جست‌وجوی استاد و پژوهشگر

Andrés Monroy-Hernándezمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Social Computing
• Public-interest Technology Platforms
• +۴ مورد دیگر

Andrés Monroy-Hernández is an Assistant Professor at Princeton University's Department of Computer Science, leading the Human-Computer Interaction Lab. He holds a PhD from MIT's Media Lab and has held roles at Microsoft Research and Snap Inc. His research focuses on social computing, public-interest technology, and augmented reality, emphasizing technologies that foster collaboration and connection. He is affiliated with Princeton's Center for Information Technology and Policy, the Keller Center, and serves on Crisis Text Line's board. Education: PhD in Media Arts and Sciences, MIT (2012) Master's in Media Arts and Sciences, MIT (2007) Bachelor's in Electronics Engineering, Tecnológico de Monterrey (2001) Research Interests: Monroy-Hernández explores social computing systems, public-interest platforms (e.g., OpenDeli for food delivery), and social AR (e.g., Capybara). His work emphasizes ethics, marginalized communities, and democratizing technology. Notable projects include Scratch (children's programming), Microsoft's Calendar.help, and Significant Otter (biosignal sharing). Awards & Recognition: MIT Technology Review Innovator Under 35 (2013) CNET's Most Influential Latinos in Tech (2013) Best Paper Awards at CHI, CSCW, HCOMP, ICWSM Grants & Labs: Active in designing open-source protocols and advising tech leaders. His lab collaborates across disciplines, addressing societal challenges through technology. Recent efforts include ethical AI and decentralized social media. Teaching: Teaches social computing and mentors students in HCI and public-interest tech.

Selma Yildirimمشاهده پروفایل

استاد آموزشی

• Mathematical Analysis
• Partial Differential Equations
• Spectral Theory
• +۱ مورد دیگر

Selma Yildirim is an Associate Instructional Professor at the University of Chicago's Department of Mathematics. Her research primarily focuses on mathematical analysis, partial differential equations, spectral theory, and mathematical physics, with an emphasis on eigenvalue problems. She has taught a wide range of courses including Calculus, Mathematical Methods in Physical Sciences, Linear Algebra, and Numerical Analysis. Her pedagogical approach incorporates blended synchronous teaching formats and educational technology like GeoGebra and Python. Her publications consistently explore eigenvalue estimation techniques for operators such as the fractional Laplacian and Klein-Gordon operators, with applications to quantum mechanics and fluid dynamics. She has also developed educational resources on metacognition and data science.

David Greenمشاهده پروفایل

استاد

• Labour Economics
• Income Inequality
• Policy Analysis
• +۵ مورد دیگر

David Green is a Professor at the Vancouver School of Economics , University of British Columbia, and an International Fellow at the Institute for Fiscal Studies in London. He holds a Ph.D. from Stanford University and has served as Chair of the BC Basic Income Panel . Research Focus : Labour economics, income inequality, wage distribution, and public policy. His work bridges macro-level employment trends with micro-level identification of causal effects. Key Contributions : Analysis of minimum wage impacts on job stability, immigrant economic integration, resource boom spillovers, and basic income policy evaluation. Article Trends include: Welfare reform and health outcomes (2022) Covid-19 occupational risks (2022) Resource boom wage spillovers (2019) Post-2000 skill demand reversal (2016) Minimum wage and job tenure dynamics (2013) Scientific Recognition : Harry Johnson Prize (2000, 1995) for best articles in the Canadian Journal of Economics. International Fellow, Institute for Fiscal Studies (London). Policy Engagement : Directed the 2017-2019 BC Basic Income Panel , producing evidence-based reports on poverty reduction and workforce participation. His empirical work frequently informs national policy debates on taxation, immigration, and social assistance.

• Wind Turbines
• Autonomous Learning
• Deep Learning
• +۸ مورد دیگر

Yolanda Vidal Segui is an Associate Professor in the Department of Mathematics at the Universitat Politècnica de Catalunya (UPC), affiliated with the Escola d'Enginyeria de Barcelona Est (EEBE). Her research focuses on wind energy systems, predictive maintenance, and structural health monitoring of wind turbines. She leads projects in the CoDAlab and WinTurCoM research groups, specializing in data-driven models, condition monitoring, and failure prognosis. Her work integrates machine learning, mathematical modeling, and sensor technology to enhance turbine reliability and energy efficiency. Dr. Vidal holds a PhD in Applied Mathematics and has authored over 350 publications. Her contributions include advancements in SCADA data analysis, vibration-based diagnostics, and AI-driven condition monitoring systems. She has received several accolades, including the WindEurope Technology Workshop recognition and the IFIT Distinction in Mechanism and Machine Science. Her research bridges academia and industry, addressing challenges in offshore wind turbine integrity and maintenance strategies. Active in professional service, she serves on conference committees and editorial boards (e.g., Mechanical Systems and Signal Processing, Wind Energy). Her work emphasizes sustainable energy solutions and has been applied in real-world scenarios like the Alpha Ventus wind farm. She also contributes to educational initiatives, developing innovative teaching materials for engineering students.

Axel Schülzgenمشاهده پروفایل

استاد

• Fiber Lasers
• Fiber Optic Sensing
• Optical Communications
• +۶ مورد دیگر

Axel Schülzgen is a Professor of Optics at CREOL, The College of Optics and Photonics, University of Central Florida. He also holds an Adjunct Research Professor position at the University of Arizona's College of Optical Sciences. His research focuses on fiber fabrication, nano-structured fibers, nonlinear materials, and applications in fiber lasers, sensing, and communications. He earned a PhD in Physics from Humboldt-University of Berlin, Germany. His expertise spans optical fiber devices, components, and structures, with a strong emphasis on advancing high-power laser delivery and sensing technologies. Research Interests: - Development of hollow-core fibers for low-loss light transmission - Anti-resonant fiber designs for multi-mode guidance - Nonlinear optical materials for fiber lasers - Applications in fiber optic sensing and medical imaging - Disordered media imaging using optical fibers Awards & Honors: OSA Fellow (Optical Society of America) SPIE Fellow (International Society for Optics and Photonics) 2021 Excellence in Graduate Teaching Award 2015 CREOL Excellence in Research Award Advising & Labs: - Current advisees: Ameen Alhalemi, Caleb Dobias, Md Abu Sufian - Notable alumni: Xiaowen Hu (2022), Stefan Gausmann (2021), and Jian Zhao (2019) - Research Group: Focuses on fiber fabrication technology, nanotechnology in fibers, and photonics applications

Hai Phanمشاهده پروفایل

دانشیار

• Federated Learning
• Privacy-Preserving Machine Learning
• Health Informatics
• +۲ مورد دیگر

Dr. Hai Phan is an Associate Professor in Data Science at New Jersey Institute of Technology's Ying Wu College of Computing. He holds a Ph.D. in Computer Science and Engineering from CNRS, University Montpellier 2 (2013), an M.S. from Konkuk University (2010), and a B.S. from HCM City University of Technology (2008). His research explores privacy-preserving machine learning and computational health analytics: Federated learning systems and optimization Privacy-enhancing technologies (differential privacy) Health informatics and social media analysis Cybersecurity defenses and adversarial learning Fair and ethical AI systems Dr. Phan's publications demonstrate strong emphasis on federated learning architectures with privacy guarantees, defenses against emerging security threats, and analysis of health-related behaviors through social media. Recent work focuses on IoT applications, large language model security, and mobile federated learning ecosystems. His research integrates techniques from distributed systems, cryptography, and machine learning. No scientific awards are mentioned in available sources. Information regarding student advising, research grants, or laboratory affiliations is not provided in available documentation.

Huazhen Fangمشاهده پروفایل

دانشیار

• Systems and Control
• Advanced Battery Management
• Energy Storage Systems
• +۵ مورد دیگر

Huazhen Fang is an Associate Professor in the Department of Mechanical Engineering at the University of Kansas School of Engineering, where he joined in 2014. He leads the Information & Smart Systems Laboratory (ISSL) and holds a courtesy appointment in the Department of Electrical Engineering & Computer Science. His research focuses on enabling intelligence for complex systems through information-driven approaches. Dr. Fang received his Ph.D. in Mechanical Engineering from the University of California, San Diego in 2014, following an M.Sc. from the University of Saskatchewan and a B.Sc. in Computer Science & Technology from Northwestern Polytechnic University in China. He was a Visiting Faculty Fellow at Mitsubishi Electric Research Laboratories in 2022. His research interests span Systems and Control, Advanced Battery Management, Energy Storage Systems, and Robotics, with particular focus on system modeling, estimation, control design, machine learning and numerical optimization. Dr. Fang's work has significant applications in energy management, cooperative robotics, and environmental observing systems. His research has been supported by the National Science Foundation, Department of Energy, Army Research Laboratory, and Mitsubishi Electric Research Laboratories. His extensive publication record shows a clear trend toward increasingly sophisticated integration of physics-based modeling with machine learning approaches, particularly in battery management systems and autonomous vehicle control. Recent work demonstrates a growing emphasis on Bayesian inference methods, distributed control architectures, and safety-critical applications of intelligent control systems. Faculty Early Career Award from National Science Foundation (2019) University Scholarly Achievement Award (2024) Miller Professional Development Award (2022) Miller Faculty Scholar Award (2018, 2019, 2023) Wesley G. Cramer Outstanding Mechanical Engineering Faculty Award (2016) Big XII Faculty Fellowship (2015) IEEE Transactions on Transportation Electrification Prize Paper Award (2024) Dr. Fang has successfully mentored numerous graduate students through the Information & Smart Systems Laboratory, with many receiving awards for their research. His research has attracted significant funding from prestigious organizations including the National Science Foundation, Department of Energy, Army Research Laboratory, and Mitsubishi Electric Research Laboratories. He currently serves as an Associate Editor for multiple prestigious journals including Information Sciences, IEEE Transactions on Industrial Electronics, and IEEE Control Systems Letters. The Information & Smart Systems Laboratory (ISSL) under Dr. Fang's leadership has established itself as a center for cutting-edge research in information-driven smart systems. The lab focuses on pushing the frontiers of information extraction, analysis and exploitation for dynamic systems to deal with system complexity and enable system intelligence. The lab actively collaborates with industry partners and local communities, emphasizing research that serves societal needs.

Edoardo Celesteمشاهده پروفایل

دانشیار

• EU Digital Law
• Comparative Digital Law
• Digital Rights and Constitutionalism
• +۵ مورد دیگر

Dr. Edoardo Celeste is an Associate Professor of Law, Technology and Innovation at Dublin City University's School of Law and Government. He leads the Erasmus Mundus Master in Law, Data and Artificial Intelligence (EMILDAI) and serves as Deputy Director of the Dublin European Law Institute. PhD, University College Dublin Law degrees: University of Rome 'La Sapienza', University of Paris II 'Panthéon-Assas', King’s College London His research focuses on digital rights, constitutionalism in digital contexts, and the intersection of law, technology, and sustainability. He coordinates the DCU Law and Tech Research Cluster and co-founded the Digital Constitutionalism Network. Recent publications examine digital sovereignty, AI sustainability, and constitutional responses to social media exclusion. His 2022 book Digital Constitutionalism: The Role of Internet Bills of Rights (Routledge) established him as a leading voice in the field. Irish Research Council Early Career Researcher of Year Award (2022) Principal Investigator for Cross-Border Data Protection Network (IRC/ESRC funded) Facebook Research grant for Digital Constitutionalism: Content Governance Standard project He supervises PhD students in digital rights, data protection, platform regulation, and digital sustainability while holding affiliations with the ADAPT Centre and UCD Centre for Human Rights.

Bo Wuمشاهده پروفایل

دانشیار

• Compilers and Programming Systems
• Heterogeneous Computing
• Machine Learning Systems
• +۴ مورد دیگر

Bo Wu is an Associate Professor in the Department of Computer Science at Colorado School of Mines. His research focuses on compilers and programming systems, particularly program optimizations for heterogeneous computing and emerging architectures, with applications in machine learning and graph processing. He joined Mines in 2014 after earning a Ph.D. from The College of William and Mary and earlier degrees from Central South University in China. Education : B.S. in Computational Science and Technology (Central South University, 2005) M.S. in Computer Science (Central South University, 2008) Ph.D. in Computer Science (The College of William and Mary, 2014) Research Interests : Wu's work emphasizes enhancing data locality in heterogeneous systems, GPU scheduling, and optimizing applications for emerging architectures. His contributions include frameworks like GraphZero for efficient graph mining and FLEP for GPU preemption. Awards & Grants : NSF SPX Award (2018) NSF CAREER Award (2018) Supercomputing Best Paper Award (2015) Multiple NSF grants for GPU-related research Advising & Grants : Wu has led several NSF-funded projects and actively participates in conference program committees (e.g., PPoPP, SC, ICS). His research spans compiler optimizations, parallel computing, and high-performance systems. Labs & Teams : While specific labs aren’t named, his work involves collaborations on GPU-based systems, graph processing frameworks, and compiler toolchains.

Babak Taheriمشاهده پروفایل

استاد

• Consumer Engagement & Journeys
• Co-creation of Value
• Contemporary Issues in Tourism & Hospitality
• +۳ مورد دیگر

Dr. Babak Taheri is a Full Professor at Texas A&M University's Department of Hospitality, Hotel Management & Tourism, affiliated with the College of Agriculture & Life Sciences. He holds an Honorary Professorship in Marketing at the University of Aberdeen and has held visiting professor roles at institutions like the University of Sassari and the University of Central Florida. His expertise spans consumer engagement, sustainability, and mixed-method research in tourism and hospitality. Education: BSc Industrial Engineering (Azad University), MSc Information Systems (Glasgow Caledonian University), PhD in Marketing (University of Strathclyde) with a focus on tourism. Additional credentials include a PhDip in Research Methods and MRes in Management Science. Research focuses on consumer behavior, co-creation value, CSR, and climate change impacts. He has published over 150 papers and serves as Associate Editor for The Service Industries Journal and International Journal of Contemporary Hospitality Management . Awards include Fellow of The Higher Education Academy and multiple Best Reviewer accolades. Leadership includes roles at Durham University, Strathclyde, and others. He secured grants exceeding $3M from Horizon 2020 and Innovate UK. Teaching spans BSc to PhD levels, emphasizing experiential learning. Media contributions include articles in The Conversation and The Irish Times. Grants & Leadership: Academic leadership in research and teaching, with grants focused on innovation and resilience in hospitality sectors.

Joshua Bardمشاهده پروفایل

دانشیار

• Robotic Fabrication
• Architectural Design
• Human-Machine Collaboration
• +۴ مورد دیگر

Joshua D. Bard is an Associate Professor and Associate Head for Design Research at Carnegie Mellon University's School of Architecture. His work bridges traditional craft and cutting-edge robotics, focusing on human-machine collaboration in construction domains. He leads Archolab, an award-winning research group exploring digital fabrication methods like 'Morphfaux' (robotic plaster techniques) and 'Spring Back' (parametric steam bending). Education: M.Arch (Distinction) from University of Michigan; B.A. in Literature & Philosophy from Wheaton College. Professional affiliations include the Manufacturing Futures Institute and rob|arch. Research emphasizes reviving historical crafts through digital tools, such as augmented reality interfaces for architectural education and thermal-tuned concrete panels via robotic processes. His teaching includes generative modeling and architectural robotics labs. Awards: Architect Magazine R+D Award, Canadian Wood Council Merit Award Key Projects: Plaster ReCast AR app, Thermally Informed Robotic Concrete Panels Collaborators: Dana Cupkova, Garth Zeglin, Steven Mankouche Current courses include 62-225 Generative Modeling and 48-555 Introduction to Architectural Robotics. His work is featured in venues like the Carnegie Museum of Art and academic journals like International Journal of Architectural Computing .

• Antenna Measurement
• Electromagnetic Field Theory
• Near-Field to Far-Field Transformation
• +۵ مورد دیگر

Dr. Alexander Paulus serves as a Researcher at the Chair of High-Frequency Engineering within the Department of Electrical Engineering at the Technical University of Munich (TUM), School of Computation, Information and Technology. Working under Prof. Dr.-Ing. Thomas Eibert, he contributes to advanced electromagnetic research and measurement systems development at TUM's Arcisstr. 21 campus in Munich. Research Expertise His core specialization lies in near-field antenna measurement and transformation techniques, with significant contributions to phase retrieval algorithms, inverse source methods, and UAV-based electromagnetic field measurements. He addresses critical challenges including probe correction with unknown antennas, sparse sampling for directive antennas, and electromagnetic modeling of environmental effects like rain attenuation. His work bridges theoretical electromagnetics with practical antenna characterization solutions. Publication Trends From 2014-2025, Paulus has published 25+ papers focusing on near-field to far-field transformations, particularly in phaseless and multi-probe scenarios. Recent work (2023-2025) demonstrates innovation in spectral filtering, sparse reconstruction, and UAV-based systems for defect localization and wet antenna modeling. His research increasingly integrates computational techniques to solve complex inverse problems in antenna measurements. Scientific Recognition No formal awards documented in available information Academic Contributions Student Mentoring: No advisees listed in provided materials Research Funding: Grant details not specified in source text Research Environment Paulus operates within TUM's Chair of High-Frequency Engineering facilities, which include advanced near-field measurement ranges, UAV-based electromagnetic characterization systems, and laboratories for metamaterials research and electromagnetic compatibility testing. His work supports applications in 5G/6G communications, aviation navigation systems, and precision antenna diagnostics.

Sarah Eblingمشاهده پروفایل

استاد

• Computational Linguistics
• Assistive Technologies
• Sign Language Technologies
• +۷ مورد دیگر

Sarah Ebling is a Full Professor of Language, Technology and Accessibility at the University of Zurich's Faculty of Arts and Social Sciences. She leads the Language, Technology and Accessibility research group within the Institute for Computational Linguistics. Her work focuses on computational linguistics applications for assistive technologies targeting disabilities such as hearing impairments, visual impairments, and cognitive disorders. Key areas include sign language technologies, automatic text simplification, and audio description systems. She directs the large-scale Swiss innovation project 'Inclusive Information and Communication Technologies' (2022-2026, CHF12 million budget) and collaborates on EU H2020 and SNSF Sinergia projects. Education: Holds a doctoral degree (summa cum laude, 2016) from the University of Zurich with research on automatic translation to Swiss German Sign Language. Completed studies in German Linguistics, Computational Linguistics, and English Linguistics at Universities of Zurich and Heidelberg, with research stays in Dublin, Chicago, and Rochester. Research emphasizes multimodal accessibility solutions, including sign language fluency assessment, gesture-based interaction, and AI-driven text adaptation. Current projects explore audio description translation systems (SwissADT), sign language corpus development (SwissSLi), and digital tools for comprehensibility assessment in simplified texts. Her work bridges computational linguistics with ethical considerations in assistive technology deployment. Grants and Leadership: Principal Investigator on major accessibility-focused grants, including the CHF12M Swiss innovation project. Supervises PhD candidates in areas like sign language assessment tools and text simplification algorithms. Active in international collaborations, publishing extensively in computational linguistics and accessibility journals/conferences. Technology Development: Created the 'DigiSpon' benchmark for language sample analysis and developed open-source tools for sign language translation baselines. Her team's innovations include the SignCLIP model connecting text and sign language via contrastive learning, and pose estimation frameworks for sign language recognition.

Prashanth Krishnamurthyمشاهده پروفایل

پژوهشگر

• Robotics
• Control Systems
• Cyber-Physical Systems
• +۳ مورد دیگر

Prashanth Krishnamurthy is a Research Scientist in the Department of Electrical and Computer Engineering at New York University Tandon School of Engineering. His research focuses on robotics, control systems, and cybersecurity, particularly in cyber-physical systems such as power grids and embedded devices. He holds a Ph.D. in Electrical Engineering from NYU. Key research areas include hardware security (e.g., detecting Trojans in chips), anomaly detection in critical infrastructure, and resilient control strategies for robotic systems. He has led or contributed to projects funded by the U.S. Department of Energy (DOE), Office of Naval Research (ONR), and others, including the Tracking Real-time Anomalies in Power Systems (TRAPS) initiative and hardware Trojan detection using short-term aging phenomena. Education: Ph.D., Electrical Engineering, NYU His work bridges theoretical advancements and practical implementations, such as developing FPGA-based testbeds for hardware security validation and creating AI-driven cybersecurity tools like the CRAKEN LLM agent. Collaborators include institutions like SRI International, Karlsruhe Institute of Technology, and the NYU Center for Cybersecurity. Grants include a $1.94M DOE grant for TRAPS and a $359K DURIP grant for hardware Trojan detection. His technical contributions span control systems, anomaly detection algorithms, and cybersecurity frameworks for embedded systems. He is actively involved in advancing secure cyber-physical systems through innovations in real-time monitoring, robust control mechanisms, and AI-augmented security solutions.

João Paulo Costeiraمشاهده پروفایل

دانشیار

• Computer Vision
• 3D Reconstruction
• Structure from Motion
• +۳ مورد دیگر

João Paulo Costeira is an Associate Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), Lisbon. He holds a PhD in Electrical and Computer Engineering from IST (1995) and was a Visiting Scientist at Carnegie Mellon University's Robotics Institute (1991–1995). His research focuses on Computer Vision, 3D Reconstruction, and Structure from Motion, with contributions to object recognition, robotics, and multimedia analysis. Education: PhD in Electrical and Computer Engineering, IST (1995); Visiting Scientist, CMU Robotics Institute (1991–1995). Roles: Coordinator of the Signal and Image Processing Group (SIPg), Co-director of the Carnegie Mellon|Portugal Dual PhD Program in ECE and Robotics (2007–2018), and Scientific Director of Carnegie Mellon|Portugal (2014–2018). Research Interests: João's work emphasizes 3D reconstruction from video, rigid and non-rigid motion analysis, and applications in robotics and urban surveillance. He has pioneered methods for motion segmentation and robust correspondence problems in computer vision. Publications: His recent work includes advancements in apple counting systems, rotation averaging for robotics, and domain adaptation for traffic density estimation. These contributions highlight his expertise in real-world computer vision challenges. Awards: None explicitly listed. However, his extensive publication record and academic leadership reflect significant scholarly impact. Advising & Grants: Supervised 13 PhD students, many co-advised with CMU faculty. Active in projects like CityCam (vehicle counting) and MultiDrone (robotics collaboration). Funded by FCT, EU, and industry partnerships. Labs/Teams: Leader of the Signal and Image Processing Group (SIPg) at ISR. Involved in NETSyS program for networked systems and robotics.