جست‌وجوی استاد و پژوهشگر

Arian Malekiمشاهده پروفایل

دانشیار

• Compressed Sensing
• Signal Processing
• Statistical Inference
• +۶ مورد دیگر

Arian Maleki is an Associate Professor in the Department of Statistics at Columbia University, affiliated with the Faculty of Arts and Sciences and the Foundations of Data Science Center. He holds a PhD from Stanford University (2010) and previously served as a postdoctoral scholar at Rice University. His research focuses on statistical inference, signal processing, and machine learning, with particular emphasis on compressed sensing, high-dimensional statistics, and algorithm design. Key areas include noise mitigation, image reconstruction, and the theoretical analysis of algorithms for inverse problems. Recent work highlights include studies on speckle noise challenges, phase transitions in compressed sensing, and certified data removal techniques. His contributions bridge theory and application, often addressing practical computational and statistical challenges in imaging and signal processing. No scientific awards are explicitly listed. Research outputs emphasize foundational advancements in statistical methodologies and algorithmic frameworks for high-dimensional data analysis. Labs/teams: Active involvement in the Foundations of Data Science Center at Columbia University.

Tianhua Xuمشاهده پروفایل

دانشیار

• Optical Communication Networks
• Optical Sensing Systems
• Optoelectronics
• +۴ مورد دیگر

Dr. Tianhua Xu is a Reader in the School of Engineering at the University of Warwick and an Honorary Lecturer in the Department of Electronic and Electrical Engineering at University College London (UCL). He holds a Ph.D. in Optical Communications and Intelligent Signal Processing from KTH Royal Institute of Technology in Sweden, followed by postdoctoral and research roles at KTH, RISE Acreo, DTU, and UCL. His research focuses on optical communication systems, intelligent signal processing, machine learning, optical sensing, and advanced energy systems. He has secured major grants, including a €1.8M EU Horizon Europe project (SPAR) and a £285K UK National Grid initiative. Dr. Xu serves as an Associate Editor for IEEE Transactions on Communications and the Journal of the European Optical Society-RP, and chairs technical groups in the Optical Society of America. He has authored over 200 publications, including invited book chapters, with a Google Scholar h-index of 32. Current projects involve smart photonic sensing, energy storage systems, and deep learning in optical networks. His lab oversees six PhD students and multiple postdoctoral researchers. He actively recruits students through global scholarships and oversees vibrant research teams in optical communications, sensing, and energy systems.

Scott T. M. Dawsonمشاهده پروفایل

استادیار

• Fluid Mechanics
• Dynamical Systems
• Control Theory
• +۵ مورد دیگر

Scott T. M. Dawson is an Assistant Professor in the Mechanical, Materials, and Aerospace Engineering Department at Illinois Institute of Technology (Illinois Tech). He holds positions in the Armour College of Engineering and leads research at the intersection of fluid mechanics, dynamical systems, control theory, and data science. His work focuses on extracting dynamic models from large datasets to analyze and control turbulent flows and unsteady aerodynamic systems. Education includes a Ph.D. and M.A. from Princeton University (2017, 2013), and B.Eng. and B.S. degrees from Monash University (2010, 2009). Prior to Illinois Tech, he was a postdoctoral scholar at Caltech’s Graduate Aerospace Laboratories under Prof. Beverley McKeon. Research interests emphasize reduced-order modeling, data-driven techniques for fluid flows, and flow control applications. His group’s work is supported by NSF, AFOSR, and DOE grants. Recent projects include sparsity-promoting methods for flow analysis, wavelet-based resolvent analysis, and neural network-driven flow control systems. Publications span over 60 peer-reviewed articles, with a focus on turbulence modeling, transient flow dynamics, and machine learning integration in fluid mechanics. Key contributions include novel algorithms for isolating amplification mechanisms in wall-bounded flows and robust neural network frameworks for closed-loop flow stabilization. Grants and collaborations include multi-year NSF CAREER funding for automated distillation of coherent flow structures. Ongoing efforts explore time-localized spectral methods, nonlinear dimensionality reduction, and hydrogen decarbonization in vehicular systems.

• Aquaponics
• Biogas Production
• Environmental Engineering
• +۵ مورد دیگر

Karel Keesman is an Associate Professor at Wageningen University & Research, specializing in mathematical and statistical methods applied to environmental engineering and biotechnology. He leads research in aquaponics, wastewater treatment, and sustainable energy systems, focusing on optimizing resource use and environmental impact. His work integrates advanced modelling techniques with real-world applications, such as bioreactor stability, nutrient cycling in aquaculture, and renewable energy storage. His research interests span aquaponics system design, desulfurization processes, and sensor-based monitoring in water networks. He actively contributes to interdisciplinary projects, including the development of off-river pumped hydro energy storage and nutrient recovery from biofloc systems. Keesman supervises multiple PhD candidates exploring topics like aquaponics sustainability, anaerobic digestion, and energy mixes in Indonesia. He has authored over 300 publications and datasets, emphasizing open-access research. His work bridges theoretical models with practical solutions for environmental challenges.

Karsten Urbanمشاهده پروفایل

استاد

• Numerical Mathematics
• Scientific Computing
• Partial Differential Equations
• +۹ مورد دیگر

Prof. Karsten Urban is a Full Professor of Numerical Mathematics at the University of Ulm, leading the Institute for Numerical Mathematics. He holds roles such as Dean of Studies in Computational Science and Engineering (CSE) and Deputy Spokesman for the Research Association for Scientific Computing in Baden-Württemberg. He is an active member of prestigious societies including the Deutsche Mathematikervereinigung (DMV) and SIAM. His academic journey includes a PhD from RWTH Aachen (1995), Habilitation (2001), and a full professorship at Ulm since 2005. Research focuses on numerical methods for PDEs, reduced basis techniques, multiscale simulations in fluid mechanics, biomechanics, quantum sciences, and financial mathematics. He has pioneered wavelet-based methods and collaborated with industries on ship propulsion and energy trading models. His work integrates mathematical rigor with real-world applications, emphasizing model reduction and computational efficiency. Editorial Roles: Managing Editor of Advances in Computational Mathematics , Editor of SN Partial Differential Equations and Applications . Awards: Teaching award of Baden-Württemberg (2005), Science-Economy Cooperation Awards (2004, 2008). Administrative Roles: Member of the University Council and ASIIN expert committee. Supervises doctoral students in numerical analysis, quantum simulations, and biomechanics. Active in interdisciplinary projects, including quantum systems (IQST) and fracture healing modeling in collaboration with biomechanics experts. His contributions bridge academia and industry, driving innovation in computational methods.

Dan M. Ionelمشاهده پروفایل

استاد

• Alternative and Renewable Energy Technologies
• Electric Machines and Power Electronic Drives
• Electromagnetic Devices
• +۳ مورد دیگر

Dan M. Ionel, Professor and inaugural L. Stanley Pigman Chair in Power at the University of Kentucky (UK), directs the Power and Energy Institute Kentucky (PEIK). He is affiliated with the SPARK Laboratory in the Department of Electrical and Computer Engineering, focusing on renewable energy, electric machines, and smart grids. Alternative and Renewable Energy Technologies Electric Machines and Power Electronic Drives Electromagnetic Devices Electric Power Systems Energy Storage Smart Grids and Buildings His research integrates machine learning with electromagnetic design for applications like electric vehicles and aircraft propulsion. Recent work includes axial flux permanent magnet machines with Halbach arrays and cryogenic thermal management systems. Scientific awards include IEEE Fellowship. Research is sponsored by NSF, DOE, NASA, and industry partners like ANSYS, EPRI, and Regal Rexnord. Collaborations span national labs (NREL, ORNL), utilities (LG&E, TVA), and aerospace entities.

Valentina Boevaمشاهده پروفایل

استادیار

• Machine Learning
• Bioinformatics
• Genomics
• +۳ مورد دیگر

Prof. Valentina Boeva is an Assistant Professor at the Department of Computer Science, ETH Zürich, specializing in biomedical informatics. Her research focuses on integrating machine learning and computational methods to address challenges in genomics, oncology, and precision medicine. She holds a position in the Professur für Biomedizininformatik (Biomedical Informatics) and is based at CAB G32.2, Universitätstrasse 6, Zürich, Switzerland. Her work emphasizes applications such as cancer biomarker discovery, tumor heterogeneity analysis, and epigenetic profiling. She teaches courses including Machine Learning Seminar, Data Science Lab, and Machine Learning for Genomics. Her research group develops computational tools like CDState and UniversalEPI to decode complex biological systems. She actively publishes in top-tier journals, with recent work on exosome-driven diagnostics and chromatin interaction modeling. Her scientific contributions span methodologies for single-cell data analysis, survival modeling, and drug response prediction. She collaborates across disciplines to bridge computational science with clinical applications in cancer research.

Lu Suمشاهده پروفایل

دانشیار

• Internet of Things
• Cyber-physical Systems
• Wireless and Mobile Sensing
• +۷ مورد دیگر

Lu Su is an Associate Professor at the School of Electrical and Computer Engineering , Purdue University , with prior appointments at SUNY Buffalo . His research spans Internet of Things , cyber-physical systems , mmWave sensing , and crowd-sourced data validation , focusing on quality-of-information aware distributed sensing and security in autonomous systems . Ph.D. in Computer Science (2013) and M.S. in Statistics (2012) from University of Illinois at Urbana-Champaign M.E. and B.E. from Harbin Institute of Technology Research Interests: IoT , cyber-physical systems , crowd sensing , security and privacy , and machine learning for sensor networks. His work addresses quality-aware information integration , adversarial attacks in autonomous vehicles , and privacy-preserving crowd-sourced systems . Recent publications focus on mmWave-based sensing (e.g., 3D pose reconstruction), federated learning (driver monitoring), and data poisoning attacks in crowd-sourced systems. His research also extends to traffic optimization and human activity recognition using wireless networks. Professional Roles: Workshop Chair (INFOCOM 2023, 2022) TPC Vice Chair (INFOCOM 2021) Program Committee Member for top conferences Editorial Board, ACM Transactions on Sensor Networks Teaching: Courses on Embedded Systems , Internet of Things , and Network Concepts at both undergraduate and graduate levels.

Yuntian Dengمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Yuntian Deng is an Assistant Professor at the University of Waterloo and a Visiting Professor at NVIDIA. He holds affiliations with Harvard SEAS as an Associate and the Vector Institute as a Faculty Affiliate. He completed his PhD in Computer Science at Harvard under Professors Alexander Rush and Stuart Shieber, followed by a postdoc under Yejin Choi. His research focuses on Natural Language Processing and Machine Learning, with notable contributions in chatbot interaction analysis (WildChat), implicit reasoning models, and markup-to-image generation. He has developed influential tools like OpenNMT and WildVis, and his work has been featured in outlets like the Washington Post and used by OpenAI and Anthropic. Education: PhD in CS (Harvard), Postdoctoral Research (University of Washington). Key achievements include the ACM Gordon Bell Prize for GenSLMs, Best Demo Runner-up at ACL 2017, and Best Paper at DAC 2020. His research emphasizes scalable datasets, efficient reasoning techniques, and real-world applications of AI models. Research interests span NLP, machine learning algorithms, and their applications in areas like dialogue systems, generative models, and ethical AI evaluation. Notable projects include WildChat (1M ChatGPT interactions), implicit chain-of-thought reasoning, and neural steganography for text-based information hiding. His articles explore topics ranging from knowledge distillation to diffusion models, with a focus on bridging theoretical advancements and practical implementations. He actively collaborates with industry partners like NVIDIA and maintains open-source tools to advance AI research accessibility.

Chengzong Pangمشاهده پروفایل

دانشیار

• Power Systems
• Electrical Engineering
• Control Systems
• +۷ مورد دیگر

Chengzong Pang is an Associate Professor and MSECE Graduate Coordinator at the Department of Electrical and Computer Engineering, College of Engineering, Wichita State University. His work focuses on power systems, electrical engineering innovations, and renewable energy integration. He specializes in transient stability analysis, control systems, and smart grid technologies. Research Interests: Dr. Pang's expertise includes advanced control strategies for power electronics (e.g., PMSM, UPQC), machine learning applications for grid stability (LSTM/SVM), and energy storage solutions for renewable integration. His research also addresses challenges in microgrid operation, subsynchronous oscillation mitigation, and battery storage systems. Key Trends in Publications: Over 20 years of publications (2002–2022) emphasize: (1) Machine learning for power system analysis, (2) Control system design for renewable integration, (3) Grid stability enhancement via advanced algorithms, and (4) Smart grid infrastructure optimization. Recent works (2021–2022) highlight transient stability prediction and ANFIS-based power quality solutions. Labs/Teams: Active in energy systems research groups focusing on renewable integration and grid modernization, though specific lab names are not explicitly stated in the provided texts.

Olga Vitekمشاهده پروفایل

استاد

• Statistical Science
• Machine Learning
• Mass Spectrometry
• +۵ مورد دیگر

Olga Vitek is a Professor at Northeastern University's Khoury College of Computer Sciences, with affiliated faculty status in the Department of Chemistry and Chemical Biology. Her research bridges statistical science and machine learning with mass spectrometry-based proteomics and systems biology, focusing on developing open-source software tools like MSstats and Cardinal for quantitative proteomic analyses and imaging. Education: PhD in Statistics (Purdue University), Postdoc at the Ruedi Aebersold Lab (Institute for Systems Biology) Leadership: Director of the Barnett Institute for Chemical and Biological Analysis Her work emphasizes: Statistical experimental design Signal detection in complex mass spectrometry data Causal inference in biomolecular networks Reproducible computational infrastructure Recent publications highlight advancements in quantitative proteomics , mass spectrometry imaging , and causal modeling , with applications spanning cancer research, immunology, and clinical diagnostics. Notable trends include deep learning integration for image analysis and open-source tool development for scalable, transparent workflows. Scientific accolades: Elected Fellow of the American Statistical Association 2021 Gilbert S. Omenn Computational Proteomics Award NSF CAREER award Chan-Zuckerberg Essential Open-source Software award Senior Member, International Society for Computational Biology

Ignacio Galiano Zurbriggenمشاهده پروفایل

استادیار

• Power Converter Modelling & Control
• Energy Systems Integration
• Renewable Energy Systems
• +۶ مورد دیگر

Ignacio Galiano Zurbriggen is an Assistant Professor in the School of Sustainable Energy Engineering at Simon Fraser University (SFU), holding a P.Eng. license. His expertise spans power electronics, energy systems integration, and sustainable energy engineering. He completed his Ph.D. in Electrical & Computer Engineering at UBC (2020), followed by M.Sc. degrees from UBC (2013) and SFU (2012), and a B.Sc. in Electrical Engineering from the National University of Córdoba (2010). His research focuses on power converter modeling/control, energy systems optimization, and multi-domain systems. Notable projects include ultra-fast MPPT algorithms for PV systems, LVRT improvements for wind turbines, and GaN-based PFC design. He teaches advanced courses like SEE 895 Special Topics III and serves as an Associate Editor for the IEEE Open Journal of Power Electronics. Recent work emphasizes grid-connected converter dynamics, microgrid optimization, and adaptive control strategies for renewable energy systems. His publications span IEEE Transactions on Power Electronics, Smart Grid, and leading conferences like APEC and ECCE.

Pia Frickerمشاهده پروفایل

دانشیار

• Computational Methodologies
• Urban Design
• Landscape Architecture
• +۸ مورد دیگر

Prof. Pia Fricker is an Associate Professor and Vice Head of the Department of Architecture at Aalto University's School of Arts, Design and Architecture in Finland. She holds the Professorship of Computational Methodologies in Landscape Architecture and Urbanism, directing the Urban Studies and Planning Programme. Her research integrates urban design, landscape architecture, and digital design culture, focusing on data-driven methods, immersive environments, and adaptive urban development. Collaborations include ETH Zurich, Singapore University of Technology and Design, and Hafencity University Hamburg. Key projects include the Metaversity and Future Smart Cities initiatives. Fricker has led over 80 publications and exhibitions globally, including at the Venice Biennale and National Design Centre Singapore. She is an editorial board member for the Journal of Digital Landscape Architecture and peer reviewer for multiple journals. Awards include the Digital Landscape Architecture Award (2018) and DLA Scientific Merit Award (2021). Her teaching emphasizes computational pedagogy and digital innovation in design education. Education: PhD in Architecture (ETH Zurich, 2021) Postgraduate in Didactics (ETH Zurich, 2011) MAS in Computer Aided Architectural Design (ETH Zurich, 2003) MSc Arch in Urban Design & Landscape Architecture (Technical University of Karlsruhe, 2001) Research Interests: Computational design, parametric modeling, mixed reality, climate-adaptive ecosystems, generative AI, and sustainable urban development. Her work bridges emerging technologies with ecological and urban challenges, emphasizing interdisciplinary collaboration. Grants & Projects: Metaversity (2023–2025, Principal Investigator) Future Smart Cities Sasakawa (2023–2024, Principal Investigator) ABRA (2020–2023, Project Member) Awards: DLA Awards (2018, 2021) DLA Review Committee Awards (2020–2022) Exhibition Recognitions (Venice Biennale, National Design Centre Singapore) Labs/Teams: Leads the Urban Studies and Planning Programme and collaborates with interdisciplinary teams on projects like the RAILCORRIDOR Singapore initiative. Active in digital twin development and AI-driven design tools.

Valentine Kabanetsمشاهده پروفایل

استاد

• Computational Complexity
• Pseudorandomness
• Circuit Lower Bounds
• +۴ مورد دیگر

Valentine Kabanets is a Professor in the School of Computing Science at Simon Fraser University. He holds a Ph.D. in Computer Science from the University of Toronto (2000), M.Sc. from Simon Fraser University (1996), and B.Sc. from the National University of Kiev (1993). His research focuses on computational complexity theory, pseudorandomness, circuit lower bounds, and cryptography. He has received prestigious awards including the Nerode Prize (2013) and multiple best-paper awards at conferences like STOC and CCC. Teaching includes courses on computability and complexity (CMPT 308), data structures and algorithms (CMPT 307), and advanced complexity theory. His research explores foundational questions such as derandomization of algorithms, connections between upper/lower bounds in complexity, and cryptographic hardness assumptions. He leads the Algorithms and Complexity Theory Lab and has supervised numerous graduate students and postdocs. Kabanets' work bridges theoretical insights with practical algorithmic advancements, contributing to both foundational computer science and applied cryptography.

Soraya Fatehiمشاهده پروفایل

استادیار

• Operations Management
• Supply Chain Logistics
• On-Demand Platforms
• +۵ مورد دیگر

Soraya Fatehi is an Assistant Professor in the Department of Operations Management at the Jindal School of Management (JSOM), University of Texas at Dallas. Her research focuses on operations management challenges in on-demand and crowdsourced systems, retail operations, and the OM-Finance interface. She holds a PhD in Operations Management from the University of Washington, an MS in Systems Engineering from the University of Arizona, and a BS in Industrial Engineering from the University of Tehran. Education: PhD, Operations Management, University of Washington, 2020 MS, Systems Engineering, University of Arizona, 2015 BS, Industrial Engineering, University of Tehran, Iran, 2013 Her research interests span the operational dynamics of on-demand platforms, crowdsourcing in logistics, sustainable transportation solutions, and the intersection of operations management with financial strategies. She has contributed to advancing methodologies for optimizing inspection policies in manufacturing and developing revenue-sharing frameworks in crowdfunding contexts. In her recent publications, Dr. Fatehi explores the integration of autonomous vehicles in food delivery, green initiatives in ride-hailing, and strategies to combat greenwashing in competitive markets. Her work also addresses the optimization of last-mile delivery through crowdsourcing and the operational management of on-demand systems. Earlier contributions include algorithmic approaches for production process inspections and revenue-sharing models in crowdfunding. Scientific Awards: Outstanding Teaching Award, Foster School of Business, University of Washington, 2019 Outstanding Research Award, Foster School of Business, University of Washington, 2018 Advising and Grants: No specific advising or grant information is provided in the text.