جست‌وجوی استاد و پژوهشگر

Rajinder Dudrahمشاهده پروفایل

استاد

• Cultural Studies
• Creative Industries
• Film Studies
• +۷ مورد دیگر

Rajinder Dudrah serves as Professor of Cultural Studies and Creative Industries within Birmingham City University's Faculty of Arts, Design and Media, a position he has held since joining BCU in 2016. He has significantly shaped the university's research landscape through founding the Centre for Equality, Diversity and Inclusion in the Arts (CEDIA), co-establishing the BCU India Innovation Group, and developing the Creative Industries research cluster. His academic trajectory includes prior appointments at the University of Manchester, University of Portsmouth, Cardiff University, and the University of Birmingham. Dudrah's research program critically examines cultural production across film, media, and creative sectors with specialized expertise in Bollywood cinema, Black British representation, diasporic media flows, and popular music cultures. His methodological approach integrates cultural theory with qualitative research on creative industries, recently expanding into AI-driven creative technologies and immersive art applications. This work consistently bridges academic inquiry with creative practice through global collaborations with artists and industry partners. Analysis of his recent publications reveals three dominant research trajectories: the digital transformation of South Asian cinemas (particularly Bollywood's adaptation to streaming platforms), the political economy of creative labor in global contexts, and multilingualism's role in cultural production. His scholarship demonstrates increasing interdisciplinary engagement with technology studies while maintaining core commitments to representation, diaspora, and postcolonial perspectives. Scientific Awards: Leverhulme Major Research Fellowship (2022-2025) Dudrah has secured substantial research funding including Principal Investigatorship for the AHRC/Innovate UK project 'India-UK Creative Industries at 75' (2022), which generated 15+ creative outputs with 30 UK-India artists. As Co-Investigator on the £4 million AHRC Creative Multilingualism consortium, he developed the Slanguages research strand examining language in creative economies. He actively mentors PhD candidates and early-career researchers through structured research development programs. His institutional leadership includes directing CEDIA's initiatives on arts sector inclusion and co-founding the BCU India Innovation Group that facilitates UK-India creative industry partnerships. Current activities center on the Leverhulme-funded 'E-Bollywood' project analyzing social media and streaming platforms' impact on Hindi cinema, alongside collaborations with Warwick University and Jawaharlal Nehru University on AI-driven artistic production.

• Recommender Systems
• Multimodal Learning
• Music Emotion Recognition
• +۳ مورد دیگر

Marta Moscati works at the Institute of Computational Perception at Johannes Kepler University Linz , focusing on advanced recommendation systems and multimodal learning. Her research spans emotion-based music recommendation, privacy-preserving machine learning, and graph neural networks. Recent work includes: Developing multimodal single-branch architectures for cold-start scenarios Creating preference obfuscation techniques in implicit feedback systems Advancing music emotion recognition with semi-supervised graph networks Contributing to the FAME Challenge for multilingual face-voice association She has published extensively in top AI venues while maintaining technical expertise in both deep learning and theoretical physics , with early work on lepton universality violation. At JKU, she contributes to: Recommendation algorithms development Multimodal representation learning research Musical affective computing applications Privacy-preserving AI frameworks

J.C. Scholtesمشاهده پروفایل

استاد

• Natural Language Processing
• Information Retrieval
• Text Mining
• +۵ مورد دیگر

J.C. Scholtes is an Extra-ordinary Professor of Text Mining at the Department of Knowledge Engineering, Faculty of Science and Engineering, University of Maastricht. He is also a Senior Research Fellow at the Dutch School for Information and Knowledge Systems (SIKS), a Board Member at IPRally, and a Venture Partner at ENDEIT Capital. M.Sc. in Computer Science from Delft University of Technology Ph.D. in Computational Linguistics from University of Amsterdam His research expertise spans Natural Language Processing, Machine Learning, and Artificial Intelligence, with applications in legal, medical, business, and regulatory domains. He focuses on text mining, machine translation, question-answering systems, and information extraction from unstructured data. Recent publications (2024–2025) emphasize context-aware machine translation, misinformation detection in recommendation systems, healthcare data analysis, and food science applications. Key themes include integrating deep learning architectures (e.g., Transformers), optimizing search and translation efficiency, and leveraging hybrid human-machine approaches. He has collaborated widely in industry and academia, notably deploying e-discovery software for institutions like the UN War Crimes Tribunals and FBI-ENRON. His career history includes leadership roles at ZyLAB (1987–2021) and prior service in the Royal Dutch Navy.

Jörn Ostermannمشاهده پروفایل

استاد

• Video Coding
• Computer Vision
• Machine Learning
• +۹ مورد دیگر

Prof. Jörn Ostermann is a Full Professor and Head of the Institut für Informationsverarbeitung at Leibniz Universität Hannover since 2003, with prior roles at AT&T Bell Labs and AT&T Labs-Research. He served as Dean of the Faculty of Electrical Engineering and Computer Science (2011–2013) and member of the Senat (since 2020). His research spans video coding, computer vision, machine learning, 3D modeling, and computer-human interfaces , with applications in SAR imaging, predictive maintenance, children's speech analysis, and cochlear implants. Key projects include Next Generation Video Coding , Conditional Coding for Learned Compression , and GreenAutoML4FAS . Notable trends in his recent publications (2025–2023) include Neural network-based video compression Uncertainty estimation in speech recognition Zero-delay coding for cochlear implants Domain adaptation for aerial image segmentation 3D mesh compression standards Error concealment in VVC coding Scientific recognitions: AT&T Standards Recognition Award (1998) ISO Award (1998) IEEE Fellow (2005) Distinguished Lecturer, IEEE CAS Society (2002/2003) MPEG Convenor (2020–2023) He co-authored a graduate textbook on Video Communications , holds >30 patents, and has led >20 research projects. His work bridges academic research and industrial standardization, particularly in MPEG and IEEE committees.

Yannick Blandinمشاهده پروفایل

استاد

• Motor Learning
• Sensorimotor Integration
• Observational Learning
• +۳ مورد دیگر

Professor Yannick Blandin is affiliated with the University of Poitiers (Faculty of Sports Sciences) and the Center for Research on Cognition and Learning (CeRCA) . His work focuses on motor learning mechanisms, observational learning, mental imagery, and feedback systems in sensorimotor integration. Ph.D. in Physical Activity Sciences (University of Montreal, 1994, directed by Dr. Luc Proteau) Current research themes: specificity of practice, eye movement analysis, point-light display applications His publications reveal trends in sensorimotor coding , motor sequence acquisition , and visual feedback optimization , often intersecting neuroscience , psychology , and sports rehabilitation . He has developed the PLAViMoP platform for visualizing and modifying motor sequences. International collaborations include Dr. Stefan Panzer (Saarland University) and Dr. Charles Shea (Texas A&M). His research spans applications in stroke rehabilitation , athletic training , and cognitive-motor interfaces .

Selma Tekirمشاهده پروفایل

دانشیار

• Natural Language Processing
• Text Mining
• Machine Learning
• +۵ مورد دیگر

Selma Tekir is an Associate Professor in the Computer Engineering Department at Izmir Institute of Technology. She completed her undergraduate studies in Computer Engineering at Ege University in 2001, followed by a master's degree from Izmir Institute of Technology in 2004, and earned her PhD from Ege University in 2010. In 2009, she worked as a visiting researcher at the University of Konstanz, Germany. Her educational background includes: B.Sc.: Computer Engineering, Ege University (2001) M.Sc.: Computer Engineering, Izmir Institute of Technology (2004) Ph.D.: Computer Engineering, Ege University (2010) Dr. Tekir's research spans multiple areas of natural language processing and computational linguistics, with a particular focus on Turkish language processing. Her work explores text mining, news analysis, information warfare, and the application of deep learning techniques to linguistic problems. She has made significant contributions to counterfactual detection in Turkish, multi-modal language models, and the integration of symbolic reasoning with neural approaches. Her research often bridges theoretical advances with practical applications, particularly in the Turkish language context which presents unique challenges as a morphologically rich agglutinative language. Analysis of her recent publications reveals a strong trend toward advancing natural language understanding systems for Turkish, developing methods for counterfactual detection, enhancing question answering systems with knowledge graphs, and applying graph neural networks to biological sequence analysis. Her work demonstrates growing interest in combining symbolic reasoning with neural approaches and exploring self-reflection capabilities in language agents. Dr. Tekir has received research funding for projects including: Consistency and Reliability Assessment in News Chains (TÜBİTAK ARDEB 3501) Application of Data Analysis and Visualization Techniques on Historical Sources (BAP) She teaches courses including CENG 381 - Stochastic Processes, CENG 613 - Scientific Research Methods in Computer Science, and SEDS 501 - Introduction to Data Science.

Anne-Lise Jouenمشاهده پروفایل

دانشیار

• Neuroscience
• Language
• Speech
• +۵ مورد دیگر

Anne-Lise Jouen is an Associate Professor at the Faculty of Sports Sciences, University of Burgundy, where she is affiliated with the INSERM U1093 Laboratory. She has held prestigious research positions at institutions including the University of Geneva, University of Tokyo, and Sorbonne University. Her academic credentials include: PhD in Neuroscience (2013) from Stem cell and Brain Research Institute, Claude Bernard University – Lyon I Master 2 in Engineering of Cognition, Creation and Learning (2009) from Grenoble INP Master 2 in Neuropsychology (2009) from Université Pierre Mendès France Master 1 in Cognitive Sciences (2008) from Université Lumière Lyon II Licence in Psychology and Cognitive Sciences (2007) from Université Lumière Lyon II DIU in Gerontechnology (2010) from Joseph Fourier University and others Dr. Jouen's research focuses on the intersection of neuroscience, language processing, and technology. She investigates how the brain processes language and spatial information through embodied cognition frameworks, employing neuroimaging techniques like fMRI, EEG, and fNIRS. Her recent work explores human-robot interaction for language learning and autism intervention, demonstrating through EEG monitoring that social robots increase functional connectivity in children's brains during language acquisition. Her publication record shows consistent research impact across multiple domains including semantic processing, speech production, and autism interventions. Notable projects include the GOLIAH platform for home-based autism intervention and research on telepresence robotics' impact on spatial cognition. Her scientific recognition includes the JSPS fellowship award for her post-doctoral research at the University of Tokyo. Dr. Jouen's current research trajectory includes: 2022-2023: Post-doctorate studying telepresence robotics and virtual reality effects on spatial representations at CAPS Laboratory 2017-2020: Speech production research using EEG (SNF Sinergia Project MoSpeeDi) at University of Geneva 2015-2017: Language learning via human-robot interactions at University of Tokyo 2013-2015: Autism social skills evaluation via ICT at Sorbonne University

Vladimir Golkovمشاهده پروفایل

پژوهشگر

• Deep Learning
• High-Dimensional Data
• Geometric Data
• +۶ مورد دیگر

Dr. Vladimir Golkov is a Postdoctoral Researcher at the Technical University of Munich (TUM) within the School of Computation, Information and Technology, specifically in Informatics 9 (Computer Vision Group). He works under the supervision of Prof. Dr. Daniel Cremers and maintains an active research profile in deep learning applications for medical imaging and biomedical data analysis. His primary research interests focus on deep learning since 2014, with specialization in high-dimensional and geometric data structures, data-processing goals beyond supervised learning (including clustering and anomaly detection), and applications in biomedicine and physics. Dr. Golkov has successfully mentored students who have gone on to pursue PhD studies at prestigious institutions including TUM, LMU, Mila, ETHZ, Cambridge, and Stanford. Analysis of his recent publications reveals a strong trend toward medical imaging applications, particularly in MRI technology, where he combines deep learning approaches with traditional physics-based methods. His work frequently bridges the gap between theoretical machine learning advances and practical medical applications, with significant contributions to diffusion MRI, sequence optimization, and 3D data processing. He has also expanded into emerging areas including large language models for medical applications and equivariant deep learning architectures. Dr. Golkov receives grant support from the Deutsche Telekom Foundation and maintains an active publication record with numerous contributions to leading conferences including ISMRM, MICCAI, and NeurIPS workshops. His research demonstrates a consistent trajectory of innovation at the intersection of computer vision, deep learning, and biomedical applications. He is actively involved in teaching and mentoring, with students from his projects advancing to top PhD programs worldwide. His office is located at Boltzmannstrasse 3, 85748 Garching, Germany (Office: 02.09.061), and he can be contacted at vladimir.golkov@tum.de.

Xi Wangمشاهده پروفایل

پژوهشگر

• Computer Vision
• Computer Graphics
• Vision Science
• +۷ مورد دیگر

Dr. Xi Wang is a postdoctoral researcher at the Technical University of Munich (TUM) within the Computer Vision Group (Informatics 9) under Prof. Daniel Cremers, and a researcher at the Computer Vision and Geometry Lab at ETH Zurich led by Prof. Marc Pollefeys. He collaborates with Prof. Luc Van Gool at INSAIT and has affiliations with MIT and Adobe Research. His research spans computer vision, computer graphics, vision science, and vision-language multimodal learning , focusing on modeling human common sense, intent-driven behavior, and egocentric scene understanding. Research Highlights: Developed multimodal world models (GEM) for fine-grained ego-motion and object dynamics Created vision-language interior design systems (I-Design) and driver trajectory prediction models Advanced 3D human-object interaction reconstruction (ROMEO) and eye-tracking-based action anticipation Explored Gaussian splatting for dynamic scene understanding (EgoGaussian) Scientific Recognition: Recipient of the ECCV 2024 Workshop Best Paper Award for ROMEO $1.6 million grant from BMBF for establishing his independent TUM research group Accepted papers at top venues: CVPR, ECCV, ICLR, ETRA, ISMAR

• Computer Science
• Artificial Intelligence
• Deep Learning
• +۴ مورد دیگر

Tengda Han is a research scientist at Google DeepMind , focusing on video understanding and visual-language models . He previously completed his PhD at the University of Oxford under Andrew Zisserman and earned a Bachelor of Engineering from Australian National University in mechanical & material engineering, with prior studies in business administration and law at Renmin University of China . Research Interests: His work explores neural networks for video analysis, including self-supervised learning , video captioning , object counting , and prompt engineering . Key projects include AutoAD for movie description, Temporal Alignment Networks , and Dense Predictive Coding for video representation. Scientific Awards: Best Paper Award at ACCV 2024 Best Poster Award at BMVC 2023 BMVA Sullivan Doctoral Thesis Prize Runner-up Collaborations & Students: He has collaborated with researchers like Andrew Zisserman , Max Bain , and Arsha Nagrani , and mentored students Junyu Xie , Toby Perrett , and Niki Amini-Naieni on projects including Shot-by-Shot and Unique Video Captioning .

• Computer Science
• Artificial Intelligence

Luis Lago is an Associate Professor at the Polytechnic School of the Autonomous University of Madrid . His office is located in EPS, B-307, and he can be contacted via email at luis.lago@uam.es . His research primarily spans Computer Science and Artificial Intelligence , with a focus on neural networks, natural language processing, and computational neuroscience. His work integrates theoretical and applied machine learning, addressing challenges in sequence modeling, formal automata, and anomaly detection. Recent publications highlight innovations in recurrent neural networks for brain-computer interfaces (e.g., P300-ERP detection) and text analysis (e.g., authorship attribution, fake news detection). No awards, grants, or student advising details are documented in the available information.

Tianxing Heمشاهده پروفایل

استادیار

• AI Safety
• AI-driven Simulation
• Large Language Models
• +۵ مورد دیگر

Tianxing He is an Assistant Professor at Tsinghua University's Institute for Interdisciplinary Information Sciences (IIIS), also known as the Yao Class, which he joined in September 2024. He also works part-time at Shanghai Qi Zhi Institute where he recruits for research-oriented positions and video game development roles. His academic journey includes a postdoc at the University of Washington under Yulia Tsvetkov, a PhD at MIT supervised by James Glass, and bachelor's and master's degrees at Shanghai Jiao Tong University in the ACM honored class under Prof. Kai Yu. Dr. He's research spans AI safety and AI-driven simulation with particular focus on large language models. His work examines LLM capabilities in cryptography (AICrypto benchmark), security vulnerabilities (jailbreaking techniques), multi-agent system integrity, and socially impactful applications like vaccine hesitancy simulation (VACSIM framework). He has developed visualization tools like MiniAgents-Pro using Unity for generative agent simulation and created watermarking techniques such as SemStamp and k-SemStamp for detecting machine-generated text. His publication record shows consistent contributions to top-tier conferences including ACL, EMNLP, NeurIPS, and ICLR, with recent work focusing on evaluating LLM capabilities across diverse domains. The research demonstrates sophisticated understanding of both the technical aspects of language models and their broader societal implications, particularly in security and ethical considerations. Scientific Awards: The Best Paper Award at the Efficient Natural Language and Speech Processing Workshop (NeurIPS ENLSP 2022) for "PCFG-based Natural Language Interface Improves Generalization for Controlled Text Generation" Dr. He actively mentors students and recruits research interns, PhD candidates, and post-bachelor researchers. He plans to teach an undergraduate NLP course in Fall 2025 and organizes the IIIS AI Safety Lunch, a monthly gathering in Beijing for those working on AI safety. His recruitment efforts target students interested in LLM social simulation, AI+game development, and AI safety research, emphasizing the need for strong self-motivation and research passion. He leads research initiatives including the IIIS AI Safety Lunch series and has developed multiple visualization and simulation frameworks. His work sits at the intersection of AI safety, language modeling, and practical applications, with team collaborations spanning multiple institutions including MIT, University of Washington, and Shanghai Jiao Tong University.

• Machine Learning
• Probabilistic Modeling
• Neural Networks
• +۴ مورد دیگر

Carl Allen is a Laplace Junior Chair in Machine Learning at École Normale Supérieure, Paris, working in the research group of Stéphane Mallat, Giulio Biroli and Garbiele Peyré. Previously, he was a postdoctoral fellow at ETH Zurich and completed his PhD in Machine Learning in 2021 at the University of Edinburgh under the supervision of Professors Tim Hospedales and Iain Murray. His educational background includes a BSc in Mathematics & Chemistry from the University of Southampton, an MSc in Mathematics and the Foundations of Computer Science (MFoCS) from the University of Oxford, and MScs in Artificial Intelligence and Data Science from the University of Edinburgh. Before transitioning to AI/ML research, he spent several years in Project Finance. Allen's research focuses on mathematically understanding mechanisms behind successful machine learning methods, particularly neural networks. He investigates how machine learning models exploit aspects of data distribution from a probabilistic perspective. His current topics include explaining how VAEs disentangle independent factors of data, identifying mathematical models behind self-supervised learning, and deriving probabilistic interpretations of softmax classification. His PhD work investigated neural representations of discrete objects and their relationships, with a main result explaining how word embeddings can seemingly be added and subtracted (e.g., queen ≈ king - man + woman), which received Best Paper (honorable mention) at ICML 2019. His research spans theoretical foundations of machine learning, with particular emphasis on representation learning, disentanglement, and probabilistic modeling of neural networks. His work connects mathematical principles with practical machine learning applications, aiming to develop more interpretable and reliable algorithms. Allen has received notable recognition including a Best Paper honorable mention at ICML 2019 and a research grant from the Hasler Foundation. He has delivered invited talks at prestigious institutions including Harvard Center of Mathematical Sciences & Applications and Astra-Zeneca. His collaborative work spans multiple institutions, including a notable internship at Samsung AI Centre, Cambridge, where he worked at the intersection of representation learning and logical reasoning. His research has significant implications for developing more interpretable, reliable, and theoretically grounded machine learning systems.

Edward Changمشاهده پروفایل

استاد

• Epilepsy
• Brain Tumors
• Neurosurgery
• +۳ مورد دیگر

Edward Chang serves as Chair of the Department of Neurological Surgery at the University of California, San Francisco (UCSF), where he is a Professor specializing in surgical therapies for epilepsy, brain tumors, and trigeminal neuralgia. His expertise centers on advanced neurophysiologic brain mapping to preserve speech and motor function during neurosurgical procedures, significantly impacting clinical outcomes for patients with complex neurological conditions. His educational foundation includes: BA in Amherst College (1997) MD from UCSF (2004) Neurosurgery Residency at UCSF (2004-2010) Fellowship in Cognitive Neurophysiology at UC Berkeley's Helen Wills Neuroscience Institute (2008-2009) Chang's research pioneers the intersection of neurosurgery and neural engineering, with primary focus on speech neuroprosthetics, brain tumor heterogeneity, and intracranial biomarker development. His team employs electrophysiological mapping, machine learning, and neurostimulation to decode neural activity for communication restoration in paralyzed patients and optimize surgical approaches for epilepsy and gliomas. This work bridges fundamental neuroscience with transformative clinical applications. Analysis of his 2025 publications reveals dominant themes in speech decoding neuroprosthetics (including multilingual systems and avatar control), glioma molecular evolution, and closed-loop deep brain stimulation for depression/OCD. These studies leverage high-density intracranial recordings to identify personalized neural biomarkers, demonstrating a clear trajectory toward individualized neuromodulation therapies and real-time brain-computer interfaces. His scientific recognition includes: Member of the National Academy of Medicine (2020) Blavatnik National Award Laureate in Life Sciences (2015) Bowes Biomedical Investigator (2018) NIH New Director's Innovation Award (2011-2016) New York Stem Cell Foundation Robertson Investigator (2015-2020) Klingenstein Fellowship Award (2011) Pathway to Independence Award (2009-2013) Alpha Omega Alpha (2003) UCSF Dean's Prize for Research (2004) Young Investigator Award from Grass Foundation-American Epilepsy Society (2011) Chang directs substantial NIH-funded research programs including the BRAIN Initiative opportunities, with grants spanning neural decoding (DP2), stem cell applications, and depression biomarker development. While specific advisees aren't listed in source materials, he mentors surgical residents and fellows within UCSF's neurological surgery program. His clinical team at UCSF specializes in comprehensive brain tumor care and epilepsy surgery, integrating cutting-edge research with patient treatment protocols. The Chang Lab operates at the forefront of translational neuroscience, maintaining active projects in speech neuroprosthetics (including the high-performance system published in Nature 2024), closed-loop DBS for psychiatric disorders, and molecular profiling of glioma progression. Current initiatives focus on scaling brain-computer interfaces for clinical deployment and developing personalized stimulation targets using stereoencephalography data.

Domagoj Ševerdijaمشاهده پروفایل

استادیار

• Combinatorial Optimization
• Computational Linguistics
• Natural Language Processing
• +۴ مورد دیگر

Domagoj Ševerdija is an Assistant Professor at the School of Applied Mathematics and Informatics at Josip Juraj Strossmayer University of Osijek, where he leads the Computer Science and Machine Learning Research Group. He holds a PhD in Electrical Engineering (2013) and a dual BS in Mathematics and Computer Science (2007) from the University of Osijek. His research spans computational linguistics, natural language processing, machine learning, and combinatorial optimization, with applications in bioinformatics, robotics, and energy systems. Recent work focuses on neural language models, domain adaptation techniques, and efficient algorithm design. Publications show strong interdisciplinary trends: computational linguistics (Croatian morphology, sentence embeddings), bioinformatics (cell typing, RNA splicing), and algorithm optimization (terrain guarding, matrix operations). Recent papers emphasize knowledge distillation, domain adaptation, and compressed representations. Awards: Best paper award in AIS - Artificial Intelligence Systems track (MIPRO 2023) Research funding includes: Computer-Assisted Corpus Linguistics (UNIOS, 2019-2020) Croatian Identity Network Framework (Adris Foundation, 2020-2021) Croatian Language in Global Cloud (Adris Foundation, 2019-2020) Leads the Computer Science and Machine Learning Research Group, coordinating projects in NLP, computational geometry, and AI applications.