جست‌وجوی استاد و پژوهشگر

Vincent Y. F. Tanمشاهده پروفایل

استاد

• Online Decision Making
• Multi-Armed Bandits
• Reinforcement Learning
• +۳ مورد دیگر

Professor Vincent Y. F. Tan holds dual appointments in the Department of Mathematics and the Department of Electrical and Computer Engineering (ECE) at the National University of Singapore (NUS). He is also affiliated with the Institute of Operations Research and Analytics (IORA) and the Institute of Data Science (IDS). His research focuses on Online Decision Making, Multi-Armed Bandits, Reinforcement Learning, Information Theory, and Statistical Signal Processing. Notably, he has been actively publishing in top-tier conferences like NeurIPS, ICML, and IEEE journals, with recent works exploring topics such as low-rank adaptation, off-policy evaluation, and queueing control. Professor Tan has advised numerous PhD students, including Fengzhuo Zhang, Yujun Shi, and Junwen Yang. He has received recognition for his teaching, including a 4.7/5.0 rating for EE5137 Stochastic Processes. His work has led to impactful publications, such as the best paper award at the ICML 2025 workshop on World Models and an oral presentation at ICLR 2025. He currently serves as a Senior Area Chair for NeurIPS 2025 and an Area Editor for the IEEE Transactions on Information Theory. His research group focuses on advancing theoretical and applied aspects of machine learning, with projects funded by grants in areas like distributed optimization and adversarial robustness. He collaborates widely, including with institutions like IIT Delhi and HKUST Guangzhou. Open positions are available for motivated postdocs and students in his research areas.

Anthony Faramelliمشاهده پروفایل

مدرس

• Psychosocial Studies
• Coloniality and Postcolonial Theory
• Digital Cultures and Media Studies
• +۳ مورد دیگر

Dr Anthony Faramelli is a Lecturer in Visual Cultures at Goldsmiths, University of London, serving as Programme Leader for the BA in Fine Art and History of Art, and co-Leader of the PhD in Visual Cultures. He holds a PhD in Critical Humanities from The London Graduate School, Kingston University (2014). His research focuses on psychosocial studies, coloniality, institutional analysis, digital cultures, and psychotherapy aesthetics, with notable works including Resistance, Revolution and Fascism: Zapatismo and Assemblage Politics (2018) and The Mass Psychology of Fascism in the Age of Machines (forthcoming). He is affiliated with the Goldsmiths Centre for Migrant Futures and the Psychosocial Studies Association. Faramelli also consults for mental health and housing organizations, applying institutional psychotherapy principles in practice. Education: PhD in Critical Humanities (2014), The London Graduate School, Kingston University Research Interests: Coloniality, institutional psychotherapy, digital surveillance, micro-fascist movements, Fanon’s ethico-aesthetic paradigm, Zapatismo, and decolonial mental health practices. His work bridges psychoanalytic theory, political philosophy, and postcolonial critique. Recent Projects: Refugee Cartographies (mapping Latin American diaspora networks), and critical analyses of the Alt-Right and algorithmic fascism. He has presented at conferences such as the IV Jornadas Deleuze in Buenos Aires and the Université Le Havre-Normandie. Professional Roles: Editor of Deleuze and Guattari Studies , Executive Board member of the Psychosocial Studies Association, and consultant for Southwark Council’s Housing Solutions.

Banu Lokmanمشاهده پروفایل

استاد

• Operational Research
• Multi-criteria Decision Making
• Optimization
• +۳ مورد دیگر

Banu Lokman is a Professor of Operational Research (OR) at the University of Portsmouth, serving as Associate Head (Research and Innovation) in the School of Organisations, Systems and People within the Faculty of Business & Law. She leads the Centre for Innovative and Sustainable Finance and contributes to the Centre for Operational Research & Logistics. Her expertise spans multi-criteria decision-making, optimization, and their applications in healthcare and sustainability. She holds editorial roles at OMEGA and the IMA Journal of Management Mathematics and organizes the NATCOR MCDM courses. Previously, she served as Deputy Director of CORL (2011–2024), Secretary of the International MCDM Society, and Board Member of INFORMS MCDM Section. She currently chairs the INFORMS MCDM Section as President-elect/Vice-President. Education: BSc, MSc, and PhD in Industrial Engineering from Middle East Technical University (METU, Turkey), followed by postdoctoral research at Aalto University (Finland). She taught at METU (2014–2019) and held visiting roles at Aalto University. Research Interests: Focuses on developing optimization methods for multi-criteria decision problems, particularly in healthcare (e.g., optimizing prostate biopsy decisions with Portsmouth NHS Trust) and sustainability. Her work emphasizes algorithms for nondominated set representation, robust efficiency analysis, and cluster ensemble methods. Key Awards: Bernard Roy Award (2022) for outstanding contributions to Multiple Criteria Decision Aiding, and Young Researcher Award (2015). Advising & Grants: Leads a healthcare-related PhD project and contributes to projects like the Social Investment Fund collaboration with Waltham Forest Council. She actively supervises students and participates in research initiatives on supply networks and data control systems. Labs & Teams: Engaged with interdisciplinary teams in operational research and logistics, particularly in applying OR to real-world challenges such as energy market optimization and MRO supply networks.

Thomas E. Bittnerمشاهده پروفایل

دانشیار

• Formal Ontology
• Applied Ontology
• Ontology of Space and Time
• +۳ مورد دیگر

Thomas E. Bittner serves as an Associate Professor in the Department of Philosophy at the University at Buffalo, specializing in formal and applied ontology with particular focus on spatial and temporal reasoning. His academic work bridges philosophy, computer science, and geographic information systems. His research interests encompass Formal and Applied Ontology , Ontology of Space, Time and Spatial Entities , Spatio-Temporal Reasoning , and Bio-Medical Ontology . He investigates fundamental questions about spatial relations, vagueness in geographic boundaries, and quantum-inspired models for geographic information systems, developing rigorous formal frameworks applicable to both philosophical and computational domains. His publication record demonstrates consistent engagement with cutting-edge topics in ontology, particularly exploring quantum geography concepts and computational implementations of formal ontologies. Key themes across his work include the representation of spatial vagueness, quantum-inspired geographic modeling, and verifiable computational ontology systems. Bittner holds a PhD from the Technical University of Vienna and teaches courses including Logic and Spatial Ontology. His contact information lists office location at 109 Park Hall on UB's North Campus with phone (716) 645-5149.

Besiki Stviliaمشاهده پروفایل

استاد

• Data Quality
• Data Curation
• Research Information Management
• +۶ مورد دیگر

Besiki Stvilia is a Professor in the School of Information at Florida State University's College of Communication and Information. He holds a Ph.D. and M.S. in Library and Information Science from the University of Illinois at Urbana-Champaign, along with an M.S. in Applied Mathematics from Tbilisi State University. His research focuses on data quality assurance, digital curation, social informatics, and knowledge organization. He has led projects on research data management, metadata frameworks, and collaborative data practices in scientific communities. Stvilia has taught courses including LIS 6205 (Information Behavior), LIS 5263 (Information Retrieval Theory), and LIS 5787 (Metadata Practices). His funded research includes grants on data quality infrastructure for repositories, researcher identity curation, and mobile wellness application behavior studies. His work emphasizes bridging theoretical models with practical applications in digital libraries and academic systems. His research spans topics like GenAI credibility assessment, ontology development for data repositories, and user engagement with social Q&A platforms. He has advised multiple collaborative projects involving interdisciplinary teams and has published extensively in journals like Journal of Documentation and Information Processing & Management .

Brian Vermeireمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics
• Aerodynamics
• High-Performance Computing
• +۶ مورد دیگر

Brian Vermeire is an Associate Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University. His research focuses on computational fluid dynamics, aerodynamics, high-performance computing, turbulence modeling, numerical methods, and optimization. He leads the Computational Aerodynamics Laboratory, emphasizing scale-resolving simulations and high-order numerical techniques. Key interests include large eddy simulation (LES), direct numerical simulation (DNS), and gradient-free optimization. His work often involves developing advanced algorithms for unstructured grids and high-performance computing platforms. Research Interests: High-order numerical methods Implicit/explicit time integration schemes Polynomial adaptation for adaptive meshing Aeroacoustic shape optimization Large eddy simulation (LES) and direct numerical simulation (DNS) Software development for CFD (e.g., PyFR) Recent work trends show strong focus on hybridized flux reconstruction methods, energy-conservative algorithms, and industrial adoption of high-fidelity simulations. Major contributions include scalable implementations for petascale computing and open-source tools like PyFR. His group collaborates on applications such as wind turbine aerodynamics and low-pressure turbine design. Labs/Teams: Computational Aerodynamics Laboratory (website: link )

Lata Narayananمشاهده پروفایل

استاد

• Distributed computing
• Graph algorithms
• Routing algorithms
• +۷ مورد دیگر

Dr. Lata Narayanan is a Professor in the Department of Computer Science and Software Engineering at Concordia University, Montreal, Canada. Her research spans theoretical and applied aspects of distributed systems, with a focus on algorithms for mobile agents, communication networks, and sensor networks. Department: Computer Science and Software Engineering University: Concordia University Research Interests Lata Narayanan specializes in algorithms for mobile robots and ad hoc networks , with expertise in routing on distributed networks , parallel algorithms , and social network analysis . Her work addresses challenges in sensor network optimization, barrier coverage, and time-energy tradeoffs for evacuation systems. Article Trends Her recent publications (2021-2025) emphasize game theory for network dynamics, cloud resource allocation , and temporal graph exploration . Key themes include strategic diversity, truck-drone delivery logistics, and energy-sharing protocols for mobile agents.

• Physical Chemistry
• Theoretical and Computational Chemistry
• Chemical Engineering
• +۶ مورد دیگر

Professor George Jackson, FRS, holds the position of Professor of Chemical Physics at the Department of Chemical Engineering, Imperial College London (since 2001), and has affiliations with multiple institutions including the University of Sheffield and Cornell University. His academic career spans roles from Lecturer (1989–1994) to Reader (1994–2001) in Physical Chemistry and Chemical Physics. He leads the Molecular Systems Engineering group and chairs the journal Molecular Physics . Research focuses on theoretical and computational chemistry, thermodynamics, and fluid phase equilibria. Key contributions include the SAFT (Statistical Associating Fluid Theory) equation of state, applied to CO2 capture, polymer physics, and pharmaceutical processes. He has pioneered models for electrolytes, solvents, and complex fluids, emphasizing sustainability and industrial applications. Notable accolades include the Rossini Award (2023), Fellowship of the Royal Society (2020), and the Bakhuis Roozeboom Medal (2019). His work has been recognized through Imperial College's President's Medal (2016), the Guggenheim Medal (2014), and over 400 citations in Google Scholar. Collaborations span institutions globally, including the Sargent Centre for Process Systems Engineering and the Thomas Young Centre. Current initiatives include the HiRECORD project for CO2 capture technology scaling and the development of phase-change solvents. His research integrates molecular simulation, process design, and sustainability assessment, addressing challenges in energy, environment, and pharmaceuticals.

Tim Coloniusمشاهده پروفایل

استاد

• Fluid Dynamics
• Cavitation and Bubble Dynamics
• Aerodynamic Sound
• +۵ مورد دیگر

Tim Colonius is the Frank and Ora Lee Marble Professor of Mechanical Engineering and Medical Engineering and holds the Cecil and Sally Drinkward Leadership Chair at the California Institute of Technology. He has been affiliated with Caltech since 1994 and currently serves as Executive Officer for Mechanical and Civil Engineering . Colonius earned his B.S. from the University of Michigan (Ann Arbor), and both his M.S. and Ph.D. from Stanford University. Research Interests: His work focuses on fluid dynamics (global instabilities, cavitation, aerodynamic sound), flow control (closed-loop control, reduced-order modeling), and biomedical applications (shock waves, lithotripsy, ultrasound). He also develops advanced numerical methods for interface capturing, immersed-boundary techniques, and high-order accuracy. Scientific Contributions: Recent publications highlight his research in multiphase flows, vortex ring collisions, turbulent jet analysis, GPU-accelerated simulations, and biomedical applications. His group uses computational and data-driven approaches to study turbulence, instabilities, and flow optimization. Scientific Awards: AIAA Aeroacoustics Award Fellow of the Acoustical Society of America Fellow of the American Physical Society (APS) NSF and DoD research grants

Garnet K. Chanمشاهده پروفایل

استاد

• Theoretical Chemistry
• Condensed Matter Physics
• Quantum Information Theory
• +۴ مورد دیگر

Garnet K. Chan is the Bren Professor of Chemistry and Director of the Rudolph A. Marcus Center for Theoretical Chemistry at the California Institute of Technology. He received his B.S. from the University of Cambridge in 1996 and his M.A. and Ph.D. from the University of Cambridge in 2000. Dr. Chan's research lies at the interface of theoretical chemistry, condensed matter physics, and quantum information theory, focusing on quantum many-particle phenomena and the numerical methods to simulate them. His group has developed numerous methodologies including density matrix renormalization and tensor network algorithms, canonical transformation-based down-foldings, local quantum chemistry methods, quantum embeddings, and new quantum Monte Carlo algorithms. His work addresses problems that appear naively exponentially hard but where understanding of physics, particularly entanglement structure, allows for calculations of polynomial cost. Analysis of his recent publications reveals a strong focus on quantum simulation techniques, particularly tensor network methods applied to strongly correlated systems. His research spans fundamental theoretical developments to practical applications in quantum computing, molecular simulation, and materials science, with increasing integration of machine learning techniques and GPU acceleration in computational chemistry frameworks. Dr. Chan leads an active research group at Caltech dedicated to simulating chemical and physical systems at the level of many-particle quantum mechanics. His group has welcomed numerous researchers including Kasra Hejazi, Zuxin Jin, Zhihao Cui, Ke Liao, Henrik Larsson, and Wenyuan Liu. He teaches courses in Physical Chemistry (Ch 21 abc) and Advanced Quantum Chemistry (Ch 225), contributing significantly to theoretical chemistry education at Caltech.

• 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.

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.

Abdullah Muzahidمشاهده پروفایل

دانشیار

• Computer Architecture
• Systems
• Artificial Intelligence
• +۵ مورد دیگر

Abdullah Muzahid is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University (since August 2024), previously serving as an Assistant Professor there since August 2018. Prior, he held an Assistant Professor role at the University of Texas at San Antonio (2012-2018). He earned his Ph.D. from the University of Illinois at Urbana-Champaign (2012), focusing on architectural support for debugging concurrency bugs under Prof. Josep Torrellas. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign (2012) M.S., Computer Science, University of Illinois at Urbana-Champaign (2009) B.S., Computer Science and Engineering, Bangladesh University of Engineering and Technology (2005) Research Interests: His work spans Computer Architecture , Systems , and Artificial Intelligence , with focus on multiprocessor architecture, parallel programming, debugging, and applying machine learning to system optimization. Recent projects include cache indexing via entropy estimation, DNN training acceleration, and hardware-software co-design for security. Awards: NSF CAREER Award (2017) Excellence in Research Award (UTSA, 2015 & 2017) W. J. Poppelbaum Award (UIUC, 2012) Intel Ph.D. Fellowship (2011) Grants & Advising: He leads NSF-funded projects on robust deep learning and stream processing systems. Advised 5 PhD graduates and currently mentors 4 PhD students. Served on program committees for ISCA, HPCA, MICRO, and as NSF panelist. Labs/Teams: Active in Texas A&M’s Computer Architecture group, collaborating on machine programming, hardware security, and AI-driven systems optimization.

Santiago Segarraمشاهده پروفایل

دانشیار

• Network Theory
• Machine Learning
• Graph Signal Processing
• +۵ مورد دیگر

Santiago Segarra is the W. M. Rice Trustee Associate Professor in the Department of Electrical and Computer Engineering at Rice University, with courtesy appointments in Computer Science and Statistics. He joined Rice in 2018 and collaborates with Microsoft Research since 2022. His expertise spans network theory, machine learning, graph signal processing, and optimization. Segarra earned his B.Sc. in Industrial Engineering from ITBA (2011), and M.S. and Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2014-2016), followed by a postdoc at MIT (2016-2018). Research Focus: His work integrates algebraic topology, signal processing, and machine learning to analyze networked systems. Key areas include social/technological network clustering, graph-based data analysis, and applications in neuroscience and communication networks. Recent projects address fair graph learning, distributed GNN training, and network topology inference. Awards: Penn’s Wolf Award for Best Dissertation (2017), Argentine National Engineering Honors (2011), and ITBA’s Best Thesis Award (2011). Grants/Sponsors: Supported by NSF, ONR, and industry collaborations. Labs/Groups: Leads the Rice Wireless group and collaborates with Microsoft Research on applied network science. Advises students in interdisciplinary research combining theory and real-world applications.

• Algorithms
• Spectral Graph Theory
• Computer Science
• +۵ مورد دیگر

Dan Spielman is the Sterling Professor of Computer Science and holds joint appointments as Professor of Statistics and Data Science and Mathematics at Yale University. He is affiliated with the Department of Mathematics within the Faculty of Arts and Sciences. His research focuses on spectral graph theory, algorithms, linear systems, and their applications in computer science, mathematics, and statistics. He has been recognized as an ACM Fellow for his contributions to theoretical computer science and mathematics. Dr. Spielman's work bridges theoretical and applied domains, with notable advancements in graph sparsification, Laplacian solvers, and the resolution of the Kadison-Singer problem. His research also encompasses algorithmic design, optimization, and probabilistic methods. Key grants include NSF funding for projects like 'Generalized Algebraic Graph Theory: Algorithms and Analysis' (2016). His scientific awards include the ACM Fellowship (2011), acknowledging his impactful contributions to algorithms and complexity theory. Spielman’s interdisciplinary approach integrates spectral graph theory with practical applications, addressing fundamental problems in computation and mathematics.