جست‌وجوی استاد و پژوهشگر

Yehuda Baruchمشاهده پروفایل

استاد

• Career studies
• HRM
• International/global HRM
• +۲ مورد دیگر

Yehuda Baruch is Professor of Management at Southampton Business School, University of Southampton, UK, and Affiliated Professor at Audencia, Nantes France. With over 175 refereed papers, six books, and approximately 50 book chapters, his scholarly impact is substantial with over 30,000 Google Scholar citations and an H-index of 84. Baruch earned his DSc from Technion, Israel, and completed postdoctoral work at City University and London Business School. His academic journey includes significant leadership roles such as Vice-president Research of the European Academy of Management (2016-2019), Associate Editor of Human Resource Management, and Editor of multiple prestigious journals. He is currently Dean of the Peer Review College at the British Academy of Management and Consulting Editor for the Journal of International Business Studies. Professor Baruch's research centers on Human Resource Management and Organizational Studies, with particular emphasis on career development and global HRM practices. His work explores how geographical location impacts career success, career ecosystems in higher education, future workplace scenarios, and the integration of AI in HRM. He has made significant contributions to understanding working from home dynamics following the pandemic and has examined gender-based discrimination across cultural contexts in the UK and Pakistan. His recent publications reveal a strong methodological rigor combined with theoretical innovation, particularly in sampling strategies and career ecosystem frameworks. Baruch's work consistently bridges academic theory with practical organizational applications across global business environments, demonstrating exceptional interdisciplinary reach. Fellow of the Academy of Social Sciences Fellow of the British Academy of Management Cooper Medal for Outstanding Contribution and Leadership (2020) Top 50 globally in business & management studies Top 2% of global highly cited scholars Hughes Lifetime Scholar Award (2024) Professor Baruch has supervised 28 PhD students (7 current, 21 completed), with 14 holding academic positions internationally across multiple continents. His research has attracted substantial funding totaling £1,171,000, including significant grants from UKRI (ESRC) for pandemic-related working from home research and from the UK Art Council for entrepreneurial careers of artists. He has served on editorial boards for 14 major journals and held multiple leadership positions in professional associations. Baruch is actively involved in multiple research centers at Southampton including the Centre for Inclusive and Sustainable Entrepreneurship and Innovation, Centre for Research on Work and Organisations, and Centre for Democratic Futures. His collaborative research spans international boundaries, connecting scholars across continents to address contemporary organizational challenges in an increasingly globalized business environment.

Pierre Duchesneمشاهده پروفایل

استاد

• Time Series Analysis
• Sampling Theory
• Multivariate Analysis
• +۲ مورد دیگر

Pierre Duchesne is a Full Professor in the Department of Mathematics and Statistics at the University of Montreal . He serves as Professor-responsibility for the M.Sc. and Ph.D. in Statistics programs (2000-2004). His research focuses on applied statistics with emphasis on: Time Series Analysis (univariate and multivariate models, serial correlation testing, portmanteau statistics) Sampling Theory (robust estimation methods, calibration estimators) Multivariate Analysis (ARCH effects, vector autoregressive models, causality testing) Applications in Econometrics and Financial Econometrics His work combines theoretical development with practical implementation through: Wavelet-based diagnostic methods Simulation studies for model validation Software development (S-PLUS/SAS) for statistical analysis Collaboration with organizations like Statistics Canada and Canadian Journal of Statistics He has served as Associate Editor for journals including Computational Statistics & Data Analysis (CSDA) and Canadian Journal of Statistics (CJS/RCS) .

• Plasma Physics
• Fusion Plasmas
• Applied Mathematics
• +۴ مورد دیگر

Nathan Garland is a Lecturer in Applied Mathematics and Physics at Griffith University, Australia. He is affiliated with the Queensland Quantum and Advanced Technologies Research Institute (QUATRI) and the Centre for Quantum Dynamics. Prior to joining Griffith, Garland conducted postdoctoral research at Los Alamos National Laboratory and served as sessional teaching staff at James Cook University. Education: PhD in Electrical and Electronic Engineering and Mathematics from James Cook University B.Eng (Hons) and B.Sc in Electrical and Electronic Engineering and Mathematics from James Cook University His research focuses on computational plasma modeling, with applications in low-temperature plasmas, tokamak fusion, electron transport in liquids, and deep learning integration for plasma simulations. He combines advanced numerical methods with experimental validation to address challenges in energy systems and plasma medicine. Recent publications highlight trends in plasma physics, machine learning-driven cross-section determination, and electron transport across gas-liquid interfaces. Garland contributes to fusion energy discourse through media appearances and peer review roles in journals like Plasma Sources Science and Technology and European Physical Journal D . Grants: Quantum Mechanics: The Missing Link? - $1.2M LANL LDRD grant (2019-2021) Digitally Disrupted Demos - $7.5K Griffith Sciences grant (2022) Supervision: Principal Supervisor for PhD project 'Better Modelling of Solvents' Associate Supervisor for PhD projects on landscape evolution modeling and non-equilibrium electron scattering Collaborations: Member of Tokamak Disruption Simulation (TDS) SciDAC Center IAEA Fusion Energy Conference Program Committee member

• Bioinformatics
• Computational Biology
• Pharmaceutical Drug Discovery & Development
• +۱۳ مورد دیگر

Leonard Wesley is an Associate Professor in the Department of Computer Science at the College Of Science, San Jose State University. His research spans interdisciplinary domains at the intersection of Bioinformatics , Computational Biology , and Machine Learning , with specific applications in Pharmaceutical Drug Discovery , Genomic Data Analysis , and Autonomous Robotics . Education: Ph.D. in Computer Science, University of Massachusetts M.S. in Computer Science, University of Massachusetts B.A. in Physics and Math, Northeastern University Research Interests include Approximate Reasoning (probabilistic, evidential, and fuzzy logic), Agent-Oriented Systems , and Sensor Fusion . His work applies these methodologies to Drug Portfolio Management , Protein Structure Scoring , and Medical Diagnostics . Publication Trends show a consistent focus on Computational Biology , Robotics , and Uncertainty Quantification over four decades. Early work in Computer Vision evolved into modern applications in Pharmaceutical Analytics and AI in Aerospace . Key Projects include SVM-based drug affinity prediction, evidence-driven decision support systems for biopharma, and real-time agent development frameworks like ROADS. He has contributed to CFD code control and Mobile Network Congestion solutions. Collaborations with institutions like NASA, Los Alamos National Laboratory, and international conferences (WMSCI, ICINCO, AIAA) highlight his cross-disciplinary impact. His teaching includes Artificial Intelligence and Bioinformatics courses.

Iason Papaioannouمشاهده پروفایل

استاد مدعو

• Uncertainty Quantification
• Structural Reliability
• Reliability-Based Optimization
• +۵ مورد دیگر

Iason Papaioannou is an Adjunct Professor in the area of Uncertainty Quantification at the Technical University of Munich (TUM), affiliated with the Engineering Risk Analysis Group. He holds a habilitation from the TUM School of Engineering and Design and has been tenured since 2021 as an Akademischer Rat. His academic journey includes a Ph.D. in Civil Engineering from TUM (2012), an M.Sc. in Computational Mechanics (2007), and a Diploma in Civil Engineering from the National Technical University of Athens (2005). His research focuses on uncertainty quantification , reliability assessment , and Bayesian updating of engineering systems. Key areas include probabilistic modeling, machine learning applications, spatial variability analysis, and geotechnical reliability. He has pioneered methods for system reliability analysis, adaptive subset simulation, and cross-entropy-based importance sampling. Teaching responsibilities include courses such as Stochastic Finite Element Methods, Structural Reliability Methods, and Elements of Machine Learning. His work integrates advanced computational techniques with practical engineering challenges, emphasizing high-dimensional uncertainty analysis and data-driven model updating.

Foto N. Afratiمشاهده پروفایل

استاد

• Parallel and distributed computation
• Processing of very large data
• MapReduce
• +۶ مورد دیگر

Professor Foto N. Afrati is a Distinguished Faculty Member at the National Technical University of Athens, specifically within the School of Electrical and Computing Engineering and the Division of Communication, Electronic and Information Engineering. She has held this position since 1993, following previous academic ranks at the same university as Associate Professor (1989-1993), Assistant Professor (1985-1989), Lecturer (1982-1985), and Research Fellow (1980-1982). She completed her PhD in Electrical Engineering at Imperial College of the University of London in March 1980, with a dissertation focused on Error Correcting Codes by Algorithms. Her academic journey also included a Diploma from Imperial College (March 1980) and an earlier Diploma in Electrical and Mechanical Engineering from the National Technical University of Athens (June 1976). Professor Afrati's research interests span several critical areas in computer science: Parallel and distributed computation Processing of very large data (including MapReduce) Data and web mining Database Systems Information integration Query optimization Computation and complexity of algorithms Approximation algorithms Her most recent publications demonstrate expertise in MapReduce environments, query optimization with views, and data exchange frameworks. These works are published in prestigious venues like EDBT, VLDB, PODS, and ICDT, with specific focus areas including adaptive sampling techniques, data source integrity, and algorithm complexity in database environments. Professor Afrati has received significant recognition in her field, including Fellow of the Association for Computing Machinery (ACM) Best Paper Award at the International Conference on Database Theory (ICDT) 2009 She has advised numerous PhD students throughout her career, including Theodoros Mitakos, Ezz Hattab, Nikos Kiourtis, and Angelos Vasilakopoulos. Her current PhD students include Victor Kyritsis and Nikos Stassinopoulos. Professor Afrati maintains strong professional networks through her various visiting positions at institutions such as Google, Stanford University, IBM Research Center, University of Helsinki, University of Paris, DIMACS, and others. She has served as associate editor and reviewer for major academic journals and conferences including IEEE TKDE, ACM Transactions of Database Systems (TODS), Journal of ACM (JACM), and Theoretical Computer Science (TCS). Her extensive work in research projects spans both national and international initiatives, with funding from sources including the European Union's Thalis project, ESPRIT working groups, HCM networks, and Greek General Secretariat of Research and Technology grants.

Amneet Bhallaمشاهده پروفایل

دانشیار

• Fluid-Structure Interaction
• Multiphase Flows
• Aquatic Locomotion
• +۷ مورد دیگر

Dr. Amneet Bhalla serves as an Associate Professor in the Department of Mechanical Engineering within the College of Engineering at San Diego State University (SDSU). His primary contact email is asbhalla@sdsu.edu, with office located in Engineering Building Room 323-G, and phone number (619) 594-2043. Education: Ph.D., Mechanical Engineering, Northwestern University (2013) M.S., Mechanical Engineering, Indian Institute of Technology Kharagpur (2009) B.S., Mechanical Engineering, Indian Institute of Technology Kharagpur (2004-2008) Postdoctoral Training: University of North Carolina at Chapel Hill (Mathematics Department) and Lawrence Berkeley National Laboratory (Computational Research Division) Research Interests: Dr. Bhalla develops advanced numerical methods and high-performance computing techniques for computational fluid dynamics (CFD) and fluid-structure interaction (FSI) problems. His work spans aquatic locomotion, renewable energy device modeling, multiphase flows, vehicular aerodynamics, and bioengineering applications. He creates mathematical models to interrogate underlying flow physics for engineering design optimization, with emphasis on open-source software development through the IBAMR library. Publication Trends: Recent publications (2023-2025) focus on robust numerical frameworks for multiphase flows with phase change, acoustic streaming, and fluid-structure interaction. Key themes include mass conservation in level set methods, adaptive mesh refinement, and solvers for non-isothermal gas-liquid-solid systems. Applications range from aquatic locomotion and renewable energy devices to microfluidics and biomedical flows, demonstrating commitment to both theoretical advances and practical engineering solutions. Scientific Awards: No awards mentioned in the provided text Advising and Grants: Dr. Bhalla secured an NSF CAREER award (2023) for "Consistent Continuum Formulation and Robust Numerical Modeling of Non-Isothermal Phase Changing Multiphase Flows". As PI of the CFD Lab, he mentors graduate students in computational mechanics, leveraging prior industrial experience at ExxonMobil Upstream Research Company. His research integrates industrial practicality with academic rigor through collaborations with national laboratories. Laboratory and Team: The Computational Fluid Dynamics and Flow Physics Laboratory (CFD Lab) develops the open-source IBAMR software—a distributed-memory parallel implementation of the immersed boundary method with adaptive mesh refinement. The lab emphasizes transparency, community engagement, and reproducibility, establishing cross-institutional collaborations while advancing computational methods for complex flow phenomena in engineering and biological systems.

HanQin Caiمشاهده پروفایل

استادیار

• Mathematical Optimization
• Data Science
• Machine Learning
• +۵ مورد دیگر

Dr. HanQin Cai is the Paul N. Somerville Endowed Assistant Professor in the Department of Statistics and Data Science at the University of Central Florida (UCF), also serving as Director of the Data Science Lab. He holds a joint appointment with the Department of Computer Science. His research focuses on theoretical and algorithmic foundations of mathematical optimization, data science, and machine learning, with emphasis on non-convex algorithms, adversarial attacks, signal/image processing, and deep learning integration. He has secured NSF grants totaling over $2.6M, including a $121K single-PI grant and a $2.49M co-PI grant. His work has been recognized with the UCF OSCaR Award (2025) and IEEE Senior Member status (2024). Education: PhD in Applied Mathematical and Computational Sciences from University of Iowa (2018), with M.S. in Mathematics (2014) and M.C.S. in Computer Science (2017). Previously served as a Postdoc at UCLA Mathematics Department under Dr. Wotao Yin. Research highlights include: query-efficient zeroth-order optimization, robust signal processing with corrupted data, adversarial attacks on neural networks, and tensor-based methods for high-dimensional data analysis. His recent publications explore advanced techniques in matrix recovery, tensor decompositions, and explainable AI. Grants & Awards: NSF DMS-2304489 (2022–2025), NSF DUE-2321986 (2024–2029), UCF OSCaR Award, IEEE Senior Membership. Labs & Teams: Directs UCF's Data Science Lab, collaborates across disciplines in statistics, computer science, and engineering.

• Computer Architecture
• Computer Systems
• Approximate Computing
• +۱۰ مورد دیگر

Pejman Lotfi-Kamran is an Associate Professor at the School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, where he also serves as the head of the school and director of Turin Cloud Services. His research focuses on computer architecture, systems, approximate computing, and cloud computing, with an emphasis on performance and energy efficiency for big-data applications. His educational background includes: Ph.D. in Computer Science, EPFL (2013) M.Sc. in Electrical and Computer Engineering, University of Tehran (2005) B.Sc. in Electrical and Computer Engineering, University of Tehran (2002) Lotfi-Kamran's research spans computer architecture innovations, including data and instruction prefetching, networks-on-chip, coherence protocols, and many-core processor design. He has pioneered work on scale-out processors, neural acceleration for GPUs, and approximate computing frameworks. His publications appear in top venues such as ISCA, HPCA, MICRO, and IEEE/ACM journals. His recent articles reflect a strong trend in improving system performance through intelligent prefetching, efficient NoC designs, and energy-aware architectures. Key themes include reducing frontend bottlenecks, optimizing cache behavior, and enhancing data delivery in large-scale systems. His work often combines cross-stack insights with hardware-software co-design for real-world impact. Scientific awards and recognitions include: 2017 CADS Best Paper Award 2016 Young Faculty Award from Iran's National Elites Foundation 2012-2013 Intel Ph.D. Fellowship 2012 and 2011 HiPEAC Paper Awards 2011 HPCA Best Student Paper Finalist Multiple academic honors from University of Tehran He has advised several graduate students including Paria Darbani, Ali Ansari, Mohammad Bakhshalipour, and Farid Samandi, many of whom have co-authored significant papers. His teaching spans institutions like Sharif University of Technology, Iran University of Science and Technology, and EPFL, covering advanced computer architecture and multiprocessor systems. He has led research projects such as AxBench and CloudSuite on Simics, and contributed to national initiatives like Iran’s National Grid. He is actively involved in tool development and continues to shape research in next-generation computing systems. He leads the Turin Cloud Services initiative at IPM and is deeply engaged in both theoretical and applied aspects of computer systems research, with ongoing work in neural acceleration, approximate computing, and scalable architectures.

Anthony Burkeمشاهده پروفایل

استاد

• Architectural Design
• Contemporary Design Theory
• Urban Technologies
• +۶ مورد دیگر

Professor Anthony Burke is a Professor of Architecture in the Faculty of Design Architecture and Building at the University of Technology Sydney (UTS), where he currently serves as Course Director for the Master of Architecture program. Previously, he held significant leadership roles including Associate Dean of International and Engagement (2017-2019) and Head of the School of Architecture (2010-2016). Internationally recognized for his work in architectural design, curation and commentary, Burke specializes in contemporary design theory at the intersection of technology, urbanism and practice, with a particular focus on ethical considerations in smart city development. Burke holds an M.Sc. in Advanced Architectural Design from Columbia University (2000) and a B.Architecture (First Class Honors) from UNSW Australia (1995). Prior to his current position at UTS, he was an Assistant Professor in Architecture at the University of California, Berkeley between 2002 and 2007. He has also served as a Visiting Professor at the Beijing Institute of Technology (2018-2020) and Institut Teknologi Bandung (2018-2019). Professor Burke's research explores the complex relationship between architecture, technology, and urban environments, with particular emphasis on how design can address societal challenges while maintaining human-centered values. His work spans architectural theory, urban technologies, and innovative practice models, often bridging academic scholarship with practical applications through numerous funded research projects and industry collaborations. He has published extensively on topics including the ethics of smart cities, architectural restoration, and the evolving role of architects in contemporary society. His recent publications reveal a consistent exploration of architecture's role in contemporary society, with recurring themes including the intersection of technology and design, ethical considerations in urban development, and innovative approaches to architectural practice. This body of work demonstrates both theoretical depth and practical relevance, connecting academic discourse with real-world applications. 2024 ACTAA Award for Lifestyle Category - Grand Designs Australia Season 11 2024 Honorary Fellow Beyond traditional academic work, Burke actively supervises PhD students and has secured significant research funding from organizations including Cumberland Council, IKEA, and the NSW Office of the Government Architect. His projects frequently involve interdisciplinary collaboration, bringing together architects, urban planners, technologists, and policymakers to address complex urban challenges. He has also founded initiatives like Open Agenda, an annual speculative design research competition for emerging Australian architects. Professor Burke extends his impact through media engagements, serving as host and presenter for popular television programs including Grand Designs Australia (Seasons 4-11), Restoration Australia (Seasons 4-7), and Culture X Design. These platforms allow him to communicate architectural concepts to broad audiences, with Grand Designs Australia reaching approximately 1 million viewers per episode in Australia and more internationally. His public outreach includes regular contributions to The Conversation, ABC News, and other media outlets, making architecture and design accessible to the general public while showcasing innovative Australian architecture.

Marco Nieمشاهده پروفایل

استاد

• Transportation Engineering
• Transportation Systems Analysis
• Urban Mobility
• +۷ مورد دیگر

Marco Nie is a Professor and Chair of the Department of Civil and Environmental Engineering at Northwestern University, where he has been a faculty member since 2006. His research focuses on Transportation Systems Analysis, emphasizing interdisciplinary approaches that integrate optimization, network science, traffic flow theory, economics, and statistics to address complex interactions between human activities, infrastructure, and urban networks. He teaches three courses: an undergraduate/graduate introduction to transportation engineering, and two graduate courses on analytical and computational tools for surface transportation systems design. Education: Marco Nie earned a BS in Structural Engineering from Tsinghua University (Beijing), followed by graduate studies in Transportation at the National University of Singapore (NUS), and a PhD in Transportation from the University of California, Davis. Research interests include improving transportation efficiency, sustainability, and equity through policy and technology. Recent work explores autonomous vehicles, modular transit systems, congestion pricing, and data-driven solutions for EV charging and ride-hail platforms. He has expressed challenges in securing research funding, noting its critical role despite inherent flaws in evaluating research impact through monetary metrics. Scientific Awards: He received the 2021 Transportation Science Meritorious Service Awards . Marco also serves on editorial boards, including Service Science (2023), and actively publishes on topics like urban mobility, freight logistics, and policy analysis. Advising & Grants: While no specific students or grants are listed, he highlights the importance of funding mechanisms for research. His work often involves collaborations with sponsors and stakeholders to address real-world transportation challenges. Labs/Teams: Affiliated with the Center for Science and Protection of Engineered Environments and engages in interdisciplinary research groups focusing on sustainable and equitable urban systems.

Christina Delimitrouمشاهده پروفایل

دانشیار

• Computer Architecture
• Cloud Computing
• Systems & Networking
• +۶ مورد دیگر

Christina Delimitrou is an Associate Professor at MIT's Department of Electrical Engineering and Computer Science (EECS) and a Principal Investigator at the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on optimizing cloud computing systems, with a strong emphasis on resource management, sustainability, and machine learning-driven solutions. Delimitrou leads projects on carbon-aware scheduling, efficient datacenter operations, and serverless computing frameworks like Ursa and Ditto. Her work bridges theoretical system design with practical deployment challenges, addressing topics such as microservices orchestration, approximation techniques for resource efficiency, and security implications of multi-tenancy in shared cloud environments. Notably, she received the Presidential Early Career Award for her contributions to improving datacenter efficiency through innovative scheduling and resource allocation strategies. Delimitrou's research group develops tools like Sage (ML-driven performance debugging) and Seer (big data analytics for cloud systems), emphasizing reproducibility and scalability. Her lab also explores edge computing, swarm robotics coordination (e.g., Hivemind), and hardware-software co-design for next-generation systems. Her academic affiliations include MIT CSAIL's Systems Community of Research, where she collaborates on large-scale software systems. Key themes in her work include QoS-aware resource management, sustainable computing practices, and leveraging approximation to enhance cloud resource utilization.

Fangneng Zhanمشاهده پروفایل

پژوهشگر ارشد

• Computer Science
• Artificial Intelligence
• Neural Rendering
• +۷ مورد دیگر

Currently a Research Fellow at Harvard University & MIT , Fangneng Zhan specializes in Neural Rendering and Generative AI . His research focuses on developing evolutive rendering frameworks, 3D-aware generative models, and multimodal synthesis techniques. Previously, he was a postdoctoral researcher at the Max Planck Institute for Informatics under Prof. Christian Theobalt. He earned his Ph.D. in Computer Science & Engineering from Nanyang Technological University, Singapore and a Bachelor's in Communication Engineering from the University of Electronic Science and Technology of China . His work spans 3D reconstruction, robotics applications , and lighting estimation , with significant contributions to SIGGRAPH , NeurIPS , and CVPR conferences. Recent research highlights include evolutive gauge transformations for neural fields, generalizable 3D style transfer via Gaussian splatting, and multimodal synthesis frameworks leveraging pre-trained models like CLIP and Stable Diffusion. He has co-authored Top50 Popular Paper in TPAMI 2023 and organized workshops at CVPR 2024 on generative models. Scientific Awards: Top50 Popular Paper, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023 Collaborative Network: Mentored students at institutions like Harvard, NTU, and ETH Zurich. His projects include datasets for lighting estimation and real-time scene text detection systems.

• Database Systems
• Data Analytics
• Machine Learning in Databases
• +۳ مورد دیگر

Jignesh M. Patel is a Professor at the University of Wisconsin, Madison, WI, USA , with over 25 years of contributions to database systems, data analytics, and hardware-aware query processing. His work bridges theoretical advancements with practical systems engineering. Research Interests span: Database systems optimization (query processing, transaction management) Hardware acceleration for analytics (eBPF, PIM, GPUs) Machine learning integration in databases (feature selection, model optimization) Efficient data structures (hashing, encoding, indexing) Multi-tenant and cloud database management Recent Work focuses on kernel-embedded databases (BPF-DB, 2025), memory-efficient dataframe processing (SplitDF, 2024), and algorithmic-hardware co-design for dense retrieval (DReX, 2025). He has pioneered techniques for adapting to data skew (VIP Hashing, 2022), leveraging static analysis in R optimization (ROSA, 2017), and rethinking benchmarking paradigms. Collaborations include key partnerships with: Systems researchers (Andrew Pavlo, José F. Martínez) Machine learning experts (Arun Kumar, Kevin Skadron) Education-focused colleagues (Adalbert Gerald Soosai Raj, Richard Halverson) Industry leaders (David J. DeWitt, Microsoft Research)

• Signal Processing
• Electronics
• Biomedical Engineering
• +۵ مورد دیگر

Marek Miśkowicz serves as a Professor at the Department of Metrology and Electronics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His primary institutional contact is miskow@agh.edu.pl, with physical location in building B-1, room 212. His research spans signal processing, biomedical engineering, and electronics, specializing in event-based sampling methodologies, time-to-digital conversion techniques, and reconstruction of bandlimited signals from nonuniform samples. Key contributions include QRS detection algorithms for ECG monitoring, POCS-based reconstruction frameworks, and event-driven control systems for industrial IoT applications. His work emphasizes resource efficiency in embedded systems and mobile health monitoring through approximate computing and adaptive sampling strategies. Recent publications (2022-2025) demonstrate consistent focus on signal reconstruction from irregular samples, with growing emphasis on spiking neural networks for event classification and industrial IoT optimization. Biomedical applications (particularly ECG analysis) and industrial control systems represent dominant application domains, while methodological innovations center on iterative reconstruction algorithms and temporal accuracy evaluation in noisy environments. No scientific awards were referenced in the source materials. No information regarding student advising or research grants was available in the provided documentation. The source texts contained no details about laboratory facilities, research teams, or collaborative groups associated with Professor Miśkowicz.