جست‌وجوی استاد و پژوهشگر

Christian Theobaltمشاهده پروفایل

استاد

• Computer Science
• Artificial Intelligence
• Computer Vision
• +۵ مورد دیگر

Christian Theobalt is a Professor of Computer Science at Saarland University and Scientific Director of the Visual Computing and Artificial Intelligence Department at the Max Planck Institute for Informatics . He leads the Saarbruecken Center for Visual Computing as a strategic partnership between Google and MPI. PhD in Computer Science (2005) from MPI-INF/Saarland University Postdoctoral Researcher at MPI (2005-2007) Visiting Assistant Professor at Stanford (2007-2009) His research focuses on the intersection of Computer Graphics, Computer Vision, and Artificial Intelligence , with specializations in: 3D/4D Human Reconstruction Neural Rendering Performance Capture Geometric Deep Learning Volumetric Video Quantum Visual Computing Recent article trends show emphasis on: Quantum computing applications in visual reconstruction Neural rendering with radiance fields Human motion capture from egocentric views Gesture-language interaction modeling Multi-modal scene understanding Real-time free-viewpoint rendering Scientific Awards : Fellow of EUROGRAPHICS (2022) CVPR Best Student Paper Honorable Mention (2020) ERC Consolidator Grant (2017) Karl Heinz Beckurts Award (2017) Busy Beaver Teaching Award (2016) ERC Starting Grant (2013) Advising over 50 students and researchers including: Current researchers: Viktor Rudnev, Linjie Lyu, Mohit Mendiratta Postdocs: Kwang In Kim, Kiran Varanasi Alumni: Franziska Mueller (Google), Dushyant Mehta (Qualcomm), Ayush Tewari (MIT) Grants include ERC grants, Google Glass Research Award, and multiple industry partnerships. His lab maintains cutting-edge facilities with: Multi-camera capture systems Quantum annealing infrastructure HDR radiance field technology GPU clusters for AI research Time-of-flight imaging systems Event camera arrays

Konrad Schindlerمشاهده پروفایل

استاد

• Photogrammetry
• Remote Sensing
• Computer Vision
• +۳ مورد دیگر

Prof. Konrad Schindler holds the position of Full Professor at the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. He is also the Head of the Institute of Geodesy and Photogrammetry (IGP), leading research and educational activities in geomatics and computer vision. His career spans roles as a Photogrammetric Engineer, scientific assistant, postdoc researcher, and academic faculty across institutions including Graz University of Technology, Monash University, and TU Darmstadt before joining ETH Zürich in 2010. Education: Undergraduate studies in Geodesy (1992–1995), Graz University of Technology, Austria MEng in Photogrammetry and Geoinformation (1995–1999), Vienna University of Technology, Austria PhD in Computer Science (2001–2003), Graz University of Technology, Austria Research focuses on Photogrammetry , Remote Sensing , Computer Vision , and Image Understanding with interdisciplinary applications in environmental monitoring, geospatial analysis, and disaster response. He develops computational methods for 3D reconstruction, fusion of multi-modal data, and AI-driven solutions for satellite imagery interpretation. His work bridges geomatic engineering and machine learning to address challenges in urban mapping, climate modeling, and biological systems analysis. Publications reflect expertise in geospatial AI, diffusion models, and benchmarking datasets for disaster resilience. Notable works include Marigold (image analysis adaptation) and BRIGHT (building damage assessment). His research emphasizes practicality and scalability, such as affordable depth estimation and global biomass datasets. He has received the 2013 Marr Prize Honourable Mention (IEEE) and the 2012 U.V. Helava Award (ISPRS), alongside several Best Presentation Awards. His contributions span technical leadership, editorial roles (ISPRS Journal), and service to Swiss remote sensing commissions. Advising and grants: While no specific advisee names or grant details are listed, his career trajectory includes mentoring postdocs and junior faculty. He teaches advanced courses in Photogrammetry , Image Interpretation , and Machine Vision , integrating cutting-edge AI techniques into curricula. His research group collaborates on global-scale projects like canopy height mapping and satellite-based climate variable assessments. Labs/Teams: As Institute Head, he oversees the IGP lab at ETH Zürich, with prior affiliations including the Digital Perception Lab (Monash University) and the Computer Vision Lab (ETH Zurich). His work often involves multi-institutional collaborations focused on geospatial AI and environmental science.

Yuki M. Asanoمشاهده پروفایل

استاد

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

Yuki M. Asano is a full Professor at the University of Technology Nuremberg , leading the Fundamental AI (FunAI) Lab . Previously, he led the QUVA Lab at the University of Amsterdam and earned his PhD at the Visual Geometry Group (VGG) of the University of Oxford under Andrea Vedaldi and Christian Rupprecht. University of Technology Nuremberg (2024–present) University of Amsterdam (prior to 2024) University of Oxford (PhD, 2020) His research spans Artificial Intelligence , Machine Learning , and Computer Vision , with a focus on Causal Representation Learning , Self-Supervised Learning , and Efficient Model Adaptation . He pioneered techniques like BISCUIT (causal variable identification) and VeRA (parameter-efficient fine-tuning). His work extends to Medical Imaging and Environmental Monitoring through applications in fetal ultrasound analysis and marine debris detection. Recent publications (2023–2025) highlight advancements in Self-Supervised Learning , Vision-Language Models , and 3D Understanding . Notable papers include TWIST & SCOUT (multimodal LLM grounding), SIGMA (masked video modeling), and GeneralAD (anomaly detection). His ICCV 2023 work on Self-Ordering Point Clouds and MoSiC (optimal-transport motion trajectories) underscores his interdisciplinary approach. He received the JUPITER compute grant (2025) and an Outstanding Paper Award at ICLR 2024 . His collaborations span institutions like MIT-IBM Watson AI Lab, Qualcomm AI Research, and University of Amsterdam.

Peichen Zhongمشاهده پروفایل

استادیار

• Materials Science
• Machine Learning
• Computational Modeling
• +۵ مورد دیگر

Dr. Peichen Zhong is an Assistant Professor in the Department of Materials Science and Engineering at the National University of Singapore (NUS). He leads the Applied Machine Learning and Materials Modeling (AM³) Group, focused on advancing computational methods for clean energy technologies. His research integrates machine learning with atomistic simulations to tackle challenges in battery materials, disordered materials, and sustainable energy systems. Education: B.S. in Physics from University of Science and Technology of China (2018); Ph.D. in Materials Science from UC Berkeley (2023, advised by Prof. Gerbrand Ceder); Postdoctoral training at Lawrence Berkeley National Lab and BIDMaP, co-advised by Persson, Cheng, and Krishnapriyan. Research Interests: Computational modeling of battery cathodes/electrolytes, AI-driven interatomic potentials, statistical mechanics in disordered materials, and generative models for scientific discovery. Key areas include Li/Na-ion batteries, solid-state reactions, and sustainable energy materials. Awards: BIDMaP Emerging Scholar Fellowship (UC Berkeley CDSS, 202?), 2023 Rising Stars in Materials Science (CMU/MIT/Stanford). Labs/Teams: The AM³ Group at NUS MSE focuses on interdisciplinary research combining theory, computation, and AI4Science. Current openings include PhD students and postdoctoral researchers.

Vineeth N Balasubramanianمشاهده پروفایل

استاد

• Deep Learning
• Machine Learning
• Computer Vision
• +۸ مورد دیگر

Vineeth N Balasubramanian is a Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Hyderabad, with affiliate faculty status in the Department of Artificial Intelligence. His research focuses on the intersection of deep learning, machine learning, and computer vision, emphasizing explainability, robustness, and real-world applications. He leads Lab 1055, which investigates problems such as Explainable and robust AI/ML systems Lifelong learning in evolving environments Multimodal vision-language models Applications in agriculture, autonomous navigation, and human behavior analysis His recent work includes causal reasoning in transformers, vision-language model capabilities, and drone-based object detection. Funded by organizations like Google, Microsoft, Intel, and DST, he has received multiple awards including the World's Top 2% Scientists (2022-23), INSA/INAE Fellowships, and Best Paper recognitions. Lab 1055 collaborates with institutions like CMU, UBC, and Monash University, contributing to cutting-edge advancements in AI.

Stanislav Smirnovمشاهده پروفایل

استاد

• Mathematical Physics
• Statistical Physics
• Probability Theory
• +۴ مورد دیگر

Stanislav Smirnov is a Professor of Mathematics at the University of Geneva, where he has been a faculty member since 2003. He also holds a part-time position at the Chebyshev Laboratory of St. Petersburg State University. As Director of the National Centre of Competence in Research SwissMAP since 2014, he leads a major research initiative funded by the Swiss National Science Foundation. His education includes a mathematics degree from St. Petersburg State University (1992) and a PhD from the California Institute of Technology (1996). Prior to joining Geneva, he held positions at Yale University, the Institute for Advanced Study in Princeton, the Max Planck Institute in Bonn, and the Royal Institute of Technology in Stockholm. Professor Smirnov's research focuses on the mathematical foundations of statistical physics, with particular emphasis on conformal invariance in two-dimensional lattice models. His work has revolutionized our understanding of critical phenomena through rigorous mathematical approaches, establishing deep connections between probability theory, complex analysis, and statistical mechanics. He develops novel discrete complex analysis techniques to prove continuum limits of lattice models at criticality. His publication record shows consistent advancement in understanding random planar geometry, percolation theory, and the Ising model, with increasing attention to Liouville quantum gravity in recent years. His work bridges deep mathematical analysis with physical insights, often providing rigorous proofs for phenomena previously understood only through physical intuition. Fields Medal (2010) for the proof of conformal invariance of percolation and the planar Ising model in statistical physics As an advisor, Professor Smirnov has mentored doctoral students including Ekin Arikök and Nikolai Bobenko. He leads the ANALYSE, PHYSIQUE MATHÉMATIQUE ET PROBABILITÉS research group at the University of Geneva. His research is supported by major grants including the NCCR SwissMAP, European Research Council's AG COMPASP, and Swiss National Science Foundation grants 200020_143434 and 200020_141329. Professor Smirnov has organized numerous influential conferences, including the "Conformal maps from probability to physics" workshop at Monte Verità (2010) and multiple "Random Planar Geometry" meetings in Les Diablerets, fostering collaboration between mathematicians and physicists working on critical phenomena and random geometry.

Maya Ramanathمشاهده پروفایل

دانشیار

• Database Systems
• Information Retrieval
• Semantic Web Data Management
• +۳ مورد دیگر

Maya Ramanath is an Associate Professor in the Department of Computer Science and Engineering at Indian Institute of Technology (IIT) Delhi. She joined IIT Delhi in 2011 after a postdoctoral research stint at the Max-Planck Institute for Informatics in Germany. Her research interests focus on database systems, information retrieval, semantic web technologies, and knowledge graph construction and applications. Education: PhD in Computer Science, Indian Institute of Science, Bangalore M.Sc.(Engg.) in Computer Science, Indian Institute of Science, Bangalore B.E. in Computer Science and Engineering, Bangalore University, Bangalore Her recent work emphasizes efficient query processing over large-scale graphs, knowledge graph applications, and natural language interfaces for semantic data. Notable contributions include algorithms for reachability approximation in web-scale graphs, speculative query planning for knowledge graphs, and exploratory querying techniques. She has collaborated extensively on projects like NAGA, ESTHETE, and KlusTree, advancing the state of the art in graph-based data management and semantic search. Publications span conferences such as ICDE, ECIR, EDBT, and VLDB, reflecting a strong focus on database systems, graph algorithms, and semantic web applications. Her work bridges theoretical foundations with practical implementations, addressing scalability and efficiency challenges in modern data management systems. Research and advising activities include supervision of projects on distributed graph processing, query optimization, and knowledge representation. She has contributed to open-source tools like LegoDB and StatiX, and her lab focuses on interdisciplinary approaches to data-centric AI.

• Food Business Management
• Food Innovation
• Innovation Management
• +۷ مورد دیگر

Dimitrios Skalkos serves as a Professor at the Department of Chemistry, University of Ioannina, holding the specific title of Professor of Innovation Management in Food Businesses. His office is located in building X2, room X2-206c on the Ioannina Campus (45110 Ioannina), and he leads the Food Chemistry Laboratory. He earned his Basic Degree in Chemistry from the University of Ioannina (1980-1985) followed by a Doctoral Degree from the University of Toledo, USA (1986-1989) with dissertation research on "Di- and Tetrahydroporphyrins: Syntheses and Antitumor Activities". Skalkos's research spans Food Business Management, Food Innovation, and Innovation Management, with specialized focus on Innovation & Entrepreneurship, Aromatic Plants, Innovative Foods, and Strategic Marketing. His work bridges academic research with industry applications in food systems, emphasizing Greek agricultural products and pandemic-era consumer behavior. His 36 publications (2020-2024) reveal strong trends in post-pandemic food innovation, consumer perception of traditional Greek foods, functional food development (particularly olive oil, cheese, and grape by-products), and sustainable agri-food supply chains. Key thematic clusters include traceability systems, bioactive properties of novel foods, and economic impacts on food choices. He has actively supervised Master's and PhD students through departmental presentations, directed 23 research programs, and organized 15 seminars and conferences. His laboratory website ( https://foods.chem.uoi.gr ) showcases ongoing food chemistry innovations.

Ali Yazdaniمشاهده پروفایل

استاد مدعو

• Condensed Matter Physics
• Quantum Fractals
• Topological Insulators
• +۵ مورد دیگر

Ali Yazdani is an Adjunct Professor at the University of Illinois Urbana-Champaign's Grainger College of Engineering, Department of Physics, and Director of the Princeton Center for Complex Materials at Princeton University. His research focuses on quantum condensed matter physics, leveraging scanning tunneling microscopy (STM) and spectroscopy to explore novel quantum phases in materials such as graphene, twisted bilayer graphene, and topological insulators. Key achievements include the first direct observation of Hofstadter's fractal energy spectrum in quantum materials (2025), studies on Majorana fermions in atomic chains, and investigations into strongly correlated Chern insulators. His work bridges theoretical predictions with experimental validation, emphasizing quantum materials' topological and correlated properties. Affiliations: Princeton University, Department of Physics; University of Illinois Urbana-Champaign, Grainger College of Engineering. Research Themes: Quantum fractals, topological insulators, superconductivity, Majorana fermions, moiré materials. Research Summary: Dr. Yazdani’s lab employs advanced STM techniques to visualize electronic wavefunctions and study correlated phases. Notable projects include: - Visualization of Hofstadter’s butterfly in twisted bilayer graphene. - Discovery of valley skyrmions in graphene quantum Hall ferromagnets. - Unconventional superconductivity in magic-angle graphene. - Development of methods to detect Majorana zero modes. Labs/Teams: Yazdani Lab at Princeton University focuses on quantum materials and topological phases, collaborating with theorists and experimentalists globally.

Smirnov Stanislavمشاهده پروفایل

استاد

• Mathematical Physics
• Probability
• Complex Analysis
• +۳ مورد دیگر

Stanislav Smirnov is a Professor at the University of Geneva's Department of Mathematics since 2003 and Head of the Chebyshev Laboratory at St. Petersburg State University since 2010. He holds a Ph.D. from the California Institute of Technology (1996) and a B.Sc./M.Sc. from St. Petersburg State University (1992). His research focuses on mathematical physics, probability, complex analysis, and dynamical systems, with groundbreaking contributions to the understanding of critical phenomena in statistical physics. Key achievements include receiving the Fields Medal (2010), the highest honor in mathematics, for his work on percolation and the Ising model. He has organized major international conferences, such as the 'St. Petersburg School in Probability & Statistical Physics' (2012) and the '2D Statistical Physics Workshop' (2013). His academic career includes positions at Yale University, the Max-Planck Institute, and KTH Royal Institute of Technology. Education: Ph.D., Mathematics, California Institute of Technology (1996) B.Sc./M.Sc., Mathematics (with honors), St. Petersburg State University (1992) Awards: Fields Medal (2010) European Research Council Advanced Grants (2008, 2013) Rollo Davidson Prize (2002) Salem Prize (2001) Smirnov’s research bridges probability theory and complex analysis, with notable results on conformal invariance in two-dimensional models. His work has advanced understanding of critical exponents, percolation thresholds, and the universality of phase transitions. He advises doctoral students and collaborates internationally on projects funded by Swiss and European grants. He co-organized over 20 conferences worldwide, including plenary talks at the International Congress of Mathematicians (2006, 2010) and the World Congress in Probability and Statistics (2012). His lab fosters interdisciplinary research, linking mathematics to physics and computer science.

Teresa Weinholtzمشاهده پروفایل

پژوهشگر

• Contemporary Artists' Books
• Materiality of Digital Art
• Literature-Culture-Arts Intersections
• +۳ مورد دیگر

Teresa Weinholtz is a researcher at the Research Centre for Communication and Culture (CECC) at Universidade Católica Portuguesa in Lisbon. She is currently pursuing a doctoral degree in Culture Studies, focusing on contemporary artists' books and their materiality, artistic practice, and cultural context. She is part of the Critique, Conflict and Cultre research cluster. PhD Candidate in Culture Studies, Universidade Católica Portuguesa Master of Arts in Book Science: Editorial Studies, Ludwig Maximilian University of Munich (2023) Bachelor of Arts in English Studies, Ludwig Maximilian University of Munich (2019) Her research explores the intersection of literature, culture, and contemporary arts, particularly through the lens of artists' books. She investigates topics like digital materiality, augmented reality, and culinary exchange in artistic contexts. Her recent work examines how artists' books address privacy, social commentary, and decolonial themes. Teresa's publications (2023-2025) focus on the evolving relationship between digital technologies and artistic book practices. Her research analyzes how immateriality in digital artists' books interacts with traditional forms, and how culinary themes in artists' books create new avenues for cultural expression and critique. She actively participates in academic presentations and conferences, including topics on decolonial artivism, virtual spaces in artists' books, and AI's role in artistic self-reflection.

Michael A Osborneمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Optimization
• Gaussian Processes
• +۳ مورد دیگر

Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML

Andreas Vlachosمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Dialogue Modelling
• +۱۰ مورد دیگر

Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.

Henrik Sandbergمشاهده پروفایل

استاد

• Cyber-Physical Systems Security
• Power Systems
• Model Reduction
• +۶ مورد دیگر

Henrik Sandberg is a Professor at the Division of Decision and Control Systems , KTH Royal Institute of Technology , Stockholm, Sweden. He holds the title of Deputy Head of Division and is affiliated with the School of Electrical Engineering and Computer Science . Education: MSc in Engineering Physics (1999) PhD in Automatic Control (2004) from Lund University Postdoctoral position at Caltech (pre-2007) Research Interests: Focus on cyber-physical systems security , power systems , model reduction , and fundamental limitations of control systems . Key sub-areas include attack detection , networked control , privacy-preserving estimation , and resilient control architectures . Publications: Over 150 papers across IEEE Transactions and Automatica , covering topics like stealthy attacks , distributed control , LQG optimization , and thermodynamic costs in filtering . Recent work includes LWE-based encrypted control and Bayesian deception mechanisms . Scientific Awards: Best Student Paper Award Finalist at IEEE CASE 2014; Best Student-Paper Award at IEEE CDC 2004. Grants & Projects: Leads the DYNACON project (WASP Cybersec cluster) and collaborates on CERCES (critical infrastructure resilience). Serves as examiner for multiple advanced courses in cybersecurity and control systems. Contact: Email: hsan@kth.se Phone: +46 (0)8 790 7294 Room: A:607, Malvinas Väg 10, Stockholm

Eva Spiesمشاهده پروفایل

استاد

• Religious plurality
• Contemporary Christianity in Africa
• Religion and development cooperation
• +۹ مورد دیگر

Prof. Dr. Eva Spies is a Professor for the Study of Religion with Focus on Africa at the University of Bayreuth within the Faculty of Humanities & Social Sciences . She holds a PhD in Cultural Anthropology from the University of Mainz and leads the Research Section Moralities in the Africa Multiple Cluster of Excellence (EXC 2052). Key Roles : Principal Investigator, Bayreuth International Graduate School of African Studies (BIGSAS) Vice Dean of Research Co-founder, German Association for the Study of Religion's Africa working group Research Interests span religious plurality, development cooperation, and transformative practices. She coined the concept of religious engineering to analyze how religious resources shape social/political worlds. Regional expertise in Niger/West Africa and Madagascar/Indian Ocean , with focus on: South-South mission Religion and pandemic response Intercultural hermeneutics Epistemological conflicts Material culture in development Projects : Africa Multiple Cluster of Excellence (2025): "Religious engineering. The making of moralities, development and religion in Niger" Humboldt Research Hub (2021): "Fighting pandemics with religion?" (with Moi University and University of Zimbabwe) Education : PhD in Cultural Anthropology, University of Mainz