جست‌وجوی استاد و پژوهشگر

Claire Le Gouesمشاهده پروفایل

استاد

• Software Engineering
• Program Analysis
• Defect Repair
• +۵ مورد دیگر

Claire Le Goues is a Professor of Computer Science at Carnegie Mellon University, primarily affiliated with the Software and Societal Systems Department (S3D) within the School of Computer Science (SCS). She serves as the Associate Department Head for Faculty within S3D and leads the squaresLab research group. Le Goues also co-directs the REUSE@CMU summer program and teaches software engineering and program analysis at undergraduate, master's, and PhD levels. Her research spans software engineering and programming languages, with a particular focus on how to construct, maintain, evolve, improve/debug, and assure high-quality software systems. Le Goues has made significant contributions to automated program repair, program analysis, and defect detection. Her work often bridges theoretical foundations with practical applications, addressing real-world challenges in software development and maintenance. Le Goues' recent publications demonstrate a clear trend toward integrating large language models and generative AI with traditional software engineering techniques. Her research examines how these technologies can enhance program repair (BatFix, AdverIntent-Agent), vulnerability detection (Interpretable Vulnerability Detection Reports), and testing (LWDIFF for WebAssembly). This represents an evolution from her earlier foundational work in program repair (GenProg) toward leveraging contemporary AI advancements. She has mentored numerous students through her squaresLab research group and has been instrumental in developing educational programs that prepare the next generation of software engineers. Le Goues is also known for her advocacy for double-blind review processes in academic conferences, having implemented this approach when co-chairing the Symposium for Search-Based Software Engineering in 2014.

Jinqiu Yangمشاهده پروفایل

دانشیار

• software reliability
• quality assurance of machine learning systems
• autonomous vehicles
• +۵ مورد دیگر

Jinqiu Yang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. Her research focuses on improving software reliability and quality assurance, particularly in the context of machine learning systems and autonomous vehicles. She leads active research projects in software testing, automated program repair, and mining software repositories, with strong connections to both academic and industrial applications. Her research interests span software reliability, quality assurance of machine learning systems including autonomous vehicles, software testing, automated program repair, text analytics of software artifacts, and mining software repositories. She has developed novel approaches for testing deep learning libraries, evaluating robustness in autonomous driving systems, and tracking the evolution of static code warnings. Her work bridges traditional software engineering with emerging challenges in AI systems, addressing critical issues of reliability and safety in complex software environments. Yang's recent publications (2021-2025) demonstrate a clear trajectory toward AI/ML system reliability, with increasing focus on autonomous vehicles, concept drift detection, and security aspects of large language models. Her work spans both theoretical foundations and practical applications, often involving empirical studies of real-world systems and development of practical tools to address identified challenges. ACM SIGSOFT Distinguished Paper Award Dr. Yang actively mentors graduate students and is currently recruiting Master's and PhD candidates. She has secured significant research funding including NSERC Discovery Grants (2019-2025), Gina Cody Research and Innovation Fellowship (2024-2026), and participation in the NSERC CREATE Program SE4AI (2021-2026). Her research is supported by multiple grants including NOVA – FRQNT-NSERC PROGRAM (2024-2027) and Volt-Age Seed Grant (2024-2026). She leads research in the O-RISA Lab at Concordia University, focusing on reliability and security aspects of intelligent software systems. Her team collaborates with industry partners including IBM, where she previously worked at IBM Watson Research Lab and IBM CAS, bringing practical experience to her academic research.

Andreas Hoppermannمشاهده پروفایل

استاد

• Product Development
• Design Methodology
• Fluid Power Engineering
• +۱ مورد دیگر

Prof. Dr.-Ing. Andreas Hoppermann serves as Professor of Design Theory at Niederrhein University of Applied Sciences within the Department of Engineering and Computer Science since 2009. He leads the Fluid Power and Tribology Laboratory, conducting research at the intersection of mechanical product development and tribological systems. His academic foundation includes a Mechanical Engineering degree and doctorate from RWTH Aachen University, where his dissertation investigated surface design and material selection for hydraulic components. Prior to academia, he worked as a research group leader in tribology at RWTH Aachen and as a project-leading development engineer at Voith Paper. Hoppermann's research centers on product development , design methodology , fluid power engineering , and tribology , with emphasis on technical product optimization, test rig development, and tribological phenomena in industrial applications. His work bridges theoretical modeling with experimental validation in hydrostatic bearing systems and fluid power components. Recent publications demonstrate a concentrated focus on grease-lubricated hydrostatic bearings, exploring non-Newtonian fluid behavior, pressure distribution, and control concepts. This research trajectory reflects growing industrial demand for efficient, maintenance-friendly bearing solutions in mobile machinery and automotive systems. His scientific recognition includes the prestigious Borchers-Plakette award from RWTH Aachen. Borchers-Plakette With over 100 supervised master's theses from 2009-2026, Hoppermann has guided research on hydraulic system optimization, tribological contact analysis, and mechanical design innovations. His BMBF-funded "Stahl-Schnecke" project advanced steel-based worm gear technology as bronze alternatives. Current research focuses on hydrostatic bearing performance and tribological testing methodologies. The Fluid Power and Tribology Laboratory provides experimental facilities for student projects and industry collaborations, featuring test rigs for hydrostatic bearings, tribological contacts, and fluid power systems. This infrastructure supports Hoppermann's applied research approach connecting academic inquiry with industrial problem-solving.

Baishakhi Rayمشاهده پروفایل

دانشیار

• Software Engineering
• Security
• Artificial Intelligence
• +۳ مورد دیگر

Baishakhi Ray is an Associate Professor of Computer Science at Columbia University, working at the intersection of AI, Software Engineering, and Security. She received her Ph.D. from the University of Texas, Austin, and has established herself as a leading researcher in applying artificial intelligence to software engineering challenges. Her educational background includes a Ph.D. from the University of Texas, Austin, which provided the foundation for her research career at the forefront of AI and software engineering. Dr. Ray's research focuses on leveraging artificial intelligence to solve fundamental challenges in software engineering and security. Her work spans multiple areas including code generation with large language models, vulnerability detection, software testing, and program analysis. She has pioneered approaches that combine deep learning with traditional software engineering techniques to create more robust, secure, and efficient software development processes. Her research has practical implications for improving code quality, enhancing software security, and accelerating development cycles through AI assistance. Her recent work demonstrates a strong emphasis on semantic-aware code generation, execution reasoning, and addressing hallucinations in code language models. She has also made significant contributions to evaluating the functionality and security of AI-generated code, identifying critical challenges in the practical adoption of AI for software development. Dr. Ray has received numerous prestigious awards recognizing her contributions to the field: IEEE TCSE Rising Star NSF CAREER award IBM faculty award VMware Faculty award Distinguished Paper awards at FSE'17, ASE'22, and ISSTA'23 ICSME Most Influential Paper award Publications featured in CACM Research Highlights As an Amazon Visiting Academic and active participant in major software engineering conferences, Dr. Ray has established herself as a thought leader in AI for software engineering. Her research has been widely covered in trade media, indicating its relevance and impact on industry practices. She has mentored numerous students through their research and has been instrumental in shaping the next generation of researchers in this interdisciplinary field. Her work demonstrates a consistent focus on bridging theoretical advances with practical applications, ensuring that her research has tangible benefits for the software development community. The trajectory of her publications shows an evolving research agenda that has successfully adapted to the rapidly changing landscape of AI and its applications to software engineering.

Sarah Fakhouryمشاهده پروفایل

پژوهشگر

• Formal Methods
• Software Engineering
• AI-Assisted Programming
• +۵ مورد دیگر

Sarah Fakhoury is a Senior Researcher in the Research in Software Engineering (RiSE) group at Microsoft Research, Redmond. Her work bridges formal methods, empirical software engineering, machine learning, and human-computer interaction to optimize developer cognitive effort in AI-assisted programming tools. Her research focuses on trustworthy AI for code generation , leveraging formal verification to ensure correctness in LLM-generated outputs. Key areas include program comprehension, source code readability, and empirical evaluation of developer-AI interaction. She develops tools like 3DGen for provably correct binary parsers and NL2Fix for natural language-based code repair. Her publications reveal strong trends in formal methods integration with AI (60% of recent work), empirical developer studies (30%), and readability/metrics innovation (10%). Keywords cluster around program verification, LLM evaluation, and cognitive load measurement. ACM/SIGSOFT Distinguished Paper Award (ICPC 2018) Fakhoury actively contributes to the academic community as PC member for ASE, ICSE, and ESEC/FSE. She co-organizes workshops like Muslims in ML at NeurIPS and mentors through SMeW. Her RiSE group collaboration with Shuvendu Lahiri and Madanlal Musuvathi drives Microsoft's trustworthy AI4Code initiatives, focusing on verifiable developer tools.

Christian Winklerمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• User Experience
• +۴ مورد دیگر

Christian Winkler is Professor for AI-based UX optimization and general business administration at Nuremberg Institute of Technology since 2022. With over 25 years of experience spanning entrepreneurship, enterprise architecture, and academia, he brings substantial industry expertise to his academic role. His career includes founding multiple technology companies including an internet service provider (WWL Internet AG) that went public in 1999, Querplex GmbH through management buyout in 2003, and datanizing GmbH as an NLP SaaS provider in 2017. Professor Winkler's research focuses on practical applications of artificial intelligence in business contexts, with particular expertise in natural language processing, user experience optimization, and data-driven marketing strategies. His work bridges theoretical AI research with real-world business applications, emphasizing accessibility of complex technologies. He has published extensively on language models (including BERT and LLaMA implementations), text analysis techniques, and social media data analysis for business insights. Recent publications reveal a strong trend toward optimizing large language models for practical deployment, with significant focus on analyzing user-generated content from social platforms like Instagram and WallStreetBets. His work demonstrates how NLP can extract valuable business intelligence from unstructured data sources while making advanced AI techniques accessible to non-technical business professionals. Winkler teaches E-Commerce, International Marketing Tools - Quantitative Methods, Applied User Experience, and Communication Management, reflecting his interdisciplinary approach that combines technical AI knowledge with business administration expertise. He is an active contributor to the data science community through conference presentations at events like m3 Konferenz, MLsummit, and data2day, as well as educational content for Heise Academy on Python and NLP topics.

Manfred Schochمشاهده پروفایل

استاد

• Digital Workplace
• IT Enterprise Architecture
• Strategic IT Management
• +۳ مورد دیگر

Prof. Dr. Manfred Schoch serves as Professor of Business Informatics at Esslingen University of Applied Sciences' Faculty of Computer Science and Information Technology. He concurrently holds leadership roles at Fraunhofer FIT's Business Informatics branch and directs the FIM Research Institute for Information Management. His career bridges academic research with industrial digital transformation projects across sectors including transportation, finance, and manufacturing. His educational foundation includes: Summa cum laude doctorate from University of Hohenheim focusing on digital workplace communication technologies M.Sc. in Information-Oriented Business Administration and B.Sc. in Business Informatics from University of Augsburg Academic exchange at University of Pittsburgh's Joseph M. Katz Graduate School of Business Prof. Schoch's research centers on three interconnected domains: technology implementation in digital workplaces, strategic IT management and enterprise architecture, and data-driven workflow automation. He employs mixed-method approaches combining quantitative network/survey analysis with qualitative interview techniques to examine technology's organizational impact. Recent work investigates pandemic-driven remote work adaptations, AI integration challenges, and behavior change support systems for workplace stress management. His publication trajectory (2022-2025) reveals intensifying focus on generative AI applications, digital workplace resilience, and ethical technology adoption. Key themes include human-AI collaboration models, technostress mitigation, and CSR-guided digitalization. His work appears in premier venues like European Journal of Information Systems and ICIS proceedings. Professional recognition includes: Hohenheim Teaching Prize (2022) for innovative digital management pedagogy He actively supervises student theses through industry-collaborative projects with companies including DB Fernverkehr and Allianz. His research funding stems from Bavarian consortium initiatives like ForDigitHealth and international partnerships with MIT and Microsoft Research. Current grants focus on sustainable hybrid work models and AI-enhanced decision systems. As FIM Research Institute director, he leads cross-disciplinary teams developing practical digital transformation frameworks. The institute maintains strong industry connections through its Digital Leadership Academy and applied consulting projects addressing real-world challenges in workflow automation and IT-business alignment.

Ashwin Ramمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction
• Wearable Computing
• Augmented Reality
• +۴ مورد دیگر

Ashwin Ram is a postdoctoral researcher at Saarland University's Human-Computer Interaction & Interactive Technologies Lab, under Prof. Jürgen Steimle. He holds a PhD in Computer Science from the National University of Singapore (NUS), advised by Prof. Shengdong Zhao, and a Bachelor's in Electronics Engineering from NIT Trichy. His research focuses on wearable augmented reality, smart glasses, and accessibility, leveraging cognitive and behavioral theories to design intelligent interfaces. Notable contributions include Mindful Moments (DIS '23, Honorable Mention), a mindfulness tool for smart glasses, and a quadruped robot guidance system for visually impaired individuals (CHI '24, Honorable Mention). He has served as an Associate Chair (AC) for UIST 2025 and CHI 2025. His work bridges HCI with wearable computing, exploring topics like video learning optimization (LSVP, IMWUT '21), sound source localization via neural networks (NCC '18), and accelerating Hawkes processes for event modeling (ICML '17 workshop). He collaborates internationally, including a research visit at UCL's Multi-Sensory Devices Group. Key achievements include 17 peer-reviewed publications (Google Scholar, ORCID: 0000-0003-1430-8770) and interdisciplinary projects like semantic floor map-based robot navigation. Beyond academia, he practices Carnatic music, plays guitar, and is fluent in Malayalam, Tamil, and English, with proficiency in French, German, and Hindi.

Sabine C. Langerمشاهده پروفایل

استاد

• Acoustics
• Modeling and Simulation
• Numerical Acoustics
• +۷ مورد دیگر

Prof. Dr.-Ing. Sabine C. Langer is a Full Professor of Acoustics and Director of the Institute of Acoustics at Technische Universität Braunschweig. She holds a PhD in Engineering and has extensive experience in academia, including leadership roles such as President of the Deutsche Gesellschaft für Akustik (DEGA) and Deputy Speaker of the DFG Collaborative Research Center 880 (SFB 880). Her research focuses on acoustics, numerical modeling, aircraft noise reduction, and innovative materials for sound absorption. She has pioneered studies on acoustic black holes, metamaterials, and AI-driven design optimization. Langer’s work also includes contributions to educational platforms, such as developing MATLAB-based sound quality analysis tools and online learning resources for engineering students. Education: Civil Engineering degree (1991–1996, TU Braunschweig), PhD in Engineering (2001, TU Braunschweig). Key positions include W2 Professor for Vibroacoustics (2013–2018) and Junior Professor for Wave Propagation and Building Acoustics (2003–2013). She led the Graduate School at SFB 880 and advised numerous research initiatives in structural acoustics and noise mitigation. Research interests span numerical acoustics, sound quality assessment, and sustainable acoustic design. Her recent work emphasizes AI integration in engineering design, stochastic modeling, and additive manufacturing of acoustic materials. She has published extensively on aircraft cabin noise prediction, vibration isolation, and metamaterial applications. Professional roles include membership in the DIN/VDI Normenausschuss Akustik and the Advisory Board of the Excellence Cluster Hearing4All. She has organized major acoustics conferences, including DAGA 2020 in Hannover, and contributed to standard-setting in noise reduction and vibration technology.

Andrea Barthمشاهده پروفایل

استاد

• Stochastic Partial Differential Equations
• Uncertainty Quantification
• Monte Carlo Methods
• +۴ مورد دیگر

Andrea Barth is a W3-Professor of Computational Methods for Uncertainty Quantification at the University of Stuttgart, leading the Research Group for Computational Methods for Uncertainty Quantification within the Excellence Cluster for Simulation Technology. She holds a Ph.D. from the University of Oslo (2009) and has held positions at ETH Zürich and the University of Stuttgart. Her work focuses on stochastic partial differential equations, numerical methods for uncertainty quantification, and applications in engineering and natural sciences. Education: Ph.D. in Mathematics, University of Oslo (2006–2009) Lecturer/Postdoc at ETH Zürich (2010–2013) Junior Professor at University of Stuttgart (2013–2017) Research Interests: Stochastic PDEs, uncertainty quantification, Monte Carlo methods, Bayesian inverse problems, and numerical analysis of random fields. Her work bridges stochastic analysis and numerical simulations, addressing challenges in modeling and simulating complex systems with uncertainties. Grants & Funding: Principal Investigator in projects like 'Data-Integrated Simulation Science' (ExC 2075) and 'Quantitative Methods for Visual Computing' (SFB/TRR 161). Her research also explores applications in porous media, carbon dioxide storage, and optical flow analysis. Supervision: Advised PhD students including Oliver König, Fabio Musco, and Robin Merkle. Current students focus on topics like deep learning for stochastic PDEs and continuous level Monte Carlo methods.

• Medical Biometry
• Health Services Research
• Mental Health
• +۵ مورد دیگر

Anne Daubmann is a Research Fellow at the Institute of Medical Biometry and Epidemiology within the Faculty of Medicine at the University Medical Center Hamburg-Eppendorf (UKE) . Her work focuses on Medical Biometry and Health Services Research , with significant contributions to Mental Health , Multiple Sclerosis , and Primary Care domains. Her research spans: Digital health interventions for cancer patients Nutritional strategies in MS management Family-oriented mental health policies AI reliability in medical guidelines Resuscitation training methodologies Scientific Awards: Certificate in Medical Biometry (2020) from GMDS and IBS-DR She holds memberships in the International Biometric Society, German Region (IBS-DR) . Her methodological expertise includes statistical analysis in clinical trials and health outcomes evaluation .

Volker Rodehorstمشاهده پروفایل

استاد

• Computer Vision
• Image Analysis
• 3D Reconstruction
• +۵ مورد دیگر

Univ.-Prof. Dr.-Ing. habil. Volker Rodehorst is a full professor of computer vision at Bauhaus-Universität Weimar, holding positions in both the Faculty of Media and Faculty of Civil Engineering. His research focuses on photogrammetric computer vision, image analysis, 3D reconstruction, and structural health monitoring with applications in civil infrastructure inspection and urban modeling. He leads projects like ev.AI.luate and InfraCloud, leveraging AI and UAS technologies for infrastructure assessment. Education: PhD (2003): Technical University of Berlin, Faculty of Civil Engineering & Applied Geosciences Habilitation (2013): TU Berlin, Faculty of Electrical Engineering & Computer Science Computer Science Diploma (1994): TU Berlin Research Interests: UAS-based structural inspection using multi-view stereo and deep learning Crack detection and segmentation in concrete structures Automated building age estimation for energy modeling Flight path planning optimization for complex structures Integration of computer vision into BIM workflows Publications: Recent work emphasizes robust algorithms for crack detection (Omnicrack30k benchmark), UAS flight path optimization, and semantic segmentation challenges in bridge inspections. Key contributions include MVCrackViT and CISOL datasets advancing structural analysis methodologies. Awards: Best Academic Performance Prize (1994) - TU Berlin ISPRS Presidential Citation (2008) for WG III/2 leadership Grants & Labs: Leads Bauhaus' 3D-RealityCapture-ScanLab and coordinates EU projects like AISTEC-PRO. Active in developing modular solutions like smoodPLAN for infrastructure inspection. Teaching: Offers courses in photogrammetric computer vision, geodesy, and parallel systems. Supervises PhD students in structural health monitoring and computer vision.

Ayush Tewariمشاهده پروفایل

استادیار

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +۳ مورد دیگر

Ayush Tewari is an Assistant Professor at the University of Cambridge. Previously, he was a postdoctoral researcher at MIT CSAIL under Bill Freeman, Josh Tenenbaum, and Vincent Sitzmann, and completed his Ph.D. at the Max Planck Institute for Informatics under Christian Theobalt. His research focuses on visual perception, developing methods to infer 3D structured representations from images and videos, aiming to bridge the gap between human perceptual capabilities and machine learning systems. Key research interests include neural rendering, inverse rendering, 3D reconstruction, and generative models. Notable contributions include advancements in Neural Radiance Fields (NeRF), diffusion models for inverse problems, and human-centric perception studies. His work has been published in top venues such as SIGGRAPH, CVPR, ICCV, and NeurIPS. Recent research trends emphasize ambiguity-aware inverse rendering, stochastic inverse problem solving using diffusion models, and integrating forward models for 3D scene inference. His work on Diffusion with Forward Models (NeurIPS 2023) proposes a novel framework for solving inverse problems without direct supervision. Awards: Best Paper Honorable Mention at BMVC 2022 (VoRF: Volumetric Relightable Faces). Labs/Projects: Core contributor to the DFM (Diffusion with Forward Models) project, advancing 3D scene understanding via probabilistic methods.

Bernd Sturmfelsمشاهده پروفایل

استاد

• Algebraic Geometry
• Combinatorics
• Commutative Algebra
• +۴ مورد دیگر

Bernd Sturmfels is a leading mathematician serving as Director of the Max Planck Institute for Mathematics in the Sciences in Leipzig since 2017. He is also Professor Emeritus of Mathematics, Statistics, and Computer Science at the University of California, Berkeley, and holds honorary professorships at the Technical University of Berlin and the University of Leipzig. His research bridges pure and applied mathematics, with foundational contributions to algebraic geometry, combinatorics, and computational biology. Education: Sturmfels earned dual Ph.D. degrees in 1987 from the University of Washington and Technische Universität Darmstadt, followed by an honorary doctorate from Goethe University Frankfurt in 2015 and additional honorary doctorates from the University of Bern (2023) and the University of Chicago (2024). Research Interests: His work spans algebraic geometry , combinatorics , commutative algebra , algebraic statistics , convex optimization , and computational biology . He explores deep connections between abstract algebraic structures and practical applications in statistics, optimization, and the life sciences. Publications and Trends: With over 300 research articles and 11 books, his recent work (2022–2025) focuses on advanced topics like Grassmannian geometry, tropical implicitization, quantum chemistry applications, and algebraic statistics. His research increasingly integrates computational methods with theoretical insights, addressing problems in machine learning, phylogenetics, and optimization. Awards and Honors: Sturmfels has received numerous prestigious awards, including: George David Birkhoff Prize in Applied Mathematics (2018) SIAM von Neumann Lecturership (2010) Humboldt Senior Research Prize (2007–2008) David and Lucile Packard Fellowship (1992–1997) Fellowships of the AMS and SIAM Membership in the Berlin-Brandenburg Academy of Sciences and Humanities Mentoring and Grants: He has supervised 60 doctoral students and numerous postdocs, with many securing positions at leading institutions. His mentoring philosophy emphasizes diversity and excellence, as highlighted in his Notices of the AMS article. Funding sources include the NSF, DARPA, and the German National Science Foundation (DFG). Labs and Teams: At the Max Planck Institute, he leads the Nonlinear Algebra group, fostering interdisciplinary collaboration between mathematics and the sciences. His team focuses on developing algebraic methods for data analysis, optimization, and theoretical physics.

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

Dr. Katrin Vogt is a researcher at the University of Konstanz, specializing in neuroethology and sensory systems. Her work focuses on understanding recurrent neural circuits governing state-dependent behavior, particularly in olfactory systems of Drosophila larvae. She leads a subproject investigating hunger-dependent serotonergic modulation in the antennal lobe, collaborating with a doctoral student. Her research bridges vertebrate and invertebrate models to identify conserved network principles across species. Dr. Vogt is part of a DFG-funded interdisciplinary team examining recurrent circuits' roles in flexible behavioral responses to environmental changes. Her research interests include neural circuitry modulation, sensory integration, and behavioral plasticity. Key projects involve analyzing how sensory inputs are modulated by internal states like hunger, and how recurrent connections enable adaptive responses. She has contributed to studies on visual and olfactory memory formation, multisensory integration, and navigational strategies in Drosophila. Publications highlight work on dopamine signaling in taste punishment, social behavior in larvae, and cross-species odor coding principles. Her findings aim to uncover fundamental mechanisms underlying sensory-driven behavior and neural plasticity in both vertebrates and invertebrates.