جستجوی استاد راهنمای دانشگاهی

Lindsey Kuperمشاهده پروفایل

استادیار

• programming languages
• distributed computing
• concurrency
• +۶ مورد دیگر

Lindsey Kuper is an Assistant Professor in the Computer Science and Engineering Department at the Baskin School of Engineering, University of California, Santa Cruz. She received her Ph.D. from Indiana University in 2015 and leads the CASL (Concurrency and Safety Lab) research group, which is part of the larger Languages, Systems, and Data (LSD) Lab. Her research focuses on programming-language-based approaches to building concurrent and distributed software systems that are elegant, correct, and efficient. Key research areas include: Library-level choreographic programming (in Haskell, Rust, and TypeScript) Dependently-typed diagrams for inductive reasoning about concurrent executions Expressing and verifying causal message delivery with refinement types Lindsey's research has been recognized with an NSF CAREER Award, a Stellar Development Foundation Academic Research Grant, a Google Faculty Research Award, and support from Amazon Web Services. Her work on "HasChor: functional choreographic programming for all" received the ICFP 2023 Distinguished Paper Award. She teaches courses in programming languages and distributed systems at UC Santa Cruz, including Foundations of Programming Languages and graduate/undergraduate Distributed Systems. Her undergraduate distributed systems lectures from 2020-2021 have been viewed over 200,000 times on YouTube. Lindsey co-founded the !!Con and !!Con West conferences of ten-minute talks on the joy, excitement, and surprise of computing, and serves on the board of the Exclamation Foundation. She has chaired numerous workshops and served on program committees for major programming languages conferences including PLDI, POPL, ICFP, and OOPSLA. Her current PhD students include Jonathan Castello, Tim Goodwin, Nathan Liittschwager, Patrick Redmond, Gan Shen, and Yan Tong. Recent graduates include Shun Kashiwa (MS, 2024) and Ali Ali (BS, 2024).

• Federated Learning
• Cybersecurity
• Distributed Systems
• +۵ مورد دیگر

J. Huang is a faculty member at the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology . Their research is centered on federated learning , cybersecurity , and distributed systems , with a strong emphasis on defending against adversarial attacks and ensuring robustness in decentralized machine learning environments. Research Interests: Federated Learning and Distributed AI Adversarial Attacks and Defenses Data Poisoning and Model Robustness Generative Adversarial Networks (GANs) Privacy-Preserving Machine Learning Trustworthy and Secure AI Systems Recent publications reflect a deep engagement with security challenges in federated learning , including gradient inversion attacks, model poisoning without data access, and optimizing client selection strategies. These works contribute to advancing the reliability and safety of distributed AI systems. Scientific Contributions: While no explicit awards are listed, the high citation counts and peer-reviewed contributions in top-tier venues like FC, SRDS, DSN, and PAKDD demonstrate significant academic impact. Collaborations: Huang collaborates with researchers such as Z. Zhao, L.Y. Chen, S. Roos, C. Hong, and others, indicating a strong international research network, particularly within Europe and Asia.

Chad Rodekohrمشاهده پروفایل

استاد

• Mechanical Engineering
• Materials Science
• Mechanical Braiding Systems
• +۵ مورد دیگر

Dr. Chad Rodekohr is a Professor of Mechanical Engineering in the College of Engineering at Anderson University, where he has taught since Fall 2022. His role encompasses teaching core engineering courses, mentoring student research, and advising the Engineering Club. He emphasizes integrating Christian faith with engineering education, preparing students for professional and spiritual life. Education: BS in Aviation Management MS in Physics Ph.D. in Mechanical Engineering Research Focus: Dr. Rodekohr specializes in experimental mechanics and materials science, with emphasis on: Braiding systems mechanics and computational modeling Tin whisker formation mechanisms in electronics Advanced microscopy techniques for materials characterization Vacuum systems and thin-film deposition processes Electronics packaging reliability and failure analysis His research merges theoretical physics with industrial applications. Publication Trends: His scholarly output (2009-2016) shows dual focus: materials science investigations (tin whisker growth, braiding mechanics, surface engineering) and science-faith integration. Early career publications emphasize experimental validation of material behaviors, while recent works explore engineering education philosophy. Professional Activities: Industry collaboration with Dodge Industrial as Manufacturing Engineer during summers Student mentoring through research projects in Sn whisker growth and carbon-fiber braiding Leadership of Engineering Club with extracurricular activities including astronomy nights and technical workshops

Gustavo Benitez Alvarezمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Mathematics
• Finite Difference Methods
• +۶ مورد دیگر

Gustavo Benitez Alvarez is a Full Professor in the Department of Exact Sciences at Federal Fluminense University (Universidade Federal Fluminense) in Brazil. His academic profile demonstrates an active research career with numerous publications spanning computational mathematics, numerical analysis, and biomedical applications. His research interests focus primarily on Numerical Analysis , Finite Difference Methods , and Computational Mathematics , with significant applications in Mathematical Oncology and Thermal Physics . He has developed innovative approaches to solving partial differential equations, particularly the Helmholtz equation, where he has created methods that eliminate pollution error in one dimension and minimize dispersion in two dimensions. His publication record shows a strong trend toward interdisciplinary research, with approximately half of his recent work applying advanced numerical methods to biomedical problems, particularly glioma (brain tumor) modeling and treatment optimization. The other half focuses on fundamental numerical methods for PDEs, computational fluid dynamics, and thermal analysis. Gustavo Benitez Alvarez's work demonstrates a consistent pattern of high-quality publications in reputable journals such as Annals of the Brazilian Academy of Sciences , Mathematical Notes , and Semina: Ciências Exatas e Tecnológicas . His research has practical applications in medical treatment planning, engineering design, and computational physics. He collaborates with researchers across multiple institutions, including co-authorship with colleagues from UFF (Universidade Federal Fluminense) and other Brazilian universities. His work on glioma modeling shows particular promise for improving radiotherapy treatment planning through more accurate tumor growth prediction.

• Computational Multiphysics
• Offshore Renewable Energy
• Finite Element Methods
• +۵ مورد دیگر

Oriol Colomes Gene serves as Assistant Professor in the Offshore Engineering section within the Faculty of Civil Engineering and Geosciences at Delft University of Technology. He leads the Computational Multiphysics in Offshore Engineering research group, focusing on advanced numerical methods for offshore renewable energy applications. His teaching responsibilities include the “Floating and Submerged Structures” module for Civil Engineering master’s students and “Introduction to Computational Dynamics of Offshore Structures” in the Offshore and Dredging Engineering master’s program. His educational background includes a combined Bachelor’s/Master’s degree in Civil Engineering (2011) and a PhD in Civil Engineering (2016) from Universitat Politècnica de Catalunya, followed by postdoctoral research at Duke University (2016-2020). Colomes Gene’s research centers on computational multiphysics with emphasis on: Development of efficient Finite Element solvers for turbulent flows, free-surface flows, and visco-elasto-plastic solids Fluid-structure interaction in complex geometries Numerical methods for multi-phase and multi-material systems Applications in offshore wind, wave energy, and floating photovoltaics His recent work demonstrates strong integration of theoretical numerical methods with practical offshore renewable energy challenges, particularly in hydroelastic analysis of floating structures and optimization of energy capture systems. His scientific recognition includes: NWO Open Technologieprogramma subsidy for the RAPID-Wind project (2025) Colomes Gene actively supervises research within his Computational Multiphysics group and contributes to major collaborative projects including SPARKLES (2024-2030), which investigates nature-positive floating solar solutions. His research is supported by significant grants targeting offshore renewable energy innovation and computational methodology advancement. His laboratory work focuses on high-performance computing for multiphysics simulations, with particular expertise in developing and validating numerical frameworks for: Free-surface flows interacting with structures Vortex-induced vibrations Complex geometry hydrodynamics Hydroelastic response of very large floating structures

Suzan Gazioğluمشاهده پروفایل

استاد

• Applied Statistics
• Statistical Modeling
• Sensitivity Analysis
• +۳ مورد دیگر

Dr. Suzan Gazioğlu is a tenured Full Professor in the Department of Mathematical Sciences at Montana Technological University, where she has served since 2004 after joining as an Assistant Professor of Statistics. Holding a Ph.D. in Statistics from the University of Glasgow (2002), she has established herself through interdisciplinary research bridging statistical theory with environmental and health applications. Education: Bachelor's in Mathematics, Yüzüncü Yıl University, Türkiye Master's in Applied Mathematics, Yüzüncü Yıl University, Türkiye Master's in Mathematical Statistics, University of Toledo, Ohio Ph.D. in Statistics, University of Glasgow, UK (2002) Her research centers on applied statistics, with pioneering work in sensitivity and uncertainty analyses for compartmental models—particularly in environmental systems like the global carbon cycle. She has expanded into secondary analysis of case-control studies in biostatistics while maintaining a strong focus on statistics education innovation, especially in online pedagogy. This dual emphasis creates a unique synergy between methodological advancement and educational impact. Analysis of her publications reveals a clear evolution toward graphical sensitivity techniques and uncertainty quantification in complex environmental models, with recent work emphasizing visualization tools like dot charts and star plots. Concurrently, she has developed influential frameworks for online statistics education, demonstrating how digital platforms can enhance statistical literacy without compromising rigor. Scientific Awards: Distinguished Researcher Award, Montana Tech (2013) Faculty Merit Award, Montana Tech (2013) Nomination for Rose and Anna Busch Faculty Achievement Award, Montana Tech (2013) Fellowship, Royal Statistical Society, UK (2002) National M.Sci. & Ph.D. Scholarship, Turkish Higher Education Council (1994-2002) Dr. Gazioğlu has mentored numerous undergraduate researchers through Montana Tech's Undergraduate Research program, guiding projects that often bridge statistical methodology with real-world applications. Her leadership extends to professional service as former President of the Montana Chapter of the American Statistical Association (2005-2006) and ongoing editorial roles, reflecting significant contributions beyond traditional academic boundaries. While grant details aren't specified, her collaborative research with environmental scientists suggests active engagement with university-wide initiatives. Her work inherently connects with Montana Tech's Center for Environmental Remediation and Assessment (CERA), leveraging institutional strengths in environmental science. This alignment with the university's research infrastructure enables her statistical innovations to directly address pressing ecological challenges through interdisciplinary collaboration.

Mohammad REIHANEHمشاهده پروفایل

استادیار

• Operations Research
• Logistics and Supply Chain Management
• Optimization Algorithms
• +۳ مورد دیگر

Professor Mohammad REIHANEH is an Assistant Professor of Operations Management at IÉSEG School of Management. He holds a Ph.D. in Operations Management from the University of Massachusetts (USA), an MSc in Industrial Engineering from Isfahan University of Technology (Iran), and a BSc in Applied Mathematics from Ferdowsi University of Mashhad (Iran). His research focuses on optimization algorithms, vehicle routing problems, scheduling theory, and logistics systems. He is a member of the LEM research group and has published extensively in top-tier journals such as the European Journal of Operational Research and the Journal of the Operational Research Society. Education: 2018: Ph.D., Operations Management, University of Massachusetts, USA 2012: MSc, Industrial Engineering, Isfahan University of Technology, Iran 2009: BSc, Applied Mathematics, Ferdowsi University of Mashhad, Iran Research Interests: Mohammad’s work emphasizes practical applications of optimization in logistics and healthcare systems. He develops exact algorithms (e.g., branch-and-price) and heuristic methods for complex routing problems, maintenance scheduling, and resource allocation in healthcare settings. His contributions bridge theoretical advancements in operations research with real-world operational challenges in industries like renewable energy (offshore wind farms) and humanitarian logistics. Publications: His recent work addresses cutting-edge challenges such as multi-period offshore wind farm routing, hemodialysis center scheduling, and food bank distribution optimization. He frequently collaborates with international researchers on projects involving vehicle routing, reliability engineering, and control chart design for manufacturing systems. Advising & Grants: No specific student advisees or grant details are listed in the provided materials.

Manfred Jaegerمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Bayesian Networks
• +۵ مورد دیگر

Manfred Jaeger is an Associate Professor at the Department of Computer Science, Technical Faculty of IT and Design, Aalborg University. His research focuses on Artificial Intelligence , Bayesian Networks , and Graph Neural Networks , with significant contributions to probabilistic reasoning and relational learning. University: Aalborg University School: Technical Faculty of IT and Design Department: Department of Computer Science Jaeger's research explores inductive and probabilistic reasoning , statistical relational learning , and model checking . His recent work integrates heterogeneous graph neural networks with relational Bayesian network encodings to enhance reasoning capabilities in complex systems. Key trends in his publications include relational deep learning , probabilistic inference , and graph-based modeling . He has contributed to applications in social network community detection , reinforcement learning for MDPs , and latent variable models for graph learning . Jaeger collaborates on projects involving incomplete data analysis , modularization of complex tasks , and probabilistic logic . His datasets on multi-multi-instance learning networks are publicly available for research use.

David N. Jansenمشاهده پروفایل

دانشیار

• Computer Science
• Formal Methods
• Model Checking
• +۵ مورد دیگر

David N. Jansen is an Associate Professor at the Institute of Software , Chinese Academy of Sciences , Beijing, China. Previously, he held an Assistant Professor position at Radboud University Nijmegen (2007–2016) and a Postdoc role at RWTH Aachen University (2007). His work bridges academic research, software development, and international collaboration. Education : PhD in Computer Science from University of Twente (1998–2003); Diploma in Mathematics (with minors in Computer Science and Comparative Linguistics) from University of Bern (1990–1997); Certificate of Proficiency in English from University of Cambridge (2000). Research interests include stochastic model checking , UML extensions for probabilistic systems , Markov reward models , and formal verification of embedded systems . He has contributed to tools like MRMC and TCM , focusing on efficient data structures and lumping techniques for system validation. Publication trends show expertise in formal methods , probabilistic verification , and equivalence relations . His work spans automated analysis of probabilistic programs , stuttering equivalence algorithms , and applications to real-world systems like the European Train Control System. Leadership and Service : He has served on doctoral advisory boards (SIKS), supervised student projects (e.g., XML/XMI interface for TCM), and volunteered in Christian organizations. His technical skills include C, C++, UML, model checking, and database systems like MySQL.

Yixuan Zhangمشاهده پروفایل

استادیار

• Human-Computer Interaction (HCI)
• Human-LLM Interaction
• Data Visualization
• +۵ مورد دیگر

Yixuan (Janice) Zhang is an Assistant Professor in the Department of Computer Science at William & Mary, holding a Ph.D. in Human-Centered Computing from Georgia Tech (2023). Her research focuses on Human-Computer Interaction (HCI), Human-LLM Interaction, Data Visualization, and Trust Dynamics, with cross-disciplinary applications in health informatics and STEM education. She has published in top venues like ACM CHI, CSCW, and IEEE VIS, earning awards including the 2022 Rising Star in EECS and Foley Scholar recognition. Research Highlights: Explores ethical implications of LLMs in education and healthcare. Investigates trust dynamics in crisis informatics and social media. Designs AI-driven tools for Alzheimer’s awareness and mental health support. Grants & Collaborations: NSF RITEL Grant ($900K) for LLM-powered collaborative learning systems. Partnerships with WHO, Microsoft Research, and Virginia educational institutions. Co-chaired ACM DIS'24 and served on CHI'24 program committees. Awards: Best Paper Award at CHI'23 Learn, Discover, Innovate grant (2023) General Assembly of Virginia grant for ADRD awareness systems. Her lab actively engages in projects like MetaAgents (CSCW'25) and EmotionPrompt (enhancing LLMs with emotional stimuli). She advises PhD students in AI ethics and education technology.

Stephanos Mavromoustakosمشاهده پروفایل

دانشیار

• Web Engineering
• Human-Computer Interaction
• Information Security
• +۳ مورد دیگر

Dr. Stephanos Mavromoustakos serves as Acting Associate Dean and Chair of Cybersecurity, Computer Science, and Software Engineering at Indiana Tech's Talwar College of Engineering and Computer Sciences. He holds an Associate Professor rank and directs the Cybersecurity Center. His academic journey includes a PhD in Computer Science from the University of Cyprus (2006) and multiple degrees from Nova Southeastern University. With over 25 years of experience, Dr. Mavromoustakos has taught at institutions like the University of Windsor and European University Cyprus. His professional leadership spans roles as department head, consultant, and CEO of Cyprosoft. He currently advises programs in Cybersecurity (BS/MS), Computer Science (BS), and Software Engineering (BS). Research interests focus on Web Engineering , Human-Computer Interaction , Information Security , and Artificial Intelligence . Notable work includes cybersecurity project development, adaptive e-Learning systems, and biometric authentication technologies. He has published extensively in journals like UPGRADE and conferences such as IEEE and ACM. Grants include NSF S-STEM ($1M), Google Cloud Platform grants, and EU projects like MEDFORIST ($2.5M). His funded projects address data security, mobile commerce, and educational technology innovations. He teaches a wide range of undergraduate and graduate courses, emphasizing practical skills in software engineering, database systems, and cybersecurity.

Detlev Ristauمشاهده پروفایل

استاد

• Optical Thin Film Technology
• Precision Metrology
• Optical Materials
• +۳ مورد دیگر

Prof. Detlev Ristau is a Professor at the Institute of Quantum Optics within the Faculty of Mathematics and Physics at Leibniz University Hannover. He holds leadership roles including Group Leader for Laser Components and Fibers and is part of the Executive Board of the Institute of Quantum Optics and the PhoenixD Research School. His research focuses on optical thin-film technologies, precision metrology, optical materials, ion deposition processes, laser damage mechanisms, and optical fibers. His work integrates advanced coating techniques, material characterization, and applications in high-power lasers and photonics. Notable contributions include optimizing nanocrystal-based fiber lasers, enhancing laser-induced damage thresholds in optical components, and developing novel thin-film designs for precision applications. He leads interdisciplinary projects in the HITec Institute, emphasizing quantum engineering and optical technologies. Ristau's research spans from fundamental material science to applied photonics, with a strong emphasis on translating innovations into practical optical systems. His publications highlight advancements in nanothermometry, fiber optics, and thin-film fabrication methodologies.

Matthew Whiteمشاهده پروفایل

دانشیار

• Nonlinear Optoelectronics
• Organic Photovoltaics
• Hybrid Perovskite Devices
• +۳ مورد دیگر

Matthew White is an Associate Professor in the Department of Physics at the University of Vermont, affiliated with the College of Engineering and Mathematical Sciences. He earned his Ph.D. from the University of Colorado, Boulder in 2009. His research focuses on nonlinear processes in optoelectronic devices and materials physics for low-cost, high-performance photovoltaics, particularly hybrid and organic photovoltaic device physics. Key research areas include perovskite solar cells, organic light-emitting diodes (OLEDs), photonic crystals, and nanomaterials synthesis. His work integrates experimental techniques with theoretical modeling to optimize device performance and stability. Recent studies explore ligand design for perovskite nanocrystals, defect engineering in photonic crystals, and microcavity effects in OLEDs. Publications highlight advancements in optoelectronic materials, including nonlinear impedance spectroscopy for ion migration analysis and band structure control in photonic systems. His research emphasizes practical applications in renewable energy and high-efficiency light-emitting technologies. No scientific awards or grants are explicitly mentioned in the provided information. He advises no listed students and has not disclosed lab affiliations beyond the UVM Device Physics group.

• Geomagnetism
• Paleomagnetism
• Geophysics
• +۳ مورد دیگر

Dr. Lennart de Groot is an Associate Professor at Utrecht University's Faculty of Geosciences, Department of Earth Sciences, leading the Paleomagnetic Laboratory at Fort Hoofddijk. His academic journey includes a BSc (2007), MSc (2008), PhD (2013, Cum Laude), and postdoctoral research at Utrecht University. Specializing in geomagnetism and paleomagnetism, his work focuses on understanding rapid fluctuations in Earth's magnetic field using advanced techniques like micromagnetic tomography and multi-method paleointensity approaches. Research Interests: Geomagnetic field dynamics, paleointensity determination, rock magnetism, and micromagnetic analysis. Key Projects: Development of pymaginverse for geomagnetic modeling, study of Mid-Miocene geomagnetic reversals, and analysis of Devonian volcanic records. His articles highlight innovations in paleomagnetic measurement techniques and their applications to ancient geomagnetic field behavior. Notable awards include the ERC Starting Grant (2019), NWO-Vidi (2019), and the William Gilbert Award (2018). He advises on grants and mentors researchers in geophysics and rock magnetism, contributing to the UN Sustainable Development Goals through Earth science research. Lab affiliations include the Paleomagnetic Laboratory Fort Hoofddijk, where cutting-edge equipment enables high-resolution studies of magnetic minerals and their paleoenvironmental records.

• Stochastic Processes
• Mathematical Biology
• Statistical Inference
• +۵ مورد دیگر

Arnab Ganguly is an Associate Professor in the Department of Mathematics at Louisiana State University (LSU), holding this position since August 2021. He previously served as an Assistant Professor at LSU (2015–2021) and the University of Louisville (2012–2015), with postdoctoral research at Brown University (2012) and ETH Zurich (2010–2012). His research focuses on stochastic processes, mathematical biology, and statistical inference, with applications in biochemical reaction networks and control systems. Ganguly holds a Ph.D. in Mathematics from the University of Wisconsin-Madison (2010), and earlier degrees from the Indian Statistical Institute, Kolkata (M.Stat, B.Stat). His work bridges theoretical probability and applied mathematics, addressing challenges in stochastic modeling, parameter inference, and multiscale systems. Key themes include Markov chain approximations, large deviation principles, and nonparametric learning techniques. He has developed efficient simulation algorithms for complex biochemical networks and contributed to understanding enzyme kinetics through stochastic averaging methods. Publications reflect a strong emphasis on stochastic differential equations, ergodic theory, and applications in systems biology. While no awards are explicitly mentioned, his extensive publication record and academic roles highlight significant contributions to the field. His research also explores hybrid spatial models, error analysis in stochastic simulations, and Bayesian nonparametric methods for dynamic systems.