جست‌وجوی استاد و پژوهشگر

Victor Akinwandeمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Victor Akinwande is a doctoral researcher at Carnegie Mellon University's Computer Science Department under advisor J. Zico Kolter. His research focuses on robust machine learning systems, causal inference for global health applications, and advancing vision-language models through generative modeling techniques. He holds an MSc in Information Technology (CMU, 2016–2018) and a BSc in Computer Science from the University of Ilorin (2011–2015). His work spans key areas including adversarial robustness, generalization bounds for prompt-based learning, and causal effect estimation in public health. Notable contributions include HyperCLIP (2024), AcceleratedLiNGAM (2024), and foundational work on subset scanning for anomaly detection in health data (2020–2022). Victor has been recognized with an Outstanding Paper Award (ICLR 2024 workshop) and a Distinguished Paper Award Nomination (AMIA 2022). His research bridges theoretical machine learning advancements with practical applications in global health systems and AI safety.

• Computer Vision
• Artificial Intelligence
• Robotics
• +۴ مورد دیگر

Randal C. Nelson is an Associate Professor in the Department of Computer Science at the University of Rochester , where he has been employed since 1988, transitioning from Assistant to Associate Professor in 1994. His research focuses on Computer Vision , Artificial Intelligence , and Robotics , with a particular emphasis on motion analysis, object recognition, and real-time visual systems. Education: Ph.D. in Computer Science from the University of Maryland (1988), thesis on Visual Navigation M.S. in Computer Science from the University of Maryland (1985) B.S. in Physics with Honor from the University of Wyoming (1981), and a second B.S. in Mathematics (1982) Research Interests span Computer Vision , Machine Learning , and Robotics , with key contributions in temporal texture analysis , perceptual grouping hierarchies , and appearance-based 3D object recognition . His work explores how visual behavior can inform intelligent systems, emphasizing efficiency through selective information processing. Recent Publications highlight advancements in tracking objects using temporal cues, recognizing non-rigid motion, and developing robust visual systems for cluttered environments. Collaborations with students like Andrea Selinger, Ramprasad Polana, and Brandon Sanders have produced pioneering studies in 3D recognition and robotic manipulation .

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۳ مورد دیگر

Antonio Norelli is a Researcher at the University of Oxford's Department of Computer Science, specializing in cutting-edge topics within Artificial Intelligence and Machine Learning. His work bridges theoretical advancements with practical applications, including AI in games, scientific discovery, and multimodal learning. He has contributed to projects like Olivaw, an Othello-playing AI, and explores foundational AI concepts such as explanatory learning and equivariant neural networks. Research interests span from reinforcement learning in strategic games to latent space translation and representation learning. He investigates how AI can emulate human-like behavior and advance scientific methodologies through automated discovery and hypothesis generation. His contributions also address challenges in zero-shot learning and cross-modal adaptation without extensive training data. Notable research includes analyzing the human-likeness of AI agents in games and quantifying the capabilities of language models. His publications reflect a focus on both technical innovation and ethical considerations in AI development. No scientific awards, grants, or lab affiliations are publicly documented. He has not listed academic advisees, though his work suggests potential collaboration opportunities in AI and machine learning domains.

Sergio Daviesمشاهده پروفایل

مدرس ارشد

• Spiking Neural Networks
• Neuromorphic Computing
• Computer Network Security
• +۳ مورد دیگر

Dr. Sergio Davies serves as a Senior Lecturer in the Department of Computing and Mathematics at Manchester Metropolitan University, leveraging extensive experience from both academic research and industry leadership. His career spans the University of Manchester's SpiNNaker project, the European Human Brain Project, and strategic roles in hardware/software development before returning to academia in 2019. His educational foundation includes an MSc-equivalent in Telecommunication Engineering from the University "Federico II" in Naples and a Ph.D. in Computer Science from the University of Manchester (2012). MSc-equivalent: Telecommunication Engineering, University "Federico II", Naples Ph.D.: Computer Science, University of Manchester (2012) Davies specializes in spiking neural networks and neuromorphic computing, with pioneering work on the SpiNNaker architecture. His current research focuses on applying these technologies to computer network security and robotics, developing event-driven systems for real-time applications like gesture recognition and intrusion detection. This work bridges theoretical neuroscience with practical engineering challenges, emphasizing low-power, real-time operation. Analysis of his 15 most recent publications (2012-2025) reveals a clear evolution from foundational SpiNNaker development toward applied security and robotics. Recent work integrates event-based vision with neuromorphic hardware for multi-object tracking and gesture recognition, while maintaining core contributions to learning algorithms like STDP. The consistent thread is practical implementation on neuromorphic platforms for security-critical applications. Professional recognition includes: Member of the Institution of Engineering and Technology (MIET) Member of the Institute of Electrical and Electronics Engineers (MIEEE) Fellow of the Higher Education Academy (FHEA) His industry experience as an IT consultant at KPMG and subsequent leadership of hardware/software teams informs his applied research approach. Though specific student advisees aren't listed, his work involves mentoring through the SpiNNaker ecosystem and Human Brain Project collaborations. Current projects focus on neuromorphic security solutions developed through university-industry partnerships. Davies operates within Manchester Metropolitan University's neuromorphic computing research group, maintaining active ties to the original SpiNNaker team at Manchester and the Human Brain Project consortium. His lab focuses on translating theoretical neural models into deployable security and robotics systems using SpiNNaker hardware.

• Data Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Mohsen Farid is a Senior Lecturer in Data Science at the College of Science and Engineering. His research spans interdisciplinary areas including artificial intelligence, machine learning, neurotechnology, and healthcare analytics. He has contributed to advancements in genomics data management, video authentication, and neurotechnological solutions for mental health conditions like PTSD. His work frequently integrates computational methods with real-world applications in healthcare and cybersecurity. Key research outputs include studies on storage-optimized genomics systems (2024), neurotechnological interventions for PTSD (2023), and deep learning analyses of histopathology images (2021). His earlier contributions include gait recognition systems (2018) and semantic mapping of legal arguments (2015). Farid’s publications reflect a strong focus on applied data science and its societal impact, particularly in healthcare, security, and biomedical engineering. While no specific awards or grants are explicitly mentioned, his extensive publication record highlights sustained contributions to interdisciplinary data-driven research. His articles often address challenges in data management, pattern recognition, and ethical applications of AI.

Riley Casperمشاهده پروفایل

استادیار

• Orthogonal Polynomials
• Integrable Systems
• Spectral Theory
• +۴ مورد دیگر

W. Riley Casper is an Assistant Professor of Mathematics at California State University, Fullerton (CSUF). His research focuses on the interplay between noncommutative algebra, algebraic geometry, and integrable systems, with applications to special functions and computational fluid dynamics. He holds a PhD from the University of Washington and dual MS/BS degrees from North Dakota State University. Dr. Casper teaches a wide range of courses at CSUF, including Calculus, Differential Equations, Real/Complex Analysis, Abstract Algebra, and Topology. His work spans both pure and applied mathematics, with recent contributions to matrix-valued differential operators, bispectrality, and prolate spheroidal wave functions. He has collaborated with institutions like Louisiana State University and the University of California, Berkeley on projects blending algebraic methods with computational techniques. Selected publications include studies on fractional differential operators, prolate spheroidal systems, and ocean data assimilation. His research has appeared in journals like Communications in Mathematical Physics and the American Journal of Mathematics. Teaching innovations include advanced problem-solving methodologies and applications of algebraic geometry in undergraduate instruction. Professional service includes organizing conferences (GAP XVI, ICM Satellite) and delivering invited talks at institutions worldwide. His work bridges theoretical mathematics with computational challenges in fluid dynamics and quantum computing.

Jacob Kauffmannمشاهده پروفایل

پژوهشگر

• Unsupervised Learning
• Explainable AI
• Clustering
• +۳ مورد دیگر

Dr. Jacob Kauffmann is a Postdoctoral Researcher at the Technical University of Berlin, specializing in research areas such as Unsupervised Learning, Explainable AI, and Anomaly Detection. His work focuses on enhancing model interpretability in unsupervised techniques and uncovering biases like the Clever Hans effect in machine learning systems. Education: M.Sc. Computer Science, Technische Universität Berlin (2017) Research Interests: Jacob’s research bridges theoretical foundations and practical applications of explainable AI (XAI) in unsupervised learning. He investigates clustering explanations, anomaly detection mechanisms, and kernel-based methods to improve model transparency. His recent work explores dataset shifts via Wasserstein distances and neural network-driven cluster analysis. Publications: Dr. Kauffmann’s articles emphasize XAI advancements, such as explainable anomaly detection and model bias analysis. Key themes include algorithmic transparency, dataset transport phenomena, and deep learning applications in unsupervised tasks. Advising & Grants: No formal advisees listed. Research supported by grants related to XAI and unsupervised learning methodologies (specific grants not detailed).

Thomas Schnakeمشاهده پروفایل

پژوهشگر

• Explainable AI
• Natural Language Processing
• XAI for Quantum Chemistry
• +۳ مورد دیگر

Thomas Schnake is a postdoctoral researcher at the Machine Learning Lab of the Technical University of Berlin and the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds a Ph.D. in Machine Learning from TU Berlin and prior degrees in Mathematics and Scientific Computing from Humboldt University of Berlin. His research focuses on Explainable AI (XAI), Natural Language Processing, and the mathematical foundations of machine learning. He has also gained industry experience at ebuero AG and GFaI e.V. in Berlin. Education: B.Sc. Mathematics & Philosophy, Humboldt University Berlin (2014) M.Sc. Mathematics, Humboldt University Berlin (2018) M.Sc. Scientific Computing, Technical University Berlin (2018) Ph.D. Machine Learning, Technical University Berlin (2024) Research Interests include: Explainable AI for complex domains like quantum chemistry and histopathology Graph neural network interpretability through walk-based explanations High-resolution data synthesis with minimal input Unsupervised anomaly detection in text and energy systems His recent publications (2021-2025) demonstrate contributions to XAI frameworks, graph neural network explanations, and transformer model interpretability. He is affiliated with two prominent institutions and maintains active research in interdisciplinary areas combining mathematics and machine learning.

Laura Dietzمشاهده پروفایل

دانشیار

• Information Retrieval
• Natural Language Processing
• Knowledge Graphs
• +۳ مورد دیگر

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire. She leads the TREMA lab and focuses on text-based machine learning, information retrieval, and environmental data science. Her academic journey includes postdoctoral roles at Mannheim University and the University of Massachusetts, alongside a PhD from the Max Planck Institute for Informatics. Education: PhD in Computer Science (Max Planck Institute, 2011), followed by postdoctoral research at UMass (2012–2015) and Mannheim (2015–2016). Tenure-track appointment at UNH since 2016. Research Interests : IR+NLP: Integrates knowledge graphs, entity retrieval, and machine learning to automatically generate comprehensive articles. This work is supported by an NSF CAREER Award (2019–2023). Watershed Data Science: Analyzes storm event impacts using time-series forecasting, with grants from the Northeast Big Data Hub. Machine Learning: Develops methods for sequential data analysis across domains like social media and environmental science. Grants & Awards : NSF CAREER Award (Utilizing Fine-Grained Knowledge Annotations) Elite Postdoc Program of BW-Stiftung (2016–2019) Best Paper at JCDL 2018 for Entity-Aspect Linking Service & Leadership : Organized HIPstIR Workshop on non-mainstream IR research Coordinated TREC Complex Answer Retrieval Track (2017–2019) Keynote speaker at ECIR, NAACL, and SPIRE Labs & Teams : Head of TREMA Lab at UNH, focusing on query-driven knowledge synthesis and environmental data science. Collaborates with faculty in Natural Resources and the Environment Department.

• French Language Practice
• Romance Studies
• Computer Vision
• +۳ مورد دیگر

Viridiane Fay is a researcher in the Institute of Modern Languages and Romance Studies at the University of Würzburg , specializing in French language practice and applied computational methods. During lecture periods, she holds office hours every Tuesday from 1 p.m. to 2 p.m., with appointments available during semester breaks. Role: French Editor, Language Practice Location: Am Hubland, 97074 Würzburg Contact: viridiane.fay@uni-wuerzburg.de Her research spans two distinct domains: French language pedagogy and computer vision/AI . While her institutional affiliation focuses on Romance Studies, her publication record reveals expertise in: Medical and underwater image processing Transformer-based architectures Federated learning and network optimization Watermarking and security algorithms Multi-modal AI applications Neuroscience signal analysis Recent work includes lightweight segmentation models (e.g., ContextFormer), drone/aerial imagery detection frameworks (CH-YOLO-Lite), and privacy-preserving AI systems (Find). Despite limited biographical details in the scrape, her 30+ publications since 2017 suggest significant technical contributions.

Dirk Hovyمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Social Science
• Ethics and Fairness in AI
• +۳ مورد دیگر

Dirk Hovy is an Associate Professor in the Department of Computing Sciences at Bocconi University. He serves as Scientific Director of BIDSA’s Data and Marketing Insights (DMI) research unit and heads the MilaNLP lab. His work focuses on natural language processing (NLP) and computational social science, with a particular interest in ethics and fairness in AI, large language models, transfer learning, and computational methods for social science. He has organized major conferences such as EMNLP 2017 and workshops on abusive language detection and computational social science. Education: PhD in Computer Science from the University of Southern California (USC), Master’s in Sociolinguistics from Germany. Research interests span NLP techniques like reinforcement learning and adversarial learning, alongside societal impacts of AI. His recent work explores sociodemographic variables in NLP models and their integration with demographic information. Notable contributions include geolocation via social media text, gender bias in NLP systems, and hate speech detection on Twitter. Labs/Teams: Leads MilaNLP and directs BIDSA’s DMI unit. Grants: Received a 2020 ERC Starting Grant to study sociodemographic variables and NLP models.

Michele Mancusiمشاهده پروفایل

پژوهشگر

• Deep Learning
• Source Separation
• Generative Models
• +۳ مورد دیگر

Michele Mancusi is a Senior Research Scientist at Sony and holds a Ph.D. in Computer Science from Sapienza University of Rome, where he remains affiliated with the GLADIA research group under Prof. Emanuele Rodolà. He earned dual degrees in Physics (Quantum Information) from Sapienza, blending computational and theoretical expertise. His research focuses on deep learning applications in audio signal processing, including source separation techniques for music and ecological monitoring. Notable contributions include data-driven methods for marine biodiversity assessment via fish vocalization analysis and innovations in diffusion models for high-resolution audio generation. His work bridges academia and industry, with publications in top venues like ISMIR, ICASSP, and ICLR. Research interests emphasize generative models (autoregressive/diffusion), music technology, and interdisciplinary applications in environmental science. Recent efforts explore coherence-oriented audio representation learning (COCOLA) and inference-time optimizations for music mastering processors (ITO-Master). Publications reflect a trend toward real-world impact, such as accelerating transformer-based translation systems and developing latent diffusion techniques for timbre transfer. Collaborations include cross-disciplinary projects with marine biologists to advance biodiversity tracking through passive acoustic monitoring.

Jean Vaillancourtمشاهده پروفایل

عضو هیئت علمی

• Probabilities
• Stochastic Equations
• Random Games
• +۲ مورد دیگر

Jean Vaillancourt is an Affiliated Professor at the Department of Decision Sciences, HEC Montréal. He holds a PhD in Mathematics from Carleton University. His research focuses on stochastic processes, probability theory, and data mining applications in social media analysis. Key areas include martingale theory, random walks in random environments, and social network dynamics. He has contributed to methodologies for estimating multi-modal histograms and predicting network restructuring following node removals. His work integrates mathematical rigor with computational techniques, appearing in venues like Stochastic Analysis and Applications and Neural, Parallel and Scientific Computations . Active on platforms like Academia.edu and ResearchGate, his research bridges theoretical probability with practical applications in data science and network analysis. Education: PhD in Mathematics, Carleton University Research Interests: Probabilities, stochastic equations, social media analysis, data mining, and network science. His work emphasizes statistical methods for pattern extraction, centrality-based network prediction, and measure-valued processes. Articles Overview: Recent publications explore martingale convergence theories, random walk periodicity detection, and cascading removals in social networks. These studies highlight his dual focus on foundational probability theory and applied data-driven analysis. Grants & Advising: Supervision activities span the last five years, though specific grants or advisees aren't detailed here. His work on road database automation and edge detection showcases interdisciplinary impact. Labs & Teams: Collaborates across disciplines, integrating mathematical modeling with computational tools. Active in HEC's research ecosystem, contributing to formal concept analysis and SAR imagery processing initiatives.

Mete Ahishaliمشاهده پروفایل

پژوهشگر

• Machine Learning
• Remote Sensing
• Medical Imaging
• +۴ مورد دیگر

Mete Ahishali is a Postdoctoral Researcher affiliated with the School of Forest Sciences at the University of Eastern Finland's Faculty of Science, Forestry and Technology. His research focuses on advanced machine learning techniques applied to remote sensing, medical imaging, and environmental monitoring. He is a core member of the Global Ecosystem Health Observatory (GEHO) research group since January 2024. His technical expertise spans hyperspectral image analysis, SAR image classification, domain adaptation networks, and medical image restoration. Key contributions include developing neural network architectures for tree mortality mapping, anomaly detection in industrial systems, and COVID-19 classification using X-ray images. Recent work demonstrates innovations in autoencoder-based band selection for hyperspectral imagery, self-attention fusion networks for cardiac diagnostics, and operational U-nets for wildfire detection. His research bridges theoretical machine learning advancements with practical applications in environmental science and healthcare. Notable technical skills include deep learning frameworks, remote sensing data processing, and medical imaging modalities. Current projects focus on scalable solutions for large-scale Earth observation data analysis and cross-modal medical imaging systems.

• Machine Learning
• Artificial Intelligence
• Computer Science
• +۵ مورد دیگر

Ethem Alpaydin is a Professor of Computer Science at Özyeğin University in Istanbul, Turkey. He is a prominent researcher in machine learning and artificial intelligence, with affiliations including membership in The Science Academy, Turkey, and Academia Europaea. He also holds a fellowship with the Asia-Pacific Artificial Intelligence Association. His research focuses on foundational aspects of machine learning, including statistical methods, neural networks, and deep learning. He has contributed to areas such as generative adversarial networks (GANs), decision pathways in neural networks, and distributed decision trees. His work bridges theoretical advancements with practical applications in domains like natural language processing and computer vision. Alpaydin has authored influential textbooks such as Introduction to Machine Learning and Maschinelles Lernen , which are widely used in academic curricula. His publications emphasize model interpretability, regularization techniques, and cross-lingual learning for languages like Turkish. Awards include recognition from leading scientific institutions for his contributions to AI and data science.