جستجوی استاد راهنمای دانشگاهی

Leland Bybeeمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Econometrics
• +۲ مورد دیگر

Leland Bybee is an Assistant Professor of Finance at the University of Chicago Booth School of Business . He leverages machine learning and natural language processing to address economic and financial questions, particularly focusing on belief measurement with applications to asset pricing and behavioral economics. Ph.D. in Financial Economics, Yale School of Management (2024) M.S. in Statistics, University of Michigan (2017) B.A. in Economics, University of Chicago (2013) His research integrates computational methods with economic theory to analyze: Textual analysis of business news for macroeconomic tracking Narrative-driven asset pricing models Memory-based belief formation using kernel methods Macroeconomic determinants of currency returns He has received multiple awards including: Dimension Fund Advisors Distinguished Paper Award BlackRock Applied Research Award HEC Top Finance Graduate Award The Brattle Group PhD Candidates Award EFA Engelbert Dockner Memorial Prize Bybee teaches Machine Learning in Finance and participates in finance seminars, contributing computational tools like regIPCA (Python) and changepointsHD (R) to the research community.

Estara Arrantمشاهده پروفایل

پژوهشگر ارشد

• Semitic Linguistics
• Medieval Jewish Studies
• Medieval Islamic Studies
• +۱۲ مورد دیگر

Dr Estara Arrant is a Cambridge University Library Leverhulme Early Career Research Fellow and Trinity Hall Postdoctoral Research Associate. She merges Semitic linguistics with data science to study medieval Jewish and Islamic textual cultures. PhD (University of Cambridge, 2021): Computational analysis of Cairo Genizah Torah codices MPhil (University of Oxford, 2018): Linguistic structure of Qur’ānic creation narratives BA: History & Modern Hebrew Her groundbreaking work combines machine learning and bioinformatics algorithms to analyze textual evolution. Current projects include: Developing the first NLP model for medieval Judaeo-Arabic Creating digital tools for computerized stemmatology She actively teaches Python and R as an Cambridge Digital Humanities associate, and her research has been published by Open Book Publishers and Brill .

Gedas Bertasiusمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Video Understanding
• +۵ مورد دیگر

Gedas Bertasius is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. Previously, he served as a postdoctoral researcher at Meta AI (Facebook AI) and earned his PhD in Computer Science from the University of Pennsylvania. His academic journey began with a bachelor’s degree in Computer Science from Dartmouth College. Dr. Bertasius specializes in computer vision and machine learning with specific interests in: Video understanding First-person vision (egocentric vision) Human behavior modeling Multimodal deep learning Transfer learning Computer vision for sports analytics Video+robotics integration His research produces practical frameworks like Video ReCap for hierarchical captioning of long videos, SiLVR for language-based video reasoning, and BASKET for fine-grained skill estimation. He focuses on developing models that can process videos across multiple temporal granularities while maintaining computational efficiency. Key research themes in his work include: Recursive video processing architectures Space-time attention mechanisms Generative video modeling LLM integration with vision systems 3D-aware representation learning Continual learning for video QA He has received notable recognition, including: CVPR 2024 Egocentric Vision (EgoVis) Distinguished Paper Award CVPR 2020 Best Paper Award Nomination First Place at CVPR 2025 Multi-Discipline Lecture Understanding Workshop Dr. Bertasius collaborates with prominent researchers like Mohit Bansal and Lorenzo Torresani . His recent publications demonstrate expertise in advancing video-language models, with applications in semantic alignment, temporal grounding, and cross-modal reasoning. For detailed information about his research, publications, and ongoing projects, please visit his official website .

Neil Shahمشاهده پروفایل

دانشگاهی

• Graph Neural Networks
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Neil Shah is a Lead Research Scientist at Snap Inc., leading initiatives in user modeling, personalization, and trust and safety across Snapchat. His research focuses on advancing machine learning algorithms for large-scale structured data, including graph and sequential representations, with applications to recommendation systems and social platform security. PhD in Computer Science, Carnegie Mellon University (2017), advised by Christos Faloutsos B.S. in Computer Science, North Carolina State University Current research interests span: Graph Neural Networks (GNNs) for real-time inference and scalable training Cross-domain recommendation systems and generative modeling Test-time augmentation and hyperbolic geometry in representation learning Explainability methods for GNNs and fairness-aware outlier detection Recent publications highlight productionized GNN frameworks (GiGL), multimodal graph benchmarks, and novel approaches to link prediction and collaborative filtering. His work has appeared at top venues like KDD, ICLR, NeurIPS, and WWW. Scientific recognition includes: Outstanding Service Award at WSDM 2022 Best Paper Honorable Mention at CHI 2019

SangHyung Ahnمشاهده پروفایل

مدرس

• Construction Process Modelling
• Building Information Modelling (BIM)
• Agent-Based Simulation
• +۳ مورد دیگر

SangHyung Ahn is a Lecturer at the School of Civil Engineering , University of Queensland (UQ), since 2017. He joined UQ as a postdoctoral research fellow in 2015 after earning his PhD in Civil Engineering (Construction Engineering and Management) from Purdue University, USA. Prior to his academic career, he worked as an assistant manager at Hyundai Engineering and Construction Co., Ltd. (2003-2007) and holds an MBA in international business from Hanyang University and a B.Sc in Civil Engineering from Korea University. Research Focus: Construction process modelling with virtual reality, decision support systems for construction, automation of data-driven simulation modelling, sensor-based operations analysis, and integration of Building Information Modelling (BIM). Teaching: Coordinates undergraduate courses Introduction to Project Management (CIVL3510) and Construction Engineering Management (CIVL4522) . Research Trends: His recent publications highlight interdisciplinary work in transportation engineering, structural design, and AI-driven simulation tools. Key themes include application of machine learning to car-following models, drone-based vehicle identification, and optimization of public transport systems using agent-based simulations. Supervision: Available for supervision, with completed supervision of PhD and Master’s theses on topics such as BIM-LCA integration, pedestrian trajectory analysis, and AI-driven driving behavior models.

Marc Lackenbyمشاهده پروفایل

استاد

• Topology
• Geometry
• Group Theory
• +۷ مورد دیگر

Prof. Marc Lackenby is a Professor of Mathematics at the Mathematical Institute, University of Oxford. His research spans topology, geometry, group theory, and their intersections, particularly focusing on low-dimensional topology and geometric algorithms. His editorial roles include serving as an editor for the International Mathematical Research Notices , Groups, Geometry and Dynamics , and the Forum of Mathematics, Pi and Sigma . He was previously an editor for the Journal of Topology (2007–2021) and the Journal of the LMS (2008–2013). Recent publications highlight his work on hyperbolic knots, triangulation complexity of 3-manifolds, and applications of machine learning to topological problems. His research bridges classical geometric topology and modern computational methods. Scientific awards include the LMS Whitehead Prize (2003), EPSRC Advanced Research Fellowship (2004–09), Philip Leverhulme Prize (2006), and the Frontiers of Science Award (2024). He was an invited speaker at the International Congress of Mathematicians (ICM) in 2010.

Sebastijan Dumancicمشاهده پروفایل

استادیار

• Program Synthesis
• Probabilistic Programming
• Neuro-Symbolic AI
• +۲ مورد دیگر

Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.

Sadaf Sobhaniمشاهده پروفایل

استادیار

• Thermal Management
• Energy Conversion
• Heat Transfer
• +۱۲ مورد دیگر

Sadaf Sobhani is an Assistant Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University. Her research focuses on thermal management and energy conversion with applications in high-efficiency, low-emission energy systems, spacecraft thermal control, and electrochemical reactors for carbon dioxide conversion. Dr. Sobhani's educational background includes a B.S. (2014), M.S. (2015), and Ph.D. (2019) in Mechanical Engineering from Stanford University. During her doctoral studies, she worked as a research associate at the NASA Ames Research Center and later joined the Lawrence Livermore National Laboratory as a postdoctoral researcher. Her research program integrates computational modeling, experimental techniques, and advanced manufacturing to investigate flow, heat transfer, and chemical reactions in porous media. She leverages the connection between micro-scale features and macro-scale transport properties to develop innovative solutions for energy systems. Her work spans multiple disciplines including combustion engineering, electrochemical systems, and thermal management for spacecraft. Dr. Sobhani's publications demonstrate a consistent focus on porous media combustion, heat transfer optimization, and advanced diagnostic techniques. Her recent work has increasingly incorporated additive manufacturing and machine learning approaches to solve complex thermal management challenges, particularly for space applications and carbon dioxide conversion systems. Gallery of Fluid Motion Award, American Physical Society (2018) Accel Innovation Scholarship, Stanford Technology Ventures Program (2017) Graduate Public Service Fellowship, Haas Center for Public Service (2016) Schneider/MAP Sustainable Energy Fellowship, Haas Center for Public Service (2016) Graduate Research Fellowship, National Science Foundation (2015) AIAA Niagara Frontier Section 2025 Young Professional of the Year Award NASA Early Career Faculty Award (2023) NASA Early Stage Innovations Award (2023) Dr. Sobhani leads an active research group and has secured significant funding including a NASA Early Career Faculty Award and a FuzeHub grant with industry partners Lithoz America and Dimensional Energy. She has developed a new spacecraft thermal management course at Cornell and is actively mentoring students in her laboratory research. The Sobhani Lab, located at 182 Grumman Hall, spans approximately 850 sq. ft. and focuses on spacecraft thermal control, combustion research, non-intrusive diagnostic methods, and ceramic additive manufacturing. The lab utilizes advanced facilities including the Cornell NanoScale Science and Technology Facility and the Cornell High Energy Synchrotron Source.

Gavin Schwarzمشاهده پروفایل

استاد

• Organizational Change
• Organizational Failure and Inertia
• Virtual Teams
• +۲ مورد دیگر

Gavin Schwarz is a Professor and Head of School at the School of Management and Governance within the UNSW Business School . He specializes in organizational change , organizational failure and inertia , and the dynamics of virtual teams , with a focus on how organizations fail during change processes and how to develop applied strategies for change management . His work spans diverse sectors including healthcare, technology, and education, with publications in leading journals such as Academy of Management Learning and Education and Administrative Science Quarterly . Education : PhD in Management (University of Queensland), MPhil (Hons) in Management (University of Auckland), BA in Management and English (University of Auckland). Grants : 2020 Brock University grant for "University communication in times of COVID-19" , 2020 UNSW Medicine grant for "Translation and change: Embedding effective change management in health" , and earlier Australian Research Council and Gordon J. Samuels Fellowship awards. His research explores the development of knowledge in organizational theories , with an emphasis on collective responses to change , HR management during crises , and technology strategy . He has contributed to understanding organizational communication , team innovation , and structural inertia . His 15 most recent publications cover topics from AI’s role in organizational change to pandemic-driven research adaptation, with keywords spanning management science , behavioral economics , and digital transformation . Scientific Awards 2021-2023 : Outstanding Reviewer Awards (Academy of Management Review) 2017-2020 : Best Reviewer Awards (Journal of Organizational Behavior) 2019, 2013, 2011 : Best Paper Finalist/Awardee (Academy of Management divisions) 2007, 2006 : Editorial Board Excellence (Academy of Management Journal) and Gordon J. Samuels Fellowship As an active supervisor in organizational change and HR development , his work supports healthcare innovation and digital transformation. He serves as Editor-in-Chief for the Journal of Applied Behavioral Science and is on the editorial boards of Academy of Management Review , Journal of Management , and Journal of Organizational Behavior . Contact: g.schwarz@unsw.edu.au

Frederik Tilmannمشاهده پروفایل

استاد

• Seismology
• Geophysics
• Earthquake Analysis
• +۳ مورد دیگر

Prof. Dr. Frederik Tilmann is a leading seismologist at the GFZ German Research Centre for Geosciences (Section 2.4 Seismology) and a professor at the Freie Universität Berlin . His work focuses on seismic waveform analysis to understand geodynamic processes in subduction zones and continental collisions. Current affiliations: Head of Seismology Section, GFZ Potsdam University Professor, Freie Universität Berlin Research interests include: Earthquake source characterization Seismic tomography methods Mantle dynamics and lithospheric deformation Machine learning applications in seismic data analysis Volcano-seismic monitoring Ocean bottom seismology techniques Recent publications highlight advancements in: Full waveform inversion for mantle dynamics Machine learning for seismic phase picking Anisotropy studies in Alpine and Himalayan regions Subduction zone microseismicity analysis Volcano-induced landslide detection Scientific awards include: Feodor-Lynen Fellowship (Humboldt Foundation) Trinity Hall College Staff Fellowship Multiple citations in high-impact journals Collaborative work spans global seismic infrastructure projects like SMART cables, the Collaborative Seismic Earth Model, and the AlpArray network. His methodology innovations in shear wave splitting and depth phase picking have become standards in computational seismology.

Pierre-Henri Parisمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Knowledge Graphs
• Information Extraction
• +۲ مورد دیگر

Pierre-Henri Paris is an Associate Professor (Maître de Conférences) at Paris-Saclay University since September 2024. Previously, he worked as a Postdoctoral Researcher at Telecom Paris (Institut Polytechnique de Paris) from September 2020 to August 2024. His academic journey includes a PhD in Artificial Intelligence from Sorbonne University and CNAM (Conservatoire National des Arts et Métiers) completed in 2020. Education: PhD in Artificial Intelligence, 2020, Sorbonne University and CNAM M.Sc. in Artificial Intelligence, 2016, CNAM M.Sc. in Mathematics, 2008, CY Cergy Paris University (incomplete) Pierre-Henri Paris's research focuses on the intersection of artificial intelligence, knowledge representation, and natural language processing. His work particularly emphasizes knowledge graphs, entity linking, and data quality. He has made significant contributions to projects like YAGO 4.5, which enhances knowledge bases with cleaner, logically consistent structures, and MAFALDA, a benchmark for fallacy classification. His research often bridges theoretical foundations with practical applications, particularly in how knowledge can be effectively represented, extracted, and utilized in complex systems. His recent publications reveal a strong focus on knowledge graph enhancement, semantic representation, and natural language understanding. The work on YAGO 4.5 demonstrates his commitment to creating more robust knowledge bases, while MAFALDA shows his interest in the intersection of language understanding and logical reasoning. His research trajectory indicates a consistent exploration of how structured knowledge can be integrated with linguistic analysis to create more intelligent systems. Advising: PhD students: Simon Coumes (2022-), Chadi Helwe (2022-2024), François Amat (2022-) Master's students: Syrine El Aoud (2021), Ayoub Mountassir (2013-2015) Bachelor's students: Khalil Halloul (2013-2014) Pierre-Henri Paris is actively involved in teaching at Paris-Saclay University, where he instructs courses including Introduction to Machine Learning, Introduction to Neural Networks, Algorithms for Data Science, Databases, and Data Warehousing. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications in artificial intelligence and data science.

• Photonics
• Integrated Waveguides
• Optical Sensors
• +۵ مورد دیگر

Professor B M Azizur Rahman is a distinguished academic in the field of photonics at City University London, where he has served as Professor of Photonics in the Department of Electrical and Electronic Engineering since 2000. Previously, he was Reader in Photonics (1996-2000) and Lecturer (1988-1996) at the same institution. His academic journey began with a BEng (1971-1976) and MSc (1976-1979) from Bangladesh University of Engineering and Technology, followed by a PhD from University College London (1979-1982). His educational background laid the foundation for his extensive research career focusing on photonics, integrated waveguides, and optical sensors. Professor Rahman has made significant contributions to fields including plasmonic biosensors, fiber optic sensing technologies, supercontinuum generation, and metamaterial-based sensing systems. His research bridges theoretical modeling with practical applications in environmental monitoring, healthcare diagnostics, and engineering solutions. An analysis of his most recent publications (2022-2025) reveals a strong focus on advanced sensing technologies with applications across multiple domains. His work demonstrates expertise in combining photonics principles with nanotechnology, artificial intelligence, and novel materials to develop highly sensitive detection systems. Key research trends include the integration of deep learning with optical sensing, development of plasmonic-enhanced biosensors, and innovative waveguide designs for improved optical performance. Professor Rahman has maintained a highly productive research career with over 443 publications documented in his ORCID profile. His work shows extensive international collaboration with researchers from institutions in the UK, Bangladesh, Thailand, and other countries. While specific grant information is not provided in the available data, his sustained publication record across high-impact journals indicates successful research funding and supervision of numerous research projects over his career. His research group appears to focus on experimental photonics, computational modeling of optical systems, and development of novel sensing platforms.

Sean Anderssonمشاهده پروفایل

استاد

• Systems and Control Theory
• Robotics
• Nanotechnology
• +۱۲ مورد دیگر

Sean Andersson is a Professor in Mechanical Engineering and Systems Engineering at the College of Engineering, Boston University, and serves as Director of the BU Robotics Lab. His research bridges systems and control theory with applications in nanotechnology , atomic force microscopy , and robotics . His work in nanobioscience focuses on single molecule tracking and high-speed imaging in atomic force and fluorescence microscopy, leveraging control theory to enhance imaging capabilities. In robotics, he develops stochastic control methods for autonomous systems operating in complex environments, emphasizing multi-agent systems , sparsely sampled data , and symbolic control frameworks . Recent publications highlight trends in receding horizon control , persistent monitoring , neural style transfer for imaging , and stochastic policy optimization . The Andersson Lab also explores compressive sensing and optimal control for sensor networks and nanoscale fluid dynamics.

Mehmet Koyutürkمشاهده پروفایل

استاد

• Systems Biology
• Computational Genomics
• Network Biology
• +۴ مورد دیگر

Mehmet Koyutürk serves as the Andrew R. Jennings Professor in the Department of Computer and Data Sciences at Case Western Reserve University's Case School of Engineering, with additional affiliation as a Member of the Cancer Genomics and Epigenomics Program at the Case Comprehensive Cancer Center. His computational research bridges algorithm development with biological applications, focusing on network-structured data analysis to address complex biomedical challenges. Dr. Koyutürk earned his Ph.D. in Computer Science from Purdue University following B.S. and M.S. degrees in Electrical Engineering and Computer Engineering from Bilkent University. His primary research domains include high-throughput biological data analysis, systems/network biology methodologies, data mining algorithms, and scientific computing optimization, with particular emphasis on phosphorylation networks, genomic interactions, and multi-omics integration. Recent publication trends reveal expanding applications of his network science expertise into Alzheimer's disease phosphoproteomics, bipolar disorder biomarker discovery, and intimate partner violence analysis, while maintaining core contributions to graph neural networks and biological link prediction. His group actively develops open-source analytical tools like RokaiXplorer for phospho-proteomic data accessibility. Scientific Recognition Andrew R. Jennings Professorship Dr. Koyutürk leads multiple NIH-funded initiatives including R01-LM012980 for phosphoproteomics analysis, U01-CA198941 (BD2K program) for big network integration, and R01-LM011247 for GWAS enhancement, complemented by NSF CAREER Award CCF-0953195. He serves on the steering committee for CWRU's Systems Biology and Bioinformatics graduate programs and as Associate Editor for IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), with extensive collaboration through Mark Chance's Center for Proteomics and Bioinformatics. His laboratory specializes in developing scalable algorithms for biological network analysis, currently advancing projects on kinase-substrate association prediction, co-phosphorylation network characterization in cancer, and network-based approaches to intimate partner violence data mining, with strong emphasis on translating computational methods into biomedical insights through open-source software dissemination.

• graph algorithms
• optimization
• high-dimensional geometry
• +۳ مورد دیگر

Yang P. Liu is an Assistant Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. Previously, he was a Postdoctoral Member at the Institute for Advanced Study and earned his PhD from Stanford University under the supervision of Aaron Sidford. He completed his undergraduate studies at MIT, graduating in May 2018. His educational background includes: PhD in Computer Science, Stanford University (Advisor: Aaron Sidford) Bachelor's degree, Massachusetts Institute of Technology (graduated May 2018) Dr. Liu's research spans the intersection of mathematics and computer science, with particular focus on graph algorithms , optimization , high-dimensional geometry , and additive combinatorics . His work often develops novel algorithmic techniques that bridge theoretical insights with practical applications. He has made significant contributions to areas such as convex optimization, linear programming, and combinatorial problems. His teaching includes courses like "A Principled Approach to Optimization" (CS 15-759), which covers rigorous treatments of convex optimization topics including gradient descent, interior point methods, linear regression, linear programming, and sparsification. His extensive publication record in top-tier conferences (FOCS, STOC, SODA) demonstrates a consistent focus on developing almost-linear time algorithms for fundamental graph problems, optimization techniques, and combinatorial theorems. Recent work shows increasing emphasis on combinatorial lines, corners theorem, and k-CSP approximability, while maintaining strong connections to optimization theory and graph algorithms. Dr. Liu has received notable recognition for his work: National Defense Science and Engineering Graduate (NDSEG) Fellowship (2018-2021) Google PhD Fellowship (2022-2023) Best Paper award at FOCS 2022 for "Maximum Flow and Minimum-Cost Flow in Almost Linear Time" Best Student Paper at STOC 2021 for "Discrepancy Minimization via a Self-Balancing Walk" His research has been supported by prestigious fellowships including the NDSEG Fellowship and Google PhD Fellowship. His work on graph algorithms, optimization, and combinatorics involves collaborations with researchers across theoretical computer science and mathematics. His publications often involve co-authors from multiple institutions, suggesting active research collaborations across the field. Dr. Liu maintains an active research program with a focus on developing efficient algorithms for fundamental computational problems. His recent work continues to push the boundaries of what's computationally feasible in graph algorithms, optimization, and combinatorial mathematics, with particular emphasis on achieving almost-linear time complexity for challenging problems.