and Editor-in-Chief of Circuit World Journal (2020–2025). Labs & Standards: Coordinator of the EMC Laboratory at Aalborg University. Member of IEC standardization Working Groups 6 and 8 (TC77A), focusing on EMC strategies for power grids.

• Large-Scale Data Management
• Data Systems
• Big Data Analysis
• +۵ مورد دیگر

Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto. His research focuses on large-scale data management, integrating machine learning into data systems, and developing efficient query processing techniques for unstructured and streaming data. He holds a PhD from the University of Toronto, an MSc from the University of Maryland at College Park, and a Bachelor's from the University of Patras in Greece. Research interests include data systems, big data analysis, video query processing, and natural language interfaces for databases. He leads projects like ReDD (Relational Deep Dive), SVQ (Streaming Video Queries), and Reliable Text-to-SQL, aiming to bridge human-readable queries with database execution. His work emphasizes scalability, intelligence, and real-world applicability. Recipient of the University of Toronto's Inventor of the Year Award (2011), he translates research into startups like Sysomos, Aislelabs, and Workorb. His contributions span over 200 publications in top venues such as SIGMOD, VLDB, and ICDE. Courses taught include advanced data systems, database design, and system internals. Current projects explore schema extraction from unstructured data, video query optimization, and cost-effective machine learning pipelines. Collaborations with industry and academic partners drive innovations in both theory and practical applications.

Eun Jeong Chaمشاهده پروفایل

دانشیار

• Structural Engineering
• Societal Risk & Hazard Mitigation
• Infrastructure Systems Risk Management
• +۳ مورد دیگر

Eun Jeong Cha is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign. She holds a Ph.D. (2012) and M.S. (2009) from Georgia Institute of Technology, and a B.S. (2006) from Seoul National University. Her research focuses on risk-informed decision-making for infrastructure resilience under natural hazards, including hurricane risk assessment under climate change, interdependent infrastructure systems analysis, and seismic risk mitigation. Education: Ph.D. in Civil Engineering, Georgia Tech (2012) M.S. in Civil Engineering, Georgia Tech (2009) B.S. in Architectural Engineering, Seoul National University (2006) Her research interests span structural reliability, disaster risk management, and the integration of climate change impacts into infrastructure design. Key areas include hurricane risk modeling, interdependent infrastructure recovery, and socially-aware retrofit prioritization. She has received awards such as the ASCE/EMI Probabilistic Methods Committee Student Paper Award (2012) and is a Fellow of the Next Generation of Hazards and Disasters Researchers (2015). Dr. Cha leads the R4 Group, advancing methodologies for resilient infrastructure systems. She actively contributes to professional societies like ASCE, serving on committees for load combinations and climate adaptation. Her work bridges engineering, risk analysis, and policy to enhance community resilience against extreme events.

Marina Bockمشاهده پروفایل

مدرس

• Numerical Modelling
• Laboratory Experiments
• Metallic Structures
• +۳ مورد دیگر

Dr. Marina Bock is a Chartered Civil Engineer and Lecturer in Civil Engineering at Aston University's College of Engineering and Physical Sciences. She specializes in structural engineering with expertise in metallic structures, additive manufacturing, and numerical modeling. Currently accepting PhD students, her work bridges academic research and industry applications in sustainable construction. Her educational background includes: PG Cert in Building and Design and Construction Technology, University of Wolverhampton (2017-2018) PhD in Local Buckling and Web Crippling Response of Stainless Steels, Universitat Politècnica de Catalunya (2010-2015) MSc in Patch Loading of Hybrid Plate Girders, Universitat Politècnica de Catalunya (2004-2010) Dr. Bock's research integrates laboratory experiments and numerical modeling to advance metallic structural systems, with pioneering work in additive manufacturing for construction. Her investigations span stainless steel design code development, corrosion prevention in reinforced concrete using hydrogels, and cold-formed steel behavior. Recent projects focus on sustainable infrastructure solutions through novel composite materials. Analysis of her 2022-2025 publications reveals dominant themes in additive manufactured aluminum structures, cold-formed steel design methodologies, and sustainable paving materials for urban heat island mitigation. Her work consistently addresses practical engineering challenges through experimental validation and code-compliant design solutions. Scientific recognition includes: IStructE Academic Research Award Commendation (2021) for research on aluminum SHS/RHS under biaxial bending Dr. Bock has secured significant research funding including a Royal Society Research Grant (£20k, 2023) for additive manufactured Al7075 aluminum and Innovate UK funding (£437k) for UV-reflective resin-based paving. Previous internal projects (£20k) focused on structural aluminum applications. She supervises PhD research in additive manufacturing and corrosion prevention while maintaining industry collaborations. Her experimental work utilizes advanced university laboratories for structural testing, with collaborations spanning European research consortia and industrial partners. Current projects involve multi-institutional teams developing reusable structural systems and solar-energy-harvesting building envelopes.

Daniel Lovelessمشاهده پروفایل

دانشیار

• Radiation effects in electronics
• Integrated circuit reliability
• Radiation-hardened circuit design
• +۵ مورد دیگر

Daniel Loveless is Associate Professor of Intelligent Systems Engineering and Director of the IU Center for Reliable and Trusted Electronics (CREATE) at Indiana University. He holds B.S. (Georgia Tech), M.S./Ph.D. (Vanderbilt) degrees in Electrical Engineering. Loveless's research focuses on radiation effects/reliability in electronic/photonic integrated circuits, radiation-hardened digital/mixed-signal design, embedded systems, FPGAs, microprocessors, SoCs, and CubeSats. With over 120 peer-reviewed publications, his work advances radiation-tolerant electronics for space/defense applications. Leadership and Honors: Director, IU Center for Reliable and Trusted Electronics Senior Member, IEEE Associate Editor, IEEE Transactions on Nuclear Science 2019 NPSS Radiation Effects Early Achievement Award Recipient of five best conference paper awards

• Orthopedics
• Musculoskeletal health and human movement
• Bone, skin and cartilage science
• +۴ مورد دیگر

Dr. Kevin J. Deluzio is an Associate Professor in the Department of Mechanical and Materials Engineering at Queen's University and serves as Dean of Smith Engineering. He holds a cross-appointment in the Centre for Health Innovation and is affiliated with the Canadian Orthopaedic Research Society and multiple biomechanics societies. His research focuses on musculoskeletal health, biomechanics of human locomotion, and knee osteoarthritis treatment evaluation. Dr. Deluzio earned his BSc (1988), MSc (1990), and PhD (1997) from Queen's University, followed by postdoctoral training at Harvard University. He previously held a faculty position at Dalhousie University, establishing the Dynamics of Human Motion Laboratory. Education: Bachelor of Science (Honours) in Mathematics and Engineering, Queen's University (1988) Master's of Science in Mechanical Engineering, Queen's University (1990) Doctor of Philosophy in Mechanical Engineering, Queen's University (1997) Post-doctorate in Orthopaedic Biomechanics, Harvard University (1999) Research Interests: Dr. Deluzio investigates biomechanical factors of musculoskeletal diseases (e.g., knee osteoarthritis), non-invasive therapies, and surgical treatments like total knee replacement. His work involves markerless motion capture systems and collaborations between engineering and medicine through the Human Mobility Research Centre at Kingston General Hospital. Grants & Awards: While no specific awards are listed, his research has been supported through academic appointments and institutional affiliations. Labs & Teams: Directs the Dynamics of Human Motion Laboratory and collaborates at the Human Mobility Research Centre, integrating engineering and medical expertise to advance musculoskeletal health solutions.

Andrea Santilliمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Representation Learning
• Machine Intelligence
• +۱۰ مورد دیگر

Andrea Santilli is a Research Scientist at Nous Research and holds a PhD in Computer Science from GLADIA at Sapienza University of Rome. His research focuses on large language models (LLMs), robustness, reliability, and multimodal learning. He previously worked at Apple MLR, Hugging Face’s BigScience, and Pi School. He earned his MSc and BSc in Computer Science from Tor Vergata University and Sapienza. Education: PhD in Computer Science, Sapienza University of Rome (2024) MSc in Computer Science, University of Roma Tor Vergata (2020) BSc in Computer Science, University of Roma Tor Vergata (2018) Research Interests: Santilli’s work spans LLM robustness , mechanistic interpretability , multimodal neural databases , and instruction-tuning . He introduced Parallel Jacobi Decoding and contributed to projects like BLOOM, Camoscio, and Fauno. His research bridges syntax-aware NLP, privacy-preserving LLMs, and cross-modal alignment. Publications: His work includes advancements in 3D-text latent space alignment (CVPR 2025), evolutionary merging (ICML 2025), and efficient decoding (ACL 2023). Over 15+ peer-reviewed papers span venues like ACL, CVPR, and ICLR. Awards: Received the Emanuele Pianta Award for his MSc thesis on continual language learning with syntax-based episodic memory. Grants & Projects: Winner of ‘Machine Learning Algorithms for Translation’ grant (2022), developing Parallel Decoding Co-PI for ‘Multimodal AI for 3D Analysis’ (2021) with Ecole Polytechnique Labs & Teams: Active in GLADIA (Sapienza), Apple MLR, and Hugging Face’s BigScience initiative. Core contributor to open-source projects like PromptSource and BLOOM.