جستجوی استاد راهنمای دانشگاهی

Maggie Makarمشاهده پروفایل

استادیار

• Machine Learning
• Causal Inference
• Healthcare Applications
• +۳ مورد دیگر

Maggie Makar is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the University of Michigan. Her research focuses on the intersection of machine learning and causal inference, particularly in developing robust and data-efficient methods for healthcare and resource-constrained settings. She received her PhD from MIT's CSAIL and a bachelor's degree in math and economics from UMass Amherst. Her work emphasizes causal reasoning to improve predictive models' reliability, with applications in healthcare decision-making and policy evaluation. Key areas include offline reinforcement learning for medical interventions, confounding variable handling, and mitigating biases in AI systems. Dr. Makar teaches courses such as CSE 598 (Causality and Machine Learning) and mentors PhD students in causal ML and healthcare AI. She actively seeks to expand her research group and collaborate on projects blending causal theory with real-world applications. Education: PhD in Computer Science, MIT (CSAIL), 2018 Bachelor's in Mathematics and Economics, University of Massachusetts Amherst, 2013 Awards: CAREER Award (NSF, 2024) CRII: SCH Grant (NSF, 2022) Research Themes: Causal inference in machine learning Data-efficient methods Healthcare applications Algorithmic fairness

Jing Zhangمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Generative AI
• +۵ مورد دیگر

Dr. Jing Zhang is a Lecturer in the School of Computing at the Australian National University (ANU) , within the ANU College of Systems & Society . Previously, he served as a Research Fellow at ANU (2021–2022) and earned his PhD in 2021 under the supervision of Nick Barnes. His academic journey includes a Master’s (2010) and Bachelor’s (2007) from Northwestern Polytechnical University . Education: PhD, Australian National University, 2021 (Supervisor: Nick Barnes) Master’s Degree, Northwestern Polytechnical University, 2010 Bachelor’s Degree, Northwestern Polytechnical University, 2007 Research Interests: Jing Zhang’s research focuses on computer vision and machine learning , with a particular emphasis on generative AI . His work addresses challenges in image, video, and audio generation/editing; explainable model adaptation; out-of-distribution detection; and adversarial attacks/defenses. He explores techniques to enhance model robustness and generalization across diverse domains. Awards & Recognition: CVPR 2020 Paper Award Nominee for “UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders” Teaching & Supervision: Jing Zhang teaches COMP8536: Advanced Topics in Computer Vision (Semester 2, 2024) and ENGN4528/6528: Computer Vision (Semester 1, 2025). He currently supervises 4 PhD students as their primary advisor, focusing on cutting-edge research in computer vision and AI. Professional Contributions: Co-organizer of tutorials on salient object detection (ACCV 2019) and uncertainty estimation (ICCV 2021) Regular reviewer for top venues: CVPR, ICCV, ECCV, TPAMI, IJCV, and others Research Groups: While not explicitly stated, his work is likely affiliated with ANU’s computer vision research groups, focusing on generative AI and robust machine learning systems.

Egidio Faloticoمشاهده پروفایل

استادیار

• Robotics
• Biorobotics
• Computational Neuroscience
• +۴ مورد دیگر

Egidio Falotico is a Tenure Track Assistant Professor (RTD-B) at the BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy. He leads the BRAin Inspired Robotics (BRAIR) Lab and holds roles such as Principal Investigator (PI) for the PROBOSCIS project and co-leader of Sub-Project 10 (Neurorobotics) in the Human Brain Project (HBP). His research focuses on brain-inspired motor control, soft robotics, and neurorobotics, integrating computational neuroscience with robotic systems. Education: Dual PhD in Biorobotics (Scuola Superiore Sant’Anna) and Cognitive Science (University Pierre et Marie Curie). Master’s in Computer Science (University of Pisa). Research interests include control mechanisms for soft robots using AI, bio-inspired motor control, and neuroscientific principles applied to robotics. He has contributed to projects like HBP, where he developed the Neurorobotics Platform, and PROBOSCIS, aiming to create elephant-trunk-inspired robots. Teaching includes leading PhD courses on Brain-Inspired Motor Control and Robotics at the University of Pisa. He has supervised EU-funded students and managed interdisciplinary teams in neurorobotics and soft robotics. Key projects involve HBP’s Neurorobotics Platform, soft robot control (e.g., PROBOSCIS), and collaborations in sensorized systems and adaptive learning algorithms. His work bridges neuroscience, AI, and robotics to advance soft robotic systems and bio-inspired models.

• Statistical Methodology
• Latent Variable Models
• Tensor Decompositions
• +۳ مورد دیگر

Zhongyuan Lyu is a Research Fellow (Postdoctoral Research Scientist) at Columbia University's Data Science Institute, mentored by Professors Yuqi Gu and Kaizheng Wang. His research focuses on statistical methodology for latent structures in mixture models, graphical models, and tensor decompositions, with applications to heterogeneous data analysis. Prior to Columbia, he earned his PhD in Mathematics from the Hong Kong University of Science and Technology under Professor Dong Xia's supervision. His academic background includes advanced work in high-dimensional data analysis, latent variable modeling, and computational statistics. Research interests emphasize developing theoretically grounded algorithms for complex data types, particularly in network science and multilayer data frameworks. Recent publications highlight contributions to spectral clustering optimization, adaptive transfer learning frameworks, and tensor-based methodologies for higher-order networks. His work bridges statistical theory and practical applications, addressing computational limits and optimal estimation challenges in modern data science problems. No scientific awards or grants are explicitly mentioned in the provided data. His current position is full-time within the Data Science Institute's research team.

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.

Umar Razaمشاهده پروفایل

مدرس ارشد

• Internet of Things
• Machine Learning
• Wireless Sensor Networks
• +۹ مورد دیگر

Dr. Umar Raza serves as a Senior Lecturer in Networking, IoT and Smart Systems at Manchester Metropolitan University's Department of Engineering, where he concurrently manages the Cisco Network Academy. His academic career includes prior lecturing roles in Robotics and Computing at Staffordshire University, with research spanning industrial applications of emerging technologies. His educational credentials include a PhD in Wireless Sensor Networks from the University of Bradford (2014), Postgraduate Certificates in Research Methods and Professional Higher Education from Staffordshire University (2005, 2007), an MSc in Electronics and Computer Systems from Huddersfield University (1995), and a BSc in Engineering Electronics from DeMontfort University Leicester. Dr. Raza's research centers on Internet of Things applications integrated with Machine Learning across industrial, agricultural, and healthcare domains. Current projects address IoT/ML solutions for domicile care monitoring, cognitive impairment assistance, natural disaster early warning systems, and smart farming in Pakistan, with emphasis on practical implementations in real-world environments. His recent publications demonstrate a pronounced interdisciplinary trajectory, particularly at the IoT-Machine Learning nexus for healthcare innovation (cardiac diagnostics, autism support wearables) and agricultural technology. Significant contributions also appear in blockchain scalability, vehicular network security, and 5G propagation modeling, reflecting both theoretical rigor and industry-relevant problem solving. Professional recognition includes: Fellow of the Higher Education Academy (FHEA) Dr. Raza actively supervises two MSc and two PhD candidates while leading multiple funded projects: Principal Investigator for KTP projects with Kindus Solutions (completed) and Trumeter Ltd Project Advisor for KTP with RAIT Ltd Lead researcher on NATO Science for Peace Security grant for natural disaster warning systems Principal Investigator for Ignite National Technology Fund smart farming initiative in Pakistan As Cisco Network Academy Manager, he drives industry-academia collaboration in networking education, developing curricula that integrate cutting-edge IoT and cybersecurity concepts while maintaining strong professional engagement through IEEE, IET, and BCS memberships.

Niki Martinelمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Learning
• Pattern Recognition
• +۲ مورد دیگر

Dr. Niki Martinel is Associate Professor of Computer Vision and Machine Learning at the University of Udine's Department of Mathematics, Computer Science and Physics. His research advances machine learning methodologies with applications spanning medical imaging, underwater enhancement, and anomaly detection. Research specialties include deep learning architectures (Capsule Networks, Mamba models), self-supervised approaches, and feature representation techniques. Recent work focuses on improving robustness in challenging imaging conditions through physics-informed models and domain adaptation techniques. Publications demonstrate consistent innovation in imaging applications, particularly medical artifact reduction and underwater enhancement. Recent work shows strong emphasis on efficient architectures for mobile deployment and physics-aware models for scientific applications. Research collaborations extend to healthcare diagnostics, sports analytics, and marine robotics, showcasing cross-disciplinary applications of computer vision technologies.

• Continual Learning
• Few-Shot Learning
• Domain Adaptation
• +۷ مورد دیگر

Mahardhika Pratama is a Professor in the Department of Computer Science at Universitas Indonesia's Faculty of Computer Science, where he leads research in machine learning and artificial intelligence. His work bridges theoretical advancements with practical applications across various domains including time series analysis, autonomous systems, and resource-constrained environments. Pratama's research focuses on overcoming fundamental challenges in machine learning systems, particularly in the areas of continual learning and few-shot learning. His work addresses the critical problem of catastrophic forgetting in neural networks while developing efficient learning systems that can adapt to new tasks with minimal data. His publications reveal a strong emphasis on developing practical solutions for real-world applications where data is scarce or constantly evolving. Analysis of his recent publications (2023-2025) shows a clear research trajectory toward more sophisticated approaches for continual learning under data scarcity, with increasing focus on transformer architectures, multi-agent reinforcement learning, and cross-domain adaptation techniques. His work demonstrates a consistent pattern of addressing both theoretical foundations and practical implementations of adaptive learning systems. His research has been supported through numerous collaborative projects with international institutions including UNSW Canberra, Swinburne University of Technology, and University of Alberta, reflecting the global impact of his work in the machine learning community.

Leonidas Guibasمشاهده پروفایل

استاد

• Computer Vision
• Computer Graphics
• Machine Learning
• +۵ مورد دیگر

Leonidas Guibas is the Paul Pigott Professor of Computer Science at Stanford University, leading the Geometric Computation group. He is a Hans Fischer Senior Fellow at the Technical University of Munich (TUM), associated with the Visual Computing Focus Group under Prof. Matthias Nießner. His research bridges computer vision, graphics, and machine learning, focusing on 3D data processing, geometric modeling, and sensor networks. Guibas holds prestigious awards including the Vannevar Bush Fellowship and ACM Allen Newell Award, and is a member of the US National Academy of Engineering and American Academy of Arts and Sciences. Education: Ph.D. from Stanford University under Donald Knuth, with prior appointments at Xerox PARC, MIT, and international institutions. His work emphasizes algorithms for sensing, reasoning, and acting in physical environments, with contributions to robotics, computational geometry, and discrete algorithms. Research interests span 3D vision (e.g., generative models for shape synthesis), deep learning architectures for spatiotemporal data, and multimodal sensor integration. Recent projects include object pose estimation, functional relationship learning in scenes, and deformation-aware 3D model retrieval. Publications highlight advancements in 3D reconstruction, point cloud analysis, and geometric deep learning. His TUM fellowship supported work on digital twins and visual computing, emphasizing high-fidelity environmental modeling. Awards also include IEEE and ACM Fellowships, underscoring his impact on theoretical and applied computer science.

Yang Yangمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Computational Social Science
• +۴ مورد دیگر

Yang Yang is an Assistant Professor at the Mendoza College of Business, University of Notre Dame. He holds a PhD in Computer Science from the same institution and previously served as Assistant Professor at Syracuse University and Research Assistant Professor at Northwestern Institute on Complex Systems (NICO) and Kellogg School of Management. His research focuses on data mining, machine learning, computational social science, and science of science, particularly exploring how social networks influence leadership attainment and innovation dynamics in scientific contexts. Yang's work bridges computer science and social sciences, examining topics like team composition effects on innovation, media's role in science communication, and gender disparities in leadership. His research has been published in top-tier venues such as PNAS, KDD, ICDM, and featured in media outlets like Forbes, The Washington Post, and WIRED. His recent technical contributions include advancements in multimodal AI, remote sensing interpretation, and video synthesis techniques. Notable projects include geo-localization systems using satellite imagery, transformative video generation models, and human-centric animation technologies leveraging diffusion models and transformers.

• Machine Learning
• Computer Science
• Recommender Systems
• +۴ مورد دیگر

Xiangyang Li is a Professor at the University of Science and Technology of China, School of Computer Science and Technology. His work spans interdisciplinary domains including computer science, machine learning, and geoscience. Research Focus: Machine Learning, Recommender Systems, Blockchain, and Computer Vision. Key Contributions: Development of novel algorithms for UWB positioning, code information retrieval benchmarks, and vision-language models. Recent publications highlight trends in large language model (LLM) integration for recommendation systems, quantum-inspired optimization, and cross-chain consensus models. His 2025 work includes collaborations on semantic-driven inference, prompt tuning, and hybrid BFT consensus for blockchain scalability.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۳ مورد دیگر

João Paulo Papa is a Professor at the Department of Computing within the Institute of Biosciences, Humanities and Exact Sciences at São Paulo State University (UNESP), Brazil. His research spans machine learning, computer vision, and quantum computing with significant applications in medical diagnostics and environmental monitoring. Over the past three years, he has published extensively in top-tier journals including IEEE Access, ACM Computing Surveys, and Neural Computing and Applications. His research interests focus on developing innovative machine learning approaches for healthcare applications, particularly in Parkinson's disease detection through speech and facial analysis, medical image processing for cancer detection, and quantum-classical hybrid models. His work demonstrates strong integration of theoretical machine learning advancements with practical medical and environmental applications. Papa has established a productive research group that has produced numerous publications in computer vision conferences and medical informatics venues. Analysis of his recent publications reveals a strong emphasis on medical applications of AI, with approximately 60% of his work focused on healthcare diagnostics, 25% on fundamental machine learning advancements, and 15% on environmental and remote sensing applications. His research shows increasing collaboration with international partners while maintaining strong roots in Brazilian academic networks. Among his notable contributions is the development of specialized machine learning architectures for medical image analysis, including TransConv for esophageal cancer detection and quantum-classical hybrid models for breast cancer diagnosis. He has also made significant contributions to the Portuguese language processing community through adaptations of large language models for Brazilian Portuguese medical applications. Prof. Papa actively supervises graduate students and collaborates with medical professionals across Brazil, translating AI research into practical clinical tools. His research group maintains strong connections with hospitals and medical research centers to ensure clinical relevance of their technical developments.

Eduardo Blancoمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۷ مورد دیگر

Eduardo Blanco is an Associate Professor in the Department of Computer Science at the University of Arizona. His research focuses on advancing Natural Language Processing (NLP) with an emphasis on understanding negation, hate speech dynamics, and computational linguistics applications. He explores how language models can better interpret ambiguous statements, predict conversation incivility, and counteract harmful online content through structured responses. Blanco’s work spans theoretical and applied NLP domains, including semantic role labeling, sentence embedding evaluation, and biomedical text analysis using protein language models. His research often integrates large language models with task-specific frameworks to address challenges in zero-shot learning and negation robustness. Key areas of investigation include: Negation processing and affirmative interpretation Social media discourse dynamics and hate speech detection NLP for biomedical applications (glycosylation site prediction) Semantic similarity alignment in sentence embeddings His recent articles highlight advancements in counterhate speech strategies, temporal QA systems, and LLM-based peer review tools. Notably, he secured the NSF CAREER Award for studying negation through positive interpretations (2022). Collaborative efforts focus on interdisciplinary teams addressing challenges in computational linguistics, with grants supporting projects like the ALIGN-SIM benchmarking framework and BEMEAE event argument extraction system.

• Meta-learning
• Deep Learning
• Bayesian Deep Learning
• +۳ مورد دیگر

Boyan Gao is a Postdoctoral Researcher at the University of Oxford's Institute of Biomedical Engineering, part of the Engineering Science faculty. His research focuses on meta-learning, deep optimisation, and Bayesian deep learning, with applications in clinical machine learning. He is affiliated with the Computational Health Informatics (CHI) Lab under Prof. David Clifton, exploring optimiser and loss function learning for novel tasks. Education: PhD in Deep Learning and Machine Learning from the University of Edinburgh (supervised by Prof. Timothy Hospedales) Research Interests: Boyan's work bridges theoretical advancements in meta-learning with practical clinical applications. Key areas include gradient-based meta-learning, optimisation algorithm adaptation for unseen tasks, and Bayesian approaches in deep learning. His recent projects address challenges in medical image segmentation, physiological signal analysis, and scalable edge-based language models. Labs & Collaborations: He collaborates within the CHI Lab, focusing on clinical machine learning applications. His work intersects with digital health initiatives, leveraging AI for healthcare diagnostics and patient monitoring.

Debarghya Mukherjeeمشاهده پروفایل

استادیار

• High-dimensional statistics
• Statistical machine learning
• Algorithmic fairness
• +۵ مورد دیگر

Debarghya Mukherjee is an Assistant Professor at the Department of Mathematics and Statistics, Boston University, with a joint appointment in the College of Arts & Sciences and the Computational Data Science (CDS) program. His research focuses on high-dimensional statistics, statistical machine learning, and algorithmic fairness. He holds a Ph.D. from the University of Michigan and completed postdoctoral work at Princeton University under Prof. Jianqing Fan. Education: Ph.D. in Statistics, University of Michigan (2017-2022) M.Stat. and B.Stat., Indian Statistical Institute, Kolkata (2012-2017) His research explores the intersection of statistical theory and modern machine learning challenges, including fairness, domain adaptation, and non-standard asymptotics. He has contributed to methodologies for high-dimensional models, spatial/temporal data dependency, and optimal knowledge transfer across domains. His work emphasizes theoretical rigor and practical applicability in fields like economics and biology. Recent publications address topics such as prediction interval aggregation under domain shifts, fairness-aware machine learning, and the theoretical foundations of deep neural networks. His teaching includes courses on generalized linear models and introductory statistics. He has no explicitly listed scientific awards but maintains active collaborations with leading institutions and researchers in statistics and machine learning.