جستجوی استاد راهنمای دانشگاهی

• Research Software Engineering
• Software Architecture
• Grid Computing
• +۳ مورد دیگر

Stephen Crouch serves as a Software Architect within the School of Electronics and Computer Science at the University of Southampton, where he actively contributes to the Web and Internet Science research group and the Southampton Research Software Group (SRSG). His role bridges technical software development with academic research infrastructure, focusing on creating robust solutions that enhance research capabilities across scientific domains while emphasizing sustainability and reproducibility in software practices. His research spans Research Software Engineering, Software Architecture, Grid Computing, Data Management, Reproducible Research, and High Performance Computing. Crouch has been instrumental in major projects including IGE (funded by the European Union's FP7 program) which developed integrated educational frameworks for research software engineering, and UNIVERSE-HPC (EPSRC-funded) addressing educational strategies in high-performance computing. His work consistently targets the intersection of software engineering principles and scientific research needs, particularly in creating maintainable systems for evolving research workflows. Analysis of his publication trajectory from 2007-2025 reveals a clear evolution from foundational grid computing research toward contemporary challenges in research software sustainability and reproducibility. Early work focused on grid interoperability and job scheduling, while recent contributions emphasize institutional strategies for research software engineering, data evolution pathways, and sustainable software practices. This progression demonstrates his adaptation to shifting research computing paradigms while maintaining core expertise in software architecture for scientific applications. Crouch has secured significant funding from the European Union and EPSRC, supporting his work in research software infrastructure. Within the Southampton Research Software Group, he plays a key role in developing institutional capacity for research software engineering, providing expertise that enables researchers across disciplines to implement reliable, efficient computational solutions. His collaborative approach is evident through extensive co-authorship with national and international research software engineering initiatives.

AYTAÇ GÜVENمشاهده پروفایل

استاد

• Fluid Mechanics
• Numerical Modeling
• Hydrology
• +۷ مورد دیگر

Professor AYTAÇ GÜVEN is a faculty member at Gaziantep University's College of Engineering, Department of Civil Engineering, specializing in Fluid Mechanics and Numerical Modeling . With a PhD from Gaziantep University (2008), he progressed from Research Assistant (2001-2008) to Professor (2017). His research focuses on Hydrology , Climate Change Impact on water resources, Dam Safety , and Hydropower Assessment using Genetic Programming and Neural Networks . He served as Department Head (2020-present) and Vice Head (2009-2014). Education: PhD (Civil Engineering, Gaziantep University, 2008) MS (Civil Engineering, Gaziantep University, 2005) BS (Civil Engineering, Gaziantep University, 2000) His research interests span: Climate change adaptation in water resources Advanced numerical modeling for hydraulic structures Hydropower potential assessments Scour and sediment yield prediction Machine learning applications in hydrology Dam failure risk analysis His 57 peer-reviewed publications (including 2 books) emphasize hybrid modeling techniques, with recent work on CMIP6 climate scenarios , steppe spillways , and dam safety tools . He has advised 5 PhD and 21 Master's theses. Scientific awards include: 2024 Stanford Career Impact Award 2018 Best PhD Thesis (Baraj Güvenliği Derneği) 2016 Gaziantep University Science Award 2010 Best Master's Thesis (Baraj Güvenliği Derneği) 2018 Best Presentation (International Academic Association)

Fadi AL-TURJMANمشاهده پروفایل

استاد

• Artificial Intelligence of Things (AIoT)
• Wireless Sensor Networks
• Blockchain Applications
• +۷ مورد دیگر

Prof. Dr. Fadi AL-TURJMAN serves as the founding Dean of the Faculty of AI and Informatics at Near East University (NEU), Cyprus. He holds multiple leadership roles including Head of the Software Engineering Department and Director of the AI and Robotics Institute and the International Research Center for AI and IoT. With a PhD in Computer Science from Queen’s University (2011), he specializes in AIoT systems, wireless networks, and blockchain integration. Affiliation: Near East University Leadership: Founding Dean for AI and Informatics, Director of AI & Robotics Institute Research Focus: His work bridges Artificial Intelligence of Things (AIoT) , Blockchain Applications , and Smart Networking . He explores cybersecurity frameworks for smart cities, quantum state optimization techniques, and novel AI-driven solutions for healthcare, agriculture, and energy systems. Key Article Trends: Recent publications emphasize Transformer models for environmental monitoring, blockchain-enabled security protocols , and evolutionary algorithms for resource optimization. His research spans interdisciplinary domains including medical diagnostics, vehicular networks, and sustainable infrastructure. Scientific Awards: Lifetime Golden Award of Dr. Suat Gunsel (2022) Multiple Best Research Awards at International Venues Labs & Teams: Directs the International Research Center for AI and IoT at NEU, leading multidisciplinary teams in developing advanced networking technologies and AI-driven solutions for global value chain applications.

Deepti Raghavanمشاهده پروفایل

استادیار

• Operating Systems
• Networking
• Machine Learning Systems
• +۲ مورد دیگر

Deepti Raghavan serves as Assistant Professor in Brown University's Department of Computer Science, specializing in operating systems, networking, and machine learning systems. Her research develops novel networked abstractions to optimize data movement in distributed applications through API redesign. Her educational background includes: PhD in Computer Science from Stanford University (2024) MEng in Computer Science from Massachusetts Institute of Technology (2018) BS in Computer Science from Massachusetts Institute of Technology (2017) Her research integrates systems infrastructure with machine learning workloads , focusing on performance-critical communication patterns. Key innovations include zero-copy serialization techniques and network orchestrators for compound AI systems, bridging theoretical advances with practical deployment in cloud environments. Publication trends (2018-2024) reveal consistent contributions to high-performance networking and ML systems, with increasing focus on AI infrastructure. Her work shows strong collaboration patterns with Stanford/MIT researchers and addresses fundamental challenges in data movement efficiency across distributed systems. Scientific recognition includes: National Science Foundation Graduate Research Fellowship (2019) Stanford School of Engineering Fellowship (2018-2019) Distinguished Artifact Award at SOSP 2023 Best Paper Award at Usenix ATC 2018 She actively recruits PhD students for her research group and teaches advanced systems courses including Cloud and Datacenter Operating Systems. Her research is supported by prestigious fellowships and likely NSF grants, with teaching responsibilities spanning graduate seminars and core undergraduate systems education. Her research group collaborates with Stanford's Systems Lab and MIT's CSAIL Networks group, maintaining strong ties to the Networks and Mobile Systems research community. Current projects focus on next-generation abstractions for AI-driven networked applications.

Pierre Lafaye de Micheauxمشاهده پروفایل

دانشیار

• Applied Statistics
• Biostatistics
• Statistical Theory
• +۱۱ مورد دیگر

Associate Professor Pierre Lafaye de Micheaux is a statistician based at the School of Mathematics and Statistics, University of New South Wales , where he has worked since 2020. He previously held academic roles at Université Paul Valéry (2020, Associate Professor), ENSAI (2015–2017, Professor), Université de Montréal (2011–2016, Associate Professor), and Grenoble Alps University (2003–present, Assistant Professor). His research spans theoretical and applied statistics , focusing on complex random vectors , neuroimaging genetics , and data science for IoT . Education: PhD in Statistics (2003, Université de Montréal & Montpellier) MSc in Biostatistics (1998, Montpellier) BSc in Mathematics and Physics (1996, Montpellier) MSc in Cognitive Neuroscience (2007, Grenoble Institute of Technology) Research interests include: Dependence Measures : Leveraging complex analysis for big data dependence testing under 3V's (Volume, Variety, Velocity). Neuroimaging Genetics : Developing statistical tools for fMRI/EEG/DTI phenotyping of genetic variation with institutions like CHeBA and INSERM. IoT Data Science : Creating Raspberry Pi-based statistical computing tools for real-time sensor data streams. Complex-Valued Inference : Building a unified framework for complex random vectors in neuroimaging and nuclear engineering. Recent publications demonstrate expertise in circular data analysis , nonparametric testing , and central limit theorem counterexamples , with applications in medical imaging and finance. He has supervised numerous PhD, MSc, and honors students on topics ranging from deep learning to stochastic processes. Scientific achievements include: Université de Montréal Provost Honor List (2003) Editor of the Journal of Statistical Software (2017–present) Co-leader of three research groups: Dependence Measures , Neuroimaging Genetics , and Data Science & IoT He has secured grants from UNSW Research Infrastructure Scheme and NSERC , with industry collaborations including BNP Paribas (credit risk) and Olea Medical (stroke treatment analytics). Current teaching includes Statistical Inference (ZZSC5905) and Data Science (DATA3001) at UNSW.

Zhongxin Liuمشاهده پروفایل

استادیار

• Intelligent Software Engineering (AI4SE)
• Code Intelligence
• Vulnerability Detection
• +۵ مورد دیگر

Zhongxin Liu is an Assistant Professor at the College of Computer Science and Technology , Zhejiang University , China. He earned his Ph.D. from the same institution in 2021. His research focuses on Intelligent Software Engineering (AI4SE) , leveraging software "big data" to improve code understanding, generation, and security through machine learning techniques. Published in top-tier venues: TSE, TOSEM, ICSE, FSE, ASE, ISSTA Active in academic service: Reviewer for TSE, TOSEM, ASEJ, etc. Visiting Professor at University of Stuttgart (2024-2025) His recent work explores Large Language Models (LLMs) for code intelligence, security hardening, and vulnerability detection. Papers emphasize cross-domain applications, zero-shot learning, and API/code dependency analysis. Scientific awards include: ACM SIGSOFT Distinguished Paper Awards (ASE 2018, 2019, 2020; ISSTA 2025) Zhejiang University Qizhen Scholar (2021) CCF TCSE Doctoral Dissertation Award (2023) Recruiting undergraduate interns, graduate students (MS/Ph.D.), and postdocs for code intelligence research. Contact: liu_zx@zju.edu.cn .

Morgane Chevalierمشاهده پروفایل

دانشیار

• Educational Robotics
• Computational Thinking
• E-learning
• +۴ مورد دیگر

Morgane Chevalier is an Associate Professor at Vaud Teacher Training College (HEP Vaud) working within the Media, Digital Uses and Computer Science Teaching unit (UER MI). She specializes in multimedia pedagogical engineering, combining project management with e-learning scripting. Her work bridges educational theory and practical implementation of digital technologies in classroom settings, with particular focus on primary education. With prior experience teaching in primary schools in Seine-Saint-Denis (France), working in human resources in Montreal (Canada), and teaching in the Canton of Vaud, Chevalier brings diverse perspectives to her academic work. She holds a Master's degree in e-learning from IPM, Lille 1 and is currently pursuing a MAS in 'Theories, practices and training devices for teachers' to refine her understanding of institutional and social contexts of teacher training in French-speaking Switzerland. Her research interests focus on three complementary areas: online homework implementation in blended learning environments and fostering teacher communities of practice; how e-learning supports teacher professionalization; and robotics as a medium for knowledge, specifically examining how classroom robot integration impacts student learning and teaching practices. She is particularly interested in emerging communities of practice around educational technology. Analysis of her recent publications reveals a strong emphasis on educational robotics as a vehicle for developing computational thinking in primary education. Her work consistently explores scaffolded learning approaches, intervention methods, and teacher perspectives on implementing robotics in classrooms. A recurring theme is the development of models to help teachers identify relevant computational thinking concepts during different phases of educational robotics activities. Chevalier serves in several organizational roles including as an Associate HEP Professor, delegate of the intermediate body to the HEP Council, delegate of the Specialized Pedagogy Study Commission, and scientific collaborator at EPFL's LSRO. She is also a member of the educanet2 Community. Her teaching spans Bachelor primary programs, MASPE education, and continuing education courses covering image creation and processing, media education, Thymio II robotics, and flipped classroom methodologies. She collaborates extensively with EPFL researchers, particularly on projects involving the Thymio educational robot, and has developed frameworks for creative computational problem solving through robotics. Her work demonstrates a commitment to translating research into practical classroom applications while supporting teacher development in the digital age.

• Natural Language Processing
• Semantic Parsing
• Large Language Models
• +۳ مورد دیگر

Rui Zhang is an Assistant Professor in Computer Science and Engineering , with research expertise spanning Natural Language Processing , Large Language Models , and Semantic Parsing . His recent work focuses on enhancing multimodal consistency , fairness in summarization , and mathematical reasoning capabilities of LLMs. Key Research Themes: Text-to-SQL and cross-domain semantic parsing Multimodal learning (vision-language models) Fairness and bias mitigation in NLP tasks Efficient model training and prompt optimization Scientific Awards: National Science Foundation CAREER Award (2024) Grants & Projects: CAREER: Trustworthy Human-Centered Summarization (NSF, 2024-2029) addressing LLM trustworthiness through user-centric summarization frameworks. Article Trends: Recent publications emphasize LLM collaboration , compressed reasoning models , and cross-domain knowledge alignment . He explores multi-agent systems , mathematical reasoning , and vision-language limitations , particularly in geometric perception. Applications span bioinformatics (Alzheimer's biomarker discovery) and democratic AI frameworks.

Giorgio Scorzelliمشاهده پروفایل

پژوهشگر

• Extreme Data Management
• Scientific Visualization
• Computational Topology
• +۶ مورد دیگر

Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.

• Systems Theory
• Mathematical Modeling
• Fuzzy Logic
• +۷ مورد دیگر

Konstantin Yuryevich Degtyarev is an Associate Professor at the Department of Software Engineering within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE) in Moscow. He has been working at HSE since 2008 and has accumulated 29 years of scientific and teaching experience. His office is located at AUK "Pokrovsky Boulevard", Pokrovsky blvd, 11, office S919, where he holds consultations on Mondays, Thursdays, and Fridays during Module 1. 1995: Candidate of Technical Sciences from Moscow State University of Instrument Engineering and Computer Science, specialty 05.13.01 "System Analysis, Management and Information Processing" Postgraduate study at Moscow State University of Instrument Engineering and Computer Science (1986) Specialty: Moscow Institute of Electronic Engineering, Faculty of Applied Mathematics, specialty "Applied Mathematics", qualification "Engineer-mathematician" Professor Degtyarev's research focuses on systems theory and mathematical modeling, with particular emphasis on fuzzy logic and soft computing applications in systems analysis. His work explores cognitive approaches to structural analysis, verbal calculations, perceptions in systems analysis, F-geometry in modeling and decision making, interval calculations, Z-numbers (according to L.A. Zadeh), and fuzzy time series in forecasting. He also has significant expertise in programming in C/C++ and approaches to teaching programming courses. His research bridges theoretical foundations with practical applications in software engineering and decision support systems. Analysis of Professor Degtyarev's publication history reveals a consistent focus on fuzzy logic applications across multiple domains. His work demonstrates an evolutionary path from foundational theoretical work on polyhedral dynamics and Q-analysis to more applied research involving Z-numbers, fuzzy time series, and fuzzy logic applications in software engineering, e-commerce reputation systems, and metro positioning services. A notable trend is the increasing application of fuzzy methodologies to practical software engineering problems, particularly in the areas of linguistic summarization, decision support, and uncertainty modeling. Gratitude from HSE University (May 2024) Gratitude from the Department of Software Engineering, HSE (November 2023) Certificate of honor from the Faculty of Computer Science of HSE (September 2019) Gratitude from the First Vice-Rector of HSE (January 2018) Best Teacher award (2024, 2011-2020) Winner of the competition for the best article in the journal 'Proceedings of the ISP RAS' for 2019 Professor Degtyarev has supervised numerous bachelor's and master's theses, primarily focusing on fuzzy logic applications, Z-numbers, linguistic summarization, and decision support systems. His students have developed various software implementations related to fuzzy modeling, time series forecasting, and expert assessment aggregation. He has also contributed significantly to educational development, creating teaching materials for courses in programming, fuzzy systems, and applied system analysis. His involvement in curriculum development includes certified assessment materials for the discipline "Fundamentals of Informatics and Programming". As a member of IEEE (since 1999) and ACM (2005-2014), Professor Degtyarev participates in various professional activities including serving as a reviewer for prestigious journals such as "Fuzzy Sets and Systems" and "IEEE Transactions on Fuzzy Systems". He has also been involved in international conferences as a program committee member and section chair for several International Conferences on Soft Computing.

Lin Jiangمشاهده پروفایل

استادیار

• Biomechanics
• Robotics
• Medical Assistive Device Development
• +۳ مورد دیگر

Dr. Lin Jiang serves as Assistant Professor in the Department of Mechanical Engineering at San José State University's Charles W. Davidson College of Engineering, where her research bridges biomechanics and robotics to develop medical assistive technologies and human-robot interaction systems. Her educational background includes a Ph.D. and M.Sc. in Mechanical Engineering from the University of Texas at Dallas (2021, 2019), complemented by an M.Sc. in Control Engineering and B.Sc. in Aerospace Engineering from Nanjing University of Aeronautics & Astronautics (2014, 2011) with a minor in Industrial Business Management. Dr. Jiang's research focuses on translating aerospace control systems expertise into medical applications, particularly in rehabilitation robotics and breastfeeding technology. Her work emphasizes human-centered design for devices like the patented SmartLact8 breast pump, with recent publications demonstrating significant contributions to teleoperated rehabilitation systems and lactation biomechanics. Her 15 most recent publications (2020-2025) reveal consistent specialization in medical robotics, with dominant themes in rehabilitation devices (knee braces, upper extremity therapy), breastfeeding technology innovation, and human-robot interaction frameworks for healthcare and driving safety applications. Scientific recognition includes: New Investigator Award from CSUPERB Small Group Project Award from SJSU College of Engineering Exemplary Teaching award from UT Dallas Diversity Award from Summer Biomechanics Conference Best Paper award at ASME IMECE 2018 Research funding includes NSF support for hospital-based human-robot interaction studies. Dr. Jiang actively contributes to IEEE HKN, BMES, ASME, and ISHRML while mentoring students through her Biomechanics and Robotics Lab at SJSU. The Biomechanics and Robotics Lab serves as the primary research hub for developing medical assistive technologies, with current projects focusing on rehabilitation robotics, breastfeeding simulation systems, and human-robot interaction protocols for clinical environments.

• intelligent systems
• Machine Learning
• evolutionary algorithms
• +۴ مورد دیگر

Teresa Cristina de Freitas Gonçalves is an Associate Professor at the Department of Informatics, School of Sciences and Technology, University of Évora, where she has been employed since 1999. She serves as an integrated researcher at the ALGORITMI research centre and is the Director of the VISTA Lab (Video, Image, Speech and text Analysis Lab), the unit of the ALGORITMI research centre at University of Évora. Her leadership roles include Director of the Master programme in Informatics Engineering and deputy Director of both the Master programme in Artificial Intelligence and Data Science and the Doctoral program in Computer Science. She earned her PhD in Computer Science from University of Évora and a MSc degree in Informatics Engineering from New University of Lisbon. Her academic journey at University of Évora has included significant leadership positions including Head of the Computer Science Department (2011-2015), Director of the Bachelor programme in Informatics Engineering (2016-2021), and Deputy Director roles for various undergraduate and graduate programs. Dr. Gonçalves' research focuses on intelligent systems, particularly Machine Learning approaches, with substantial contributions in evolutionary algorithms, information extraction and retrieval, and supervised learning across multiple data modalities including tabular data, text (in both Portuguese and English), and images (medical and satellite). Her work bridges theoretical advances with practical applications in healthcare, remote sensing, and natural language processing. She has successfully supervised 6 doctoral theses, 19 master theses, and 3 postdocs, and currently mentors 5 doctoral and 6 master students from diverse international backgrounds including Bangladesh, Cabo Verde, Nepal, Philippines, India, Sri Lanka, China, Mongolia, and Portugal. Her publication record includes over 100 scientific articles indexed by Scopus with 640 citations and an h-index of 12, demonstrating significant international impact with 56% of her work involving international collaboration. Her recent research shows a strong trend toward applying advanced machine learning techniques to healthcare applications, information retrieval systems, and remote sensing analysis, with particular emphasis on transformer networks, learning-to-rank methodologies, and multimodal data analysis. Dr. Gonçalves has made substantial contributions to the academic community through her service as a reviewer for over 50 articles in prestigious international journals and conferences, and as chair for major international conferences including IDEAL 2023, PROPOR 2020, SKIMA 2017 and 2018, and CLEF 2016. She serves on the board of APRP (Associação Portuguesa de reconhecimento de Padrões) and as a jury member for APRP prizes for best MSc and PhD theses. Her current research portfolio includes coordination of the Horizon Europe MSCA Staff Exchange HarmonicAI project and local coordination of WP6 in the NewSpace Portugal mobilising agenda. She is also actively involved in numerous other international research initiatives including Interreg VI-B Sudoe SenforFire, PRR CANTE, La Caixa INCOME, Erasmus+ KA220-HED REDINEST, Interreg POCTEP TID4AGRO, and ATTRACT DIH projects. Previously, she led the FCT AI in the Public Administration SNS24.Scout.IA project and coordinated the FEDER R&D NIIAA project. As Director of the VISTA Lab, Dr. Gonçalves leads a dynamic research team focused on video, image, speech, and text analysis. The lab serves as the Évora hub of the ALGORITMI research centre and has established strong international collaborations. Under her leadership, the VISTA Lab has developed innovative approaches in medical image analysis, natural language processing for Portuguese, and satellite image classification, with applications spanning healthcare, environmental monitoring, and public administration.

Gérard Berryمشاهده پروفایل

استاد

• Computer Science
• Lambda Calculus
• Functional Programming
• +۵ مورد دیگر

Gérard Berry (born December 25, 1948) is a distinguished French computer scientist currently serving as Professor at the Collège de France, holding the permanent chair Algorithmes, machines et langages (Algorithms, Machines, and Languages) since 2012. He previously held the Informatique et sciences numériques chair (2009-2010) and the Technological Innovation Liliane Bettencourt chair (2007-2008) at the same institution. Before joining Collège de France full-time, he served as Director of Research at INRIA Sophia Antipolis (2009-2012) and at École des Mines de Paris (1977-2001). His research spans over 30 years in three main fields: lambda calculus and functional programming, parallel and real-time programming languages, and design automation for synchronous digital circuits. He is particularly renowned for developing the Esterel programming language. His work bridges theoretical computer science with practical industrial applications. Berry's research has evolved to include current work in Hop and HipHop for Web programming, formal verification of compilers, and languages for computer music. His publications demonstrate consistent contributions to programming language theory, formal methods, and their applications in hardware and software systems. Gold Medal of CNRS (2014) Chevalier de l'Ordre de la Légion d'Honneur (2012) Member of French Academy of Sciences (2002) Member of Academia Europaea (1993) Monpetit Prize of Académie des sciences (1990) Berry has advised 17 PhD students and reviewed numerous theses. His industrial experience includes serving as Chief Scientist Officer of Esterel Technologies (2000-2009), where he directed the implementation of the Esterel v7 compiler. He has also held significant leadership roles including President of the Scientific Council of IRCAM and membership on the Scientific Council of the National Education. His teaching at Collège de France has covered topics ranging from the foundations of computation to the societal impact of digital technology, with courses including The Informatics of Time and Events and Proving Programs: Why? When? How? His laboratory work has focused on developing practical applications of theoretical computer science concepts.

Lynn Kamerlinمشاهده پروفایل

استاد

• Computational Biophysics
• Molecular Evolution
• Enzyme Engineering
• +۱۲ مورد دیگر

Lynn Kamerlin is a Professor at the Georgia Institute of Technology and co-leads the Kamerlin Laboratory, which operates across Georgia Tech and Lund University. Her work integrates computational chemistry and biophysics to address fundamental questions in enzyme evolution, catalysis, and protein design. Education MNatSc in Chemistry, University of Birmingham (UK) PhD in Chemistry, University of Birmingham (UK) Her research spans computational biophysics , focusing on mechanistic biochemistry , protein evolution , and enzyme engineering . Key methodologies include machine learning , molecular dynamics simulations , EVB/QM/MM modeling , and natural language models for protein structure prediction. Recent publications highlight trends in AI-driven enzyme design , conformational dynamics , and mechanistic studies of phosphoryl transfer reactions . Tools like WatCon and Q-RepEx demonstrate her commitment to method development in computational biology. Scientific Awards Georgia Research Alliance Eminent Scholar (2022-Present) Wallenberg Scholar (2020-2024) ERC Starting Grant (2012-2017) Wallenberg Academy Fellowship (2014-2019, prolonged 2019-2024) Young Academy of Europe Chair (2014-2015) Fellow of the Royal Society of Chemistry (2017) Her lab collaborates with experimental groups worldwide, leveraging enhanced sampling techniques and structural bioinformatics to engineer enzymes with tailored properties. Grants from the Swedish Research Council and European Research Council underpin her research on enzyme evolution and catalytic mechanisms. Current projects include computational design of thermostable enzymes , allosteric modulators for biomedical targets, and modular protein scaffolds . The lab also investigates non-canonical amino acid incorporation and FAIR data principles in biomolecular simulations.

Zhiyuan Liمشاهده پروفایل

استاد

• Program Analysis
• Compiler Design
• Parallel Computing
• +۶ مورد دیگر

Zhiyuan Li is a Professor in the Department of Computer Sciences at Purdue University's College of Engineering. His primary research and teaching focus on program analysis, transformation, and run-time management for high-performance computing and multicore systems, as well as reliable software for networked embedded systems. Professor Li teaches graduate-level courses including CS502: Compiling and Programming Systems and CS591RS1: Research Seminar for First-year Graduate Students. Office: LWSN 3154H Contact: li@cs.purdue.edu Phone: +1 765-494-7822 Professor Li's research spans multiple areas within computer science, with particular emphasis on compiler design, program analysis, and parallel computing. His work addresses fundamental challenges in enabling efficient execution of applications on modern parallel architectures, including multicore processors and large-scale distributed systems. He has made significant contributions to techniques for data dependence analysis, loop parallelization, array privatization, and memory optimization in compilers. His research also extends to reliable software development for embedded and sensor network systems, where resource constraints and reliability requirements present unique challenges. Professor Li's publication record demonstrates consistent contributions to top-tier conferences and journals in computer science, particularly in the areas of parallel computing, compiler optimization, and high-performance numerical methods. His work shows a progression from foundational compiler techniques to applications in scientific computing domains such as computational fluid dynamics for jet engine noise simulation. This interdisciplinary approach connects low-level program analysis with real-world engineering applications requiring petascale computing resources. Principal Investigator for NSF/PetaApps project on jet engine noise simulation Principal Investigator for Intel-sponsored research on data dependence profiling Extensive service on program committees for major conferences including ICS, PPoPP, and LCTES Professor Li has been actively involved in mentoring graduate students through research projects and course instruction. His jet engine noise simulation project specifically mentions training three Ph.D. graduate students and involving undergraduate research assistants. As coordinator for the first-year graduate research seminar, he plays a significant role in guiding new students through the transition to graduate research work in computer science. His laboratory work focuses on developing compiler techniques and runtime systems for parallel and high-performance computing. The research infrastructure includes implementations in GCC for fast data dependence profiling and support for SIMD/SSE instructions, demonstrating practical applications of theoretical compiler techniques.