جست‌وجوی استاد و پژوهشگر

Yves Blaquièreمشاهده پروفایل

استاد

• Microelectronics
• VLSI and ASIC Design
• FPGA-Based Systems
• +۶ مورد دیگر

Yves Blaquière is a Professor in the Department of Electrical Engineering at École de Technologie Supérieure. He is affiliated with the Communications and Microelectronic Integration Laboratory (LaCIME), focusing on microelectronics, integrated circuit design, and power integrity in advanced electronic systems. His research spans VLSI/ASIC design, FPGA-based reconfigurable computing, MEMS for avionics, and radiation effects on electronics. Aeronautics and Aerospace Intelligent and Autonomous Systems Microelectronics and VLSI Power Integrity Modeling MEMS Switch Design Radiation-Resilient Circuits His recent publications highlight innovations in GHz-range power integrity for SiP, FPGA-based SHEPWM inverters, and MEMS switches for avionic power systems. Collaborations with researchers like Frédéric Nabki and Nicolas Constantin reflect his focus on industrial applications and technology transfer. Professor Blaquière co-supervises PhD candidates including Hachem Bensalem, Gabriel Nobert, and Abdurrashid Hassan Shuaibu, covering topics such as heterogeneous optimization, power converter modeling, and MEMS switch development. His work contributes to wafer-scale prototyping platforms like WaferBoard and advanced tools for radiation testing in FPGAs. LaCIME, under his involvement, emphasizes equity, diversity, and inclusion, offering students opportunities to engage in cutting-edge projects from materials to communication protocols. The lab's expertise includes micro/nanofabrication, photonic microsystems, and signal processing.

• Software Quality
• Quantum Computing
• Information Systems
• +۵ مورد دیگر

Mario Piattini is a Full Professor at the School of Computer Science of the University of Castilla-La Mancha (UCLM) in Spain, where he has served since 2002. He is the founder of the Alarcos research group and has held leadership roles including Director of the Mixed Center for Software Research and Development UCLM-Indra and Director of the Institute of Technologies and Information Systems (ITSI) at UCLM. He has also served as an associate professor at Universidad Complutense and Universidad Carlos III de Madrid. His educational background includes a PhD and degree in Computer Science from Universidad Politécnica de Madrid, a Psychology degree from UNED, and a Doctor Honoris Causa from Universidad de La Plata (Argentina). He holds multiple master's degrees in IT Audit, Human Resources Management, and Project Management, along with professional certifications including CISA, CISM, CRISC, CGEIT, PMP, and data governance certifications from DAMA. Professor Piattini is a leading expert in software quality, information systems, and security, with a recent strong focus on quantum computing and quantum software engineering. His research spans software engineering methodologies, data quality, AI systems, and the emerging field of quantum software development. He has been recognized as one of the top 15 scholars in systems and software engineering (2004-2008) and among the most active software engineering researchers (2010-2017). His recent publications reveal a significant shift toward quantum computing, with numerous articles on quantum software engineering, quantum-classical hybrid systems, quantum testing frameworks, and quantum software architecture. His work bridges theoretical foundations with practical engineering approaches for the emerging quantum computing paradigm. His scientific recognition includes multiple prestigious awards: Premio Nacional a la Trayectoria Profesional del Ingeniero Informático Premio Gabriel Alonso Herrera from JCCM for research trajectory Premio Grace Hooper from COIICLM Premio Aritmel from SCIE Premio FIUM from Universidad de Murcia Premio a la Trayectoria Profesional de ISACA Madrid As an academic leader, Piattini has founded several spinoff companies including Cronos Ibérica (now Alten), Kybele Consulting, Lucentia Lab, DQTeam, AQCLab (the first ENAC-accredited laboratory for software product quality and data evaluation), and I2SC. He serves as secretary of CTN71/SC7 and is a member of various ISO/IEC and UNE standardization committees, contributing significantly to software quality standards development. His research group has established AQCLab, which has been evaluating software quality for 25 years, demonstrating his long-term commitment to practical applications of software engineering research. Through his leadership in both academic and industrial contexts, Piattini has created a robust ecosystem connecting theoretical research with real-world software quality practices.

Alan Mantoothمشاهده پروفایل

استاد

• Analog and Mixed-Signal IC Design
• Power Electronics
• Semiconductor Device Modeling
• +۳ مورد دیگر

Alan Mantooth is a Distinguished Professor holding the Twenty-First Century Research Leadership Chair in Engineering within the Department of Electrical Engineering at the University of Arkansas, Fayetteville. He serves as Director of the National Center for Reliable Electric Power Transmission (NCREPT), Executive Director for GRAPES (NSF I/UCRC) and SEEDS (DoE Center), and Deputy Director of the NSF Engineering Research Center for Power Optimization of Electro-Thermal Systems (POETS). His educational background includes: B.S. in Electrical Engineering, University of Arkansas M.S. in Electrical Engineering, University of Arkansas Ph.D. in Electrical Engineering, Georgia Institute of Technology Dr. Mantooth's research centers on analog/mixed-signal IC design, power electronics CAD, and semiconductor device modeling with emphasis on harsh-environment applications. His pioneering work in silicon carbide (SiC) and gallium nitride (GaN) power systems has enabled high-temperature operation for electric vehicles and renewable energy infrastructure, significantly advancing reliability in extreme conditions. His 2025 publications reveal strong trends toward AI-driven power electronics (e.g., SolarFormer++ for PV profiling), wide-bandgap device modeling (β-Ga2O3, SiC), and innovative packaging solutions. Key themes include reliability engineering for extreme environments, multi-physics optimization, and explainable AI for safety-critical power systems. Major scientific recognition includes: IEEE Fellow (2009) for power electronic device modeling Three R&D 100 Awards (2009, 2014, 2016) for SiC power modules IEEE Power Electronics Society Technical Achievement Award (2019) Multiple university teaching/research awards including SEC Faculty Achievement Award (2015) As an exceptional mentor (UA Outstanding Mentor 2006-2008), he co-founded Lynguent and Ozark Integrated Circuits. His centers NCREPT, GRAPES, and SEEDS have secured major funding from NSF, DoE, and industry partners, supporting over 350 refereed publications and numerous patents. Current research focuses on AI-enhanced power electronics, recyclable packaging, and next-generation wide-bandgap device characterization. He leads the NCREPT test facility and multi-institutional teams developing grid-connected power electronic systems, secure energy delivery architectures, and thermal management solutions for high-power-density applications, with direct impact on electric transportation and renewable energy integration.

Ajitha Rajanمشاهده پروفایل

استاد

• Software Testing
• Software Verification
• Automated Testing
• +۱۲ مورد دیگر

Ajitha Rajan is a Professor (Personal Chair of Software Testing and Verification) at the School of Informatics, University of Edinburgh. Previously, she was a post-doctoral researcher at Oxford University's Computer Science Department and Laboratoire d'Informatique de Grenoble (LIG) in France. She earned her PhD in Computer Science from the University of Minnesota in August 2009 under the supervision of Prof. Mats Heimdahl. Her research spans two main directions: Automated Software Testing Techniques covering test input generation, test oracles, and coverage metrics; and Biomedical Artificial Intelligence focusing on cancer survival models, interpretability for biological sequences, and medical images. Her work bridges software engineering and biomedical applications, particularly in the development of trustworthy AI systems for healthcare. Professor Rajan leads several significant research projects including a Royal Society Industry Fellowship (2022-2025) on AutoTest for autonomous vehicle perception safety, the H2020 European Project KATY (2021-2025) on AI for genomics and personalized medicine where she serves as Edinburgh Lead PI, and an EPSRC Trustworthy Autonomous Systems Node project (2020-2024). Her recent publications demonstrate strong activity across software testing, explainable AI, and biomedical applications, with numerous papers accepted to top conferences in 2025 including ML for Healthcare, IJCAI, and ESEM. Among her scientific recognitions, she received the Best Reviewer Award at ISSTA'25. Her work has been consistently published in leading venues including ICSE, ICASSP, and Communications Biology. Professor Rajan actively mentors PhD students working on diverse topics from automated testing of speech recognition systems to explainable AI for medical image analysis and cancer immunotherapy. She teaches undergraduate courses in Software Testing, Computer Programming, and Embedded Systems, and has been instrumental in establishing several fully funded PhD positions through Centres for Doctoral Training at the University of Edinburgh.

Carlo Alberto Avizzanoمشاهده پروفایل

دانشیار

• Robotics and Automation
• Human-Robot Interaction
• Computer Vision
• +۶ مورد دیگر

Carlo Alberto Avizzano serves as Associate Professor in Robotics and Automation at the University of Pisa's School of Engineering, Department of Information Engineering. He coordinates the Department of Excellence in Robotics & Artificial Intelligence (MUR) and leads the Intelligent Automation System Research Group. Research spans robotics, human-robot interaction, computer vision, and control systems Specializes in creating intelligent automation systems with cognitive capabilities Integrates AI, machine learning, and mechatronics for robust autonomous systems His research focuses on developing robots that learn from human examples, adapt to changing environments, and interact through advanced perception systems. Current work emphasizes wearable robotics, UAVs, and industrial automation solutions with applications in medical rehabilitation, firefighting, and manufacturing. He employs distributed computing architectures integrating sensors, real-time control, and knowledge transfer algorithms. Publications reveal strong emphasis on practical implementations: 15 recent works cover exoskeleton design (2024), UAV firefighting systems (2024), industrial bin-picking datasets (2024), and haptic interfaces (2012-2023). Key trends show progression from virtual reality systems (2006-2008) toward modern AI-integrated robotics with industrial and medical applications. Teaching responsibilities include PhD courses in Sensors for Construction, Python Programming for HealthScience, and Digital Perception; plus undergraduate Mechatronics and Computer Vision labs. He serves on PhD boards for Emerging Digital Technologies and Health Science Technology. Extensive patent portfolio including haptic interfaces (2012), sailing simulators (2006), and UAV systems (2024) Research directly translated to commercial products and spin-off companies

Danica M Ommenمشاهده پروفایل

دانشیار

• Forensic Statistics
• Computational Statistics
• Handwriting Analysis
• +۳ مورد دیگر

Danica M Ommen is an Associate Professor at Iowa State University, specializing in forensic statistics and computational methodologies. Her research bridges machine learning, handwriting analysis, and source identification frameworks. Education: Ph.D. in Computational Science and Statistics (2017), M.S. in Mathematics (2014), B.S. in Mathematics (2012), all from South Dakota State University. Affiliations: Chair of the OSAC Statistics Task Group; Vice-Chair of the ASA Advisory Committee on Forensic Science. Her work focuses on statistical modeling for forensic evidence , particularly in handwriting identification, aluminum powder analysis, and digital device forensics. Recent publications explore interpretable deep learning, synthetic data anchoring, and ensemble methods for likelihood ratios. The 15 most recent articles (2023–2025) span forensic machine learning, handwriting kinematics, multi-camera smartphone identification, and Bayesian frameworks. Keywords include Forensic Science , Machine Learning , and Computational Statistics , with subfields like Score-Based Likelihood Ratios and Smartphone Forensics .

Andrej Bogdanovمشاهده پروفایل

استاد

• Cryptography
• Computational Complexity
• Pseudorandomness
• +۶ مورد دیگر

Andrej Bogdanov is a Professor at the University of Ottawa in the School of Electrical Engineering and Computer Science . He earned his B.S. and M.Eng. from MIT and Ph.D. from UC Berkeley . Before joining Ottawa, he held positions at the Chinese University of Hong Kong , ITCS (Tsinghua) , DIMACS (Rutgers) , and the Institute for Advanced Study . He has served as a Visiting Professor at the Tokyo Institute of Technology (2013) and the Simons Institute (2017, 2021). Research Interests : Computational complexity, cryptography foundations, pseudorandomness, one-way functions, property testing, quantum algorithms, and sublinear-time algorithms. Teaching : Courses on Discrete Mathematics, Great Algorithms, Computational Complexity, and Cryptography at University of Ottawa, Chinese University of Hong Kong, and Rutgers University. Publications : 15+ recent works in TCC , CRYPTO , ICALP , RANDOM , and journals like Journal of Cryptology and Theory of Computing . Service : Program co-chair for SAC 2026 , and committee member for major conferences including CRYPTO , TCC , Eurocrypt , and FOCS . Advising : 12 current and former Ph.D./M.Phil. students, with postdoctoral advisees at institutions like IIT Palakkad and Academia Sinica . His work bridges theoretical computer science with applications in cryptography, quantum computing, and network security.

Stefan Winterمشاهده پروفایل

پژوهشگر

• Flaky Tests
• Robustness Testing
• Fault Injection
• +۵ مورد دیگر

Stefan Winter is a postdoctoral researcher and software engineer at LMU Munich, Germany, with a Ph.D. (Dr.-Ing.) in computer science from TU Darmstadt. He focuses on software dependability, particularly addressing non-deterministic behavior in software systems, such as flaky tests and reproducibility challenges in experimental research. Education: Ph.D. (Dr.-Ing.) in Computer Science from TU Darmstadt under Prof. Neeraj Suri. Current Role: Researcher at LMU Munich in Dirk Beyer’s group. His research spans test automation, robustness testing, fault injection, and operating systems, with a recent emphasis on mitigating flaky tests and ensuring deterministic software behavior. His work has been published in venues like ASE, ESEC/FSE, and ICSE. Stefan actively contributes to the academic community as a committee member, artifact evaluation co-chair, and session chair across conferences such as ECOOP, ISSTA, and SPLASH. He maintains expertise in reproducibility, experimental validity, and software testing frameworks.

• AI Security
• AI Safety & Alignment
• Cooperative AI
• +۶ مورد دیگر

Sahar Abdelnabi is an AI Security Researcher at Microsoft and will join the ELLIS Institute Tübingen as a Faculty/Principal Investigator. She is co-affiliated with the Max-Planck Institute for Intelligent Systems and Tübingen AI Center , leading the COMPASS Research Group focused on safe, aligned, and steerable AI agents with emphasis on security, human-AI interaction, and cooperative systems. Her research spans three pillars: (1) Probing AI failures through biases, emergent risks, and misuse scenarios; (2) Developing defenses like white-box control methods and reasoning enhancements; and (3) Leveraging AI for societal good through scientific discovery. Key contributions include coining indirect prompt injection vulnerabilities (2023), pioneering generative AI watermarking (2020), and receiving the ACL2025 Best Paper Award for work on LLM sampling heuristics. PhD in Computer Science (2019-2024) from CISPA Helmholtz Center , advised by Prof. Dr. Mario Fritz MSc in Computer Science from Saarland University Research Highlights Her work bridges AI security and safety with sociopolitical implications, focusing on prompt injection , cooperative multi-agent systems , and contextual integrity . She has been recognized by policymakers and industry leaders, including NIST , OWASP , and Microsoft's AI Bug Bounty Program . Scientific Awards Best Paper Award at ACL2025 Best Paper Award at AISec'23 Workshop Spotlight Paper at NeurIPS Datasets and Benchmarks 2024 Academic Leadership She actively contributes to the AI and security communities through: Program Committee: IEEE S&P (2026) , SaTML (2024-2026), USENIX Security (2025) Organized IEEE SaTML'25 LLMail-Inject Challenge Reviewed for top conferences: ICLR , NeurIPS , CVPR

Debabrota Basuمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +۶ مورد دیگر

Debabrota Basu is a tenured faculty member (Inria Starting Faculty Position - ISFP) at the Scool team (previously called SequeL) of Inria Centre at University of Lille in France. He teaches postgraduate-level courses on privacy, responsible machine learning, and research methods in AI at École normale supérieure-PSL University, Université de Lille, and Centrale Lille. He is also a member of the ELLIS Society (European Laboratory for Learning and Intelligent Systems) and the Paris unit of ELLIS. Dr. Basu earned his PhD in Computer Science from the Department of Computer Science, School of Computing, National University of Singapore, advised by Stéphane Bressan and Pierre Senellart. Prior to that, he obtained a B.E. degree with Honours in Electronics and Telecommunication Engineering from Jadavpur University. Before joining Inria, he was a postdoctoral researcher at Chalmers University of Technology's Data Science and AI Division. Dr. Basu's research focuses on constructing algorithms for developing efficient, robust, private, and ethical learning machines that solve real-world problems. His methodological approach blends statistics, machine learning, and optimization. His application interests span sustainable agro-ecology, medical and pharmaceutical applications, energy-efficient autonomous systems, and algorithmic audits. Recent collaborations include the Inria-Indian Statistical Institute associate team SeRAI for developing Sequential Testing and Learning Algorithms for Verifiably Robust and Responsible AI, and the Inria-INRAE collaboration on Resilient Agricultural Decision Making under Environmental Risks. His publication record demonstrates expertise in bandit algorithms, reinforcement learning, and privacy-preserving machine learning. Recent work shows a strong theoretical foundation with practical applications, particularly in pure exploration bandits, differential privacy mechanisms, constrained optimization, and fairness verification. His research bridges the gap between theoretical guarantees and real-world deployment challenges across multiple domains. Dr. Basu has received notable recognition for his contributions: Best Student Paper Award at ACM EAAMO 2022 for 'On Meritocracy in Optimal Set Selection' Young researcher (JCJC) grant from the French National Research Agency (ANR) in 2022 in 'Artificial Intelligence and Data Science' He leads the project 'RL under Real-life Constraints: Regrets and Algorithms' and supervises PhD students and postdoctoral researchers. His research is supported by multiple projects including REPUBLIC ('Vers l'IA responsable avec l'apprentissage par renforcement sous contraintes') and 'Foundations of robustness and reliability in artificial intelligence.' Dr. Basu actively collaborates with institutions worldwide, including establishing the RELIANT associate team with Kyoto University for investigating structured multi-armed bandit problems.

• Multi-objective optimisation
• Evolutionary computation
• Explainable artificial intelligence
• +۴ مورد دیگر

Dr David Walker is a Senior Lecturer in Computer Science at the University of Exeter and a member of the Institute for Data Science and Artificial Intelligence . He also contributes to the Environmental Intelligence @Exeter research network. Education: PhD in Computer Science, University of Exeter (2008–2013) BSc (Hons) in Computer Science, University of Exeter (2004–2007) Research Interests Dr Walker’s work sits at the intersection of multi-objective optimisation , evolutionary computation , explainable AI and hyper-heuristics . He develops algorithms and visual analytics that help engineers and scientists understand complex optimisation landscapes, with recent emphasis on renewable-energy planning (especially offshore wind farms) and trustworthy AI systems. Publication Trends Between 2022 and 2025 he produced a prolific stream of articles on explainable optimisation , many-objective wind-farm design and visual analytics for evolutionary algorithms . These works combine rigorous algorithmic innovation with real-world case studies, demonstrating a clear trajectory toward transparent, human-centred AI for engineering decision-making. Scientific Awards No specific awards or fellowships are mentioned in the provided material. Advising & Funding No explicit list of PhD students, post-docs or grant awards is supplied. Laboratory & Teams Dr Walker is affiliated with the Institute for Data Science and Artificial Intelligence and the Environmental Intelligence @Exeter network, indicating collaborative, interdisciplinary research environments.

• Civil Engineering
• Climate Science
• Machine Learning
• +۶ مورد دیگر

Brennan Bean is an Assistant Professor in the Mathematics and Statistics Department at Utah State University's College of Arts & Sciences. His work focuses on geospatial modeling, statistical methods for extreme weather analysis, and machine learning applications in structural and environmental engineering. Recent publications highlight expertise in snow load prediction, Bayesian entropy, and interdisciplinary data science. Notable contributions include optimizing design methods for insulated concrete wall panels and addressing deployment challenges for ML models in engineering contexts. Research trends span geospatial data integration, climate change impact assessments, and educational interventions in STEM. Key subfields include ground snow load mapping, extreme value statistics, climate downscaling, and high-dimensional ecological modeling.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +۳ مورد دیگر

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

Eunji Limمشاهده پروفایل

دانشیار

• Operations Management
• Supply Chain Management
• Business Analytics
• +۲ مورد دیگر

Eunji Lim is an Associate Professor in the Department of Decision Sciences and Marketing at the Robert B. Willumstad School of Business, Adelphi University, located in Garden City, New York. She has been a faculty member at Adelphi since 2018, initially as an Assistant Professor and promoted to Associate Professor in 2023. Her prior academic appointments include Assistant Professor roles at Kean University (2013–2018) and the University of Miami (2008–2013). Education Ph.D. in Operations Research (implied by department) , Stanford University (2008) Research Interests Lim’s research lies at the intersection of operations research, statistics, and management science. She focuses on simulation-based decision making for operations and supply-chain systems, leveraging large-scale stochastic simulation to optimize complex processes. A second major theme is nonparametric function estimation under shape constraints , where she develops statistically rigorous methods to estimate convex, monotonic, or smooth functions from noisy data. These methodologies find applications in business analytics, revenue management, inventory control, and engineering design. Publication Trends Her recent articles (2013–2025) reveal a consistent emphasis on convex and isotonic regression , simulation analytics , and stochastic optimization . A clear trajectory emerges from theoretical foundations—convergence rates, consistency, and asymptotic distributions—toward practical algorithms for high-dimensional and noisy environments. Interdisciplinary impacts are evident in publications targeting both statistics and operations-research audiences, with cross-cutting keywords such as simulation optimization, shape-restricted inference, and robust estimation. Scientific Awards No specific awards or fellowships are listed in the provided materials. Advising & Grants No information on current or former PhD/Master’s students, postdocs, or funded research grants is provided. Labs & Teams No laboratory or research-group affiliations beyond her departmental appointment are mentioned.

• Software Verification
• Formal Methods
• Program Synthesis
• +۴ مورد دیگر

Madhusudan Parthasarathy is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign, College of Engineering. His research focuses on software verification, formal methods, and logic in computer science, with significant contributions to trustworthy AI systems, program synthesis, and security. Ph.D. in Theoretical Computer Science (2002), Institute of Mathematical Sciences, University of Madras Research interests include automating software verification, building correct-by-design systems, and exploring synergies between machine learning and program synthesis. He pioneered visibly pushdown languages , impacting XML processing and program verification. His tools like VEX and Strand advanced security and heap reasoning. Recent articles focus on blockchain verification, timed automata, and learning logics from data. His work has been widely cited, with the visibly pushdown language paper alone generating over 940 scholarly entries. Best Paper Award, 19th USENIX Security Symposium (2010) He has advised numerous students and postdocs, with former advisees now at institutions like Purdue University and Google. His outreach initiatives include the ConTraIL privacy-preserving contact tracing project and the MASSIVELY EMPOWERED CLASSROOMS MOOC platform for Indian undergraduates. He teaches courses like CS 521: Advanced Topics in Programming Systems and CS 474: Logic in Computer Science , while actively serving on program committees for top-tier conferences such as POPL and PLDI.