جست‌وجوی استاد و پژوهشگر

Yue Luمشاهده پروفایل

استاد

• Applied Mathematics
• Control Theory and Stochastic Systems
• Data Science
• +۵ مورد دیگر

Yue Lu is the Gordon McKay Professor of Electrical Engineering and Applied Mathematics at Harvard University, serving as Faculty Director of Graduate Studies. His research spans applied mathematics, control theory, machine learning, and signal processing, with a focus on high-dimensional data analysis and algorithmic foundations. He leads the Signal and Information Processing lab in Maxwell Dworkin 113. Notable recognitions include being named a Harvard College Professor (2024) and achieving tenure in 2019. His work addresses topics like random matrix theory, optimization in machine learning, and statistical signal processing. Recent contributions include studies on neural networks, kernel methods, and phase retrieval algorithms. He has published extensively on the theoretical underpinnings of modern learning systems, with a particular emphasis on universality principles and asymptotic analysis. Research Highlights: Developed frameworks for analyzing approximate message passing algorithms Advanced theories of in-context learning and feature learning dynamics Contributed to understanding phase transitions in high-dimensional estimation problems Explored optimal regularization strategies for sparse regression Awards: Harvard College Professor (2024) Full Tenure in Electrical Engineering (2019) His lab focuses on bridging theory and applications in signal processing and AI, with projects ranging from imaging systems to brain network analysis. Ongoing work explores the statistical physics of learning and scalable algorithms for large-scale inference problems.

J. Nathan Kutzمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Mathematical Neuroscience
• +۸ مورد دیگر

J. Nathan Kutz is the Robert Bolles and Yasuko Endo Professor of Applied Mathematics at the University of Washington, affiliated with the College of Arts & Sciences. He also holds positions as Director of the AI Institute in Dynamic Systems, Boeing Professor of AI and Data-Driven Engineering, and Professor of Electrical and Computer Engineering. His research focuses on data-driven dynamical systems, machine learning, computational neuroscience, and optical physics. Kutz leads the Kutz Research Group, which explores topics such as reduced-order modeling, dynamic mode decomposition, and sparsity-driven dynamics. His academic contributions span interdisciplinary fields, including neuroscience (e.g., neural network functionality and neurodegenerative diseases), fluid dynamics (flow classification and control), and computer vision (video processing and gesture recognition). He has advised numerous PhD students and postdoctoral researchers, fostering collaborative projects across applied mathematics, physics, and engineering. Kutz's work emphasizes equation-free modeling and sparse sampling techniques, enabling efficient analysis of complex systems. His group develops open-source tools and frameworks for data assimilation and control, with applications in robotics, metamaterials, and biological systems.

• Computational Neuroscience
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Conor Houghton is an Associate Professor in Computer Science at the University of Bristol, affiliated with the School of Engineering Mathematics and Technology. His work bridges computational neuroscience, machine learning, and artificial intelligence. He explores neural mechanisms in cognition, language evolution, and network dynamics through interdisciplinary methods. Research interests include neural coding, evolutionary models, and applications of deep learning to cognitive processes. Key research themes involve modeling neural systems (e.g., cerebellar circuits, auditory processing), analyzing spike train data, and developing algorithms for information processing. His publications span topics like Bayesian inference in neural systems, cooperative evolutionary dynamics, and sparse autoencoder architectures. Collaborations with colleagues in neuroscience and computer science highlight his interdisciplinary approach. Publications demonstrate expertise in computational neuroscience (e.g., cerebellar state estimation, synaptic plasticity) and machine learning (e.g., reservoir computing, language models). His work often combines theoretical models with empirical data analysis, addressing questions in both biological and artificial systems.

Stefanie Jegelkaمشاهده پروفایل

دانشیار

• Algorithmic Machine Learning
• Optimization Algorithms
• Discrete and Combinatorial Machine Learning
• +۷ مورد دیگر

Stefanie Jegelka is an Associate Professor (on leave) at MIT's Department of Electrical Engineering and Computer Science, and a Humboldt Professor at Technical University of Munich (TU Munich). She is a member of CSAIL (Computer Science and Artificial Intelligence Laboratory), IDSS (Institute for Data, Systems, and Society), and Machine Learning at MIT. Her research spans algorithmic machine learning with focus on modeling, optimization algorithms, theory, and applications. Dr. Jegelka completed her PhD at the Max Planck Institutes in Tuebingen and ETH Zurich, followed by a postdoc at UC Berkeley's AMPlab and computer vision group. Her academic journey reflects a strong foundation in both theoretical and applied aspects of machine learning. Her research focuses on exploiting mathematical structure for discrete and combinatorial machine learning problems, robustness, and scaling machine learning algorithms. Key areas include submodular optimization, graph neural networks, invariant and equivariant learning, and representation theory in machine learning. Her work bridges theoretical foundations with practical applications across various domains, with particular emphasis on how mathematical structure can enhance algorithmic performance. Dr. Jegelka's recent publications demonstrate significant contributions to understanding the theoretical properties of graph neural networks, developing methods for invariant learning, and advancing representation learning techniques. Her work consistently shows strong connections between mathematical structure and machine learning performance, with applications spanning natural language processing, computer vision, and scientific domains. Sloan Research Fellowship NSF CAREER Award DARPA Young Faculty Award Dr. Jegelka has advised numerous graduate students and postdocs, many of whom have gone on to successful careers in academia and industry. Her research has been supported by prestigious grants from NSF, DARPA, ONR, and industry partners including Google, Two Sigma, and Adobe. She serves as Program Chair for ICML 2022 and has held numerous editorial and organizational roles in the machine learning community. She leads a research group investigating fundamental questions in machine learning, particularly how mathematical structure can be leveraged to develop more efficient, robust, and scalable learning algorithms. Her group collaborates across disciplines, connecting theoretical machine learning with applications in science and engineering, and has produced influential work on submodularity, graph representation learning, and invariant learning methods.

• Computer Vision
• Computer Graphics
• Machine Learning
• +۶ مورد دیگر

Dimitrios Tzionas is an Assistant Professor for 3D Computer Vision at the University of Amsterdam (UvA), leading research at the intersection of Computer Vision, Computer Graphics, and Machine Learning. His work focuses on reconstructing and modeling 3D human-body interactions with objects and scenes, supporting applications in Robotics, Mixed Reality, and the Metaverse. Academic Affiliation: University of Amsterdam (since 2022), Max Planck Institute for Intelligent Systems (2016-2022) Education: PhD in Computer Science (University of Bonn, 2017), MSc in Electrical & Computer Engineering (Aristotle University, Thessaloniki) His research emphasizes whole-body 3D interaction , including pose estimation, grasp synthesis, and contact-aware modeling. He develops methods like InteractVLM for semantic contact estimation and PICO for dense 3D contact datasets, leveraging vision-language models and optimization frameworks. Recent publications at CVPR, ICCV, and SIGGRAPH Asia highlight advancements in 3D reconstruction, avatar synthesis (PuzzleAvatar), and interactive dynamics (InterDyn). His work is supported by an ERC Starting Grant (2024-2030) and industry partnerships (Google, NVIDIA). Scientific Awards ERC Starting Grant (2024) ELLIS Scholar (2025) Outstanding Reviewer at CVPR, ICCV, ECCV He advises multiple PhD students, including Alvaro Budria and Lixin Xue , and collaborates with institutions like ETH Zurich and MPI-IS. His team's tools (e.g., ICON, GRAB) are widely adopted in academic and industrial research.

Matt Fredriksonمشاهده پروفایل

دانشیار

• Machine Learning
• Security
• Privacy
• +۲ مورد دیگر

Matt Fredrikson is an Associate Professor in the Computer Science Department at Carnegie Mellon University , affiliated with CyLab and the Principles of Programming Group . His research bridges security, privacy, and formal methods in machine learning and software systems. PhD in Computer Science, University of Wisconsin–Madison (2015) M.S. in Computer Science, University of Wisconsin–Madison (2012) Bachelor's in Mathematics and Computer Science, Duquesne University (2007) His work focuses on privacy in machine learning , particularly adversarial inference and differential privacy limitations. He develops formal methods for privacy-aware programming , using logics with counting to model adversarial uncertainty. Additionally, he explores probabilistic program analysis to enhance machine learning security and reliability. Recent publications highlight his contributions to LLM security , including attacks on alignment and robustness certification. His 2025 paper LLM Whisperer reveals biases in LLM responses, while 2024 works address certifiable robustness and automated adversarial attacks on coding models. Best Paper Award, USENIX Security Symposium 2014 He advises students on topics spanning AI ethics , program verification , and IoT security . Courses taught include Software Foundations of Security and Privacy and Bug Catching: Automated Program Verification and Testing .

Sara Ahmadiمشاهده پروفایل

مدرس

• Brain-Computer Interfaces
• Deep Learning
• Neuroscience
• +۳ مورد دیگر

Dr. Sara Ahmadi is a Lecturer in Artificial Intelligence at Radboud University, affiliated with the Donders Institute for Brain, Cognition and Behaviour. Her primary research focuses on Brain-Computer Interfaces (BCI), deep learning techniques, and neurotechnology. She actively contributes to advancing BCI systems through studies on Bayesian methods, auditory attention decoding, and gaze-independent signal processing. Her research interests span dynamic stopping algorithms for BCI, multimodal attention tracking, and optimizing VEP-based systems. She teaches courses such as 'AI in the Professional Workfield' and 'Digital Signal Processing,' reflecting her expertise in both theoretical and applied AI. Dr. Ahmadi’s work bridges neuroscience and engineering, with a focus on improving BCI usability and robustness in volatile environments. Her recent studies include pilot experiments on noise-tagging for covert attention monitoring and reinforcement learning models for decision-making systems. She is part of the Brain Computer Interfacing research group at the Donders Institute, collaborating on cutting-edge neurotechnology projects.

• Hydrology
• Remote Sensing
• Flood Risk Management
• +۷ مورد دیگر

Fiachra O'Loughlin is an Assistant Professor in the School of Civil Engineering at University College Dublin, specializing in Hydrology and Remote Sensing. He directs the Dooge Centre for Water Resources Research, focusing on water resources engineering, hydraulic studies, and climate adaptation. His research integrates hydrological modeling with remote sensing to address extreme weather impacts. Previously, he held positions at the University of Bristol and the University of the West of England, teaching hydrological modeling and flood risk management. PhD from UCD Professional Certificates in University Teaching & Learning Research interests include flood risk prediction, multi-hazard risk assessment, and peatland dynamics. Key projects involve developing high-accuracy terrain maps and optimizing hydrological models for ecological relevance. He coordinates modules like Applied Hydrology and Hydraulic Engineering Design, and leads grants on climate change adaptation. His work bridges academic research with policy, particularly through Earth Institute collaborations. Publications emphasize advanced modeling techniques, remote sensing applications, and climate resilience strategies. Notable contributions include quantifying multi-hazard risks and improving flood forecasting via satellite data integration.

Kai Liuمشاهده پروفایل

استادیار

• Machine Learning
• Optimization
• Data Mining
• +۴ مورد دیگر

Kai Liu is an Assistant Professor in the Computer Science Division at Clemson University . He holds a Ph.D. in Computer Science from the Colorado School of Mines (adviser: Dr. Hua Wang). His research focuses on the intersection of Machine Learning and Optimization , particularly in designing robust, efficient, and scalable algorithms for data mining applications. Key areas include non-negative matrix factorization, tensor completion, and healthcare analytics such as Alzheimer's disease analysis. Teaching: Liu has taught advanced courses including Optimization for Machine Learning , Advanced Machine Learning , and Applied Data Science at Clemson University. He also contributed to undergraduate courses like Discrete Structures . Research Contributions: His work emphasizes theoretical guarantees for algorithms, with applications in bioinformatics (e.g., HIV-1 drug resistance prediction), computer vision, and healthcare. Notable methods include spherical principal component analysis and accelerated tensor completion techniques. Professional Service: Liu serves on program committees for top conferences including ICML, NeurIPS, AAAI, and CVPR. He is a recognized expert in first-order optimization methods and high-dimensional statistics. Personal Interests: Beyond academia, Liu engages in traditional Chinese culture (calligraphy, painting, poetry), enjoys hiking, and is an avid reader of biographies. He is also a marathon runner and long-time supporter of Real Madrid.

Haiyan Wangمشاهده پروفایل

استاد

• Machine Learning
• Nonparametric Inference
• High-Dimensional Data Analysis
• +۳ مورد دیگر

Dr. Haiyan Wang is Professor of Statistics at Kansas State University's College of Arts and Sciences. She earned her Ph.D. from Pennsylvania State University (2004) and leads research in machine learning, nonparametric statistics, and high-dimensional data analysis with applications in genomics, lipidomics, and image processing. She directs the Statistical Consulting program and has developed R packages for functional data analysis and high-dimensional testing. Her methodological research spans: 1) Inference for heteroscedastic functional data 2) Rank-based high-dimensional testing 3) Test-based clustering 4) Image quality assessment 5) Variable selection for high-dimensional data. She maintains active collaborations in plant science, bioinformatics, and materials engineering. As Graduate Council representative, she oversees statistics curriculum development. She has advised 19+ graduate students and teaches courses spanning statistical theory, machine learning, and computational statistics.

• Computational Social Science
• Natural Language Processing
• Social Network Analysis
• +۳ مورد دیگر

Roshni Chakraborty is an Assistant Professor at the Institute of Computer Science, University of Tartu, Estonia, where she leads research in the Computational Social Science Group. Her work bridges natural language processing, social network analysis, and machine learning to address challenges in social media analytics, disaster response, and misinformation detection. Research Interests: Her expertise spans: Computational Social Science : Analyzing social dynamics via digital footprints. Disaster Informatics : Developing NLP tools for crisis-related tweet summarization (e.g., ATSumm, IKDSumm). Political & Media Analysis : Detecting biases in community-driven fact-checking and news stance. Network Science : Advancing signed network modeling (SigGAN) and cross-network alignment (HCNA). Publication Trends: Recent articles (2022-2025) focus on generative AI for fact-checking, disaster tweet summarization benchmarks, and political bias analysis. Her work emphasizes practical NLP solutions for social good, leveraging transformer models, adversarial learning, and ontology-driven approaches. Academic Activities: She actively recruits PhD students and research interns for projects in social computing. Her group collaborates on datasets like BD2TSumm and Adsumm, fostering open research in crisis informatics.

Christian Micheloniمشاهده پروفایل

دانشیار

• Pattern Recognition
• Active Vision
• Neural Networks
• +۳ مورد دیگر

Christian Micheloni is an Associate Professor at the University of Udine's Department of Mathematics and Computer Science. He holds a Laurea (cum Laude) and Ph.D. in Computer Science from the University of Udine (2002 and 2006 respectively). His research focuses on Active Vision, Pattern Recognition, and Resource Aware Networks. He has been involved in European projects like MUSAS (2008-2010) and HAMLet (2006-2008), and collaborates with international research centers and industry. Key research areas include person re-identification using neural networks, trajectory analysis, and camera network optimization. He has received the Best Paper Award at the International Conference on Distributed Smart Cameras (2014) and serves as an editor for the IEEE Journal of Selected Topics in Signal Processing. His work emphasizes real-time surveillance systems and adaptive sensor networks to manage computational and power constraints. Micheloni teaches courses like Artificial Vision and advises post-doctoral researchers. He maintains an active lab (AVires Lab) and offers post-doctoral positions. His publications span journals like IEEE Transactions on Pattern Analysis and Machine Intelligence, and conferences such as CVPR and ICCV.

Mubarak Shahمشاهده پروفایل

استاد

• Computer Vision
• Video Surveillance
• Visual Tracking
• +۷ مورد دیگر

Dr. Mubarak Shah is the Trustee Chair Professor of Computer Science and Founding Director of the Center for Research in Computer Vision (CRCV) at the University of Central Florida. His research focuses on computer vision, including video surveillance, visual tracking, human activity recognition, and UAV video analysis. He has held prestigious fellowships from ACM, NAI, AAAS, IAPR, and IEEE. Roles: Trustee Chair Professor, Director of CRCV, Graduate Faculty Member Affiliations: University of Central Florida, College of Engineering and Computer Science Research interests span visual analysis of crowded scenes, video registration, and privacy-aware diffusion models. He has pioneered geolocalization techniques and multimodal learning frameworks. His work impacts security, autonomous systems, and medical imaging. Recent publications emphasize diffusion models, 3D object detection, and adversarial learning. His work has been presented at CVPR, ECCV, and NeurIPS. Awards: Pegasus Professor (2006) ACM SIGMM Technical Award (2019) Multiple fellowships and recognitions from IEEE, AAAS, and others Advising and mentorship include over 50 graduate students and NSF REU programs. He leads CRCV, a hub for interdisciplinary vision research, and contributes to high-impact datasets like MAVREC. Labs/Teams: CRCV hosts cutting-edge projects in geolocalization, action recognition, and medical vision. Collaborative efforts include global partnerships in AI and robotics.

• Artificial Intelligence
• Data Science
• Applied Mathematics
• +۳ مورد دیگر

Jialin Liu is an Assistant Professor in the Department of Statistics and Data Science at the University of Central Florida (UCF) and a member of the UCF AI Initiative. His academic background includes a B.S. in Automation from Tsinghua University (2015) and a Ph.D. in Applied Mathematics from UCLA (2020). Education: B.S. in Automation, Tsinghua University (2015) Ph.D. in Applied Mathematics, University of California, Los Angeles (2020) Liu's research bridges AI, data science, and mathematics. He focuses on developing AI/data-driven methodologies to address computational mathematical problems such as optimization, differential equations, and numerical linear algebra. His work emphasizes the need for stable, safe, and explainable data-driven methods while establishing rigorous theoretical foundations for integrating AI into mathematics and science. The 15 most recent publications highlight interdisciplinary advancements in areas like matrix completion, graph neural networks for optimization, sparse coding for phase restoration, and theoretical frameworks for learning-to-optimize. These works span algorithm design, mathematical foundations of AI, and applications in computational mathematics.

Rosina Weberمشاهده پروفایل

استاد

• Explainable Artificial Intelligence (XAI)
• Case-Based Reasoning
• Machine Learning
• +۶ مورد دیگر

Rosina Weber is a Professor of Information Science and Computer Science at Drexel University's College of Computing & Informatics. She specializes in explainable artificial intelligence (XAI), case-based reasoning (CBR), and combining neural/symbolic methods for use-inspired AI applications. Her research addresses interpretability challenges, medical decision support, and ethical AI frameworks. Education: PhD in Production Engineering (Federal University of Santa Catarina, Brazil, 1998) MS in Production Engineering (Federal University of Santa Catarina, Brazil) BA in Business Administration (Federal University of Rio Grande do Sul, Brazil) Research Interests: Focus areas include XAI, causal discovery, medical AI applications, and AI ethics. Recent work emphasizes interpretable models for clinical diagnostics and military medical triage scenarios. Key Projects: Co-PI for DARPA ITM project with Parallax Advanced Research Former co-lead in DHS-EPA Center for Microbial Risk Assessment Swedish Vinnova-funded international XAI collaboration Awards: 2023 Research Excellence Award (College-level) Best Paper Awards (various conferences) Grants & Collaboration: Holds active grants from DARPA, NCATS-NIH, and Swedish Vinnova. Her work has been featured in Good Day Philadelphia and NBC Nightly News . Labs/Teams: Leads AI research teams focused on biomedical data translation, CBR integration, and XAI implementation in healthcare systems.