جست‌وجوی استاد و پژوهشگر

Mike Tehranchiمشاهده پروفایل

مدرس

• Mathematical Finance
• Stochastic Analysis
• Probability Theory
• +۳ مورد دیگر

Dr. Mike Tehranchi is a faculty member at the University of Cambridge, affiliated with the Statistical Laboratory within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) . His research focuses on mathematical finance, stochastic processes, and probability theory. He holds a Lecturer position and is actively involved in academic research, with notable contributions to financial models, term structure analysis, and stochastic calculus. His work bridges theoretical probability and applied finance, addressing topics such as interest rate modeling, implied volatility, and optimal investment strategies. Tehranchi’s research often intersects with optimization, statistical methods, and interdisciplinary applications in astrophysics and fluid dynamics. He maintains an active publication record and contributes to the academic community through his role in the Statistical Laboratory. Key research trends in his articles include the analysis of financial derivatives, stochastic processes in market dynamics, and the application of advanced mathematical techniques to real-world financial problems. His work emphasizes rigorous theoretical foundations while addressing practical challenges in quantitative finance. Dr. Tehranchi has no listed students or academic awards in the provided texts. He can be reached via email and is based in Room D1.04 at the Statistical Laboratory.

Susanna V. Haziotمشاهده پروفایل

استادیار

• Fluid Dynamics
• Partial Differential Equations
• Geophysical Fluid Dynamics
• +۳ مورد دیگر

Susanna V. Haziot is an Assistant Professor in the Department of Mathematics at Princeton University. Her research focuses on fluid dynamics, partial differential equations, and geophysical fluid dynamics with applications to oceanographic phenomena. She investigates wave propagation, vortex dynamics, and nonlinear systems, particularly in contexts like Arctic and Antarctic ocean currents, Muskat problems, and water wave theory. Her work combines analytical techniques, such as bifurcation theory and stability analysis, with geophysical modeling to address challenges in climate science and coastal engineering. Notable contributions include studies on solitary waves with constant vorticity, critical layers in stratified fluids, and the application of stereographic projections to model ocean currents. Dr. Haziot’s publications span topics from mathematical analysis of free boundary problems to historical perspectives on traveling water waves, reflecting her interdisciplinary approach. While no awards or grants are explicitly listed in the provided materials, her extensive publication record highlights her active role in advancing theoretical and applied fluid dynamics research. Her affiliations include the mathematics department at Princeton, where she contributes to both teaching and research initiatives. No doctoral advisees are listed in the provided text, though her work likely involves collaboration with graduate students and research groups focused on environmental fluid dynamics.

• Machine Learning
• Medical Imaging
• Biomedical Engineering
• +۳ مورد دیگر

Dr. Cheng Ouyang is a Departmental Lecturer at the University of Oxford's Institute of Biomedical Engineering, part of the Department of Engineering Science. Affiliated with St. Peter's College, his research focuses on developing data-efficient, robust machine learning approaches for medical imaging and signal analysis. Key interests include domain generalization, few-/zero-shot learning, uncertainty modeling, and multimodal learning applied to medical data such as ultrasound and MRI. Prior to Oxford, he conducted postdoctoral research in cardiac imaging at Imperial College London, where he also earned his PhD in Computing. His work emphasizes practical medical applications, such as accelerating MRI reconstruction and enhancing ECG classification through multimodal techniques. Recent contributions include the CMRxRecon2024 dataset for cardiac MRI and federated learning approaches for low-dose CT denoising. His methods address challenges in generalizability, stability, and user interaction in clinical AI systems. Awards and recognitions are pending explicit mentions in the text. Dr. Ouyang's research spans foundational machine learning theory and applied biomedical engineering, with a focus on bridging gaps between algorithmic innovation and clinical utility. His lab collaborates across disciplines to advance medical imaging analysis and decision support systems.

Daniele Loiaconoمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۶ مورد دیگر

Daniele Loiacono is an Associate Professor at Politecnico di Milano's Department of Electronics, Information, and Bioengineering (DEIB), affiliated with the Artificial Intelligence and Robotics Lab (AIRLab). His research focuses on interdisciplinary applications of Artificial Intelligence, Machine Learning, and Deep Learning in medical imaging, radiation therapy, and procedural content generation for games. He leads projects in synthetic image generation for radiotherapy quality assurance, automated treatment planning, and bias analysis in medical AI systems. Key research areas include medical image synthesis using GANs, radiation therapy optimization, and algorithmic game design. His contributions span clinical applications such as total marrow irradiation (TMI) planning and lymph-node segmentation, alongside innovations in shader generation and interactive evolutionary tools for game development. Loiacono collaborates on multi-center studies to validate AI-driven workflows in healthcare and has pioneered methods combining lean Six Sigma with machine learning for treatment process improvement. His work bridges clinical medicine and computer science, addressing challenges in radiation oncology, anatomical imaging, and procedural content automation. The AIRLab serves as a hub for his research, integrating AI advancements into real-world medical and engineering solutions.

• Fluid Mechanics
• Non-Newtonian Fluids
• Multiphase Flow
• +۴ مورد دیگر

Outi Tammisola is a Professor at KTH Royal Institute of Technology, specializing in Fluid Mechanics within the Department of Mechanics. Her research focuses on non-Newtonian fluids, viscoelasticity, multiphase flows, and microfluidics. She is actively involved in teaching and course coordination for mechanics and fluid dynamics courses, including Mechanics I, Mechanics II, and Wave Motion and Hydrodynamic Stability. Her work bridges numerical methods, experimental validation, and industrial applications, with a strong emphasis on understanding complex fluid behaviors in both theoretical and applied contexts. Dr. Tammisola’s research interests span topics such as elastoviscoplastic fluids, particle migration in microchannels, and interfacial phenomena in viscoelastic systems. Her recent publications address challenges in multiphase flow dynamics, including droplet coalescence, bubble migration, and the rheology of non-Newtonian fluids. She contributes to advancements in numerical methods, such as phase-field modeling and immersed boundary techniques, to simulate complex fluid-structure interactions. Her articles highlight trends in understanding fluid behavior under extreme conditions, such as high aspect ratio microchannels and porous media. Collaborations with experimentalists and computational scientists ensure her work remains both innovative and grounded in real-world applications. While no scientific awards are explicitly mentioned, her extensive publication record reflects significant contributions to the field of fluid mechanics. Outi Tammisola is also engaged in academic leadership, overseeing degree projects and contributing to the development of educational programs at KTH. Her research group likely explores cutting-edge topics in microfluidics and viscoelastic turbulence, though specific lab or team affiliations are not detailed in the provided texts.

Lin Wangمشاهده پروفایل

مدرس

• Machine Learning
• Audio-Visual Signal Processing
• Robotic Perception
• +۳ مورد دیگر

Dr. Lin Wang is a Lecturer in Applied Data Science and Signal Processing at Queen Mary University of London (QMUL), affiliated with the School of Electronic Engineering and Computer Science. He leads the Machine Listening Lab and is a member of the Centre for Multimodal AI, Centre for Intelligent Sensing (CIS), and Institute of Coding (IoC). His research focuses on audio-visual signal processing, robotic perception, and machine learning, with applications in healthcare, drone-based sensing, and human activity recognition. Dr. Wang holds a PhD from Dalian University of Technology and has held postdoctoral positions at QMUL, the University of Sussex, and the Alexander von Humboldt Foundation in Germany. Education and Roles: PhD in Signal Processing, Dalian University of Technology (2010) Postdoc at Queen Mary University of London (2014–2017) Postdoc at University of Sussex (2017–2018) Alexander von Humboldt Fellow at University of Oldenburg (2011–2013) Fellow of the Higher Education Academy (UK) Research Interests: Audio-visual signal processing for drones and wearable devices Machine listening and robotic perception Machine learning for healthcare and environmental monitoring Human activity recognition using multimodal sensors Awards and Grants: Early Career Champion on AI&Data, UK Acoustics Network Outstanding Article Award, Frontiers in Computer Science (2022) EPSRC grant: Bioacoustic Monitoring Using Drones (£46,821, 2022–2023) Innovate UK grant: Music Source Separation (£48,144, 2024–2025) Teaching and Students: Dr. Wang teaches Applied Statistics , Website Design and Authoring , and Machine Learning for Visual Data Analysis . He supervises PhD students including Ashish Alex (speech separation), Michael Clayton (drone audition), and Dmitrii Mukhutdinov (audio-visual processing). Labs and Teams: He co-leads the Machine Listening Lab and is part of the Centre for Multimodal AI, focusing on interdisciplinary projects in robotics, acoustics, and AI.

Alex Warstadtمشاهده پروفایل

استادیار

• Computational Linguistics
• Natural Language Processing
• Cognitive Modeling
• +۵ مورد دیگر

Alex Warstadt is an Assistant Professor at the University of California San Diego, holding appointments in the Department of Linguistics and the Halıcıoğlu Data Science Institute (HDSI). His research focuses on computational linguistics, applying advances in Large Language Models (LLMs) to understand human language acquisition, processing, and structure. Key contributions include developing the CoLA and BLiMP benchmarks for evaluating grammatical ability in LLMs, and the BabyLM Challenge to promote data-efficient language models. His work bridges theoretical linguistics, experimental methods, and computational modeling, particularly in pragmatics and discourse structure. Education: He earned B.A.s in Linguistics and Music Theory from Brown University and a Ph.D. in Linguistics from New York University (NYU), with a dissertation on 'Artificial Neural Networks as Models of Human Language Acquisition.' Postdoctoral work at ETH Zürich furthered his interdisciplinary research. He leads the LeM🍋N Lab at UC San Diego, which investigates language learning, meaning representation, and natural language processing through interdisciplinary collaboration. Research Interests: Warstadt’s research emphasizes leveraging machine learning to explore developmental linguistics, computational cognitive modeling, and pragmatic phenomena such as relevance and presupposition. His lab’s work aims to create models that align with human learning processes while advancing efficient NLP techniques. Recent projects include studying multimodal input effects and optimizing models for developmental plausibility. Labs/Teams: Director of the Learning, Meaning, and Natural Language (LeM🍋N) Lab, focusing on interdisciplinary research across linguistics, cognitive science, and data science.

Wesley Willettمشاهده پروفایل

دانشیار

• Information visualization
• Human-computer interaction
• New media
• +۳ مورد دیگر

Wesley Willett is an Associate Professor in the Department of Computer Science at the University of Calgary, holding the NSERC CRC II Chair in Visual Analytics. His primary research focuses on information visualization, human-computer interaction, and new media applications. He leads the Data Experience Lab and Interactions Lab, exploring innovative methods for data representation and interaction in augmented/virtual reality environments. Education includes a B.S. in Computer Science from the University of Colorado (2006) and a Ph.D. in Computer Science from UC Berkeley (2012). His work bridges technical innovation with user-centered design principles, emphasizing ethical considerations in data visualization and inclusive representation. Key research contributions include: spatial visualization techniques for large environments, gesture-based interfaces for AR/VR, and physical data representations through projects like Cetonia (swarm robotics visualization) and Data Embroidery. His work has been recognized with Best Paper awards at CHI 2015 and Pervasive 2010. Current research emphasizes immersive analytics, wearable visualization systems, and demographically diverse anthropographics. He collaborates with urban designers, neurologists, and environmental scientists to apply visualization in diverse domains like epilepsy surgery planning and air quality monitoring.

Larry Heckمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۵ مورد دیگر

Larry Heck is a Professor at the Georgia Institute of Technology with joint appointments in the School of Electrical and Computer Engineering and the School of Interactive Computing. He holds the Rhesa S. Farmer Advanced Computing Concepts Chair and is a Georgia Research Alliance Eminent Scholar. His research focuses on machine learning, deep learning, natural language processing, conversational systems, and speech/speaker recognition. He directs the AI Virtual Assistant (AVA) Lab, advancing next-generation AI assistants. Dr. Heck has held leadership roles in industry, including at Microsoft, Google, Samsung, and Viv Labs, and has over 50 U.S. patents. Education: BSEE from Texas Tech University (1986) MSEE and PhD in Electrical Engineering from Georgia Tech (1991) Research Interests: Dr. Heck’s work bridges machine learning and human-centric AI, with emphasis on conversational systems, multimodal interaction, and real-world applications. His AVA Lab develops AI assistants that integrate visual, auditory, and contextual cues for natural interaction. Recent projects include multimodal sensor integration, dialogue systems for caregiving networks, and embodied AI for avatar animation. Awards: IEEE Fellow (2020) Academy of Distinguished Engineering Alumni, Georgia Tech (2017) Distinguished Engineer Award, Texas Tech University (2017) Advising & Grants: While primarily focused on industry collaboration, Dr. Heck mentors students through Georgia Tech’s interdisciplinary programs. His research is funded by government agencies and corporate partnerships, including the NSA and DARPA. Labs & Teams: The AVA Lab collaborates with academia and industry to create AI systems that understand context, gestures, and environment. Current initiatives include multimodal dialogue datasets (e.g., OKCV, SensorQA) and reinforcement learning frameworks for real-time systems.

Lu Shijianمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Learning
• Image and Video Analytics
• +۱۴ مورد دیگر

Lu Shijian is an Associate Professor (tenured) at the School of Computer Science and Engineering , Nanyang Technological University (NTU) , Singapore. He holds a PhD in Electrical and Computer Engineering from the National University of Singapore and leads the Visual Intelligence Lab (VILab) , focusing on humanlike visual perception, understanding, and creation. University: Nanyang Technological University School: School of Computer Science and Engineering Academic Rank: Associate Professor (tenured) Email: Shijian.Lu@ntu.edu.sg Office: N4-02C-101, NTU, Singapore His research spans computer vision, deep learning, image and video analytics, visual intelligence, and machine learning , with key topics including scene text detection, unsupervised domain adaptation, image synthesis, satellite image analytics, and facial expression recognition. His work integrates supervised, semi-supervised, and self-supervised learning across 2D images, 3D point clouds, and multi-spectral data. The recent publications highlight a strong trend in domain adaptation, generative modeling, 3D vision, and multimodal AI . His lab produces high-impact work accepted at top venues like CVPR, ICCV, ECCV, NeurIPS, and TPAMI, with applications in autonomous systems, image editing, and robust AI. Top winner of ICFHR2014 Competition on Word Recognition from Historical Documents Top winner of ICDAR 2013 Robust Reading Competition (scene text segmentation) Top winner of ICDAR 2013 Document Image Binarization Contest (DIBCO 2013) Top winner of H-DIBCO 2010 – Handwritten Document Image Binarization Competition Top winner of ICDAR 2009 Document Image Binarization Contest (DIBCO 2009) Lu advises several PhD students and serves as an Associate Editor for Pattern Recognition and Neurocomputing . He has held leadership roles in top conferences as Senior Program Committee member (IJCAI, AAAI) and Area Chair (ICDAR, WACV). His lab, the Visual Intelligence Lab , is actively recruiting PhD students and conducting cutting-edge research in visual intelligence, with recent work on 3D Gaussian splatting, backdoor attacks, and vision-language models.

Sauleh Eetemadiمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Translation
• Statistical Machine Translation
• +۶ مورد دیگر

Dr. Sauleh Eetemadi is an Assistant Professor in the School of Computer Science at the University of Birmingham Dubai, contributing to both teaching and research in computer science. His academic and professional journey bridges industry innovation and higher education, with affiliations at Microsoft Research, IUST, and now the University of Birmingham. His educational background includes: PhD in Electrical and Computer Engineering, Michigan State University, 2016 MSc in Electrical and Computer Engineering, Michigan State University, 2005 BS in Computer Engineering, Sharif University of Technology, 2002 Dr. Eetemadi's research focuses on Natural Language Processing (NLP) and Machine Translation , particularly in the area of data selection for statistical and neural models. His work integrates algorithmic design, artificial intelligence, and large-scale language systems. He has taught courses such as Advanced Programming, Data Structures, Algorithm Design, AI, and NLP, reflecting a broad yet deep engagement with core computer science disciplines. Although specific publications are not listed in the provided text, his long-term work at Microsoft Research on developing a world-class machine translation service suggests a strong publication record in NLP, data optimization, and AI-driven language technologies. His research likely spans topics such as bilingual corpora filtering, model efficiency, and scalable translation systems. He has no listed scientific awards in the provided content. Dr. Eetemadi has advised students during his tenure at IUST and continues to mentor in his current role, though specific student names are not mentioned. He has not received any explicitly mentioned grants in the text, but his 13-year tenure at Microsoft Research implies involvement in major industrial research initiatives and collaborative projects. His transition from industry R&D to academia highlights a commitment to both practical innovation and academic education. He is associated with the Natural Language Processing group at Microsoft Research and currently contributes to research activities within the School of Computer Science at the University of Birmingham Dubai, though no formal lab or research team name is specified in the source.

Gerd Stummeمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Formal Concept Analysis
• +۶ مورد دیگر

Gerd Stumme is a Full Professor of Computer Science at University of Kassel , leading the Chair on Knowledge and Data Engineering . He serves as Executive Director of the Research Center for Information Systems Design (ITeG) , director of the International Centre for Higher Education Research (INCHER) , and founding member of the Hessian Institute for Artificial Intelligence (hessian.AI) . His research spans the intersection of Data Science, AI, and Mathematics , focusing on semantic/structural analysis of social networks, concept hierarchies, and mathematical structures (graphs, ordered sets) for knowledge acquisition. He pioneered work on Semantic Web, Web Mining, Social Bookmarking , and Recommender Systems , and has recently revisited mathematical foundations for knowledge representation. Recent publications analyze ordinal motifs in lattices , controversy mapping , and social network structures , with applications to business models, journalism, and AI. His work often integrates graph theory and formal concept analysis . He is a core developer of BibSonomy , a social bookmarking and publication-sharing system, and has contributed to FolkRank and TriAS algorithms for collaborative knowledge management.

Özlem Gürsesمشاهده پروفایل

استاد

• Insurance Law
• Reinsurance Law
• Marine Insurance Law
• +۷ مورد دیگر

Özlem Gürses is Professor of Commercial Law at The Dickson Poon School of Law, King's College London. She previously taught at Norwich and Southampton Law Schools and holds law degrees from the University of Istanbul, an LLM in Maritime Law (with distinction) from the University of Southampton, and a PhD in Law from the same institution. She is a leading scholar in insurance and reinsurance law, with a focus on marine, compulsory, and emerging risk insurance. Her research interests include: Insurance and reinsurance law Marine and non-marine insurance Compulsory motor vehicle insurance Insurance of offshore installations and commercial risks Artificial intelligence in insurance contracts Her recent publications analyze business interruption insurance during the pandemic, public-private partnerships in reinsurance, subrogation immunity, and the legal treatment of emerging risks. She is the sole author of key texts such as The Law of Compulsory Motor Vehicle Insurance (2019), Marine Insurance Law (3rd ed, 2023), and Reinsuring Clauses (2nd ed, 2025), and has edited major reference works including the Edward Elgar Research Handbook on Marine Insurance Law (2024). She is actively involved in the legal academic community: Member of the Presidential Council, International Insurance Law Association (AIDA) Chair, Reinsurance Working Party of AIDA Vice-Chair, British Insurance Law Association Committee Professor Gürses teaches insurance law at both undergraduate and postgraduate levels and currently supervises four PhD students. She also lectures internationally in Germany, Singapore, China, Greece, and Italy. She has organized academic conferences and participates in research on the intersection of law, technology, and sustainability.

Antonia Krefeld-Schwalbمشاهده پروفایل

استادیار

• Cognitive Science
• Consumer Behavior
• Sustainable Marketing
• +۵ مورد دیگر

Antonia Krefeld-Schwalb is an Assistant Professor at the Department of Marketing Management, Rotterdam School of Management, Erasmus University. With a background in cognitive science and management, her research bridges computational modeling, eye-tracking, and consumer decision-making to address sustainability challenges. Current Affiliation: Assistant Professor, Rotterdam School of Management Research Focus: Sustainable consumer behavior, decision-making processes, and methodological improvements Key Collaborations: Columbia University, University of Geneva, Erasmus Sustainability Program Her cognitive science training informs methodological approaches like mouse/eye tracking and computational modeling applied to marketing problems. She investigates structural parameter interdependencies, external validity threats in surveys, and climate risk communication effectiveness. Recent research trends include climate adaptation strategies, sustainable behavior interventions, and meta-scientific analyses of statistical practices in consumer research. She advocates for heterogeneous population sampling and preregistration to enhance validity. Scientific Honors: Veni Grant (NWO) She develops targeted sustainability interventions through collaborations like the Erasmus Sustainability Program. Her work appears in journals such as PNAS, Journal of Marketing Research, and Psychological Review.

Sai Reddyمشاهده پروفایل

دانشیار

• Systems Immunology
• Synthetic Immunology
• Antibody Repertoire Sequencing
• +۵ مورد دیگر

Sai Reddy is an Associate Professor of Systems and Synthetic Immunology at ETH Zurich's Department of Biosystems Science and Engineering (D-BSSE) in Basel, Switzerland, where he leads the Laboratory for Systems and Synthetic Immunology. Since September 2018, he has served as Vice Director of the Botnar Research Centre for Child Health (BRCCH), driving child health innovation through interdisciplinary research. Education: Bachelor of Science (Biomedical Engineering) from Northwestern University (2003) Master of Science (Biomedical Engineering) from Northwestern University (2004) Ph.D. (Bioengineering and Biotechnology) from École Polytechnique Fédérale de Lausanne (EPFL) (2008), supervised by Prof. Melody Swartz and Prof. Jeffrey Hubbell Post-doctoral fellowship under Prof. George Georgiou at the University of Texas, Austin (2008) His research pioneers the integration of computational modeling and cellular engineering to decode immune complexity. Systems Immunology employs quantitative measurements and machine learning to analyze adaptive immunity through antibody repertoire sequencing, while Synthetic Immunology reprograms immune cells via molecular engineering for cellular immunotherapy and directed evolution applications. Both fields converge on immunogenome manipulation to advance therapeutic design. Scientific Awards: KPMG tomorrow’s market award (2007) for nanoparticle vaccine technology As principal investigator, Prof. Reddy mentors graduate researchers in his laboratory; no specific advisees or grant awards are detailed in the text. His leadership at BRCCH amplifies translational impact on child health, though operational grant mechanisms remain undisclosed. The Laboratory for Systems and Synthetic Immunology operates from ETH Zurich's Basel campus at Klingelbergstrasse 48, Switzerland, focusing on high-throughput sequencing and synthetic biology methods to engineer immune responses.