جست‌وجوی استاد و پژوهشگر

• Face Recognition
• Biometrics
• Synthetic Data Generation
• +۴ مورد دیگر

Ivan DeAndres-Tame is an Assistant Professor in the Department of Signal Theory and Communications at the School of Engineering, Universidad Autónoma de Madrid. His research focuses on advancing biometric systems with particular emphasis on face recognition and behavioral biometrics using cutting-edge computer vision and machine learning techniques. His research interests span face recognition systems, synthetic data generation for biometric applications, explainable AI in biometric contexts, and keystroke verification systems. DeAndres-Tame has made significant contributions to the field through his leadership in major international challenges including the FRCSyn Challenge at CVPR and WACV, and the Keystroke Verification Challenge at IEEE BigData. His work bridges theoretical advances with practical applications in biometric security systems. Analysis of his recent publications reveals a strong focus on evaluating and improving face recognition systems using synthetic data, with a growing interest in the intersection of large language models and biometric systems. His research demonstrates consistent innovation in benchmarking methodologies and evaluation frameworks for biometric systems. Throughout his career, DeAndres-Tame has collaborated extensively with leading researchers in the biometrics community, including Ruben Tolosana, Rubén Vera-Rodríguez, Aythami Morales, Julian Fiérrez, and Javier Ortega-Garcia, contributing to the advancement of biometric technologies through both theoretical and applied research.

• Machine Learning
• Information Theory
• Deep Learning
• +۴ مورد دیگر

Giulio Franzese is an Assistant Professor in the Data Science department at EURECOM. His research focuses on generative models, information theory, deep learning, and their applications in telecommunications and AI-driven networks. He has contributed to advancements in diffusion processes, multi-modal data alignment, and 6G network architectures. Notable projects include the ADROIT6G initiative for next-generation networks and methodologies like INFO-SEDD for scalable information metric estimation. His work spans theoretical foundations (e.g., infinite-dimensional generative models) and applied systems (e.g., GNSS for rail transportation). His publications reflect interdisciplinary strengths in machine learning, statistical theory, and engineering applications. He holds a robust publication record with over 30 articles since 2014, including impactful works on uncertainty quantification in deep learning and entropy-based optimization. Key Research Themes: Generative Diffusion Models Information-Theoretic Metrics AI for 6G Networks Selected Projects: ADROIT6G: Distributed AI-Driven 6G Architecture RFMI: Text-to-Image Alignment Framework

• Computer Science
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Yongtao Wu is a Doctoral Assistant and student in the doctoral program in Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL). He is affiliated with the Laboratory for Information and Inference Systems (LIONS), part of the School of Engineering (STI) and the Institute of Electrical Engineering (IEM). His research focuses on machine learning, adversarial learning, computer vision, and deep learning, with applications in vision-language models, robustness analysis, and optimization algorithms. His work explores topics such as diffusion-based purification for vision-language models, adversarial attacks on large models, and parameter-efficient fine-tuning techniques. He contributes to advancing foundational AI models and their robustness in real-world scenarios. His research also intersects with cybersecurity, structural engineering predictions, and quantum computing methodologies. Yongtao is actively involved in the LIONS lab, collaborating on projects that bridge theoretical advancements with practical applications in computer science and engineering. His doctoral studies emphasize interdisciplinary approaches to solving complex problems in AI and machine learning.

• Natural Language Processing
• Vision-Language Models
• Medical AI
• +۳ مورد دیگر

Parminder Bhatia is a prominent research scientist at Amazon with over 49 publications and 1,400+ citations spanning natural language processing, vision-language models, and medical AI. As a key contributor to Amazon's AI research initiatives, Bhatia has developed influential frameworks including A³Tune for medical vision-language alignment, SIMA for visual-language modality improvement, and ReCode for evaluating code generation robustness. Their work bridges theoretical advances with practical applications across healthcare, software engineering, and multimodal systems. Bhatia's research primarily focuses on enhancing large language models through innovative alignment techniques, efficient fine-tuning strategies, and robustness evaluation frameworks. Key contributions include solving attention distribution challenges in medical VLMs, improving cross-file context understanding for code completion, and developing self-improvement mechanisms for visual-language alignment without external dependencies. Their work demonstrates consistent innovation in addressing fundamental limitations of current AI systems while maintaining practical applicability across diverse domains. Analysis of Bhatia's 15 most recent publications reveals a strong emphasis on medical AI applications (40%), code generation/analysis (30%), and foundational LLM improvements (30%). The research shows an evolving trajectory from basic NLP tasks toward complex multimodal integration, with increasing focus on practical constraints like computational efficiency, robustness to perturbations, and adaptation to specialized domains. Notably, over 60% of recent work involves medical applications, establishing Bhatia as a leader in healthcare AI.

• Digital Photogrammetry
• Computer Vision
• Geospatial Imaging
• +۱۰ مورد دیگر

Dr. Jackson David Cothren is a Professor in the Department of Geosciences at the University of Arkansas, where he also serves as the Leica Geosystems Chair in Geospatial Imaging. He holds dual leadership roles as Director of the Center for Advanced Spatial Technologies (CAST) and the Arkansas High Performance Computing Center (HPCC). His academic affiliations are deeply rooted in geospatial science, computer vision, and high-performance computing, bridging engineering and environmental applications. Ph.D. in Geodetic Science and Surveying, The Ohio State University M.S. in Geodetic Science and Surveying, The Ohio State University B.S. in Applied Mathematics, United States Air Force Academy Dr. Cothren's research spans digital photogrammetry, computer vision, UAV-based geospatial monitoring, and spatial archaeometry. He investigates non-traditional sensor modeling, feature extraction, surface generation, and integration with enterprise geospatial systems. His work increasingly incorporates deep learning, transformer models, and AI-driven analytics for applications in renewable energy, autonomous systems, and environmental sustainability. His recent publications highlight innovations in solar PV profiling, aerial image segmentation, and fairness-aware domain adaptation. The trends in his recent scholarly output reflect a strong shift toward machine learning and AI in geospatial analysis, particularly using transformer architectures for high-resolution imaging and cross-domain adaptation. His work integrates Lidar, GPS, and InSAR for deformation monitoring and leverages HPC for large-scale data processing. Applications span archaeology, agriculture, transportation, and energy infrastructure. Dr. Cothren has received numerous competitive grants from NSF, NEH, and USDA, supporting interdisciplinary research in geospatial analytics, smart transportation, and cultural heritage. His projects emphasize data-driven decision-making, community engagement, and workforce development in geospatial technologies. Principal Investigator, NSF E-RISE Rll: Arkansas Smart Transportation Research Incubator (2025–2029) Lead, RII Track-1: DART – Data Analytics that are Robust and Trusted (NSF, 2020–2025) Director, OPEN-GATE: Expanding Geospatial Education (NSF, 2016–2020) He mentors a broad interdisciplinary team and leads collaborative research initiatives involving computer vision, environmental science, and archaeology. His labs and research centers—CAST and HPCC—serve as hubs for innovation in spatial technologies, high-performance computing, and data-intensive research across the university and beyond. These centers support large-scale projects in archaeo-geophysics, UAV monitoring, and enterprise GIS integration.

Bing Liuمشاهده پروفایل

استاد مدعو

• Natural Language Processing
• Large Language Models
• Dialogue Systems
• +۴ مورد دیگر

Bing Liu serves as Director of Applied Research at Scale AI and Adjunct Professor in the Computer Science and Engineering department at the University of California, Santa Cruz. Previously, he held leadership roles at Meta (GenAI and Reality Labs), Google Research, and Capio.ai (acquired by Twilio), with expertise spanning generative AI, NLP, and spoken dialogue systems. His educational background includes: Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University B.Eng in Electrical and Electronic Engineering (First Class Honors) from Nanyang Technological University, Singapore Exchange studies at KTH Royal Institute of Technology, Sweden Liu's research focuses on advancing large language models, dialogue systems, and reinforcement learning for conversational AI. His work bridges theoretical innovation with industrial-scale applications, particularly in zero-shot learning, multilingual capabilities, and evaluation frameworks for generative AI. He has pioneered techniques in dialogue state tracking, knowledge-enriched task-oriented systems, and continual learning to address catastrophic forgetting in neural dialogue models. Analysis of his 15 most recent publications reveals a clear trajectory toward industrial-scale generative AI: early work (2018-2021) established foundational methods in task-oriented dialogue systems, while recent publications (2022-2024) focus on LLM post-training, multimodal integration, and evaluation benchmarks like Humanity's Last Exam. Key thematic clusters include cross-lingual transfer, knowledge grounding in dialogue, and robust evaluation frameworks for frontier models. As an educator, Liu guides graduate research at UC Santa Cruz in LLM and multimodal AI while serving in critical conference roles including Publication Chair for ACL 2024 and Area Chair for ACL 2023's Large Language Models track. His industry leadership includes building Scale AI's 40+ member research team and driving Meta's Llama3 development, managing multimillion-dollar data roadmaps that scaled the Meta AI Assistant to 700M MAU. Liu directs Scale AI's applied research lab focused on GenAI data and evaluation, having previously built Meta's NLU team for AR/VR voice assistants deployed across Portal, Oculus, and Ray-Ban Smart Glasses. His current work centers on creating evaluation leaderboards adopted by top AI labs and developing the data engine for next-generation generative models.

• Data Mining
• Social Network Analysis
• Machine Learning
• +۳ مورد دیگر

Jaideep Srivastava is a Professor affiliated with Qatar Foundation (Doha, Qatar), University of Minnesota (Minneapolis, USA), and holds a PhD from University of California Berkeley. His research spans data mining, social network analysis, time series modeling, and health informatics. Recent work focuses on clinical deterioration prediction, sleep research, and computational analysis of pandemic behaviors. Key contributions in clustering algorithms and graph neural networks Active in multimodal learning and misinformation detection His publications from 2024-2022 demonstrate expertise in hierarchical clustering , large language model applications , and health data analytics . Articles often integrate machine learning , social network dynamics , and clinical monitoring systems . Current projects involve Covid-19 in-hospital mortality prediction , virtual influencer analysis , and low-light imaging techniques . Collaborations span institutions in Qatar, USA, and India with applications in urban mobility and precision medicine.

• Time Series Analysis
• Machine Learning
• Solar Physics
• +۳ مورد دیگر

Shah Hamdi serves as an Assistant Professor in the Computer Science Department at Utah State University's College of Engineering. His research bridges machine learning and space physics, with emphasis on time series analysis for solar phenomena prediction and explainable AI systems. His primary research focuses include Time Series Analysis for space weather forecasting, Solar Physics applications in flare and particle event prediction, Explainable AI through counterfactual methods, and Natural Language Processing for social media analysis. He develops novel frameworks for multivariate time series classification, data augmentation of imbalanced datasets, and interpretable model architectures that handle complex spatio-temporal patterns. Analysis of his recent publications reveals three dominant trends: (1) Application of graph neural networks and multimodal fusion to solar flare prediction using photospheric magnetic field data, (2) Development of shapelet-based and saliency-guided counterfactual explanation techniques for time series classification, and (3) Creation of generative models like ChronoGAN and AVATAR for synthetic time series data augmentation. His work consistently addresses challenges in imbalanced data and space weather forecasting accuracy. Dr. Hamdi leads collaborative research initiatives including the CAIG project for synthetic data generation in solar energetic particle events. His grant portfolio demonstrates expertise in securing funding for interdisciplinary space weather and machine learning projects, while his advising focuses on training graduate students in time series analysis and explainable AI methodologies for real-world applications.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Dr Zheyuan Liu is a Research Fellow at the Australian Institute for Machine Learning within the Faculty of Sciences, Engineering and Technology at The University of Adelaide. His research focuses on machine learning, computer vision, and multimodal learning, particularly in areas like federated learning, semantic segmentation, and vision-language models. His research interests include: Federated fine-tuning of large language models Text-to-video generation with diffusion models Weakly supervised semantic segmentation techniques Multimodal image retrieval systems Audio-visual source localization Recent publications demonstrate expertise in cross-domain adaptation, contrastive learning, and re-ranking methods. He is eligible to supervise Masters and PhD students.

Wong K.K.Y.مشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Prof. Kenneth K.Y. Wong is an Associate Professor and Division Associate Head (AI & Data Science Division) at the School of Computing and Data Science (CDS), The University of Hong Kong. He holds a BEng from The Chinese University of Hong Kong and MPhil/PhD from the University of Cambridge in Computer Vision. His career spans over 20 years in academia, including roles as Associate Head of the Department (2008-2012) and Programme Director of the BEng(CompSci) programme (2008-2015). Research Focus: Computer Vision, Machine Learning, Medical Imaging, Generative Models Key Roles: Director of Computer Vision Laboratory, Associate Editor of IJCV His research emphasizes camera calibration, 3D model reconstruction, generative AI, and medical image understanding. Over 160 peer-reviewed publications appear in top venues like CVPR, ECCV, and TPAMI. Active in professional service, he serves on program committees of major AI/conference events. Grants include projects in medical imaging (e.g., mitral valve analysis) and autonomous driving (DriveGPT4 series). His lab focuses on cutting-edge topics like video diffusion models, text-to-image synthesis, and 3D avatar generation.

Hannah Kernerمشاهده پروفایل

استادیار

• Machine Learning
• Remote Sensing
• Geospatial Data Analysis
• +۱۲ مورد دیگر

Hannah Kerner is an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University, where she leads cutting-edge research at the intersection of machine learning and remote sensing. Her work applies AI to global challenges such as food security, climate change, and planetary exploration, with strong collaborations through NASA Harvest and NASA ACRES consortia. Research Interests: Her research focuses on algorithmic advances for sparse and heterogeneous geospatial data, including agricultural land cover classification, novelty/anomaly detection, change detection, geographic fairness in AI, and AI-assisted planetary exploration. She develops methods to improve model generalization, mitigate geographic bias, and enable scalable ground-truth labeling via projects like Street2Sat. Publication Trends: Her recent publications demonstrate a strong trend toward foundational models for Earth monitoring (e.g., Galileo), fairness in AI (geographic and directional bias), automated detection of natural phenomena (volcanic features, seagrass), and the development of benchmark datasets (Fields of the World). Her work spans computer vision, NLP, and environmental science, emphasizing real-world impact. Scientific Awards: Forbes 30 Under 30 Best Paper Award at CVPR 2024 Oral Presentation at WACV 2023 (2.5% acceptance rate) Spotlight Poster at ICML 2024 (3.5% acceptance rate) Top 10 project by IRCAI for SDG impact Advising and Grants: Dr. Kerner actively mentors a large research group, advising numerous PhD, MS, and undergraduate students across ASU and partner institutions. Her research is supported by major funders including NASA, NSF, Google, and the Taylor Geospatial Engine, reflecting its high impact and interdisciplinary nature. Labs and Teams: She leads a dynamic research group at ASU focused on geospatial machine learning and is a key contributor to the NASA Harvest and ACRES consortia, which apply satellite observations to global agricultural monitoring and food security.

Michael Lyuمشاهده پروفایل

استاد

• Software Engineering
• Cloud Computing
• AIOps
• +۵ مورد دیگر

Michael Lyu is a Professor at The Chinese University of Hong Kong specializing in software engineering with a focus on cloud reliability, AIOps, and log analysis. His research bridges the gap between theoretical advances and practical industrial applications in large-scale cloud systems. His research interests span Software Engineering , Cloud Computing Reliability , AIOps , and Log Analysis . Dr. Lyu's work addresses critical challenges in modern cloud operations, including failure diagnosis, anomaly detection, and reliability engineering. His recent research has pivoted toward leveraging large language models for software engineering tasks, particularly in code generation and log analysis. His publication portfolio demonstrates consistent contributions to major software engineering conferences (ASE, ICSE, ESEC/FSE) from 2018-2025, with a noticeable increase in LLM-related research since 2023. The trend shows a clear evolution from traditional software engineering topics toward AI-driven approaches for cloud operations. ICSE 2021 Keynote: "Reliability-Driven AIOps for Cloud Resilience" ASE 2023: Maat: Performance Metric Anomaly Anticipation for Cloud Services ASE 2024: LILAC: Log Parsing using LLMs with Adaptive Parsing Cache Dr. Lyu actively mentors students, with numerous co-authored publications showing his advisees as first authors. His work receives significant attention in both academic and industrial software engineering communities, addressing practical problems faced by large-scale cloud service providers. His research group appears focused on developing data-driven approaches for improving cloud system reliability through advanced analytics of logs, traces, and KPIs.

• Machine Learning
• Deep Learning
• AI Safety
• +۵ مورد دیگر

Saikat Barua is a Senior Researcher at North South University in Dhaka, Bangladesh, affiliated with the Department of Electrical Engineering and Computer Science. His research focuses on designing intelligent systems aligned with human values, aiming to create beneficial AI that collaborates with humanity for a better future. With expertise spanning Machine Learning, Deep Learning, AI Safety, Quantum Error Correction, and Natural Language Processing, Barua contributes significantly to advancing artificial intelligence with strong emphasis on ethical and practical applications. Barua's research centers on developing transparent, reliable, and human-centered AI systems across multiple cutting-edge domains. His work in explainable AI (XAI) addresses the critical need for interpretability in complex machine learning models, while his investigations into quantum machine learning explore novel approaches to hybrid quantum-classical systems. He actively researches autonomous agents and large language models, examining their security vulnerabilities and potential for beneficial societal impact. A recurring theme in his work is bridging the gap between sophisticated AI capabilities and human understanding, ensuring technologies remain accessible and trustworthy. His publication record reveals a strategic focus on making AI more transparent, secure, and energy-efficient across diverse applications. Barua has developed integrated environments for knowledge analysis (KAXAI), energy-efficient medical imaging frameworks (ELMAGIC), and quantum error correction techniques (RESCUED). His recent work increasingly addresses the security challenges of agentic systems and the practical implementation of quantum machine learning. The trajectory of his research demonstrates consistent progression from foundational AI techniques toward increasingly complex, interdisciplinary applications with real-world impact. As an active researcher with nine publications accumulating over 11,988 reads and 14 citations, Barua engages deeply with the scientific community through ResearchGate. His research questions explore critical intersections of AI with healthcare empathy, machine learning data collection, human resource management, and responsible research practices. This comprehensive engagement reflects his commitment to advancing both the technical frontiers of AI and its thoughtful integration into societal frameworks, positioning him as a thoughtful contributor to the evolving landscape of artificial intelligence research.

Zhiguang Caoمشاهده پروفایل

استادیار

• Artificial Intelligence
• Data Science
• Decision Making & Optimization
• +۳ مورد دیگر

Dr. Zhiguang Cao is an Assistant Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). With a PhD from Nanyang Technological University (2017), his work bridges Artificial Intelligence , Deep Learning , and Combinatorial Optimization to tackle complex logistics and urban mobility challenges. Research Advisor to PhD/Master's students: XIAO Xiangjie, YI Hang, ZHANG Ni Recipient of the World’s Top 2% Scientist recognition (2024), IEEE Transactions on Industrial Informatics Outstanding Paper Award (2022), and Nobert Wiener Review Award (2022) Completed degrees: PhD (NTU), MSc (NTU), BEng (GDUT, China) Research Interests focus on Learning to Optimize (L2Opt) and Neural Combinatorial Optimization , with applications in: Vehicle Routing Problems (TSP, CVRP, PDP) Job Shop Scheduling and Multimodal Optimization UAV Routing , Drone Delivery , and Stochastic Programming His 15 most recent publications (2024-2025) span top venues like NeurIPS , ICML , IJCAI , and IEEE Transactions journals, covering topics from multi-task vehicle routing to graph domain adaptation and learning with large language models . Scientific Awards : World’s Top 2% Scientist (Stanford, 2024) IEEE Transactions on Industrial Informatics Outstanding Paper (2022) Nobert Wiener Review Award (2022) BMW Summer School Excellent Research Work (2014) IEEE Singapore Chapter Tennis Prize (2012) Dr. Cao advises the L2Opt Research Group , which recruits PhD students and remote interns. He has served as Area Chair for ICLR, SIGKDD, ICML, and Senior PC for AAAI and IJCAI. His ongoing projects include Trustworthy Human-AI Optimization (AISG, 2025-2027) and Mobile Crowdsourcing Emergency Supplies (SMU-UofT, 2025-2026).

• Virtual Reality
• Human-Computer Interaction
• Rehabilitation Technology
• +۳ مورد دیگر

Simon Hoermann is a researcher with extensive contributions to virtual reality, human-computer interaction, and immersive technology applications. His work spans disciplines including rehabilitation, education, and emergency response training through serious games and XR systems. Affiliation: University of Otago Research interests focus on: Virtual reality for motor rehabilitation Collaborative human-agent interaction in immersive environments Passive haptic feedback systems Serious games for education and therapy Recent publications include 15 articles (2017-2025) examining: VR psychotherapy environments with virtual supporters XR for firefighter training and volcanic hazard education Large language model ethics and bias mitigation Biomechanical locomotion interfaces Avatar-based collaboration systems Hoermann contributes to: Systematic reviews in VR rehabilitation Design frameworks for prosocial XR applications Human factors analysis in immersive training