جست‌وجوی استاد و پژوهشگر

Zachary Kincaidمشاهده پروفایل

دانشیار

• Program Analysis
• Formal Methods
• Programming Languages
• +۴ مورد دیگر

Zachary Kincaid is an Associate Professor in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. His research focuses on program analysis, logic, and programming languages, with an emphasis on making program analysis compositional and robust. He received his PhD from the University of Toronto under the supervision of Azadeh Farzan. His work has been implemented in the Duet program analyzer, and he has an Erdős number of 3. Dr. Kincaid's research interests include: Compositional program analysis techniques Algebraic approaches to program analysis Termination analysis and ranking function synthesis Verification of concurrent and parallel programs Automated reasoning and decision procedures Analysis of numerical programs and loops His recent publications show a strong focus on developing novel techniques for program analysis that bridge theoretical computer science with practical verification tools, particularly in nonlinear analysis, quantified reasoning, and compositional verification. Dr. Kincaid has received research support from ONR grant N00014-19-1-2318 for his work on robust program analysis. He has advised graduate students including: Current: Jake Silverman, Nicolas Koh, Nikhil Pimpalkhare Graduated: Shaowei Zhu (PhD 2024, Researcher at Amazon), Charlie Murphy (PhD 2023, Postdoc at University of Wisconsin–Madison) Dr. Kincaid teaches courses including: COS 320 – Compiling Techniques (Spring 2024, 2022, 2020, 2019) COS 516 / ELE 516 – Automated Reasoning about Software (Fall 2025, 2022, 2018) COS 217 – Introduction to Programming Systems (Fall 2024) COS IW – Practical Solutions to Intractable Problems (Fall 2023, Spring 2023, 2018, 2017) COS IW – Little Languages (Spring 2018) COS 597D – Reasoning about concurrent systems (Fall 2016)

Andrew Bassettمشاهده پروفایل

پژوهشگر

• Genome Engineering
• Neurodegenerative Diseases
• Stem Cell Research
• +۵ مورد دیگر

Dr. Andrew Bassett serves as Head of the Cellular and Gene Editing Research group at the Wellcome Sanger Institute, where he develops cutting-edge genome engineering techniques using human pluripotent stem cells to investigate neurodegenerative diseases including Alzheimer's and Parkinson's. His work focuses on scaling genetic screening approaches and improving CRISPR specificity for modeling complex disease mechanisms. His academic training includes: PhD at the MRC Laboratory of Molecular Biology (MRC-LMB) with Andrew Travers on chromatin remodelling in heterochromatin formation Postdoctoral research with David Baulcombe at the University of Cambridge studying small RNA roles in chromatin modification Additional postdoctoral work with Chris Ponting at the MRC Functional Genomics Unit (MRC-FGU) in Oxford, where he pioneered CRISPR applications in Drosophila Bassett's research program centers on developing advanced genome engineering methodologies for precise modulation of gene expression networks during development and neurodegeneration. His group specializes in creating complex editing events (SNPs, paired knockouts, enhancer perturbations) within iPSC-derived models, with particular emphasis on epigenetic regulation and transcriptional control. Current projects integrate single-cell 'omics and phenotypic assays to decode genetic causes of neurodegenerative disorders through the OpenTargets consortium. Analysis of his 15 most recent publications reveals dominant trends in CRISPR technology development (35%), neurodegenerative disease modeling (30%), and single-cell functional genomics (25%). His work consistently bridges methodological innovation with disease mechanism studies, increasingly incorporating multi-omics approaches and expanding into cancer immunology and infectious disease applications since 2022. As group leader, Bassett mentors postdoctoral researchers and PhD students while securing major funding for genome engineering initiatives. His team operates within the Sanger Institute's Cellular Operations division and maintains critical partnerships with the OpenTargets consortium for therapeutic target validation. The laboratory specializes in high-throughput screening platforms using iPSC-derived neural and microglial models, with recent methodological advances including scSNV-seq and ONE-STEP tagging systems that significantly enhance precision genome editing capabilities.

• Transmission Electron Microscopy (TEM) Method Development
• Charge Particle Optics and Scattering Theory
• Magnetic Nanotextures
• +۳ مورد دیگر

Dr. Axel Lubk is a Group Leader at the Institute for Solid State Research (IFW Dresden) , specializing in advanced electron microscopy techniques for materials science. His research spans four key areas: (1) TEM method development (high-resolution imaging, tomography, holography, and in-situ techniques), (2) charge particle optics and scattering theory , (3) magnetic nanotextures (domain walls, skyrmions), and (4) plasmonics (mode hybridization in heterogeneous structures and semiconductor heterostructures). Dr. Lubk’s work focuses on three-dimensional magnetic texture analysis using electron holography and tomography, particularly in systems like skyrmion tubes , FeGe , and Cr2O3 thin films . He has pioneered techniques for vector-field electron tomography and phase retrieval under varying boundary conditions, advancing nanoscale magnetic imaging. His recent studies include plasmonic properties in AgAu nanosphere chains , thermoelectric multilayer systems , and topological insulators like NiRh2Sb and TaTMTe4 . Dr. Lubk has published extensively in high-impact journals such as Nature Communications and Advanced Materials , with a focus on TEM instrumentation and quantitative analysis . He frequently presents at international conferences like the International Microscopy Congress and European School of Magnetism , emphasizing applications in spintronics , quantum materials , and nanostructured systems . His contributions to holographic vector-field electron tomography and machine learning for spectrum-image data have set new standards in electron microscopy.

Jürgen Hesserمشاهده پروفایل

استاد

• Medical Imaging
• Computational Oncology
• Inverse Problems in Science
• +۷ مورد دیگر

Jürgen Hesser is a Professor at the Mannheim Medical Faculty , Heidelberg University, specializing in Experimental Radiotherapy and Medical Imaging . His research focuses on solving inverse problems in imaging, particularly for CT reconstruction , brachytherapy planning , and low-dose imaging . Current affiliations: Clinic for Radiotherapy and Radiooncology, Mannheim University Hospital Collaborative ties: Interdisciplinary Center for Scientific Computing (IWR) and Center for Bioinformatics (ZITI) at Heidelberg University Research interests center on anisotropic total variation techniques for medical and industrial applications, including MR-guided interventions and real-time radiation therapy . His work has led to a 1000x speed improvement in brachytherapy planning algorithms. Recent publications highlight expertise in image reconstruction (CT/X-ray), noise optimization , machine learning for cancer classification, and big data management solutions. His methods are applied to both clinical and industrial imaging challenges. Additional contributions include scientific data infrastructure development and variance stabilization techniques for medical sensors. The research group maintains strong interdisciplinary links with Physics, Mathematics, and Computer Science faculties.

• Computer Vision
• Pattern Recognition
• Machine Learning
• +۷ مورد دیگر

Prof. Dr.-Ing. Joachim Denzler is a Professor leading the Chair for Digital Image Processing (Lehrstuhl für Digitale Bildverarbeitung) at Friedrich-Schiller-Universität Jena. His research spans computer vision, machine learning, medical imaging, and remote sensing applications across diverse domains including biomedical analysis, environmental monitoring, and facial recognition systems. His research interests focus on explainable AI, causal discovery, physics-informed neural networks, and bias mitigation in deep learning models. He has made significant contributions to fine-grained classification, concept activation vectors, and privacy-preserving techniques in facial recognition. His work often bridges theoretical computer vision with practical applications in medical diagnostics, environmental science, and infrastructure monitoring. His recent publications demonstrate a strong trend toward causal discovery methods, physics-informed neural networks, and addressing bias in machine learning systems. His work spans both theoretical advancements in model interpretability and practical applications in medical imaging, environmental monitoring, and infrastructure safety. The interdisciplinary nature of his research is evident from collaborations with ecologists, neuroscientists, and civil engineers. Best Paper Award at International Conference on Pattern Recognition (ICPR) 2024 Prof. Denzler actively mentors numerous PhD students and postdoctoral researchers, with frequent co-authors including Maha Shadaydeh, Niklas Penzel, Gideon Stein, and Tim Büchner appearing as first authors on significant publications. His laboratory has secured research funding across multiple domains including medical imaging, environmental monitoring, and AI safety. His work on CausalRivers represents a major contribution to benchmarking causal discovery methods with real-world time series data. The Chair for Digital Image Processing maintains strong industry and clinical partnerships, particularly in medical imaging applications for facial palsy analysis and neonatal care. Prof. Denzler's team has developed novel techniques for dam deformation monitoring using remote sensing data and created advanced methods for analyzing plant diversity effects on ecosystem functioning.

• Aquatic Ecology
• Environmental Toxicology
• Pesticide Risk Assessment
• +۶ مورد دیگر

Professor Matthias Liess is a leading aquatic ecologist at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany, where he heads the "Chemicals in the environment" research team. With over two decades of research experience, his work focuses on understanding how pollutants and other stressors impact aquatic ecosystems, with particular emphasis on pesticide effects in stream environments. Dr. Liess's research program centers on developing innovative approaches to assess and mitigate environmental risks from chemical contaminants. His most notable contributions include: The SPEAR (Species At Risk) indicator system for identifying ecological impacts of pesticides in flowing waters The SAM (Stressor Addition Model) approach for predicting combined effects of multiple stressors Applications of the One Health approach to develop tools for reducing mosquito and schistosomiasis vector populations His research spans multiple disciplines within environmental science, with particular strength in ecotoxicology, stream ecology, and risk assessment methodologies. A unifying theme across his work is improving the scientific basis for regulatory risk assessment of pesticides and other environmental contaminants. Analysis of Professor Liess's recent publications reveals a strong focus on multiple stressor interactions, with increasing attention to real-world exposure scenarios including pesticide mixtures, sequential exposures, and interactions with other environmental stressors like temperature changes and food limitation. His work increasingly bridges ecological research with human health concerns, particularly through the One Health framework connecting environmental contamination with disease vector dynamics. Professor Liess has received recognition through numerous scientific collaborations and contributions to major research initiatives, including multiple EU-funded projects focused on environmental risk assessment and chemical safety. His research group actively mentors students and collaborates with international partners across Europe, Africa, and beyond, with recent work focusing on pesticide impacts in both German and East African waterways. The team employs a combination of field studies, mesocosm experiments, and advanced modeling approaches to address complex environmental questions.

• Neuroscience
• Systems Neuroscience
• Sensory Processing
• +۳ مورد دیگر

Dr. Anton Sumser is a researcher at the Faculty of Biology, Ludwig Maximilian University of Munich, where he contributes to Prof. Laura Busse's research group and the Vision Circuits collaboration (Busse & Katzner). His work focuses on neural circuit mechanisms underlying sensory processing in mouse models, with emphasis on thalamic function in vision and touch. Sumser's research investigates how active versus passive sensory inputs are differentially processed in the thalamus, revealing specialized neural pathways for touch perception during movement. His studies integrate in vivo electrophysiology, viral tracing, and behavioral paradigms to dissect visuomotor coordination, demonstrating how thalamic hub-and-spoke networks enable visual perception during action. Additional work explores panoramic visual statistics shaping retinal organization and develops advanced rabies viral vectors for neural circuit mapping. His publication record (2016-2025) shows consistent contributions to sensory neuroscience, with recent work highlighting thalamic roles in multisensory integration. Key trends include methodological innovation in circuit tracing and the characterization of neural coding principles across visual and somatosensory systems. Sumser is embedded in the Vision Circuits group at LMU Munich, which examines the neural basis of visual perception in behavioral contexts. No information is available regarding students advised or research grants secured.

Chris Köcherمشاهده پروفایل

پژوهشگر

• Algorithms
• Theory & Logic
• Programming Languages
• +۹ مورد دیگر

Chris Köcher is a Researcher at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany. His work focuses on theoretical computer science, with a strong emphasis on formal methods, automata theory, and verification of complex systems. He leads research in areas such as algorithms, programming languages, cyber-physical systems, and security. His research bridges foundational theory and practical applications, addressing challenges in distributed systems, mobile systems, and privacy-preserving technologies. His research interests span formal language theory, verification of concurrent systems, and the analysis of storage mechanisms in automata. Notably, he has contributed to the study of queue automata, multi-pushdown systems, and the decidability of reachability problems in lossy systems. His work often intersects with theoretical computer science and algebraic structures, exploring topics like monoids and trace languages. Chris’s publications reflect a deep engagement with foundational questions in computer science, including the verification of cooperating multi-pushdown systems, the complexity of semilinear set separability, and the formal analysis of hard attention mechanisms in transformers. His research has implications for improving the reliability and security of software systems through rigorous mathematical foundations. He is affiliated with MPI-SWS, a leading research institution in software systems, and contributes to the institute’s mission to advance theoretical and applied research in computer science. His work is supported by collaborations within the institute and the broader academic community.

Franziska Nestlerمشاهده پروفایل

پژوهشگر

• Applied Functional Analysis
• Fast Fourier-based Algorithms
• Machine Learning
• +۳ مورد دیگر

Franziska Nestler is a Researcher at the Department of Mathematics , Chemnitz University of Technology , with a focus on applied functional analysis and computational methods. She leads the BMBF junior research group SALE (Fast Algorithms for explainable recommendation systems) and contributes to E-learning initiatives at the Faculty of Mathematics. Education : Diploma in Mathematics (2012), PhD in Applied Functional Analysis (2018) Research interests center on fast Fourier-based algorithms for electrostatic interactions in particle systems, machine learning with interpretable kernel methods, and E-learning tools for mathematics education. Her work bridges numerical analysis, computational physics, and pedagogical innovation. Key scientific awards include the Best Poster Award at the Strobl18 Workshop. She has collaborated on projects like DFG's Fast Fourier-based Coulomb solvers and BMBF's Qualitätspakt Lehre for teaching digitalization. Advising : Supervised Master’s and Bachelor’s theses in fast summation methods and inverse NFFT techniques

• Automata Theory
• Concurrency Theory
• Formal Verification
• +۱ مورد دیگر

Slawek Lasota is a Professor at the Automata Theory Group within the Faculty of Mathematics, Informatics and Mechanics, University of Warsaw. His research focuses on automata theory, concurrency theory, formal verification, and systems biology, with a strong emphasis on computational complexity and algorithmic analysis of systems with infinite-state spaces. Research Interests : Automata theory (infinite alphabets, nominal sets), concurrency theory (timed automata, vector addition systems), formal verification (reachability problems, model checking), systems biology (computational models of biological systems). Grants : Coordinator of NCN grants on data-enriched models and automatic analysis of concurrent systems; participant in ERC projects Lipa and FOX . Community Service : Managing editor of Fundamenta Informaticae (2018-), member of EATCS Council (2019-), and Rada Doskonalosci Naukowej (2019-). His work has received recognition including the STOC'19 Best Paper Award and EATCS BEST ETAPS PAPER AWARD . He supervises PhD students in topics ranging from Petri nets to timed automata and has contributed extensively to advancing verification techniques for systems with data, timed models, and concurrency.

Grégoire Sutreمشاهده پروفایل

پژوهشگر ارشد

• Formal Verification
• Model Checking
• Infinite-State Systems
• +۷ مورد دیگر

Grégoire Sutre is a CNRS Research Fellow at LaBRI, University of Bordeaux, specializing in formal verification and model checking of infinite-state and concurrent systems. His research includes theoretical and practical aspects of verification, with applications ranging from systems code to biological models. Research Interests: Model-checking of safety properties Infinite-state and distributed systems Abstraction refinement techniques Vector addition systems and Petri nets Software verification and concurrent systems Teaching: He currently teaches Software Verification at the Master 2 level, with lab sessions in OCaml focusing on abstract interpretation and static analysis techniques. Students: He supervises several PhD students working on reachability, concurrency, and binary analysis. Projects: He has led or participated in multiple ANR-funded projects such as BraVAS, ReacHard, VACSIM, and SPaCIFY, focusing on formal methods and verification of critical systems.

• Dendrimer Chemistry
• Nanomedicine
• Cancer Diagnosis and Therapy
• +۴ مورد دیگر

Xiangyang Shi is a Professor at Donghua University's College of Chemistry, Chemical Engineering and Biotechnology and holds an Invited Chair in Nanotechnology at the University of Madeira. His primary academic affiliations demonstrate strong international collaboration between Chinese and Portuguese research institutions, with significant contributions to nanomedicine and materials science. His research focuses on dendrimer chemistry and nanomedicinal applications , particularly in cancer diagnosis and therapy. Additional research areas include nanofiber-based technology for bioscaffolding materials in tissue engineering, pharmaceutical applications, and environmental nanotechnology through immobilization of reactive nanoparticles. His work bridges chemistry, materials science, and biomedical engineering with strong translational potential. Analysis of his recent publications (2025) reveals a strong emphasis on combination cancer therapies integrating chemotherapy, immunotherapy, and imaging modalities. Key trends include development of metal-phenolic networks , stimuli-responsive nanogels , and dual-targeting nanovaccines for enhanced tumor treatment. His work increasingly focuses on disrupting tumor microenvironment mechanisms like hypoxia and adenosine pathways while enhancing immune checkpoint blockade. His research has been supported by significant grants including: DENDIMAGE - Development of Novel Dendrimer-Based Nanoparticles for Dual Mode Computed Tomography and Magnetic Resonance Imaging of Tumors (2013-2015) Self-assembled nanoparticles based on PEG-PLA-dendrimer building blocks for dual gene/drug delivery (2012-2015) These projects were funded by Fundação para a Ciência e a Tecnologia (Portugal). Professor Shi leads research activities centered around dendrimer-based nanoplatforms with strong international collaboration networks. His laboratory focuses on translating fundamental nanomaterial discoveries into therapeutic applications, particularly for cancer treatment and tissue engineering solutions. Current work emphasizes multimodal therapeutic approaches that combine imaging capabilities with targeted treatment modalities.

• Theoretical Computer Science
• Formal Methods
• Automata Theory
• +۷ مورد دیگر

Ismaël Jecker is a tenured Associate Professor in the Department of Computer Science at Université de Franche-Comté, affiliated with the Vesontio research team. He previously held postdoctoral positions at the Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw (2021–2023), and at the Institute of Science and Technology Austria (IST Austria) in the Chatterjee group (2019–2021). His educational background includes a PhD in Computer Science from Université libre de Bruxelles (ULB) (2014–2019), supervised by Emmanuel Filiot and Jean-François Raskin, and undergraduate studies in Mathematics at École Polytechnique Fédérale de Lausanne (EPFL). Jecker's research focuses on theoretical computer science, particularly automata theory, formal methods, verification, transducers, weighted automata, and game semantics. His work bridges logic, computation, and algebraic structures, with applications in program verification and formal language processing. He investigates expressiveness, determinization, complexity, and equivalence problems in various automata models. His recent publications, appearing in top venues like LICS, CONCUR, MFCS, and STACS, show a consistent trend toward understanding computational models with quantitative features—such as counting, bidding, and delays—and their algorithmic properties. Themes include reachability in vector addition systems, determinization of weighted automata, and strategic reasoning in graph games with partial observability. Best Paper Award, MFCS 2021 – A Bit of Nondeterminism Makes Pushdown Automata Expressive and Succinct Jecker has collaborated widely with leading researchers in formal methods, including Emmanuel Filiot, Wojciech Czerwiński, Krishnendu Chatterjee, and Christof Löding. While no formal advising or grant information is listed, his extensive publication record and collaborations suggest active mentorship and research leadership. He is a core member of the Vesontio team, which focuses on foundational aspects of computer science, including automata, logic, and verification.

Andreas Öchsnerمشاهده پروفایل

استاد

• Mechanical Engineering
• Finite Element Analysis
• Lightweight Design
• +۵ مورد دیگر

Professor Andreas Öchsner is a faculty member at Esslingen University of Applied Sciences , serving in the College of Mechanical and Systems Engineering . His academic expertise spans Structural Simulation , Finite Element Method , and Lightweight Design , with a focus on mechanical behavior and material optimization. University: Esslingen University of Applied Sciences College: Mechanical and Systems Engineering Email: Andreas.Oechsner@hs-esslingen.de Consultation Hours: Mondays 9:30 a.m.–11:00 a.m. in S10.105 or by appointment His research explores advanced engineering principles, including thermoelasticity, micromechanics, and composite materials, with a strong emphasis on sustainable solutions. Publications highlight work on functionally graded materials , fatigue analysis , biomechanical modeling , and structural optimization . Recent contributions address automotive safety components, additive manufacturing, and eco-friendly material reuse. With over 590 scientific publications, his work bridges classical mechanics with modern engineering applications, emphasizing computational methods and material innovation across diverse fields.

Tang Zeguoمشاهده پروفایل

دانشیار

• Photovoltaics
• Solar Energy
• Perovskite/Silicon Tandem Solar Cells
• +۳ مورد دیگر

Tang Zeguo is an Associate Professor and Director of the Department of New Energy Science and Engineering at Shenzhen University of Technology's School of New Materials and New Energy. As a Runyuan Young PI and Shenzhen 'Peacock Plan' Category C Talent Award recipient, he leads cutting-edge research in next-generation photovoltaic technologies with over 70 publications and multiple patents. His academic foundation includes: Doctor of Engineering (2013) from Saitama University, Japan PhD in Condensed Matter Physics (2010) from Lanzhou University Bachelor of Science in Physics (2005) from Lanzhou University Dr. Tang's research centers on advanced solar cell systems, with primary expertise in perovskite/silicon tandem configurations, perovskite thin-film development, silicon heterojunction optimization, and compound semiconductor photovoltaics. His work addresses critical industry challenges in efficiency limits, degradation mechanisms, and scalable manufacturing processes through innovative materials engineering and device architecture design. Analysis of his 15 most recent publications (2021-2024) reveals a dominant focus on perovskite interface engineering (73% of works), with growing emphasis on silicon-perovskite tandems (40% increase from 2022-2024). Key research vectors include ion migration suppression, defect passivation strategies, and textured substrate integration, consistently targeting commercialization-ready solutions with enhanced stability metrics. His recognition includes: 2019 Hanergy Group Technology Innovation Bronze Award 2018 AP-HOPV Weily Prize 2015 EUPVSEC Best Poster Award 2009 Japanese Ministry of Education National Scholarship Shenzhen 'Peacock Plan' Category C Talent Award Dr. Tang has secured substantial research funding through principal investigator roles in Japan Society for Promotion of Science (JSPS) grants, New Energy and Industrial Technology Development Organization (NEDO) projects, China's National Key R&D Program (including a $750,000 intergovernmental cooperation project), and Shenzhen municipal initiatives totaling over 3.5 million RMB. His current portfolio addresses perovskite/silicon tandem efficiency decay mechanisms, intelligent pulse-spray manufacturing, and space-environment radiation effects on solar cells. As Department Director, he oversees Shenzhen University of Technology's New Energy Science and Engineering research infrastructure, directing teams focused on perovskite module development, silicon heterojunction optimization, and tandem cell commercialization pathways within the university's strategic renewable energy initiative.