جست‌وجوی استاد و پژوهشگر

• Computational Mathematics
• Partial Differential Equations
• Finite Element Methods
• +۴ مورد دیگر

Jichun Li is a Professor in the Department of Mathematical Sciences at the University of Nevada Las Vegas, with a prolific research career spanning computational mathematics, image processing, and computer vision. His work bridges theoretical mathematics with practical applications in medical imaging, environmental science, and biometrics. Li's research interests center on computational mathematics with particular focus on partial differential equations and finite element methods, alongside significant contributions to image processing and computer vision. His work demonstrates a unique integration of mathematical theory with practical applications, particularly in medical imaging where his techniques enable improved early cancer diagnosis through advanced lesion segmentation. In environmental science, his research on land surface albedo dynamics provides critical insights into climate change impacts in sensitive regions like the Tibetan Plateau. His recent work on face recognition with synthetic data addresses contemporary challenges in biometric security systems. Analysis of Li's publication trends reveals a strategic evolution from foundational mathematical research toward interdisciplinary applications. While maintaining strong theoretical contributions in computational mathematics, particularly in PDEs and finite element methods, he has increasingly focused on medical imaging applications since 2020, developing novel techniques for cancer diagnosis and ultrasmall object detection in CT scans. His 2022-2025 publications show growing emphasis on synthetic data applications, particularly in face recognition challenges, demonstrating adaptability to emerging AI trends. Professor Li maintains an extensive collaborative network, with frequent co-authorship patterns suggesting mentorship relationships with researchers like Bo Yan, Weimin Tan, Guannan Chen, and Encai Zhang across multiple publications. His work appears in leading journals including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Multimedia, and Computational Mathematics and Applications, reflecting both the theoretical depth and practical relevance of his research.

Leon Derczynskiمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Misinformation Detection
• +۳ مورد دیگر

Leon Derczynski is an Associate Professor of Computer Science at the IT University of Copenhagen , with a dual role as Principal Research Scientist/LLMSEC at NVIDIA . He leads the Strømberg NLP research group and coordinates NLP South at ITU, while also being affiliated with the Machine Learning group. Specializes in Natural Language Processing , Machine Learning , and LLM security Focus on Misinformation detection , Clinical text mining , and Danish language technology Research grants include Verif-AI (2.9M DKK), ClinRead (544K DKK), and LITHME (EU COST action, €11K). He has coordinated major projects like COMRADES and PHEME , and contributed to uComp and TrendMiner . Scientific recognition includes the University of Sheffield Exceptional Contribution Award (twice), WEBIST Best Student Paper award , and FP7 funding . His technical work includes the garak.ai LLM vulnerability scanner and generalised-brown clustering library. Actively supervises students and maintains numerous GitHub repositories (86 public projects) related to NLP, machine learning, and computational linguistics. He has delivered keynotes and guest lectures , including at Innopolis University (Russia) and PET (Danish Security and Intelligence Service).

Len Dawsمشاهده پروفایل

استاد مدعو

• Machine Learning
• Adversarial Machine Learning
• Noisy Label Learning
• +۳ مورد دیگر

Len Daws serves as an Adjunct Professor in the Department of Computer Science at Rowan University's College of Science & Mathematics while concurrently holding a Sr. Engineering Manager position at Lockheed Martin in Moorestown, NJ. With over 12 years of teaching experience across New Jersey state and community colleges, he is currently in his sixth year instructing the Computers & Society course at Rowan University, focusing on large-scale software system development and integration. His research specializes in machine learning robustness under adversarial conditions, particularly addressing noisy label learning challenges and cybersecurity threats in AI systems. Key contributions include modeling label noise dependencies across multiple annotators, developing contributor-aware defenses against backdoor attacks, and analyzing fundamental trade-offs between attack strength and detectability in adversarial settings. Analysis of his publication history reveals a consistent trajectory toward securing machine learning pipelines against sophisticated threats, with recent work emphasizing labeler-dependent noise modeling and spectral ranking techniques for truth extraction from unreliable sources. His scholarship bridges theoretical machine learning with practical defense mechanisms for real-world AI deployment.

Shahana Ibrahimمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۵ مورد دیگر

Shahana Ibrahim is a tenure-track Assistant Professor at the University of Central Florida under the AI Initiative, holding a joint appointment in the Department of Electrical and Computer Engineering and Computer Science. Her research develops provable methods for robust machine learning systems with applications in critical real-world scenarios. Education: Ph.D. in Electrical and Computer Engineering, Oregon State University (advised by Dr. Xiao Fu) Prior industry experience: System Validation Engineer at Texas Instruments (2012-2017) and NVIDIA GPU intern (2018) Her research spans machine learning, signal processing, and optimization with core expertise in weakly supervised learning, tensor decomposition, and stochastic algorithms. She focuses on enhancing AI reliability through theoretical guarantees for noisy data environments, particularly addressing label noise, incomplete annotations, and structured factorization challenges. Her work bridges signal processing theory with modern AI to solve practical problems in data quality and system robustness. Recent publications (2023-2025) reveal strong thematic consistency in handling imperfect supervision. Key trends include crowdsourced label modeling, instance-dependent noise characterization, and tensor/matrix completion techniques. Her approach uniquely integrates signal processing perspectives with deep learning, emphasizing identifiability conditions and geometric regularization to extract reliable patterns from corrupted data. Scientific Awards: Outstanding PhD Dissertation Award from EECS, Oregon State University (2024) Dr. Ibrahim actively mentors graduate researchers including Faizul and Grey, who co-authored her ICIP 2025 and IEEE CAMSAP 2023 publications. She secured the AI-BTO DARPA grant (December 2024) for physics-informed machine learning research on intrinsically disordered proteins. Current funding supports multiple RA/TA positions for PhD students in her lab. She serves on program committees for AISTATS, AAAI AI for Social Impact, and WiML at NeurIPS. Her research group develops end-to-end learning frameworks for noisy data environments, with active projects funded by DARPA focusing on biomedical applications and robust AI validation. The lab maintains strong industry connections through NVIDIA and Texas Instruments collaborations.

• Machine Learning
• Climate Science
• Energy Systems
• +۳ مورد دیگر

Sancho Salcedo Sanz is a Full Professor at the Universidad de Alcalá, affiliated with the Signal Theory and Communications Department and the GHEODE Research Group. His work focuses on applying machine learning and optimization techniques to energy systems, climate science, and environmental modeling. He holds PhDs from Universidad Complutense de Madrid (2019) and Universidad Carlos III de Madrid (2002). Key research interests include deep learning for energy price prediction, spatio-temporal climate analysis, and hybrid models for renewable energy forecasting. His GHEODE group develops optimization algorithms for network design and distributed systems. Recent publications highlight advancements in extreme weather prediction, smart grid optimization, and explainable AI for environmental monitoring. He has pioneered methodologies like Autoencoder-based flow analogues for heatwave reconstruction and multi-method ensembles for energy demand modeling. Labs/Teams: Leader of the GHEODE Group, specializing in modern heuristics and network design. Collaborates extensively on interdisciplinary projects combining AI with environmental and engineering applications.

Mayank Goswamiمشاهده پروفایل

دانشیار

• Algorithms
• Computational Geometry
• Theoretical Machine Learning
• +۱۲ مورد دیگر

Mayank Goswami is an Associate Professor at the Department of Computer Science, City University of New York (CUNY) Queens College and Graduate Center. He earned his Ph.D. in Applied Mathematics and Statistics from Stony Brook University, advised by Joe Mitchell and David Gu, followed by a post at the Max-Planck Institute for Informatics in Germany. His research spans Algorithms , Computational Geometry , Theoretical Machine Learning , and Conformal Geometry , with a focus on geometric optimization, data structures, and adversarial robustness in AI. His work addresses problems like diverse solution design, Teichmuller maps, and noise-tolerant learning models. Publications highlight trends in Geometric algorithms for NP-hard problems (FCT 2025, ICALP 2025) Theoretical ML for manifold topology and adversarial robustness (TMLR 2025, AISTATS 2022) Efficient data structures for similarity search and sorting (WADS 2024, APPROX 2024) He has received a Best Paper Award at FCT 2025 and NSF grants for projects on geometric solution diversity (CCF-1751847) and dynamic optimality conjectures (CCF-1910873). His students include Paul Cesaretti and GiBeom Park, and he organizes the Queens College CS Colloquium and Fall Workshop on Computational Geometry (FWCG 2018, 2024–2025).

Justin Downsمشاهده پروفایل

مدرس

• Computer Vision
• Artificial Intelligence
• Geospatial Analysis
• +۱ مورد دیگر

LCDR Justin Downs is a faculty member in the Computer Science Department at the United States Naval Academy, teaching courses including Computer Networks (IC322), Software Engineering (IC470), and Human Data Interaction (SD322). He earned his MS in Computer Science from the Naval Postgraduate School with specialization in computer vision and AI. His research focuses on computer vision techniques for object detection in noisy environments and geospatial-temporal analysis of satellite data using TensorFlow. His work at the National Reconnaissance Office involved managing machine-learning truth labels for national imagery and developing novel ground-truth collection methods. With operational experience as an Air Defense Coordinator aboard USS Nimitz and as a Navy Search and Rescue pilot, his work bridges technical computing and defense applications. He maintains active research in AI/ML applications for defense systems.

Alvaro Gonzalez-Jimenezمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Medical Imaging
• +۳ مورد دیگر

Alvaro Gonzalez-Jimenez is a Post-Doc Researcher at University Hospital of Basel and a Research Associate at the Applied Artificial Intelligence (AAI) Research Lab at Lucerne University of Applied Sciences and Arts (HSLU). He completed his PhD in Biomedical Engineering at the University of Basel in 2025 with Summa Cum Laude distinction. His academic journey includes a Master's degree in Computer Science from Grenoble Institute of Technology and a Bachelor's degree from the University of Sevilla. His educational background includes: 2021-2025: PhD in Biomedical Engineering, University of Basel ("Learning Dermatology with Noisy and Limited Data") 2019-2020: MSc in Computer Science, Grenoble Institute of Technology 2014-2015: BSc in Computer Science, Budapest University of Technology and Economics (ERASMUS) 2013-2018: BSc in Computer Science, University of Sevilla Gonzalez-Jimenez's research focuses on the intersection of machine learning and dermatology, with particular emphasis on developing methods that can learn effectively from noisy and limited medical data. His work spans several key areas including: Robustness in AI systems for medical applications Hyperbolic geometry for medical anomaly detection Medical image segmentation with noisy labels Dermatology-focused AI applications His recent publications demonstrate a strong trend toward addressing critical challenges in medical AI, particularly in dermatology. His work frequently explores innovative approaches to handle limited and noisy data in medical contexts, with a growing emphasis on geometric deep learning and robust optimization techniques. His research has significant implications for making AI-assisted dermatological diagnosis more accessible and reliable, especially in resource-limited settings. His scientific achievements include: Best Paper Award at MICCAI 2023 Early Acceptance (Top 9%) at MICCAI 2025 for "Is Hyperbolic Space All You Need for Medical Anomaly Detection?" Early Acceptance (Top 11%) at MICCAI 2024 for "PASSION for Dermatology" Gonzalez-Jimenez is actively involved in several research projects including PASSION (focusing on dermatology in Sub-Saharan Africa), SelfClean (for data cleaning), and T-Loss (for robust medical image segmentation). He co-organizes the first Hyperbolic Learning for Medical Imaging (HyperMI) tutorial at MICCAI 2025, reflecting his leadership in emerging AI methodologies for medical applications.

Anne Göhringمشاهده پروفایل

پژوهشگر

• Computational Linguistics
• Natural Language Processing
• Machine Translation
• +۵ مورد دیگر

Anne Göhring is an Academic Associate at the University of Zurich, working within the Language, Technology and Accessibility research team at the Institute of Computational Linguistics. With over a decade of experience in computational linguistics and natural language processing, she has established herself as a specialist in machine translation, sign language processing, and German language analysis. Her work bridges theoretical linguistics with practical applications, particularly focusing on accessibility technologies and resource-poor languages. Her educational background includes: 2001-2010: Studies in Spanish Linguistics and Literature, Computational Linguistics 2006-2007: Erasmus study year at Universidad Complutense Madrid Dr. Göhring's research spans multiple domains of computational linguistics with a strong emphasis on practical applications. She has made significant contributions to machine translation systems, particularly for hybrid approaches and low-resource languages like Quechua. Her recent work has increasingly focused on sign language processing, developing corpora and translation systems to improve accessibility. She also conducts important research in semantic role labeling, sentiment analysis, and German language processing, with applications ranging from social media analysis to veterinary text mining. Analysis of her recent publications reveals a clear trajectory toward accessibility-focused NLP research, particularly in sign language technologies. While maintaining expertise in German language processing and machine translation, her 2023-2024 work shows a strong emphasis on sign language corpora development (SwissSLi) and pose-based identification systems. Earlier work focused more on semantic analysis, sentiment detection, and cross-lingual processing, demonstrating remarkable versatility across NLP subfields. Dr. Göhring has been actively involved in numerous research projects: 2011-2014: SNSF project SQUOIA on Hybrid Machine Translation 2015: CTI project SentiSpider on expectation-based sentiment analysis 2016-2018: What's up, Switzerland? corpus project 2018-2022: Sentiment inference research 2021-2024: NCCR Evolving Language Project: IMAGINE 2022-2026: Flagship IICT Throughout her career, Dr. Göhring has supervised numerous student projects and taught courses ranging from introductory computational linguistics to specialized seminars on machine translation and NLP for medicine. Her teaching portfolio demonstrates commitment to both foundational knowledge and cutting-edge applications in the field. She is an active member of the Language, Technology and Accessibility research group at the University of Zurich, collaborating extensively with researchers like Manfred Klenner, Sarah Ebling, and Martin Volk on diverse NLP challenges.

Yangming Liمشاهده پروفایل

پژوهشگر ارشد

• Image Analysis
• Machine Learning
• Mathematical Modeling
• +۷ مورد دیگر

Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.

Hubert Brücklمشاهده پروفایل

استاد

• Thin Films
• Magnetism
• Sensors
• +۳ مورد دیگر

Hubert Brückl is a Professor and Head of the Department for Integrated Sensor Systems at the University for Continuing Education Krems. He holds a PhD and habilitation in Physics from the University of Regensburg and has held key roles at institutions like the Technical University of Darmstadt, IFW Dresden, and the AIT Austrian Institute of Technology. His research focuses on thin films, magnetism, sensors, and micro/nanotechnology, with significant contributions to machine learning applications in materials science. Brückl's scientific career includes leadership in over 15 funded research projects, such as the development of AI-based corrosion monitoring systems and advanced magnetic sensors. He has authored numerous publications in high-impact journals, including Nature Scientific Reports , Physical Review Applied , and IEEE Transactions on Magnetics . His work spans interdisciplinary areas, combining physics, engineering, and data science to create innovative sensor technologies. Current projects emphasize AI-driven predictive modeling of materials properties and high-precision magnetic sensor fabrication. Brückl's lab integrates cutting-edge facilities for nanotechnology and sensor development, collaborating with industry partners like Siemens and aerospace firms. His research addresses challenges in sustainable materials, biomedical diagnostics, and industrial monitoring systems.

• Deep Learning
• Robustness in Machine Learning
• Label Noise
• +۴ مورد دیگر

Erik Englesson is a Researcher at the Division of Robotics, Perception and Learning at KTH Royal Institute of Technology. His work is supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP-AI/MLX). He holds a PhD from KTH Royal Institute of Technology, focusing on label noise in image classification from an aleatoric uncertainty perspective. His research interests center on robustness and uncertainty quantification in deep learning, particularly addressing label noise and combining aleatoric and epistemic uncertainties. He teaches Probabilistic Graphical Models (DD2420) at KTH. Recent publications explore topics like gradient-based explanation methods, autoencoder parameterization, and noise-robust classification strategies. His work frequently intersects with computer vision and theoretical deep learning principles. Englesson collaborates with prominent researchers like Hossein Azizpour and receives funding from strategic initiatives in AI and autonomous systems. His research bridges foundational theory with practical applications in healthcare imaging and robust model design.

Andrea M. Tonelloمشاهده پروفایل

استاد

• Power Line Communications
• Wireless Communications
• Embedded Communication Systems
• +۱۲ مورد دیگر

Andrea M. Tonello is a Full Professor at the Institute of Networked and Embedded Systems, University of Klagenfurt, Austria, where he chairs the Embedded Communication Systems Lab. He previously held positions at the University of Udine, Italy, where he was an Associate Professor and founded the Wireless and Power Line Communication Lab (WiPLi Lab). His research spans power line communications, wireless systems, embedded communications, smart grids, and machine learning applications in signal processing. Doctor of Engineering, University of Padova (1996) Doctor of Research, Telecommunications, University of Padova (2003) His research interests focus on next-generation communication systems, including power line and wireless networks, signal processing, machine learning for communications, UAV systems, and smart grid technologies. He has made significant contributions to PLC channel modeling, full-duplex communications, and information-theoretic learning for communication systems. His work integrates theoretical innovation with practical implementation in real-world networks. The most recent publications highlight a strong trend toward integrating machine learning and information theory into communication systems, particularly in power line and wireless networks. Themes include f-divergence based classification, mutual information estimation, neural decoding (MIND), noise-robust receivers, and topology-aware machine learning for PLC quality prediction. There is also a notable focus on UAV control, full-duplex PLC, and digital pre-distortion techniques for high-speed converters. IET 2016 Premium Award Best Paper Award, ISPLC 2016 Best Student Paper Award, ISPLC 2016 Aerospace Best Paper Award, 2018 Best Paper Award, ISPLC 2021 Best PhD Dissertation Award, 2019 IEEE ComSoc Distinguished Lecturer (2018) Two Awards from IEEE ComSoc TC-PLC (2019) University of Klagenfurt Technology Scholarships (2019) Dr. Tonello has supervised numerous PhD and Master’s students, including notable advisees such as Nunzio A. Letizia, Davide Righini, and Babak Salamat. He has led over 10 institutional and multiple industrial research projects with a total funding exceeding 20 million euros. He played a key role in promoting international academic collaboration, including Erasmus agreements, joint PhD programs with INSA Rennes and Ecole Polytechnique de Grenoble, and a joint master’s program with the University of Klagenfurt. He founded and led the WiPLi Lab at the University of Udine, which received around 3 million euros in funding and involved over 60 researchers and students. He also founded WiTiKee s.r.l., a spin-off company specializing in PLC for smart grids. Currently, he chairs the Embedded Communication Systems Lab at the University of Klagenfurt, focusing on next-generation networked and embedded communication technologies.

Ivan Bratkoمشاهده پروفایل

استاد

• Machine Learning
• Knowledge-Based Systems
• Qualitative Modelling
• +۵ مورد دیگر

Ivan Bratko is a Professor of Computer Science at the University of Ljubljana's Faculty of Computer and Information Science. He founded the Artificial Intelligence Laboratory in 1985 and served as its head until 2017, remaining an active member. Until 2002, he also directed the AI group at the Jožef Stefan Institute. His academic journey includes B.Sc., M.Sc., and Ph.D. degrees in electrical engineering and computer science, all from the University of Ljubljana. Bratko's research spans machine learning, knowledge-based systems, qualitative modeling, intelligent robotics, heuristic programming, and computer chess. His work focuses on learning from noisy data, combining learning with qualitative reasoning, constructive induction, Inductive Logic Programming, and applications in medicine and dynamic system control. He has authored over 200 scientific papers and influential books including Prolog Programming for Artificial Intelligence (third edition, 2001), KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems (MIT Press, 1989), and Machine Learning and Data Mining: Methods and Applications (Wiley, 1998). His publication portfolio demonstrates consistent contributions to AI, with recent work emphasizing argument-based machine learning, qualitative modeling applications, and medical AI systems. These publications reveal strong interdisciplinary connections between theoretical AI and practical applications in environmental science, healthcare, and robotics. Fellow of the European Coordinating Committee for Artificial Intelligence (ECCAI) Member of the Slovene Academy of Arts and Sciences (SAZU) Former editorial board member of Artificial Intelligence , Machine Learning , Journal of AI Research , and other leading journals Co-founder and first chairman of the Slovenian AI Society (SLAIS) Bratko has secured numerous research projects including ARRS programs on artificial intelligence (2009-2020), the PARKINSCHECK project for Parkinson's disease detection, and European projects like X-MEDIA and XPERO. His laboratory serves as the central hub for AI research at the University of Ljubljana, fostering collaborations across medical, environmental, and industrial domains. He has mentored numerous researchers and maintained active collaborations through visiting positions at institutions including Edinburgh University, University of New South Wales, and Delft University of Technology.

Berrin Yanıkoğluمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Handwriting Recognition
• +۱۱ مورد دیگر

Berrin Yanıkoğlu is a Professor in the Computer Science and Engineering Program at Sabancı University's Faculty of Engineering and Natural Sciences, Istanbul, Turkey. She is the founding Director of the Center of Excellence in Data Analytics (VERIM) and actively contributes to research and academic leadership. PhD in Computer Science, Dartmouth College, USA (1993) MS in Computer Science, Dartmouth College, USA (1992) B.E. in Computer Engineering and B.A. in Mathematics, Boğaziçi University, Turkey (1988) Galatasaray High School (1983) Her research lies at the intersection of computer vision and machine learning, with a strong focus on handwriting recognition (including Ottoman scripts), biometric verification and privacy, image understanding (face, plant, medical), and natural language processing applications such as hate speech detection and text summarization. She explores both supervised and self-supervised learning paradigms, particularly variational contrastive learning methods. The recent publications reflect a trend toward robust, semi-supervised, and unsupervised methods for learning from noisy or limited-label data, especially in the domains of document analysis and image understanding. Her work integrates deep learning with variational inference to improve model performance in real-world scenarios with imperfect data. She serves as an Editor for the Turkish Journal of Electrical Engineering and Computer Science , contributing to scholarly communication in her field. Prof. Yanıkoğlu advises graduate students, including PhD candidate Mehmet Can Yılmaz, and leads research initiatives at VERIM. She has previously worked at Rockefeller University, Xerox Imaging Systems, and IBM Almaden Research Center, bringing extensive industry and academic experience to her role. She leads the Center of Excellence in Data Analytics (VERIM) at Sabancı University, which supports interdisciplinary research in data science, machine learning, and AI applications across domains such as healthcare, document analysis, and social media monitoring.