جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Compressed Sensing
• Privacy-Preserving Techniques
• +۴ مورد دیگر

Jia Liang is a researcher at Henan Polytechnic University's School of Electrical Engineering and Automation, with a focus on Machine Learning , Compressed Sensing , and Privacy-Preserving Techniques . His work bridges Computer Science and Signal Processing , particularly in Radar Imaging and Medical Image Analysis . Key Collaborations: Di Xiao, Ying Luo, Qun Zhang, Hui Huang Technical Expertise: Federated Learning, SAR Imaging, Compressive Sensing, Adversarial Learning His research emphasizes secure data processing in IoT and cloud environments, with recent innovations in cross-disciplinary applications like biosignal analysis for cysticercosis diagnosis . Publications span top venues including IEEE Transactions on Aerospace Systems and Remote Sensing . Notable trends include privacy-preserving machine learning for federated systems and 3D radar imaging of rotating targets, alongside medical imaging solutions for chest radiographs and optical coherence tomography .

• Geometric Deep Learning
• Spectral Shape Analysis
• Shape Matching
• +۷ مورد دیگر

Riccardo Marin is a Postdoctoral Researcher at the Computer Vision Group, Technical University of Munich (TUM), where he conducts advanced research in geometric deep learning and 3D shape analysis. He is affiliated with the Department of Computer Science within the College of Engineering. His research focuses on Spectral Shape Analysis , Shape Matching , Geometric Deep Learning , and Virtual Humans . He develops novel methods for 3D human modeling, registration, and generative modeling using neural fields and diffusion techniques. Recent publications highlight his work in creating realistic 3D avatars from single images, scalable neural registration pipelines, and continuous surface representations for deformable human modeling. His research bridges computer vision, machine learning, and computer graphics. ELLIS Member Alexander von Humboldt Foundation Alumni Marie Skłodowska-Curie Alumni Riccardo Marin has been supported by prestigious fellowships and research networks. He contributes to open science through public repositories and code releases. He is actively involved in developing cutting-edge tools for spectral shape analysis and 3D human modeling, with applications in virtual reality, animation, and human-computer interaction. He is associated with research projects and software including FARM (Functional Automatic Registration Method), NICP (Neural ICP), NSF (Neural Surface Fields), and Diff-FMAPs, indicating strong technical and algorithmic contributions in the field of 3D geometry and deep learning.

Xiao Zhangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Statistical Machine Learning
• Convex Optimization
• +۴ مورد دیگر

Xiao Zhang is a tenure-track faculty member at the CISPA Helmholtz Center for Information Security and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). He obtained his Ph.D. in Computer Science from the University of Virginia in 2022 under Prof. David Evans, following an M.S. in Statistics from the same institution and a B.S. in Mathematics from Tsinghua University. Research Focus Zhang's research spans foundational and applied aspects of trustworthy machine learning, with emphasis on: Security vulnerabilities in ML systems (adversarial attacks, data poisoning) Robustness guarantees for learning algorithms Optimization methods for deep learning Privacy-preserving techniques Multimodal and physical-world applications His recent work focuses on developing theoretically-grounded defenses against emerging threats to AI systems. Publication Trends Zhang's 15 most recent publications (2019-2025) demonstrate consistent focus on adversarial machine learning, with growing emphasis on: 1) Physical-world attacks/defenses, 2) Multimodal model security, and 3) Certified robustness guarantees. The work consistently appears at top-tier venues (NeurIPS, ICLR, CCS). Academic Activities He actively recruits students and collaborators for trustworthy ML research, with open positions for PhD candidates and research assistants. No awards or specific grant information is documented in the provided materials.

Pierluigi D'Acuntoمشاهده پروفایل

استادیار

• Structural Engineering
• Architectural Design
• Computational Design
• +۴ مورد دیگر

Prof. Pierluigi D'Acunto holds the Rudolf Mössbauer Tenure-Track Assistant Professorship in Structural Design at the Technical University of Munich (TUM), within the TUM School of Engineering and Design. His research focuses on computational methods at the intersection of architecture and civil engineering, with an emphasis on innovative material systems, additive manufacturing, and robotic fabrication. Education: He completed a combined degree in Architecture and Civil Engineering at the University of Pisa (2007), earned a Master of Architecture from the Architectural Association in London (2012), and received his doctorate from ETH Zurich (2018). He previously served as a postdoctoral fellow at ETH's Chair of Structural Design before joining TUM in 2021. Research Interests: D'Acunto explores form-force relationships in structures, equilibrium-based design frameworks (e.g., Vector-based 3D Graphic Statics), and the integration of AI/machine learning with structural design tools. His work addresses sustainability through material-efficient construction and circular economy principles. Awards: ETH Medal (2019), IASS Design Excellence Award (2016), Rotary Foundation Scholarship (2011) Key Projects: Digital Bamboo Pavilion, CantiBox robotic timber assembly, Injection 3D Concrete Printing methodologies His publications emphasize data-driven design, fabrication-aware strategies, and the application of advanced robotics in construction. He collaborates closely with industry partners to translate theoretical frameworks into real-world prototypes and built projects.

Carlos Cinelliمشاهده پروفایل

استادیار

• Causal Inference
• Statistics
• Machine Learning
• +۴ مورد دیگر

Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington. He is also a Data Science Fellow at the eScience Institute and Affiliate Faculty at the Center for Statistics and the Social Sciences. He obtained his Ph.D. in Statistics from the University of California, Los Angeles, where he was advised by Chad Hazlett and Judea Pearl. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences. Key interests include: Causal inference challenges in social and health sciences Intersections of causality with machine learning and AI Sensitivity analysis for omitted variable bias Robust statistical methods for observational studies Instrumental variables and Mendelian randomization Generalizability of experimental findings His publications demonstrate a consistent focus on developing practical sensitivity analysis tools and advancing causal methodology, with recent work emphasizing applications in machine learning and econometrics. Honors include: Best Paper Award at SBE 2024 in Econometrics UCLA Dissertation Year Fellowship (2020) He actively advises PhD students and has received research funding from: NSF/MMS Royalty Research Fund He leads development of several open-source software packages for sensitivity analysis and maintains active collaborations with researchers at UCLA and other institutions.

Armeen Taebمشاهده پروفایل

استادیار

• Selective Inference
• Multiple Testing
• Graphical Models
• +۷ مورد دیگر

Armeen Taeb is an Assistant Professor in the Department of Statistics at the University of Washington . Previously, he was a postdoctoral fellow at ETH Zürich under the ETH Foundations of Data Science, mentored by Peter Bühlmann. He earned his PhD in Electrical Engineering at Caltech under Venkat Chandrasekaran's supervision. Research Interests : His work bridges optimization and statistics , focusing on Graphical and latent-variable modeling Provably optimal causal model learning False positive error control in non-traditional settings Domain adaptation Applications in physical sciences Article Trends : His publications span causal inference (2022-2025), graphical models (2017-2025), convex optimization (2018-2025), and statistical robustness (2020). Recent work (2025) addresses extremal graphical modeling and selective inference challenges. Scientific Awards : ETH Zürich Foundations of Data Science Postdoctoral Fellowship (2019-2021) Caltech Resnick Institute Fellowship (2016-2018) W. P. Carey & Co. Prize for Applied Mathematics (2020) Caltech Graduate Fellowship (2013-2014) Grants : National Science Foundation DMS-2413074 (PI), University of Washington Royalty Research Fund (PI). Service : President of the Institute of Mathematical Statistics New Researcher Group; co-organized IMS New Researchers Conferences (2024-2025).

• Haptic Interfaces
• Human-Computer Interaction
• Multimodal Interfaces
• +۵ مورد دیگر

Jeremy R. Cooperstock is a Professor at McGill University in Montreal, Canada, with a distinguished career spanning over three decades in human-computer interaction, haptics, and multimodal interface research. His work has established him as a leading figure in the development of practical haptic communication systems and accessibility technologies. Professor Cooperstock's research focuses on creating novel interfaces that enhance user experience through touch, sound, and visual feedback. His laboratory has pioneered significant advancements in vibrotactile feedback systems, telepresence technologies, and audio-haptic integration for improved user awareness and communication. His work bridges fundamental research in human perception with practical applications across multiple domains including accessibility, social interaction, and immersive environments. His recent publications demonstrate a strong trajectory in developing practical haptic applications for everyday use, with notable contributions to web accessibility frameworks, social touch interfaces, and context-aware haptic feedback systems. Cooperstock's research has evolved from foundational work in multimodal telepresence to increasingly applied research addressing real-world challenges in human communication and interaction. Throughout his career, Cooperstock has mentored numerous graduate students who have become significant contributors in the field. His collaborative approach is evident in his extensive publication record with students and colleagues across multiple institutions. His research group has consistently produced high-impact work published in top-tier venues including IEEE Transactions on Haptics, CHI, UIST, and WHC.

• Latent Variable Models
• Dynamic Latent Variable Models
• Latent Class Methods
• +۸ مورد دیگر

Augustin Kelava is a Professor at the Department of Quantitative Methods, Eberhard Karls University of Tübingen. He has held this position since 2018 and leads the Methods Center as Managing Director. Previously, he was Professor at the Hector Institute for Empirical Educational Research (2013-2018) and Junior Professor at Technical University of Darmstadt (2011-2013). PhD in Psychology (Goethe University Frankfurt, 2009) Diploma in Psychology (Goethe University Frankfurt, 2004) Kelava specializes in latent variable modeling, machine learning in social sciences, and educational research. His work spans dynamic latent class models, Bayesian regularization techniques, and prediction of human behavior using intensive longitudinal data. He contributes to psychometric theory (e.g., item response theory extensions) and applies these methods to diverse fields including sports science and emotion regulation. Editor of "Testtheorie und Fragebogenkonstruktion" (3rd ed., Springer, 2020) Key researcher in the Cluster of Excellence "Machine Learning in Science" Active in methodological conferences (FGME 2017, SEM 2019) Review activities for 20+ journals and foundations including Psychometrika, DFG, and SNSF His recent publications focus on integrating machine learning with psychometrics, addressing identifiability in complex models, and evaluating personality assessment validity for large language models. He collaborates with researchers across psychology, education, and computational fields.

• Differential Geometry
• Geometric Analysis
• Mathematical Relativity
• +۱ مورد دیگر

Dr. Armando Cabrera Pacheco is a Research Fellow at the University of Tübingen, affiliated with the Tübingen AI Center and Department of Computer Science within the Faculty of Mathematics and Natural Sciences. His interdisciplinary work bridges geometric analysis, relativity theory, and machine learning foundations. Education: Ph.D. in Mathematics, University of Miami, USA (2016) M.Sc. in Mathematics, University of Miami, USA (2012) Bachelor's degree in Mathematics, Universidad Autónoma de Yucatán, México (2009) Research Focus: His research explores the geometric structure of spacetime in general relativity, stability theorems for black holes, and Riemannian manifold extensions. Recently, he investigates foundational machine learning concepts like loss aggregation geometries and axiomatic learning frameworks, creating novel intersections between differential geometry and algorithmic theory. Publication Trends: Early work (2016-2021) centered on geometric analysis of relativistic spacetimes, positive mass theorems, and asymptotically hyperbolic manifolds. Recent publications (2023-2024) demonstrate a pivot toward machine learning theory, developing geometric frameworks for loss functions and aggregation algorithms while retaining physics-inspired mathematical rigor. Awards: Teaching Award for Mathematics for Machine Learning, University of Tübingen (Winter 2022-2023) Teaching & Leadership: He has taught graduate courses in Riemannian Geometry and Mathematics for Machine Learning, receiving recognition for pedagogical excellence. Actively organizes international conferences including "Mathematical Relativity: A Riemannian Approach" (CIMAT-Guanajuato, 2019) and "Encuentro Iberoamericano de Geometría Diferencial" (Granada, 2019).

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +۱۷ مورد دیگر

Prof. Dr. Antonio Krüger is the CEO of the German Research Center for Artificial Intelligence (DFKI) and Director of the Ubiquitous Media Technology Lab (UMTL) at the Saarland Informatics Campus in Saarbrücken, Germany. He is a leading researcher in human-computer interaction, virtual and augmented reality, multimodal interfaces, and intelligent systems. His research interests span a broad range of topics including virtual reality (VR), augmented reality (AR), gesture interaction, haptic feedback, EEG and eye tracking, gamification, cognitive load estimation, brain-computer interfaces, and smart environments. His work emphasizes user-centered design, perceptual illusions, and real-world applications in domains such as automotive, healthcare, and education. His recent publications (2023–2024) demonstrate a strong focus on advancing VR interaction through techniques like hand redirection, synthetic gesture generation for vehicles, implicit intent recognition using multimodal sensing, and personalized object referencing. These works reflect trends toward seamless, natural, and adaptive interfaces that leverage AI, perception, and real-time feedback. Prof. Krüger supervises a large team of researchers and students, including Mansi Sharma, Amr Gomaa, André Zenner, and Martin Feick, indicating active mentorship and collaborative research leadership. His lab, UMTL, develops innovative technologies in immersive environments, wearable systems, and intelligent user assistance, contributing to both academic knowledge and practical applications.

Michael Mumaمشاهده پروفایل

استاد

• Robust Data Science
• Signal Processing
• Machine Learning
• +۵ مورد دیگر

Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.

Marie Brandenburgمشاهده پروفایل

استاد

• Combinatorics
• Discrete Geometry
• Tropical Geometry
• +۶ مورد دیگر

Jun.-Prof. Dr. Marie Brandenburg is a Junior Professor for Combinatorics at Ruhr-Universität Bochum's Faculty of Mathematics, leading the Research Team Stump within the Algebra division. Her work focuses on polyhedral geometry and its applications in machine learning, optimization, economics, and game theory. She holds an office at IB 2/155 in the Department of Mathematics. Research Interests : Combinatorics & Discrete Geometry, Tropical/Algebraic Geometry, Polyhedral Geometry, Machine Learning applications, Optimization, Game Theory, and Economic Modeling. Her interdisciplinary approach bridges abstract geometry with practical fields like AI and market design. Teaching : Recently taught a seminar on Discrete Geometry (Summer 2025). Supervises graduate students in algebraic combinatorics and related areas. Labs/Teams : Member of the Research Team Stump, part of Ruhr University's Algebra group. Collaborates with the Floer Center of Geometry and the HDM@RUB student laboratory.

Frank Haußerمشاهده پروفایل

استاد

• Numerical Mathematics
• Scientific Computing
• Mathematical Modeling
• +۴ مورد دیگر

Prof. Dr. Frank Haußer is a Professor at Beuth University of Applied Sciences Berlin in the Department II Mathematics - Physics - Chemistry. He serves as the academic advisor for the Applied Mathematics program and teaches courses including Numerical Mathematics, Mathematical Methods of Digital Image Processing, and Computational Engineering. His consultation hours are held during semesters and by appointment, with availability both in-person and online. Haußer's research focuses on: Numerical mathematics and scientific computing Mathematical modeling with applications in MATLAB/Octave Machine learning for medical imaging and insect monitoring Digital image processing techniques for biomedical applications Partial differential equations and computational engineering methods He has authored a textbook on mathematical modeling with MATLAB/Octave and leads interdisciplinary projects at the intersection of mathematics and technology. Analysis of his 15 most recent publications shows strong emphasis on: Medical imaging algorithms (especially retinal OCT analysis) Nanostructure dynamics and material science Computational physics and finite element methods Machine learning applications in biology and medicine Advanced mathematical modeling techniques His work consistently bridges theoretical mathematics with practical engineering applications. Haußer actively supervises student research, including: Machine learning for insect classification and localization Medical image processing algorithms Computational methods in engineering Finite element analysis applications Optimization and simulation techniques He has guided over 30 bachelor's and master's theses since 2009. His research projects include: KInsekt (2020-2023): AI-based insect monitoring funded by BMUV BeCRF (2015-2017): Medical image quality validation funded by BMWi QM ROCT (2013-2015): Automated OCT quality management funded by IFAF These interdisciplinary collaborations involve institutions across Germany.

• Computer Networking
• Bio-inspired Computing
• Network Management
• +۶ مورد دیگر

William Donnelly is a professor at Waterford Institute of Technology in Waterford, Ireland, with a distinguished research career spanning over three decades. His work demonstrates a clear evolution from traditional telecommunications and network management to bio-inspired computing approaches, and more recently to precision agriculture applications and computer graphics. His primary research interests focus on Computer Networking , Bio-inspired Computing , and Precision Agriculture . Early in his career, he specialized in telecommunications management networks (TMN) and service management. He then pioneered work applying biological concepts like chemotaxis and quorum sensing to networking problems, developing bio-inspired routing protocols and service management frameworks. In the 2010s, his research shifted toward precision agriculture applications, particularly dairy farming, where he applied wireless sensor networks, fog computing, and edge analytics to monitor animal behavior and optimize farming practices. Most recently, he has transitioned into computer graphics, focusing on real-time rendering techniques with publications in 2023-2024 on spatiotemporal sampling methods. Analysis of his publication trends reveals a researcher who consistently identifies emerging technological challenges and applies innovative cross-disciplinary approaches to solve them. His work demonstrates remarkable adaptability, moving from telecommunications standards to biological metaphors, then to agricultural technology applications, and finally to computer graphics - always maintaining a focus on optimization, efficiency, and practical implementation. Throughout his career, Donnelly has maintained strong collaborative relationships, particularly with Sasitharan Balasubramaniam and Dmitri Botvich, with whom he has co-authored numerous papers exploring bio-inspired networking approaches. His recent collaborations in computer graphics include Alan Wolfe, Judith Bütepage, and Jon Valdés.

• Statistical Relational Artificial Intelligence
• Human-aware AI
• Explainable AI
• +۱۲ مورد دیگر

Tanya Braun is a Junior Professor in the Institute of Computer Science at the University of Münster, Department of Mathematics and Computer Science. She leads the Data Science research group, focusing on statistical-relational AI, human-aware AI, and text understanding. Her work bridges formal AI methods with real-world applications in healthcare, digital humanities, and public sector systems. Education: Bachelor's and Master's in Computational Informatics, Hamburg University of Technology Doctorate in Computer Science, University of Lübeck (2020), thesis: 'Rescued from a Sea of Queries - Exact Inference in Probabilistic Relational Models' Her research centers on probabilistic inference in relational domains , with a focus on lifted inference techniques that exploit symmetries to scale reasoning. She investigates human-aware AI , particularly how AI systems can reconcile learned models with human expectations to improve explainability and trust. Her work on text understanding addresses challenges in data-scarce settings such as digital humanities, where traditional large language models fail. She has developed methods for identifying and enriching subjective content descriptions, topic modeling in specialized domains, and feedback-driven model improvement. The 15 most recent publications highlight a strong trajectory in lifted inference, model compression, privacy-preserving AI, and explainability . Her work integrates formal AI foundations with practical concerns in high-stakes domains like healthcare. She frequently publishes in top venues such as AAAI, IJCAI, ECAI, and Artificial Intelligence, often in collaboration with Ralf Möller, Marcel Gehrke, and Jan Speller. Scientific Awards: No specific awards listed in the provided text. Tanya Braun actively advises students and leads the HAPPI project, which focuses on human-AI model reconciliation using lifted probabilistic inference. She has supervised multiple theses and mentored researchers including Jan Speller (PostDoc), Nazlı Nur Karabulut, and Sagad Hamid. She has secured funding from the Ministry of Culture and Science of North Rhine-Westphalia for her research. She is deeply involved in academic service: serving as program co-chair for KI 2025, guest-editing special issues in journals like Künstliche Intelligenz and Annals of Mathematics and Artificial Intelligence , and organizing major conferences including ICCS and KR. Labs and Teams: She leads the Data Science Group at the University of Münster, which conducts research in AI, probabilistic modeling, and data science. The group is actively involved in teaching and mentoring students in advanced AI topics.