جست‌وجوی استاد و پژوهشگر

Erika Ábrahámمشاهده پروفایل

استاد

• Computer Science
• Satisfiability Checking
• SMT Solving
• +۷ مورد دیگر

Erika Ábrahám is a Full Professor at RWTH Aachen University , Germany, leading the Theory of Hybrid Systems research group. Her academic journey includes positions as a Junior Professor (2008-2013) and postdoctoral researcher at institutions like Jülich Research Centre and Albert-Ludwigs-University Freiburg. Her research interests span formal methods, SMT solving, hybrid systems verification, and probabilistic systems. She has contributed to symbolic computation, railway timetables, and probabilistic hyperproperties, with recent work focusing on cylindrical algebraic decomposition, conflict-driven search algorithms, and stochastic modeling. Scientific contributions include 15+ articles (2021-2025) on topics like probabilistic hyperproperties , hybrid automata , and symbolic arithmetic , often published in LNCS, Springer, and Elsevier venues. She has collaborated on tools like HyPro and SMT-RAT, and edited proceedings for conferences including NASA Formal Methods and QEST.

Tomer Korenمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization

Tomer Koren is an Associate Professor at the Blavatnik School of Computer Science , Tel Aviv University, and a Senior Research Scientist at Google Research, Tel Aviv. His research focuses on machine learning and optimization , particularly in stochastic optimization, online learning, and reinforcement learning. Recent work includes advancements in learning rate annealing, bandit optimization, continual learning, and private online learning, with publications at top venues such as ICML, NeurIPS, and COLT. Key subfields span gradient methods , robust optimization , and privacy-preserving algorithms . Scientific achievements include the Outstanding Paper Award at ALT 2025 and Spotlight presentations at ICML 2025 and 2024 . He received his PhD from the Technion—Israel Institute of Technology , advised by Prof. Elad Hazan. Collaborations include work with researchers at Google and Tel Aviv University, with a focus on theoretical foundations and practical algorithm design. His current affiliations and research integrate both academia and industrial innovation.

• Electromechanics
• Electromagnetic Field Modeling
• Electric Drives
• +۷ مورد دیگر

Mykhailo Igorovich Kotsur is an Associate Professor at the Department of Electrical and Electronic Devices within the Electrical Engineering Faculty of Zaporizhzhia National Technical University. With over 14 years of academic service since 2011, he has established himself as a specialist in electromechanics and electromagnetic field modeling. His work bridges theoretical research with practical applications in industrial power systems, particularly focusing on energy efficiency improvements in electrical machinery and power distribution systems. Education: 2008: Graduated from Zaporizhia National Technical University with a degree in "Electrical Machines and Devices" 2012: Defended PhD thesis at Sevastopol National Technical University in specialty 05.09.03 "Electrical Complexes and Systems" on "Improving the Efficiency of the Pulse Regulation System of an Asynchronous Motor with a Phase Rotor" Professor Kotsur's research focuses on the investigation of electromagnetic, energy and thermal processes in electromechanical complexes and systems. His work particularly addresses challenges in electric drive systems, power quality issues related to harmonic distortions, and energy efficiency optimization in industrial applications. He has developed novel approaches for modeling electromagnetic fields in electrical machines and power distribution systems, with special attention to busbar systems and crane power supply networks. His methodologies combine theoretical analysis with practical field simulation techniques to solve real-world engineering problems. Analysis of Professor Kotsur's 15 most recent publications reveals a strong trend toward advanced electromagnetic field simulation methods applied to industrial power systems. His work consistently focuses on improving the accuracy of parameter determination for electrical systems, particularly busbar configurations and electric drive components. A significant portion of his recent research addresses the impact of higher current harmonics on industrial equipment performance, with practical applications in overhead crane systems and workshop power networks. His publications demonstrate a progression from fundamental electromagnetic modeling to increasingly sophisticated applications in energy-efficient industrial systems. Professor Kotsur actively contributes to academic training through teaching courses including "Automation of the design of electrical and electronic devices," "Methodology of scientific research in electromechanics," and "Optimization of engineering and design solutions in electromechanics." He has developed numerous instructional materials and laboratory guides for students specializing in electrical engineering and electromechanics. His work with students extends to supervising qualification projects and providing guidance on research methodology in the field of electromechanical systems. His research has resulted in multiple Ukrainian patents related to electric drive systems and control mechanisms, demonstrating the practical application of his theoretical work. These innovations focus on improving energy efficiency in industrial applications, particularly in systems involving asynchronous motors with phase rotors.

Mykhailo Petrovych Chornoborodovمشاهده پروفایل

دانشیار

• Radar
• Digital Signal Processing
• Databases
• +۱ مورد دیگر

Mykhailo Petrovych Chornoborodov is an Associate Professor at Zaporizhia National Technical University, where he has been working since 2010. He holds the academic degree of Candidate of Technical Sciences in the specialty 05.12.17 "Radio and television systems." His primary affiliation is with the university's radio engineering department, where he contributes to both teaching and research activities in radar systems and signal processing. Education: Zaporizhia State Technical University, Radio Instrumentation, 1999 Academic Degree: Candidate of Technical Sciences (2009) Academic Title: Associate Professor Dr. Chornoborodov's research focuses on radar systems, digital signal processing, databases, and military doctrine. His work primarily addresses challenges in radar technology, particularly in interference compensation and signal processing techniques. He has developed methods for improving radar performance, including compensation of impulse interference in coherent-pulse radars and enhancement of tactical and technical characteristics of radar systems like the "Pelican" and 35D6. His research bridges theoretical signal processing with practical military and defense applications. His publication record since 2003 demonstrates consistent contributions to radar and signal processing fields. The research spans military applications, meteorological monitoring, and advanced signal processing methods, with particular emphasis on weight functions for discrete Fourier transforms and pseudorandom sequence design. His most recent work (2019) focuses on pseudorandom sequence synthesis and new code sequences for phase modulation, showing continued innovation in the field. Scientific Interests: Radar, Digital Signal Processing, Databases, Military Doctrine Teaching: Devices for generating, shaping and transmitting radio signals; Signal reception and processing devices; Theory of radio engineering systems; Radio and TV broadcasting systems and networks Dr. Chornoborodov maintains active participation in scientific conferences, including the III Scientific and Technical Conference "State and Development of the Navy of the Armed Forces of Ukraine at the Current Stage" (2003), the II International Radio Electronics Forum "Applied Radio Electronics. State and Prospects of Development" (2005), and multiple scientific and technical conferences of the Kharkiv University of Air Forces (2005-2014). His scholarly contributions are documented through his ORCID (0000-0003-1230-9374) and Scopus (35762538500) identifiers.

• Electrical Power Engineering
• Power Systems
• Smart Grids
• +۳ مورد دیگر

Stefan Čubonović serves as a Lecturer at the Department of Electrical Power Engineering within the Faculty of Technical Sciences, University of Kragujevac, Serbia. His teaching portfolio includes core electrical engineering courses such as Power Transformers, DC and Asynchronous Machines, Synchronous Machines, Distribution Networks, and Electric Drives (2020-2022). His research centers on advanced power system analysis with specialization in distribution network state estimation under non-ideal measurement conditions and machine learning applications for hydroelectric power forecasting. Current work focuses on mitigating non-Gaussian noise impacts in state estimators and developing hybrid neural network models for renewable energy output prediction, demonstrating strong interdisciplinary integration of power engineering and data science. Recent publications (2022-2025) reveal a clear trajectory toward intelligent grid solutions, with 70% of work addressing hydroelectric forecasting and state estimation challenges. The research combines theoretical numerical methods with practical implementations for distribution system monitoring and optimization. Scientific recognition includes: 2nd place in best papers category at ENERGETIKA 2024 His experimental work spans synchrophasor device development for low-voltage networks, spatial sensor imaging platforms, and graphical monitoring environments for distribution system dynamics, reflecting hands-on engagement with power system instrumentation and data acquisition.

Marko Šućurovićمشاهده پروفایل

استادیار

• Electrical Power Engineering
• Electrical Machines
• Electric Motor Drives
• +۵ مورد دیگر

Marko Šućurović is an Assistant Professor at the Department of Electrical Power Engineering, Faculty of Technical Sciences in Čačak, University of Kragujevac, Serbia. His expertise spans electrical power engineering with a focus on electrical machines, motor drives, and energy efficiency. He teaches multiple courses including Electrical Installations and Lighting, Electric Motor Drives, and Distribution Networks, while actively contributing to research and development projects in his field. Education: Undergraduate studies in Electrical Power Engineering at Faculty of Technical Sciences in Čačak (2007-2011), average grade 8.82 Master's degree in Electrical and Computer Engineering, module Electrical Power Engineering (2011-2012), thesis: "Measurement and visualization of the rotating magnetic field of the stator of a three-phase and two-phase asynchronous motor" Currently pursuing PhD at Faculty of Electronics in Niš, Department of Electrical Engineering and Computer Science Šućurović's research focuses on Electrical Power Engineering , with particular emphasis on electrical machines and drives , energy efficiency , and electrical installations . His work bridges theoretical concepts with practical applications through educational laboratory setups. He has developed specialized tools for visualizing electromagnetic phenomena and has contributed significantly to understanding thermal effects in electrical systems, cable engineering, and renewable energy integration. His research often combines experimental work with analytical modeling to address real-world engineering challenges. His recent publications (2020-2025) demonstrate a strong focus on energy efficiency , educational methodologies in electrical engineering, and advanced analysis of electrical systems . A significant portion of his work addresses practical implementation challenges in power systems, with increasing attention to renewable energy integration. His research shows a clear progression from fundamental electrical machine analysis toward more complex system-level energy efficiency solutions and innovative educational approaches. Projects: Researcher on project TR33016 "Research, development and implementation of programs and measures for energy efficiency of electric drives" Participant in Erasmus+ Proof-of-Concept project "Torque sensor based on magnetomechanical effect in commercial steel" (TorqSens) Implementation of project "Introduction of laboratory exercises and information technologies in teaching electrical installations and lighting" Šućurović has developed several educational laboratory setups, including systems for measuring photovoltaic-thermal panel characteristics, educational pump systems with pressure and flow regulation capabilities, and equipment for testing low-voltage electrical installations. His work emphasizes practical, hands-on learning approaches in electrical engineering education.

Alexander Tyurinمشاهده پروفایل

استادیار

• Optimization Methods
• Machine Learning
• Federated Learning
• +۵ مورد دیگر

Alexander Tyurin is an Assistant Professor at the Skolkovo Institute of Science and Technology (Skoltech) and Senior Research Scientist/Team Leader at the Artificial Intelligence Research Institute (AIRI). His research focuses on modern optimization and machine learning, including federated learning, stochastic optimization, neural network training, and asynchronous optimization methods. Education Ph.D. in Computer Science from Higher School of Economics (2017-2020) Master of Computer Science from Higher School of Economics (2015-2017) Bachelor of Computer Science from Lomonosov Moscow State University (2011-2015) Research Focus Tyurin's work centers on developing theoretically-grounded optimization algorithms for complex machine learning systems. Key research areas include: Distributed and federated learning systems with communication efficiency Theoretical analysis of stochastic and asynchronous optimization methods Time complexity improvements for large-scale nonconvex problems Bidirectional compression techniques for distributed training Publication Trends His recent publications demonstrate a strong focus on theoretical aspects of distributed optimization, with consistent contributions to: Optimal time complexity proofs for asynchronous methods Communication-efficient algorithms for federated settings Nonconvex optimization under heterogeneous computing environments Theoretical advancements in gradient descent variants Awards and Recognition Best Reviewer Award at ICML 2022 Best Reviewer Award at NeurIPS 2022 Professional Activities Tyurin serves as reviewer for top machine learning venues including ICML, NeurIPS, ICLR, JMLR, and Machine Learning journal. He teaches Optimization Methods in Machine Learning at Skoltech and leads a research team at AIRI focused on optimization theory.

Diana Marculescuمشاهده پروفایل

استاد

• energy-aware computing
• reliability-aware computing
• hardware aware machine learning
• +۱۰ مورد دیگر

Diana Marculescu is Department Chair, Cockrell Family Chair for Engineering Leadership #5, and Professor, Motorola Regents Chair in Electrical and Computer Engineering #2 at the University of Texas at Austin. She previously served as the David Edward Schramm Professor at Carnegie Mellon University, where she was Founding Director of the College of Engineering Center for Faculty Success (2015-2019) and Associate Department Head for Academic Affairs (2014-2018). Her educational background includes: Dipl.Ing. degree in computer science from Polytechnic University of Bucharest, Romania (1991) Ph.D. degree in computer engineering from University of Southern California, Los Angeles (1998) Dr. Marculescu's research bridges hardware design and machine learning, focusing on energy- and reliability-aware computing, hardware-aware machine learning, and computing for sustainability. She leads the EnyAC (ENergY Aware Computing) research group and founded the iMAGiNE Consortium for industry-university collaboration in engineering intelligent machines from cloud to edge. Her work addresses the critical need for efficient AI systems across diverse hardware platforms. Analysis of her recent publications reveals a strong trend toward hardware-efficient machine learning, with emphasis on neural architecture search, model compression, and energy-aware deep learning systems. Her research tackles the fundamental challenge of co-designing machine learning models and hardware to achieve optimal performance and efficiency. Dr. Marculescu has received numerous prestigious awards: National Science Foundation Faculty Career Award (2000-2004) ACM SIGDA Technical Leadership Award (2003) Carnegie Institute of Technology George Tallman Ladd Research Award (2004) IEEE Circuits and Systems Society Distinguished Lecturer (2004-2005) Australian Research Council Future Fellowship (2013-2017) Marie R. Pistilli Women in EDA Achievement Award (2014) Barbara Lazarus Award from Carnegie Mellon University (2018) Fellow of ACM, IEEE, and AAAS Dr. Marculescu has advised more than a dozen doctoral students, 35+ master students, and more than two dozen undergraduates. Her research has been supported by significant grants including the NSF Career Award and Australian Research Council Future Fellowship. She has served as Technical Program Chair and General Chair for multiple major conferences including ACM/IEEE International Symposium on Low Power Electronics and Design, IEEE/ACM International Symposium on Networks-on-Chip, and IEEE/ACM International Conference on Computer-Aided Design. She leads the EnyAC research group which focuses on sustainable computing and computing for sustainability, and founded the iMAGiNE Consortium to foster industry-university collaboration in engineering intelligent machines from cloud to edge.

• Theoretical Computer Science
• Distributed Computing
• Descriptive Complexity Theory
• +۳ مورد دیگر

Fabian Reiter is an Associate Professor at Gustave Eiffel University , specializing in Theoretical Computer Science within the DataBases, Automata, algorithm Analysis and Models (BAAM) team at the Gaspard Monge Laboratory of Computer Science (LIGM) . His research bridges logic and distributed computing , focusing on extending descriptive complexity theory to distributed systems. His work establishes equivalences between distributed algorithm classes and logical formula classes, mirroring classical results like Fagin’s theorem . This includes groundbreaking connections between asynchronous automata and least fixpoint logic . Academic appointments : 2019–now : Associate Professor at Gustave Eiffel University 2019-03–2019-07 : Postdoc at Technical University of Munich (funded by ERC project PaVeS ) 2018–2019 : Postdoc at University of Paris-Saclay (funded by ERC project EQualIS ) Education : PhD (2014–2017) under Olivier Carton at IRIF, Paris Diderot University Master’s (2013–2014) under Fabian Kuhn and Andreas Podelski at University of Freiburg

Vipul Goyalمشاهده پروفایل

دانشیار

• Cryptography
• Quantum Cryptography
• Security & Privacy
• +۶ مورد دیگر

Vipul Goyal is an Associate Professor in the Computer Science Department at Carnegie Mellon University and a Senior Scientist at NTT Research . He previously spent 7 years at Microsoft Research before joining CMU. His academic journey includes a PhD from UCLA under advisors Rafi Ostrovsky and Amit Sahai , and a B.Tech. from IIT (BHU) Varanasi . Research Interests: Cryptography, Quantum Cryptography, Security & Privacy, Theoretical Computer Science Grants: NSF, DARPA, Department of Energy NETL, JP Morgan, Cisco, PNC, Ripple, DoS Networks, CyLab initiatives Education: PhD in Computer Science (UCLA, 2009) B.Tech. in Computer Science (IIT BHU Varanasi) His recent work focuses on Quantum Cryptography , Secure Multi-Party Computation , and Unclonable Security . Notable awards include the 2019 JP Morgan Faculty Award and the 2016 ACM CCS Test of Time Award . He advises the CMU Blockchain Group and has served on program committees for Crypto 2025 , FOCS 2024 , and others. Scientific Awards: 2019 JP Morgan Faculty Award 2016 ACM CCS Test of Time Award Microsoft Research Graduate Fellowship Google Outstanding Graduate Student Award Forbes 30 Under 30 in Science and Healthcare (2013) Students & Postdocs: He has mentored numerous PhD students and Postdocs , many of whom are now faculty at institutions like Tsinghua University, Max Planck Institute, and UT Austin. Former research assistants include Yifan Song and Elisa Masserova .

Stoo Seppمشاهده پروفایل

مدرس

• Educational Technology
• Cognitive Science
• Instructional Design
• +۴ مورد دیگر

Stoo Sepp is a Lecturer in Educational Technology and Learning Design at the University of British Columbia's Master of Educational Technology (MET) program. With experience since 2018, he specializes in integrating cognitive science principles into digital education frameworks. University of British Columbia Master of Educational Technology His research bridges cognitive science and educational technology , focusing on: working memory optimization , self-regulated learning , and gesture-based interaction in digital environments. Recent work explores educational data privacy and decentralized learning platforms . Key article trends span: 2019-2020: Cognitive load and movement integration 2022: Open educational resources evolution 2023-2025: Gesture analytics and translingual assessment tools Professional Networks : Active in #EdTech communities Contributor to #OpenEd movements Collaborator in cognitive load research

• Cryptography
• Computer Security
• Electronic Voting Systems
• +۲ مورد دیگر

Daniel Rausch is a Researcher at the University of Stuttgart , affiliated with the Institute of Information Security . His work focuses on cryptographic protocols, particularly in electronic voting systems, universal composability, and accountable security frameworks. Key research areas include Cryptography , Computer Security , and Formal Verification . Recent publications explore password-hardened encryption, zero-knowledge proofs, and tally-hiding mechanisms for e-voting. Scientific contributions span: Provably secure password-hardened encryption with round-optimal schemes Accountable bulletin boards with UC security guarantees Zero-knowledge proofs for ballot validity in ElGamal-encrypted elections Publicly tally-hiding protocols preserving vote privacy UC-IITM model unification for composability frameworks Collaborations include researchers like Ralf Küsters , Nicolas Huber , and Marvin Bechtold . Notable implementations include Fabric*BB for distributed ledger applications and Kryvos for verifiable elections.

Ileana Nicolaeمشاهده پروفایل

مدرس ارشد

• Power Systems
• Electromagnetic Compatibility
• Signal Processing
• +۳ مورد دیگر

Dr. Ing. Ileana Nicolae is a Senior Lecturer at the Faculty of Automatic Control, Computers and Electronics , University of Craiova, Romania. Her research focuses on power systems, electromagnetic compatibility, and signal processing techniques. Key Research Areas: Transformer overvoltage analysis, harmonic pollution, wavelet packet transforms, photovoltaic grid integration, and industrial power diagnostics. Recent Work: 2025 studies on lightning-induced transformer overvoltages and active filtering solutions, with 2024 contributions to harmonic analysis in municipal hospitals and conductor maintenance in transmission networks. Technical Expertise: Advanced control algorithms (FOC/SVPWM), EMC testing, and wavelet-based signal processing for noise reduction. Collaborative Projects: Remote radiation monitoring systems using Arduino platforms and grid compatibility analysis for renewable energy systems.

Rajiv Sethiمشاهده پروفایل

استاد

• Microeconomic theory
• Communication
• Crime
• +۱۵ مورد دیگر

Rajiv Sethi is Professor of Economics at Barnard College, Columbia University, and an External Professor on the Science Steering Committee at the Santa Fe Institute (SFI). His research centers on microeconomic theory with applications to communication, crime, policing, and inequality, including current work on the geography of lethal force. His primary research interests span microeconomic theory, agent-based modeling, behavior, evolution, finance, law and policy, network theory, polarization, misinformation, cyberhate, risk and uncertainty, social science, strategy, and decision making. He co-authored Shadows of Doubt: Stereotypes, Crime, and the Pursuit of Justice (Harvard University Press, 2019) with Brendan O'Flaherty, examining how stereotypes influence criminal justice outcomes. Recent publications reveal consistent thematic evolution: early work focused on network theory and inequality, while current research integrates forecasting systems (e.g., hybrid geopolitical event prediction) with critical analyses of discrimination in criminal justice and lethal force. His 2023-2025 output shows intensifying focus on policing dynamics, stereotype administration, and wealth distribution mechanisms. Scientific Awards No scientific awards mentioned in source material Advising and Grants Source text provides no information on student supervision, grant funding, or research advising activities. Labs and Teams Sethi's SFI affiliation indicates participation in complex systems research collaborations, particularly through the Science Steering Committee. His work on 'The Economy as an Evolving Complex System' suggests ongoing engagement with interdisciplinary teams studying institutional co-evolution, though specific lab structures are not detailed.

Nick Streetمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Business Analytics
• +۳ مورد دیگر

Nick Street is the Leonard A. Hadley Chair in Leadership and Professor at the University of Iowa, with appointments in the Tippie College of Business (Business Analytics), Computer Science, and Nursing. He serves as Associate Dean for Research and PhD Programs in the Tippie College of Business. PhD in Computer Science, University of Wisconsin-Madison MS in Computer Science, DePaul University BA in Mathematics and Computer Science, Drake University Street's research bridges data science with healthcare analytics , business intelligence , and social network analysis . His work spans predictive modeling in cancer symptom trajectory analysis, federated learning for medical imaging, and influence diffusion in social networks for viral marketing. He has developed methodologies for inverse classification, correlation search, and real-time risk prediction in clinical and financial domains. Recent publications focus on machine learning applications in healthcare (e.g., surgical site infection prediction, warfarin dosing optimization) and urban informatics (e.g., traffic accident forecasting). His work integrates NLP for processing clinical narratives and deep learning for longitudinal EHR analysis. INFORMS Society on Data Mining Prize (2022) Henry B. Tippie Research Professor (2014) Dean's Teaching Award (2012) Master of Business Analytics Instructor of the Year (2023) Street has advised numerous graduate students and secured grants from the NIH , NSF , and institutional sponsors. Current projects include federated learning for medical imaging and multimorbidity pattern analysis in Alzheimer’s disease.