جست‌وجوی استاد و پژوهشگر

• Communication Theory
• Channel Coding
• Low-Density Parity-Check (LDPC) Codes
• +۳ مورد دیگر

Richard D. Wesel is an Associate Dean for Academic and Student Affairs at a College of Engineering, where he has held leadership roles such as Vice-Chair of the Electrical Engineering Department. With Ph.D. and M.S. degrees in Electrical Engineering from Stanford University and MIT respectively, his research focuses on communication theory and channel coding, particularly low-density parity-check (LDPC) codes and turbo codes for efficient data transmission over noisy channels. Education: MIT (B.S., M.S.), Stanford (Ph.D.) Leadership: Associate Dean, Former Vice-Chair of Electrical Engineering Wesel's research explores advanced techniques for broadcast and multiple-access communication systems, emphasizing applications in wireless LANs, satellite communications, and optical networks. His work integrates machine learning with traditional decoding algorithms, as seen in recent publications on neural-network-optimized LDPC decoding and parallel trellis-stage-combining methods for high-throughput systems. His articles demonstrate a focus on finite-blocklength coding, including CRC-aided list decoding for convolutional and polar codes, and voltage optimization strategies for flash memory reliability. Wesel has authored over 100 publications and led a research group that won the 2006 Design Automation Conference's top award for optical multiple access design. Scientific Awards: National Science Foundation CAREER Award Okawa Foundation Award As an educator, Wesel received the TRW Excellence in Teaching Award in 2000 and has contributed to academic governance through roles on the Faculty Executive Committee and Undergraduate Council. His work bridges theoretical communication systems with practical implementations, including FPGA-based decoders and adaptive coding for fading channels.

Alysson Neves Bessaniمشاهده پروفایل

دانشیار

• Distributed Systems
• Dependability
• Fault Tolerance
• +۵ مورد دیگر

Alysson Neves Bessani is an Associate Professor at the Informatics Department of Faculdade de Ciências da Universidade de Lisboa, Portugal, and a member of the LaSIGE research group. His work focuses on distributed systems, Byzantine fault tolerance, and cybersecurity, with significant contributions to blockchain consensus and intrusion-tolerant architectures. Academic Rank: Associate Professor University: Universidade de Lisboa School: Faculdade de Ciências Department: Informatics Department Research Groups: LaSIGE, Navigators Research Interests span distributed systems design, Byzantine fault tolerance, adaptive consensus protocols, and secure multi-cloud storage. His work bridges theoretical foundations with practical implementations like the BFT-SMaRt library and the Vawlt startup. Scientific Awards include multiple Test-of-Time Awards (DSN'24, DSN'21), IBM Faculty Award (2017), and Best Student Paper at Middleware'19. He has advised numerous PhD and Master’s students, contributing to advancements in fault-tolerant systems. Publications (15 most recent) reveal trends in Byzantine consensus optimization, blockchain integration, and AI-driven threat detection. His interdisciplinary work combines distributed computing with genomics and IoT security, reflecting a broad impact across computer science.

• Power Systems
• Renewable Energy
• Smart Grids
• +۷ مورد دیگر

Dr. Aris Dimeas is a Researcher at the National Technical University of Athens in the Department of Electric Power and Industrial Applications . He holds a diploma and PhD in Electrical and Computer Engineering from NTUA and has extensive experience in power systems operations, renewable energy integration, and smart grid technologies. Specialized in AI applications for power systems Developed control software for demand side management Consultant for PPC (2007-2012) Research Focus : Smart grids and digital twin implementations Renewable energy market dynamics Microgrid optimization and control algorithms Collaborations : Active participant in EU research projects, collaborating with HEDNO and other energy grid operators on electronic meters and intelligent network deployments. Teaching : Instructs courses on electric energy systems, power system analysis, and energy management.

• Control Systems
• Petroleum Engineering
• Machine Learning
• +۳ مورد دیگر

Alexey Pavlov is a Professor of Petroleum Cybernetics at the Department of Geosciences and Petroleum, Norwegian University of Science and Technology (NTNU). He holds an MSc in Applied Mathematics from St. Petersburg State University, a PhD in Mechanical Engineering from Eindhoven University of Technology, and has industrial R&D experience from Statoil and Ford Motor Co. Education: MSc (Applied Mathematics, St. Petersburg State University), PhD (Mechanical Engineering, Eindhoven University) His research focuses on control systems for petroleum engineering applications, including nonlinear control theory, iterative learning control, and data-driven optimization methods. Publications reveal a strong emphasis on real-time drilling optimization, well integrity monitoring, and synchronization in networked systems. Recent work trends include machine learning integration for oil well monitoring, moment matching in model reduction, and extremum seeking control for multi-agent systems. Collaborations span institutions like Ford Motor Co., Statoil, and Eindhoven University of Technology. Current affiliations include the Department of Geosciences and Petroleum at NTNU. No scientific awards or advisee information is explicitly mentioned in the provided texts.

• Artificial Intelligence
• Robotics
• Data Science
• +۴ مورد دیگر

Cecilio Angulo Bahón is a full Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Barcelona School of Industrial Engineering (ETSEIB) and the Department of Systems, Automatics and Industrial Informatics Engineering . He leads research in Artificial Intelligence and Robotics , with significant contributions to healthcare data analytics, digital twins, and human-robot collaboration. His research spans machine learning for medical data harmonization, generative adversarial networks in health informatics, and evolutionary algorithms for control systems. Recent publications focus on synthetic healthcare data generation, climate-resilient agriculture , and UMAP-based data analysis . His work bridges AI theory with practical applications in industrial and healthcare domains. Scientific awards include the Sant Jordi 2023 Digital Polytechnic Initiative Award . He has supervised doctoral candidates like Carlos Flores-Vázquez and N. Raya, with key collaborations at the IDEAI-UPC Intelligent Data Science and AI Research Group and the Institute of Robotics and Industrial Informatics (CSIC-UPC).

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +۶ مورد دیگر

Thomas Hellstrom is a Professor at the Department of Computer Science , Umeå University, Sweden. He leads the Intelligent Robotics group and is affiliated with the Center for Transdisciplinary AI . His research spans human-robot interaction (HRI) , deep learning applications , robot ethics , and field robotics for agricultural and forestry automation. Coordinated EU projects: INTRO (FP7/ITN), SOCRATES (H2020), CROPS, SWEEPER Developed intelligent walker for stroke patients with CMTS/MT-FoU/Umeå Stroke Center Key contributions in robot learning , causal reasoning , and natural language understanding Research Focus : His work emphasizes understandability in robot behavior, including causal modeling , multi-modal communication , and ethical frameworks for autonomous systems. Current project ROCC (Swedish Research Council) explores robot causality, while SOCRATES addressed social robotics in eldercare. Scientific Awards : • Erdös-Bacon-Sabbath number ≤ 13 Grants & Funding : • ROCC (2023, 3.7M SEK, Principal Investigator) • SCAI (2022, 3.7M SEK, Co-Applicant) • VINNOVA (2019, 3.47M SEK, Co-Applicant)

Sujith Vijayanمشاهده پروفایل

دانشیار

• Computational modeling
• Brain oscillatory dynamics in health and disease
• Development of stimulation and pharmacological therapies for brain disorders
• +۷ مورد دیگر

Sujith Vijayan is an Associate Professor at the School of Neuroscience , part of the Virginia Tech College of Science . His research focuses on neural dynamics during sleep and active behavior , leveraging computational modeling , signal processing , and invasive/non-invasive recording to explore memory consolidation, emotional regulation, and therapeutic applications for neurological disorders. Research Interests include: Sleep-mediated memory processing Brain-computer interface (BMI) learning Pathological brain rhythms in PTSD and Parkinson’s Neural stimulation therapy development REM sleep emotional memory mechanisms His 15 most recent publications reveal a trajectory from anesthetic-induced neural oscillations (2013), through schizophrenia network analysis (2015), to targeted memory reactivation during sleep (2017). Key scientific contributions include the 2022 NSF CAREER award for BCI learning enhancement via sleep manipulation . Lab members include graduate students in Biomedical Engineering, TBMH, and Neuroscience programs, with alumni now at institutions like Barrow Neurological Institute and University of Arizona’s Sleep Lab.

Manuel Penschuckمشاهده پروفایل

پژوهشگر ارشد

• Algorithm Engineering
• Random Graphs
• Parallel and Distributed Computing
• +۳ مورد دیگر

Manuel Penschuck is a Research Fellow at the Institute of Computer Science , Goethe University Frankfurt, Germany. His research focuses on algorithm engineering, graph theory, and scalable network generation, with emphasis on parallel computing, I/O-efficient algorithms, and random graph models. He actively contributes to conferences like ESA, SEA, and IPDPS, and has co-authored publications in top venues including LIPIcs , IEEE Transactions , and SIAM . His work includes engineering algorithms for non-linear preferential attachment , parallel shuffling , and hyperbolic graph generation . He has co-organized program committees for ESA, EuroPar, and SEA, and his collaborations span institutions such as MPI-INF, TU Darmstadt, and Australian National University. Recent publications highlight advances in uniform graph sampling, geometric network models, and distributed systems. His research integrates theoretical rigor with practical implementation, addressing challenges in big data and high-performance computing. He is a key contributor to the Networkit toolkit for large-scale network analysis.

• Signal Processing
• Wireless Communications
• MIMO Communications
• +۴ مورد دیگر

Joakim Jaldén is a Professor at the Division of Information Science and Engineering, School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology. He holds a Ph.D. in Electrical Engineering from KTH (2007) and completed post-doctoral studies at Vienna University of Technology (2007-2009). With affiliations at Stanford University and ETH Zürich, his academic journey reflects global expertise. 2002: M.Sc. in Electrical Engineering, KTH 2007: Ph.D. in Electrical Engineering, KTH 2007-2009: Post-Doctoral Researcher, Vienna University of Technology Jaldén's research spans Signal Processing , Wireless Communications , and Biomedical Data Analysis . He pioneered MIMO communications and later developed ELISpot/FluoroSpot analysis algorithms commercialized by Mabtech AB. His work on cell migration tracking (IEEE ISBI 2012) and distributed optimization (ECO-PANDA method) demonstrates interdisciplinary impact. Key publication trends include Hidden Markov Models for DNA sequencing, Reinforcement Learning in communication systems, and Low-Complexity Beamforming for MU-MIMO networks. His 2024 work on mmWave MIMO beam coherence showcases continued leadership in wireless channel modeling. Scientific recognition includes: IEEE Signal Processing Society 2006 Young Author Best Paper Award Ingvar Carlsson Career Award 2009 (Swedish Foundation for Strategic Research) IEEE ISBI 2012 Best Paper Award Bitplane Awards (2013-2015) for cell tracking challenges As Program Director of KTH's 5-year Electrical Engineering Degree Program (CELTE) since 2016 and Vice-Chair of EECS Faculty Board , Jaldén leads academic initiatives. His collaborations with industry (e.g., Mabtech AB) and roles as examiner for advanced courses in communication systems highlight his educational impact.

Yoseph Barashمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Biology
• Bioinformatics
• +۴ مورد دیگر

Yoseph Barash is an Associate Professor at the University of Pennsylvania, jointly appointed in the Perelman School of Medicine's Department of Genetics and the School of Engineering and Applied Science's Department of Computer and Information Science. He leads the BioCiphers Lab, integrating machine learning with experimental biology to decode RNA biogenesis and splicing regulation in human disease. Education: B.Sc. in Physics and Computer Science, Hebrew University Ph.D. in Machine Learning, Hebrew University (2006) His research spans Machine Learning , Computational Biology , and Bioinformatics , focusing on predictive models for RNA splicing and its role in diseases like cancer and neurological disorders. He pioneered the splicing code (Barash et al., Nature 2010) and extended it to genetic variations (Xiong et al., Science 2015). Recent publications emphasize RNA splicing variations in cancer, tool development (e.g., MAJIQ V3, MAJIQ-CLIN), and machine learning for drug discovery (e.g., trametinib sensitivity in AML). His work bridges computational innovation with wet-lab validation, enabling novel high-throughput assays. Scientific Awards: Lap-Chee Tsui Publication Award (2010) NSERC EWR Steacie Fellowship Canadian Institute for Advanced Research Fellowship He advises companies in RNA therapeutics and has licensed splicing quantification tools to Pfizer, GSK, and Biogen. His lab collaborates extensively, training students and postdocs in interdisciplinary approaches to RNA biology.

Rafał Weronمشاهده پروفایل

استاد

• Forecasting
• Agent-based modeling
• Computational economics
• +۴ مورد دیگر

Rafał Weron is a Full Professor at Wrocław University of Science and Technology, where he has held leadership roles since 2015, including Head of the Department of Operations Research and Business Intelligence and Chairman of the Scientific Discipline Council for Management and Quality Sciences. His expertise spans electricity price forecasting, computational economics, and risk management, with significant contributions to probabilistic forecasting methods. As a globally recognized scholar, he has received prestigious awards such as the Hugo Steinhaus Prize (2018) and the Tao Hong Award (2017). Key Affiliations : Wrocław University of Science and Technology; Polish Academy of Sciences (Statistics and Econometrics Committee); Polish Mathematical Society. Research Trends: Weron's work focuses on electricity price forecasting, leveraging machine learning and statistical models to enhance accuracy and reliability. His publications emphasize probabilistic forecasting frameworks, quantile regression, and hybrid modeling techniques, reflecting a commitment to methodological rigor and practical applications in energy markets. Scientific Awards: Top 1% globally ranked economist (IDEAS/RePEc, 2013-2022) World's Top 2% Most Widely Cited Scientist (2019-2021) 'Hugo Steinhaus' Prize (2018) Tao Hong Award (2017) Emerald Citation of Excellence (2017) Minister of Science & Higher Education Prize (2016) Commission of National Education Medal (2016)

• Databases
• Data Management
• Provenance Analysis
• +۶ مورد دیگر

Val Tannen is a Professor at the University of Pennsylvania, specializing in database systems, provenance analysis, and programming languages. His research focuses on data management, query languages, and systems like DBSP and ORCHESTRA. Collaborations include work with co-authors such as Zachary Ives, Susan Davidson, and Todd Green. Key research interests include provenance for databases, incremental view maintenance, and data integration. His work bridges theoretical foundations and practical applications in systems like DBSP for stream processing and ORCHESTRA for collaborative data sharing. Publications span provenance frameworks, query optimization, and distributed systems. While no awards are explicitly listed, his contributions to database theory and systems are widely recognized.

Constantin Grigoمشاهده پروفایل

پژوهشگر

• Machine Learning
• Uncertainty Quantification
• Stochastic Differential Equ://grigo.userweb.mwn.de/ueber-mich/
• +۲ مورد دیگر

Constantin Grigo is a PhD researcher at the Technical University of Munich (TU Munich), actively engaged in the Continuum Mechanics group. His work focuses on Uncertainty Quantification (UQ) and Machine Learning (ML), particularly for applications in maritime safety, bicycle traffic modeling, and stochastic systems. He has presented his research at major conferences like SIAM UQ and WCCM, and has been recognized with Student Travel Awards from SIAM UQ 2018 and SIAM CSE 2019. Education: Master of Science in Physics, LMU Munich (2015) Bachelor of Science in Physics, LMU Munich (2012) Year abroad at Grenoble INP (2010-2011) Research Interests: Probabilistic machine learning for coarse-graining high-dimensional systems Bayesian model and dimension reduction Stochastic differential equations in heterogeneous media Microscopic traffic simulation for bicycles and autonomous vehicles Digital twin applications for maritime and urban mobility Reduced-order modeling of random materials Selected Awards: SIAM UQ 2018: Student Travel Award Winner SIAM CSE 2019: Student Travel Award Winner His publications span topics such as data-driven scenario specification for autonomous vehicles, bicycle maneuver prediction using neural networks, and physics-constrained surrogates for UQ. He also contributes to open-source simulation tools like SUMO for traffic modeling.

Erica Goldمشاهده پروفایل

مدرس

• Phonetics
• Sociolinguistics
• Forensic Speech Science
• +۴ مورد دیگر

Erica Gold serves as a Lecturer in the Department of Linguistics within the College of Humanities, Arts, Behavioral and Social Sciences at California State University San Marcos. Her academic background includes a Ph.D. in Linguistics and MSc in Forensic Speech Science from the University of York, UK, and a B.A. in Linguistics from UC San Diego. Ph.D. in Linguistics; University of York, U.K. MSc in Forensic Speech Science; University of York, U.K. B.A. in Linguistics; University of California San Diego Dr. Gold's research focuses on the intersection of phonetics and sociolinguistics, employing acoustic, auditory, and data-intensive approaches to inform both linguistic theory and forensic applications. Her primary interests include phonetic variation in vowels, speaking tempo, hesitation markers, and voice quality as they relate to regional and social accents/dialects, with particular attention to inter-speaker and intra-speaker variability across different populations. Her work bridges theoretical linguistics with practical forensic speaker identification applications. Analysis of her publication record reveals consistent specialization in forensic phonetics, with particular emphasis on speaker identification methodologies, regional dialect variation (especially West Yorkshire English), and the application of acoustic analysis to forensic evidence. Her research demonstrates methodological rigor through the development and validation of speech tempo measures, voice quality analysis, and large-scale regional speech databases for forensic reference populations. Dr. Gold serves as an Editorial Assistant for Babel: The Language Magazine, which publishes linguistics content for lay audiences, and is a Full Member of the OSAC subcommittee on Speaker Recognition for the National Institute for Standards and Technology, where she contributes to developing forensic speaker recognition standards. She teaches foundational linguistics courses including LING 101: Mysteries of Human Language and LING 200: English Grammar and Syntax, bringing her research expertise in phonetics and forensic applications into the classroom. Her professional activities demonstrate strong engagement with both academic linguistics and practical forensic applications through publications, committee work, and database development.

Yasmine Abdinمشاهده پروفایل

استادیار

• Polymer Matrix Composite Materials
• Composites Design and Manufacturing
• Structural Reliability
• +۹ مورد دیگر

Dr. Yasmine Abdin serves as an Assistant Professor in the Department of Materials Engineering within the Faculty of Applied Science at the University of British Columbia (UBC). Her research focuses on advancing polymer matrix composite materials through innovative digital simulation and probabilistic design methodologies. Her academic credentials include: B.Sc. from KU Leuven M.Sc. from KU Leuven Ph.D. from KU Leuven Dr. Abdin's research program centers on overcoming limitations in composite material durability through probabilistic design frameworks and multi-scale modeling. She integrates finite element analysis, machine learning, and Industry 4.0 technologies to predict structural reliability under stochastic service conditions, with emphasis on damage tolerance, manufacturing-process-structure relationships, and optimization of carbon fiber production from sustainable precursors like lignin and asphaltenes. Her recent publications (2023-2025) demonstrate strong focus on sustainable composite manufacturing, including carbon fiber production from renewable resources, 4D printing of shape memory polymers, flax fiber-reinforced composites, cellulose nanofibril modification, and fatigue behavior analysis. Key thematic trends include the convergence of digital twin technologies with composite manufacturing, sustainable precursor development, and the application of machine learning to enhance modeling efficiency in structural reliability prediction. Information regarding doctoral students, research grants, laboratory facilities, or scientific awards was not provided in available sources.