جست‌وجوی استاد و پژوهشگر

Karol Myszkowskiمشاهده پروفایل

استاد

• Realistic Image Synthesis
• Global Illumination
• High Dynamic Range Imaging
• +۷ مورد دیگر

Karol Myszkowski is a Professor in the Department of Computer Graphics at the Max Planck Institute for Informatics in Saarbrücken, Germany. His research spans realistic image synthesis, high dynamic range imaging, and perception issues in computer graphics. He leads a research group that investigates the intersection of human visual perception and computer graphics techniques to develop more realistic rendering solutions. Institution: Max Planck Institute for Informatics Department: Computer Graphics (Department 4) Location: Campus E1 4, Room 229, Saarbrücken, Germany His research interests focus on realistic image synthesis and global illumination, high dynamic range imaging techniques, stereo 3D visualization, eye tracking applications, computational displays, appearance fabrication, and perception issues in computer graphics. He investigates how human visual perception interacts with rendering techniques to create more efficient and realistic graphics solutions for virtual and augmented reality applications. Recent publications (2023-2025) show a strong trend toward neural rendering techniques, particularly for high dynamic range content processing and generation. His work increasingly integrates deep learning approaches with traditional computer graphics methods, focusing on perception-guided solutions that optimize visual quality according to human visual system characteristics. Key areas include HDR image/video processing, neural radiance fields, image/video deblurring, and multisensory perception studies. Current PhD Students: Martin Bálint, Daniel Jiménez Navarro Former PhD Students: 17 including Mojtaba Bemana, Chao Wang, and Krzysztof Wolski Current Postdoc: Colin Groth Former Postdocs: 11 including Gurprit Singh and Bin Chen His research group collaborates extensively within the Max Planck Institute for Informatics and with other international institutions. The team focuses on bridging the gap between computer graphics techniques and human visual perception, developing novel methods for realistic image synthesis, high dynamic range imaging, and perception-aware rendering solutions for virtual and augmented reality applications.

Pietro Michiardiمشاهده پروفایل

استاد

• Scalable Machine Learning Algorithms
• Computational Statistics
• Parallel and Distributed Systems
• +۳ مورد دیگر

Pietro Michiardi is a Professor and Department Head of Data Science at EURECOM, a leading institution in telecommunications and computer science. His work focuses on scalable machine learning algorithms, computational statistics, and parallel/distributed systems. He teaches courses on distributed systems and algorithmic machine learning, emphasizing cloud computing and parallel frameworks. His research explores cutting-edge topics like generative diffusion models, information-theoretic approaches to data alignment, and anomaly detection in multivariate time series. Recent work includes advancements in score-based generative models and cognitive-dissonance-aware knowledge updates in large language models. Michiardi has been recognized with several accolades, including the Best Paper Award at ITC2016 and the Etoiles de l'Europe award for the FP7 BIGFOOT project. His contributions span foundational studies in distributed systems scheduling (e.g., HFSP for Hadoop) and innovative methods for cloud caching and resource management. While no advising/grants details are explicitly listed, his extensive publication record (over 227 works) reflects impactful contributions across machine learning theory and practical system optimizations.

• Statistical Learning Theory
• Recommender Systems
• Matrix Completion
• +۵ مورد دیگر

Antoine LEDENT is a tenure-track Assistant Professor in the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU), holding a PhD in Mathematics from the University of Luxembourg (2017) and undergraduate/graduate degrees from Clare College, University of Cambridge. His academic trajectory includes a postdoctoral position at TU Kaiserslautern under Prof. Marius Kloft, establishing his foundation in theoretical computer science. His educational background spans rigorous mathematical training at Cambridge followed by doctoral research on stochastic differential equations at Luxembourg, directly informing his current theoretical work. This path transitioned into computer science through postdoctoral research in algorithmic learning theory. Professor LEDENT specializes in Statistical Learning Theory and Recommender Systems, with groundbreaking contributions to generalization bounds for deep neural networks, contrastive learning frameworks, and matrix completion theory. His research uniquely bridges abstract mathematical principles with practical AI applications, particularly in high-dimensional data analysis and robust recommendation engines, emphasizing provable guarantees over empirical results. Analysis of his 15 most recent publications reveals a dominant focus on theoretical machine learning, with 70% concerning generalization analysis in deep learning and matrix completion. His work consistently targets top-tier venues (ICML, NeurIPS, AAAI), demonstrating expertise in transforming mathematical insights into scalable AI solutions, especially for non-IID data and uncertainty-aware systems. Key recognitions include: Outstanding Meta Reviewer (ICML 2025, top 2%) Area Chair appointments for NeurIPS/ICML 2025 Action Editor for TMLR (2024) Certificate of Excellence in Reviewing (KDD 2023) Top Reviewer distinctions at NeurIPS/ICML/ICLR (2022-2024) He actively mentors PhD candidates including AISG Scholarship recipient NONG Minh Hieu and SENARATH ARACHCHIGE Dilan Dinushka, offering fully funded 4-year positions with pathways to Presidential Scholarships. His recruitment targets mathematically strong candidates for projects in Statistical Learning Theory, Matrix Completion, or Recommender Systems, with stipends exceeding 6000 SGD/month for exceptional performers. Based in Singapore's AI hub at SMU—ranked #41 globally in AI (CSRankings)—he operates within SCIS's dynamic ecosystem that connects academia with regional AI startups and industry partners, leveraging Singapore's strategic position at the Asia-Pacific research nexus.

Steffen Schneiderمشاهده پروفایل

دانشیار

• Dynamical systems
• Representation learning
• Neural dynamics
• +۳ مورد دیگر

Dr. Steffen Schneider is an Associate Professor at the Ludwig Maximilian University of Munich , affiliated with the Graduate School of Systemic Neuroscience (GSN) and the Helmholtz Center Munich, Computational Health Center . His research focuses on developing machine learning algorithms for representation learning, nonlinear system dynamics, and neural interpretability in biological datasets. His work spans dynamical systems , self-supervised learning , and identifiability theory , with a strong emphasis on compressing multi-modal data into foundation models and studying their mechanistic interpretability . Recent publications demonstrate applications in neural dynamics , biomedical modeling , and speech recognition . His team at Helmholtz Center Munich explores system identification and neural embeddings , with collaborations across neuroscience and computational health. Contact: steffen.schneider@helmholtz-munich.de | Research Website

Shaojie Tangمشاهده پروفایل

استاد

• Machine Learning
• Responsible AI
• Combinatorial Optimization
• +۶ مورد دیگر

Shaojie Tang is a Professor in the Management Science and Systems department at the School of Management, University at Buffalo. He serves as the Associate Director of the AI and Management Science Institute for Artificial Intelligence and Data Science. His research focuses on Machine Learning, Responsible AI, and Combinatorial Optimization, with applications in business and data science. PhD from Illinois Institute of Technology His work spans interdisciplinary domains including Federated Learning, Recommender Systems, and Optimization techniques. Key subfields involve Personalized AI, Tensor Completion, and Algorithm Design for collaborative systems. Recent publications from 2025 and 2024 highlight his leadership in Federated Learning, Neural Network Decoupling, and Strategic AI Deployment. Trends include balancing personalization with collaboration, enhancing model robustness, and cross-modal adaptation in distributed environments. Professional affiliations include IEEE, ACM, and INFORMS. He advises graduate students in theoretical and applied AI research, though specific advisees are not listed publicly. His research teams operate under the Institute for Artificial Intelligence and Data Science, advancing scalable solutions for dynamic business challenges.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Amir Zamir is a Tenure-Track Assistant Professor of Computer Science at the Swiss Federal Institute of Technology (EPFL) and leads the Visual Intelligence and Learning Lab (VILAB). Previously affiliated with UC Berkeley, Stanford, and UCF, his research spans computer vision, machine learning, and AI, focusing on embodied intelligence and multimodal learning. Current Appointments: EPFL (Tenure-Track Assistant Professor) Industry Experience: Chief Scientist at Aurora Solar (2015-2022), Chief Scientist at Duranta His research emphasizes advancing vision systems beyond narrow approaches toward general multi-modal/multi-task models that operate in real-world environments. Key projects include 4M (Multimodal Foundation Models), Taskonomy (Task Transfer), Gibson (Sim-to-Real), and Omnidata (Steerable Datasets). Current work explores flexible-length tokenization (FlexTok), visual personalization (ViPer), and robust cross-domain learning . Recent publications demonstrate expertise in autoregressive image generation , vision-language modeling , and embodied robotics . Awards include the ECCV Young Researcher Award , NVIDIA Pioneering Research Award , and multiple CVPR/SIGGRAPH best paper recognitions . He advises PhD students in vision systems, AI, and robotics, and teaches courses on Visual Intelligence, AI Product Management, and Autonomous Robotics.

• Database Systems
• Stream Processing
• Internet of Things (IoT)
• +۷ مورد دیگر

Steffen Zeuch is a researcher at Humboldt University of Berlin, Germany, with a strong research focus on database systems, stream processing, and Internet of Things (IoT) data management. He is a key contributor to the NebulaStream platform, an extensible, high-performance system for multi-modal edge applications. His work spans query optimization, GPU acceleration, fault tolerance, and distributed state management in stream processing environments. Research interests include database systems, stream processing, IoT data management, query optimization, GPU computing, and hardware-aware execution. His research addresses challenges in real-time analytics, efficient data placement, adaptive compilation, and complex event processing in distributed and edge environments. The publication trends in his recent articles highlight a consistent focus on stream processing systems—especially NebulaStream—with increasing attention to GPU acceleration, adaptive optimization, fault tolerance, and complex event processing. His work bridges theoretical query optimization with practical system implementation, emphasizing performance, scalability, and deployment in real-world IoT and edge infrastructures. Steffen Zeuch has made significant contributions to top-tier venues such as VLDB, SIGMOD, EDBT, and DEBS. His collaborative work, primarily with Volker Markl and other members of the database group at Humboldt University, demonstrates strong research leadership and technical depth in data-intensive systems. He has led and contributed to projects involving system design, performance benchmarking, and real-world deployment of stream processing platforms. His work on tutorials and system demonstrations indicates active engagement in community education and dissemination. While no specific lab or team name is mentioned, his research is centered around the NebulaStream project, a comprehensive platform for managing and analyzing data across fog, edge, and cloud environments. This platform supports complex analytics beyond traditional cloud boundaries, enabling scalable and efficient IoT applications.

• Natural Language Processing
• Biomedical Informatics
• Medical Text Simplification
• +۴ مورد دیگر

Byron C. Wallace is a prominent researcher in Natural Language Processing with a focus on biomedical and clinical applications . His work spans model distillation, factuality evaluation, and medical text simplification. Key contributions include developing methods for tracing teacher models in distillation and creating benchmarks like FactPICO and RedHOT. Research interests include LLM interpretability , medical evidence synthesis , and cross-modal alignment in clinical NLP His recent publications analyze syntactic template repetition in LLMs, token erasure effects , and definition-augmented biomedical NER Wallace's work has advanced zero-shot summarization techniques, pharmacovigilance automation , and patient data privacy analysis in clinical models. Current projects explore chain-of-thought distillation , multilingual medical simplification , and interpretable feature extraction from EHR data. Key collaborators include researchers from institutions like Allen Institute for AI, MIT, and various biomedical NLP teams. His methodological innovations in instance attribution , data augmentation , and counterfactual reasoning have influenced modern NLP research paradigms.

Sreyasee Das Bhattacharjeeمشاهده پروفایل

استادیار

• Multimodal Data Analytics
• Affective Computing
• Generative AI
• +۱۰ مورد دیگر

Sreyasee Das Bhattacharjee is an Assistant Professor of Teaching in the Department of Computer Science and Engineering at the University at Buffalo (SUNY Buffalo), within the School of Engineering and Applied Sciences. She leads research in multimodal data analytics, affective computing, and generative AI, with extensive collaborations across institutions like NASA, JPL, Roswell Park, and UCSF. She is actively involved in academic leadership as the MS-AI Program Director at the Institute of AI and Data Science UB and serves on the Faculty Senate. Education: Ph.D., Indian Institute of Technology, Madras M.Tech., Indian Institute of Technology, Delhi M.Sc. in Mathematics, Chennai Mathematical Institute, India Her research focuses on developing AI systems for human-centered applications, particularly in emotion recognition, educational data mining, and immersive technologies. She investigates multimodal generative models, human-machine collaboration, and AI-powered interactive agents, often leveraging wearable sensors and large-scale datasets. Her work bridges computer vision, machine learning, and real-world healthcare and educational challenges. The most recent publications highlight a consistent trajectory in multimodal AI, with emphasis on robust emotion recognition under incomplete data, generative modeling for affective states, and applications in healthcare and education. Key themes include diffusion models, knowledge distillation, explainable AI, and personalized interactive systems, reflecting a strong interdisciplinary approach combining computer science with psychology and medicine. Scientific Awards and Honors: Best Paper Award, IEEE Big Data 2019 and 2017 IBM PhD Fellowship Award 2008 Australian Endeavour Research Fellowship 2007 Fellow, New Faculty Academy, UB 2021 NSF Travel Award 2025 National Scholarship, Govt of India 2000 Dr. Das Bhattacharjee has secured significant external funding, including a $1M NSF grant for a STEM program for refugee and underprivileged students, and leads projects on AI for elder care and plant health monitoring. She mentors a diverse group of graduate and undergraduate students, many of whom have gone on to top institutions and companies like Amazon and CMU. She also serves as Associate Editor for JMIR mHealth and Frontiers in AI, and has held leadership roles in major conferences as Area Chair and Meta Reviewer. She leads the Visual Computing Lab at UB, where her team develops cutting-edge AI systems for multimodal understanding, affective computing, and educational technology. The lab fosters interdisciplinary collaboration and innovation, with ongoing projects in BCI-AI integration, immersive agents in VR, and biosignal-based emotion modeling.

Ivor Simpsonمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Medical Image Analysis
• +۱۲ مورد دیگر

Dr. Ivor Simpson is an Associate Professor in Informatics at the University of Sussex, affiliated with the School of Engineering and Informatics and part of the AI research group. He serves as the Academic Lead for Sussex AI and convenes the MRes in Advanced Artificial Intelligence. His academic career includes prior roles as a Senior Lecturer (2022–present) and Lecturer (2020–2022) in Artificial Intelligence at the same institution. His educational background includes a DPhil in Medical Image Analysis from the University of Oxford (2008–2012) and an MEng in Computer Science from the University of Southampton (2004–2008). Prior to his academic appointments, he worked as a Senior Research Scientist and Head of Machine Learning at Anthropics Technology (2014–2019) and as a Research Associate at University College London (2012–2014). Ivor’s research lies at the intersection of machine learning and applied statistics, with a focus on probabilistic modeling for imaging and temporal data. His work emphasizes uncertainty quantification, interpretable AI, and deep generative models, applied to medical image analysis, computer vision, and ecological monitoring. He is particularly interested in inverse problems, morphometry, image registration, and multi-modal MRI analysis. His recent publications (2022–2025) reflect a strong trend toward uncertainty-aware models in medical imaging, ecoacoustics, and computer vision. Topics include compressed sensing MRI, variational autoencoders, deep ensembles, and interactive ecoacoustic dashboards. These works demonstrate interdisciplinary collaboration and methodological innovation, particularly in quantifying spatial uncertainty and building interpretable representations. MICCAI Young Scientist Award (2013) for work on probabilistic non-linear registration Dr. Simpson actively supervises students, currently serving as primary supervisor for 6 PhD candidates and secondary supervisor for several others. He has led or participated in multiple research grants from EPSRC, NERC, and Innovate UK, supporting projects in ecoacoustics, MRI modeling, and machine learning applications. His teaching includes advanced machine learning, computer vision, and the MRes in Advanced AI. He leads a research team focused on interdisciplinary AI applications and collaborates widely across academia and industry.

Russell Poldrackمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Neuroinformatics
• Decision-Making
• +۵ مورد دیگر

Russell Poldrack is the Albert Ray Lang Professor of Psychology at Stanford University , with an additional courtesy appointment in Psychiatry and Behavioral Sciences. He serves as Department Chair in Psychology, Associate Director of the Stanford Data Science Initiative, and Director of the Center for Open and Reproducible Science. A British Academy Fellow (2023), Poldrack leads research at the intersection of cognitive neuroscience and neuroinformatics , focusing on decision-making, executive control, and open science frameworks like NeuroSynth and OpenNeuro. Developed fMRIPrep pipeline for standardized neuroimaging preprocessing Championed reproducibility in fMRI analysis through multiverse analysis frameworks Created hyve visualization engine for composite neuroimaging representations His recent publications address decision-making mechanisms (eLife 2025), fMRI reliability (Nature Human Behaviour 2024), and machine learning reproducibility (Science Advances 2024). Poldrack's lab has produced over 150 published reuses of shared neuroimaging data through the OpenNeuro platform, while his Poldrack Lab continues to explore self-regulation ontology in clinical populations. He currently oversees 13 independent studies through Stanford's Neuroimaging Data Science program. Major awards: OHBM Open Science Award , Wiley Young Investigator (2005) Professional roles: Board of Scientific Counselors, NIH NIMH; Former Human Connectome Project EAB Chair Methodological contributions: BIDS standard adoption, MRIQC quality control protocols

Massimo Bertozziمشاهده پروفایل

دانشیار

• Computer Vision
• Real-Time Image Processing
• Autonomous Driving
• +۱۲ مورد دیگر

Massimo Bertozzi is an Associate Professor in the Department of Engineering and Architecture at the University of Parma, Italy. He has held a permanent academic position since 1997 and is a leading researcher in computer vision and intelligent vehicles. His work spans real-time image processing, autonomous driving, pedestrian detection, and embedded systems. He earned his Dr.Eng. in Electronic Engineering from the University of Parma in 1994 and completed his PhD in Information Technologies in 1998. His academic journey progressed from PhD student to assistant researcher and eventually to Associate Professor. His research focuses on vision-based systems for autonomous vehicles, including stereo vision, CNNs, SWIR imaging, and sensor fusion. He has been instrumental in pioneering projects such as ARGO, VIAC, TerraMax, and V-Charge. His current work includes collaboration with the IMP Lab on deep learning for computer vision. His recent publications (2022–2025) demonstrate a strong trend in AI-driven image synthesis, disentangled representations, 3D reconstruction, and real-time autonomous driving algorithms. Key themes include diffusion models, GANs, transformers, and efficient neural architectures applied to vision tasks. Scientific Awards: Best Paper Award, CONTROLO 2022 Best Paper Award, VISAPP 2022 He has advised numerous researchers and co-authored with PhD students and collaborators such as Tomaso Fontanini, Leonardo Rossi, and Alex Ergasti. He has not been mentioned to have received major external grants, but his involvement in EU-funded projects (PReVENT, PEGASE, V-Charge) indicates significant collaborative funding. He teaches courses including Computer Science and Programming Laboratory, Fundamentals of Computer Vision, and Deep Learning for Engineering Applications. He is affiliated with the Computer Engineering Group and the IMP Lab at the University of Parma, contributing to a vibrant research environment focused on intelligent systems and autonomous technologies.

Isabel Valeraمشاهده پروفایل

استاد

• Machine Learning
• Fairness in AI
• Causal Inference
• +۷ مورد دیگر

Isabel Valera is a full Professor in the Department of Computer Science at Saarland University, Saarbrücken, Germany. She also holds an adjunct faculty position at the Max Planck Institute for Software Systems (MPI-SWS) and is a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), contributing to the Robust Machine Learning Program and the Saarbrücken AI & Machine Learning (Sam) Unit. Research Interests: Her work lies at the intersection of machine learning, fairness, and causality. She focuses on developing methods for algorithmic recourse , fair decision-making , causal modeling , and robust learning . She is particularly interested in designing models that provide actionable explanations and ensure equitable outcomes in AI systems. Her research leverages probabilistic modeling, variational inference, and deep generative architectures. Publication Trends: Her recent publications (2021–2023) show a strong emphasis on algorithmic recourse , fairness under uncertainty , causal representation learning , and multimodal and graph-based generative models . She frequently publishes in top-tier venues such as NeurIPS, ICML, ICLR, AAAI, and FAccT, often in collaboration with leading researchers like Bernhard Schölkopf and Zoubin Ghahramani. Scientific Awards and Fellowships: Humboldt Post-Doctoral Fellowship Minerva Fast Track Fellowship (Max Planck Society) ELLIS Fellow Advising and Grants: She has mentored several early-career researchers who are now active contributors in machine learning, including Adrián Javaloy and Amir-Hossein Karimi. While specific grant details are not listed, her leadership roles and fellowships indicate sustained funding support from major research organizations. She has served as a co-editor for major conferences such as AISTATS and ECML PKDD, demonstrating her active role in the academic community. Labs and Research Groups: She leads or has led research groups at both Saarland University and the Max Planck Institutes (MPI for Intelligent Systems and MPI for Software Systems), focusing on foundational and applied aspects of machine learning with societal impact.

• Nationalism Studies
• Holocaust Studies
• Legal History
• +۳ مورد دیگر

Maria Kovacs is a Professor in the Nationalism Studies Department at Central European University (CEU). She actively participates in academic events, including the book launch of "Belated Obeisance - Jurist Victims of the Holocaust" (Kesei fohajtas – a holokauszt jogasz aldozatai) at the Hungarian Bar Association on March 7, supported by CEU. Her research interests center on Nationalism Studies, Holocaust Studies, and Legal History as evidenced by institutional affiliation and event participation. The department focuses on 21st-century nationalist movements and historical memory, though specific subfields are not detailed in the text. Her Google Scholar profile shows 15 recent publications (2025-2023) predominantly in Cognitive Psychology and Developmental Psychology, revealing a strong experimental focus on infant/toddler theory of mind, perspective taking, and bilingualism effects. Methodologies include eye-tracking, tablet-based games, and multi-lab studies, with recurring themes in mental state attribution and combinatorial cognition. No scientific awards were mentioned in the provided text. Details about student advising, grant funding, or research teams are absent from the sources. Her institutional role appears focused on historical and political analysis despite the cognitive science publications, though interdisciplinary connections are not specified.

Aishwarya Agrawalمشاهده پروفایل

استادیار

• Computer Vision
• Natural Language Processing
• Deep Learning
• +۷ مورد دیگر

Aishwarya Agrawal is an Assistant Professor in the Department of Computer Science and Operations Research at the University of Montreal and a core academic member of Mila – Quebec AI Institute, where she holds the Canada CIFAR AI Chair. She also works as a Research Scientist at Google DeepMind, spending one day per week there. Her research sits at the intersection of computer vision, natural language processing, and deep learning, aiming to build AI systems that can both 'see' and 'talk'. Her research interests include vision-language reasoning, data-efficient pretraining, robust evaluation, improving model reliability and explainability, and aligning AI systems with diverse cultural values. She has made significant contributions to visual question answering, particularly in addressing dataset bias through work like the VQA-CP dataset. The recent publications show a strong focus on evaluating and improving vision-language models, especially in areas like cultural alignment, consistency, reliability, and robustness. There is a clear trend toward developing better evaluation methodologies and enhancing model behavior in complex, real-world scenarios. Her group actively publishes in top-tier conferences including CVPR, NeurIPS, ICML, and ACL. Canada CIFAR AI Chair Award Georgia Tech 2020 Sigma Xi Best Ph.D. Thesis Award Georgia Tech 2020 College of Computing Dissertation Award Runner-up, 2019 AAAI/ACM SIGAI Dissertation Award NVIDIA Graduate Fellowship 2018-2019 Rising Stars in EECS 2018 Aishwarya advises several PhD and MSc students, including Rabiul Awal, Qian Yang, Le Zhang, and Oscar Mañas. She has received research grants, such as one from Samsung for compositional reasoning in multimodal transformers. She is actively recruiting graduate students and postdocs through Mila’s supervision request process. She co-organized the annual VQA Challenge and Workshop from 2016 to 2021 and is involved in broader AI community initiatives, including panels and talks on leadership and AI research trends. Her lab, part of Mila, fosters a collaborative environment focused on advancing multimodal AI.