جستجوی استاد راهنمای دانشگاهی

Professor Rob Dwyer-Joyce is a leading academic in Tribology and Lubrication Engineering at the University of Sheffield , School of Mechanical, Aerospace and Civil Engineering. He serves as Director of the Centre for Doctoral Training in Integrated Tribology and manages the Leonardo Centre for Tribology. A Fellow of both the Royal Academy of Engineering and the Institution of Mechanical Engineers, his work focuses on developing ultrasonic sensors for real-time lubrication and wear monitoring in industrial systems. Academic Affiliation: University of Sheffield (since 1994) Education: BEng Mechanical Engineering (Imperial College), PhD Tribology Industry Experience: Former British Gas engineer (Rough gas field) His research interests center on industrial wear problems , lubrication metrology , and acoustic sensor development . Key applications include wind turbine bearings, marine diesel engines, and automotive systems. His team’s innovations in tribo-acoustic sensors have enabled non-invasive oil film thickness and viscosity measurements in challenging environments. Recent scientific contributions span lithium-ion battery monitoring, wind turbine bearing dynamics, and marine engine lubrication, with over 30 publications since 2020. Awards include the EPSRC Advanced Career Fellowship in Tribo-Acoustic Sensors and recognition as a Royal Academy of Engineering Fellow . Contact: r.dwyer-joyce@sheffield.ac.uk

• Tribology
• Lubrication Engineering
• Acoustic Sensor Development
• +۳ مورد دیگر

Professor Guoyin Li is a faculty member at the School of Mathematics & Statistics , University of New South Wales (UNSW Sydney). He holds a Ph.D. from The Chinese University of Hong Kong (2007) and has been at UNSW since 2011, currently serving as Professor and Research Director. Research Interests His work spans optimization , variational analysis , and multilinear algebra , with applications in robust optimization , structural engineering , and machine learning . He specializes in nonconvex nonsmooth optimization , tensor eigenvalue problems , and exact semi-definite programming relaxations . Recent Publications His articles focus on robust optimization for structural design, nonlinear approximation techniques, and conic programming for uncertain data. Key journals include Foundations of Computational Mathematics , Mathematical Programming , and Computer Methods in Applied Mechanics and Engineering . Awards and Grants Fellow of the Australian Mathematical Society (2023) 2022 AustMS Medal 2024 Marguerite Frank Award ARC Discovery Grants (2021-2023, 2025-2027) ARC Research Hub Project (2017-2021) Professional Roles He serves on editorial boards of SIAM Journal on Optimization , Optimization Letters , and Journal of Optimization Theory and Applications , and has delivered plenary lectures at international conferences in Austria, Spain, and Canada.

• Optimization
• Variational Analysis
• Multilinear Algebra
• +۵ مورد دیگر

Carolyn Conner Seepersad serves as the J. Mike Walker Professor of Mechanical Engineering at the University of Texas at Austin and directs the Center for Additive Manufacturing and Design Innovation. She holds membership in the U.T. System Academy of Distinguished Teachers and maintains active leadership in the additive manufacturing community through roles such as co-organizer of the Solid Freeform Fabrication Symposium and ASME Design Engineering Division Executive Committee membership. Her academic credentials include: PhD in Mechanical Engineering from Georgia Tech (2004) MA/BA in Philosophy, Politics and Economics from Oxford University (1998, Rhodes Scholar) BS in Mechanical Engineering from West Virginia University (1996) Dr. Seepersad's research centers on computational design methodologies and additive manufacturing innovation , with particular expertise in simulation-based design of complex systems, environmentally conscious product development, and materials engineering. Her work bridges theoretical design frameworks with practical manufacturing applications, emphasizing sustainability and performance optimization across aerospace, automotive, and energy systems. Current projects explore reactive extrusion additive manufacturing, negative stiffness materials, and machine learning integration for process-aware design. Analysis of her 15 most recent publications reveals a dominant focus on process innovation in additive manufacturing (70%), particularly stereolithography and selective laser sintering, with growing emphasis on data-driven design approaches (20%) and sustainable engineering applications (10%). Her work demonstrates consistent progression from fundamental material design toward integrated system optimization and industrial scalability. Her scientific recognition includes: International Outstanding Young Researcher Award in Freeform and Additive Manufacturing (2009) UT System Regents’ Teaching Award (2010) ASME Design Automation Committee Outstanding Young Investigator Award (2010) ASEE Outstanding New Mechanical Engineering Educator Award (2013) Multiple ASME and ASEE best paper awards U.T. System Academy of Distinguished Teachers membership Dr. Seepersad maintains an extensive advising portfolio with 48 graduate students (16 PhD, 24 MS, and 8 current) plus 2 postdoctoral researchers, reflecting sustained research productivity and educational impact. Her Product, Process, and Materials Design Lab fosters interdisciplinary collaboration between mechanical engineering, materials science, and computational design teams.

• Design for Additive Manufacturing
• Engineering Design
• Additive Manufacturing
• +۴ مورد دیگر

Dr. Roy Lederman is an Assistant Professor at the Department of Statistics and Data Science , Yale University. He is affiliated with the Quantitative Biology Institute (QBio) , the Applied Math Program , the Institute for Foundations of Data Science (FDS) , and the Wu Tsai Institute (WTI) . He was awarded the Sloan Research Fellowship (2023) . He previously held a Gibbs Assistant Professorship at Yale (2014-2015) and a postdoc at Princeton University (2015-2018) . Education: PhD in Applied Mathematics, Yale University (2014); dual BSc in Physics and Electrical Engineering, Tel-Aviv University. Teaching: Courses include Computational Tools for Data Science, Signal Processing, and Mathematical Machine Learning. Research Areas: Dr. Lederman works at the intersection of computational biology , structural biology , Bayesian inference , numerical analysis , and machine learning . His recent work focuses on cryo-EM and hyper-molecules for studying molecular heterogeneity, alternating diffusion for common variable recovery, and Zernike polynomials for 3D imaging. He also develops Hamiltonian Monte Carlo methods and randomized DNA sequencing algorithms . Publications Trends: His publications (15 most recent) emphasize structural biology and cryo-EM applications, machine learning (Bayesian deep learning, diffusion maps), numerical analysis (Fourier/Laplace transforms), and computational biology (DNA sequencing algorithms). Key sub-fields include heterogeneity analysis , manifold learning , Hamiltonian Monte Carlo , and Zernike polynomials . Scientific Awards: Sloan Research Fellow (2023) Dr. Lederman actively mentors graduate students and postdocs at Yale, and co-organizes the One World Cryo-EM seminar series . His lab develops open-source software (e.g., prolate function implementation ) and explores theoretical bounds on transforms and common variable recovery in multi-sensor experiments.

• Computational Biology
• Structural Biology
• Bayesian Inference
• +۱۰ مورد دیگر

Víctor Adrián Braberman is a Full-time Associate Professor at the Department of Computer Science, Faculty of Exact and Natural Sciences, University of Buenos Aires (UBA), and a CONICET researcher. He serves as Co-director of the LaFHIS (Tools and Foundations for Software Engineering) Research Lab at UBA, where he leads significant research in formal methods and software engineering. His academic career demonstrates sustained excellence in both teaching and research within Argentina's premier academic institution. Braberman's research focuses on Formal Verification , particularly Model Checking of Timed Systems, Controller Synthesis, Formal Specification of event-based properties, Aspect-oriented modeling, and Software Architectures. His work also extends to Software Analysis , including Memory Consumption Prediction and Static and Dynamic Program Analysis. These interests position him at the intersection of theoretical computer science and practical software engineering applications, addressing critical challenges in system reliability and performance. His recent publications (2022-2025) reveal a clear trajectory toward integrating artificial intelligence with formal methods , particularly through the application of reinforcement learning to controller synthesis problems and the exploration of Large Language Models for software verification and falsification. The research shows increasing attention to scalability challenges in formal methods and the integration of probabilistic approaches to handle uncertainty in system environments. Automated Reasoning Amazon Research Award (ARA) (2024) Braberman has successfully supervised numerous PhD and Licentiate students, establishing a strong academic lineage in formal methods research in Argentina. His research has been supported by substantial grants including European Community projects (MEALS), ANPCyT PICT grants, UBACyT projects, and Microsoft Research funding. His leadership extends to directing major research initiatives in formal software engineering. As Co-director of LaFHIS, Braberman oversees a vibrant research ecosystem that bridges theoretical computer science with practical software engineering challenges. The lab maintains strong international collaborations, particularly with European institutions, and has secured competitive funding from both national and international sources, demonstrating the global relevance of their work in formal methods and software engineering.

• Formal Verification
• Model Checking of Timed Systems
• Controller Synthesis
• +۷ مورد دیگر

Vera Pantelic is an Adjunct Assistant Professor in the Department of Computing and Software at McMaster University. Her research focuses on software engineering practices for model-based development in automotive systems, particularly centralized Electrical/Electronic (E/E) architectures, Simulink modeling, and supervisory control of probabilistic discrete event systems. Education: Not explicitly mentioned in the text. Her scholarly activity includes extensive contributions to conferences and journals in automotive software engineering, model transformation, and real-time systems. Her work addresses challenges in modularity, documentation, and compliance within automotive embedded systems. Her recent publications emphasize advancements in centralized E/E architectures, model-driven testing, and assurance cases for automotive safety. She collaborates on topics integrating software engineering principles with automotive domain requirements. Scientific Awards: No specific awards mentioned in the text. She serves as an advisor in software engineering, though specific student names are not listed. Her projects involve simulation-based testing, model refactoring, and compliance frameworks, supported by industry partnerships and academic grants. Her work contributes to labs and teams focused on automotive software reliability and model-driven engineering. No explicit lab or team affiliations are detailed in the provided text.

• Software Engineering
• Model-Driven Engineering
• Automotive Embedded Systems
• +۳ مورد دیگر

Qiwei Yao is a Professor of Statistics at the Department of Statistics, London School of Economics (LSE), where he maintains an active research program in statistical methodology and applications. His office is located in Columbia House, Room 7.16 at LSE's Houghton Street campus in London. Professor Yao's research focuses on statistical inference for complex time series, with particular expertise in high-dimensional time series, dynamic networks, spatio-temporal processes, functional time series, nonlinear time series, and high-frequency data. His work bridges theoretical statistics with practical applications, especially in financial econometrics. He has developed innovative methodologies for dimension reduction, factor modeling, and network analysis that have become influential in the field. His recent publications reveal a strong trend toward developing statistical methods for increasingly complex data structures, particularly focusing on high-dimensional and network-based time series. His work integrates machine learning techniques with traditional statistical approaches, as evidenced by papers on deep learning for Markov property testing and tensor decompositions for matrix time series. There's also a clear emphasis on privacy-preserving methods and differential privacy in network analysis. Professor Yao has secured substantial research funding through multiple EPSRC Programme Grants and Research Projects, including the EPSRC Programme Grant for 'Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS)' and 'Network Stochastic Processes and Time Series (NeST)'. He also leads the EPSRC Research Project on 'Statistical Network Analysis: Model Selection, Differential Privacy, and Dynamic Structures' and has collaborated with industry partners like Andurand Capital Management on projects such as 'Forecasting Oil Prices Based on Quantitative Methods'. His research has significant applications across various domains, particularly in energy forecasting (electricity load prediction), financial modeling (volatility modeling, oil price forecasting), and ecological modeling (spatio-temporal population dynamics). Professor Yao maintains strong collaborative relationships with researchers across multiple institutions and disciplines.

• Statistical Inference
• Time Series Analysis
• High-Dimensional Statistics
• +۶ مورد دیگر

Anne Elisabeth Haxthausen is an Associate Professor at the Software Systems Engineering section within DTU Compute , Technical University of Denmark . Her work focuses on formal methods, railway control systems, and safety-critical software engineering. Founder and leader of the DTU Railway Verification Group Member of European Technical Working Group on Formal Methods in Railway Control Editorial board member for Springer Formal Aspects of Computing Journal Active in the Overture Language Board Her research emphasizes formal verification of railway interlocking systems, particularly through compositional approaches and automated tools. She has contributed to projects like RobustRailS, Overture, and RAISE, focusing on model-based development and verification. She serves as a tutor for bachelor students and contributes to the advisory committee for DTU's Computer Science and Engineering MSc program. Her recent publications explore challenges in verifying autonomous and AI-driven railway technologies.

• Formal methods
• Specification languages
• Semantics
• +۶ مورد دیگر

Arto Anttila is an Associate Professor in the Department of Linguistics at Stanford University and holds an Adjunct Professor (dosentti) position in General Linguistics at the University of Helsinki. His research spans multiple linguistic subfields with particular focus on the interfaces between phonology, syntax, and prosody. Dr. Anttila's research interests include phonology, morphology, syntax, metrics, and language variation. His work often explores how phonological constraints interact with syntactic structures and how variation manifests across different linguistic domains. He has made significant contributions to Optimality Theory, MaxEnt grammar, and probabilistic approaches to phonology. His recent publications demonstrate a strong focus on metrical patterns, stress systems, syllable structure, and the relationship between prosody and syntax. Anttila frequently collaborates with researchers like Giorgio Magri, Adams Bodomo, and Ryan Heuser, examining phenomena across diverse languages including English, Finnish, and Dagaare (an African language). Anttila has developed several computational tools for linguistic research, including CoGeTo (Convex Geometry Tools for constraint-based phonology), MetricalTree (for English phrasal stress prediction), Prosodic (for automatic metrical scansion), T-Order Generator, and OTOrder. These tools reflect his interest in the mathematical and computational aspects of linguistic theory.

• Phonology
• Morphology
• Syntax
• +۲ مورد دیگر

Dr. Seyed Mojtaba Hoseyni is a Lecturer in Process Safety and Loss Prevention at the School of Chemical, Materials and Biological Engineering, University of Sheffield. Previously a Postdoctoral Research Associate at the same institution (2022-2024), he holds a PhD in Energy Engineering from Politecnico di Milano (2021). His research focuses on enhancing system resilience, risk assessment, and decision-making under uncertainty in engineering systems, particularly for decarbonization applications. Royal Academy of Engineering Global Talent (Exceptional Promise) in Chemical and Process Engineering His work spans hydrogen safety, climate change risk, nuclear engineering safety, and predictive maintenance. Recent publications emphasize integrating resilience metrics into HAZOP analysis and optimizing sensor placement for risk-informed decision-making. Teaching activities include the Hazards and Protections module (CPE61020). Specializes in RAMS (Reliability, Availability, Maintainability, and Safety) analysis Develops safety frameworks for hydrogen energy systems Applies advanced computational techniques to nuclear and industrial safety

• Process Safety
• Hydrogen Safety
• Risk Assessment
• +۶ مورد دیگر

Professor Pavel V. Tsvetkov is a faculty member at Texas A&M University , holding the rank of Professor of Nuclear Engineering and serving as the Director of the Graduate Program in Nuclear Engineering . He is also an Affiliated Faculty member of the Multidisciplinary Engineering program . His office is located in the AIEN M205B building, and he can be contacted at tsvetkov@tamu.edu . Educational Background: Ph.D. in Nuclear Engineering, Texas A&M University (2002) M.S. in Theoretical & Experimental Reactor Physics, Moscow State Engineering Physics Institute (1995) Research Interests: Professor Tsvetkov's research spans a wide range of advanced nuclear engineering topics. His primary focus includes system analysis and optimization methods , complex engineered systems , and symbiotic nuclear energy systems . He is deeply involved in waste minimization and sustainability , particularly through the development of high-temperature gas-cooled reactors (HTGRs) and molten salt reactors (MSRs) . His work also explores direct nuclear energy conversion systems and the integration of AI and deep learning into nuclear reactor control and monitoring systems. Research Trends in Publications: Over the past few years, Professor Tsvetkov has published extensively on the application of machine learning and deep learning in nuclear engineering. His recent works focus on autonomous reactor control , reactor dynamics simulation , and remote monitoring systems using satellite data and AI. He has also contributed to the design and analysis of microreactors for space applications and molten salt reactor dynamics . Scientific Awards: George Armistead, Jr ’23 Faculty Excellence Teaching Award Advising and Grants: As Director of the Graduate Program in Nuclear Engineering, Professor Tsvetkov plays a key role in mentoring and advising graduate students. While specific student names are not listed, his leadership in the program and extensive research output suggest active involvement in student research and training. Labs and Teams: Professor Tsvetkov is associated with the Nuclear Power Engineering group at Texas A&M University, contributing to both academic and applied research in nuclear systems design and safety.

• Nuclear Engineering
• System Analysis & Optimization Methods
• Complex Engineered Systems
• +۶ مورد دیگر

Farzin Zareian is a Professor in Civil and Environmental Engineering at the Samueli School of Engineering, University of California, Irvine. His research focuses on performance-based earthquake engineering, collapse analysis, structural reliability, and structural control. Ph.D. in Structural Engineering, Stanford University (2006) M.S. and B.S. in Civil/Earthquake Engineering, Sharif University of Technology (1997, 1995) Zareian's research integrates analytical and experimental approaches to advance earthquake resilience in structural systems. Key areas include base plate connections, seismic response of asymmetric structures, and validation of simulated ground motions. His work with the Performance Based Earthquake Engineering Laboratory emphasizes practical applications in structural safety. Recent publications highlight innovations in column base modeling, seismic demand prediction, and infrastructure recovery frameworks. Notable trends in his 2021–2025 articles include machine learning for ground motion prediction, resettable structural systems, and probabilistic drift profile analysis for multistory buildings.

• Structural Engineering
• Earthquake Engineering
• Performance-Based Earthquake Engineering
• +۲ مورد دیگر

Rasmus Pagh is a Professor at the Department of Computer Science, University of Copenhagen, specializing in algorithms and complexity. His career includes a 2002 PhD from Aarhus University under Peter Bro Miltersen and a tenure at IT University of Copenhagen until 2020. He leads theoretical research with practical applications in big data, databases, and modern computer architecture parallelism. His research interests span algorithms, data structures, and privacy-preserving computing. Recent work includes the ERC-funded project on Scalable Similarity Search and contributions to the BARC center for basic algorithms research. He has collaborated with Google Research (2019-2020) and focuses on theoretical foundations with real-world impact. Key research trends in his 2023-2024 publications include privacy-preserving data analysis probabilistic data structures distributed secure computation noise-robust coding hashing efficiency continual privacy mechanisms Scientific recognition includes 2024 ACM Fellowship ERC grant leadership multiple top-tier conference publications

• Algorithms
• Data Structures
• Big Data
• +۷ مورد دیگر

Dr. Reza Samavi is an Associate Professor at Toronto Metropolitan University's Department of Electrical, Computer, and Biomedical Engineering, Faculty of Engineering & Architectural Science. He is also a Faculty Affiliate with the Vector Institute for Artificial Intelligence and directs the Trustworthy AI Research Lab (TAILab). Previously, he served as Assistant Professor and eHealth Graduate Program Coordinator at McMaster University's Department of Computing and Software (2014-2020). Holding a PhD in Computer Science (University of Toronto, 2013), his academic journey bridges industry experience with rigorous scholarly contributions. His research lies at the critical intersection of Trustworthy AI , Machine Learning Security , and Medical Informatics . He investigates Safety & Security of ML Algorithms Privacy-Preserving AI Systems Transparency Frameworks for Medical AI Blockchain-enabled Privacy Auditing Game Theory for Model Robustness Optimization-based Anonymization Techniques The TAILab research group under his leadership has produced groundbreaking work in Uncertainty Quantification for Neural Networks Robustness Against Adversarial Attacks Medical Image Analysis Clinical Decision Support Systems Emergency Medicine Predictive Modeling His recent projects focus on enhancing migrant youth mental health through LLM-based conversation agents and developing certified robustness guarantees for ensemble networks. Dr. Samavi's scholarly excellence is recognized through Privacy Technologies Research Award (IBM) Privacy By Design Research Award (Ontario IPC) Bridging Divides Emerging Research Grant (TMU) NSERC PGS-D Recipient (Co-supervised student) SOSCIP Accelerator Grant He has secured major funding from NSERC , SOSCIP , MITACS , HHS , and IDEaS programs. As a dedicated educator, Dr. Samavi teaches graduate courses in Secure Machine Learning and Software Testing while mentoring 15+ graduate students across PhD , MASc , and MEng programs. His lab has presented at premier venues including AAAI , IJCAI , and IEEE Transactions while maintaining active collaborations with institutions like Harvard, ETH Zurich, and the University of Waterloo.

• Information Security
• Machine Learning
• Trustworthy AI
• +۵ مورد دیگر

Dr. Bei Jiang is an Associate Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta , Canada. She holds the Canada CIFAR AI Chair and is affiliated with the Alberta Machine Intelligence Institute (Amii) . Her academic journey includes a PhD in Biostatistics (2014) from the University of Michigan, MS (2008) and BS (2004) from the University of Alberta and Beijing University of Technology, respectively. Current Positions : 2021–Present (Associate Professor), 2022–Present (CIFAR AI Chair) Past Appointments : Assistant Professor (2015–2021), Postdoctoral Fellow at Columbia University (2014–2015), Research Assistant at University of Michigan (2009–2013) Research Interests : Dr. Jiang specializes in methods for joint modeling of longitudinal and health outcome data , Bayesian hierarchical modeling , functional and imaging data analysis , and statistical machine learning . Her work integrates kernel machine regression , differential privacy , and synthetic data generation to address challenges in heterogeneous health data and neuroimaging. Scientific Awards : Highlights include the 2015 SAMSI New Research Fellow , multiple Rackham Conference Travel Awards (2013, 2012), and prestigious NSERC scholarships (2009–2012). She has also received the J Gordin Kaplan Graduate Award (2008) and Statistical Society of Canada Travel Award (2008). Grants : $375,000 (CIFAR AI Chairs, 2022–2027), $480,000 (MITACS Accelerate, 2022–2025), and $210,000 (Canadian Statistical Sciences Institute, 2022–2025) Students and Postdocs : She mentors numerous PhD , MSc , and Postdoctoral Fellows , including Junxi Zhang (2023–Present), Enze Shi (2022–Present), and former advisees like Wenxing Guo (now Lecturer at University of Essex) and Yafei Wang (Assistant Professor at University of Alberta).

• Biostatistics
• Functional Data Analysis
• Statistical Machine Learning
• +۶ مورد دیگر