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Gary ScavoneView profile

Professor

• Music Technology
• Musical Acoustics
• Acoustical Signal Processing
• +3 more

Dr. Gary Scavone is a Professor and Department Chair in the Music department at McGill University's Schulich School of Music. He holds a PhD in Computer-Based Music Theory & Acoustics and MS in Electrical Engineering from Stanford University, alongside degrees from Syracuse University in Music and Electrical Engineering. His research focuses on music technology, including acoustic modeling, sound synthesis, and instrument design. He directs the Computational Acoustic Modeling Laboratory (CAML), which explores advanced techniques for simulating musical instruments and developing software tools. As a saxophonist, he specializes in contemporary concert music performance. Research interests include physically-based sound synthesis, wind instrument acoustics, and digital waveguide modeling. He has contributed to studies on brass and woodwind impedance measurements, violin soundpost dynamics, and free-reed instrument modeling. His work bridges engineering and artistry, with applications in music pedagogy, instrument design optimization, and virtual acoustic replication. Key contributions include open-source projects for wind instrument modeling and the development of tools for automated timbre assessment. His research often combines experimental methods with computational simulations, addressing challenges in both theoretical and applied music acoustics. Current projects focus on deep learning for friction modeling, impedance measurement systems, and cross-cultural instrument analysis.

Zhengjie MiaoView profile

Assistant Professor

• Data Management
• Artificial Intelligence
• Machine Learning
• +3 more

Zhengjie Miao is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on data management and artificial intelligence, particularly developing algorithms and tools to enhance data science pipelines. He is part of the SFU Data Science Research Group and previously worked as a Research Scientist at Megagon Labs. Education: PhD in Computer Science from Duke University (advisors: Sudeepa Roy, Jun Yang), M.S. from Columbia University (advisor: Eugene Wu), B.S. from Peking University. Research interests span data management systems, machine learning integration in data science, and human-in-the-loop data preparation. His work emphasizes improving data usability through techniques like data lineage tracking, explainable query systems, and fairness-aware data processing. Recent publications highlight advancements in collaborative annotation with LLMs, automated data standardization, and contrastive learning frameworks for column annotation. Key contributions include the Rotom framework for meta-learned data augmentation, the Watchog system for column annotation, and CAPE for query explanation. His research bridges database theory with practical applications in education and industry.

Guillaume LajoieView profile

Associate Professor

• Artificial Intelligence
• Neuroscience
• Machine Learning
• +3 more

Guillaume Lajoie is an Associate Professor in the Department of Mathematics and Statistics at Université de Montréal and a Core Academic Member of Mila – Quebec Artificial Intelligence Institute. He holds a Canada CIFAR AI Research Chair and a Canada Research Chair in Neural Computation and Interfacing. His research focuses on the intersection of AI and neuroscience, particularly in understanding neural network dynamics and developing brain-machine interfaces for clinical and scientific applications. He is affiliated with the Centre de recherches mathématiques (CRM), the Interdisciplinary Center for Research on the Brain and Learning (CIRCA), and the UNIQUE initiative. Education: PhD in Applied Mathematics from the University of Washington (Seattle), postdoctoral fellowships at the Max Planck Institute for Dynamics and the University of Washington Institute for Neuroengineering. Awards include the FRQS Scholar designation and leadership roles in strategic research initiatives like UNIQUE and CIRCA. Research interests include neural computations, recurrent neural networks, neurotechnology, and responsible AI development. Supervised students include François Paugam (PhD), Giancarlo Kerg (PhD), and others. Key grants include projects on adaptive neuroprosthetics, neural decoding, and Canada Research Chairs funding.

Henrik WyschkaView profile

Researcher

• Shape Optimization with Fluid Dynamic Applications
• Algorithms for p-Laplace Problems
• Lipschitz Sequences
• +3 more

Henrik Wyschka is a doctoral researcher and scientific staff member at the University of Hamburg's Department of Mathematics, affiliated with the Faculty of Mathematics, Computer Science and Natural Sciences. His research focuses on shape optimization with fluid dynamic applications, algorithms for p-Laplace problems, and Lipschitz sequences. He is part of the DFG Research Training Group 2583, "Modeling, Simulation and Optimization with Fluid Dynamic Applications," under project O3. Education: M.Sc. (2021) and B.Sc. (2019) in Technomathematics from TU Hamburg and University of Hamburg. His work integrates theoretical analysis and numerical methods to advance computational techniques in shape optimization. Key contributions include presentations at major conferences (e.g., IFIP TC7, SIAM, GAMM) and publications on trust-region methods for p-harmonic optimization and high-order descent direction algorithms. His research emphasizes efficient numerical solutions for fluid dynamics-related challenges. Funding: Supported by the DFG through Graduiertenkolleg 2583. Office location: Geomatikum Building, Room 1517. ORCID: 0009-0000-2242-2464 .

William GasarchView profile

Professor

• Complexity Theory
• Concrete Complexity Theory
• Combinatorics
• +3 more

William Gasarch is a Professor of Computer Science at the University of Maryland, College Park, with an affiliation in Mathematics. He holds a Ph.D. from Harvard University (1985) and a B.S. from SUNY Stony Brook (1980). His research focuses on complexity theory, combinatorics, and Ramsey Theory, with applications to computer science theory. Education: Ph.D., Harvard University, 1985 (Computer Science) B.S., SUNY Stony Brook, 1980 (Math and Applied Math) Research Interests: Gasarch specializes in theoretical computer science, particularly applying Ramsey Theory to computational problems. His work bridges combinatorics, logic, and algorithm design, emphasizing foundational questions in complexity theory. He is known for exploring how Ramsey Theory informs program termination proofs and lower bounds in computational models. Labs/Teams: Affiliated with the Computer Science department’s theory group, he contributes to collaborative projects on Ramsey applications and combinatorial algorithms. His REU (Research Experiences for Undergraduates) program highlights mentorship in combinatorial algorithms. Advising & Grants: Mentored numerous high school and undergraduate students, including Breakthrough Prize winner Jacob Lurie and Intel Science Talent finalist Louis Wasserman. Active in NSF-funded initiatives through UMD’s theory group (details unspecified).

Olga VekslerView profile

Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +6 more

Olga Veksler is a Professor at the University of Waterloo's Department of Computer Science, part of the Faculty of Mathematics. She holds a Ph.D. and M.Sc. from Cornell University (1999) and a B.A. from New York University (1995). Her research focuses on computer vision, machine learning, and discrete optimization, with notable contributions to image segmentation, graph algorithms, and deep learning integration. Her work emphasizes semantic segmentation, salient object detection, and efficient optimization techniques for graphical models. Education: Ph.D. in Computer Science, Cornell University, 1999 M.Sc. in Computer Science, Cornell University, 1999 B.A. in Computer Science, New York University, 1995 Her research explores intersections between machine learning and traditional computer vision challenges, particularly leveraging graph-based optimization and CRF models. Recent trends in her work include weakly supervised learning, sparse non-local CRF applications, and test-time adaptation strategies for salient object detection. She has pioneered methods for shape priors in multi-object segmentation and efficient graph-cut algorithms. Her advising and grant activities are foundational to her research, though specific grant details are not listed here. She maintains a lab focused on advancing computer vision through algorithmic innovation, with contributions to both theoretical frameworks and practical applications in medical imaging and scene understanding.

Bela BollobasView profile

Professor

• Extremal Graph Theory
• Random Graphs
• Probabilistic Combinatorics
• +3 more

Béla Bollobás is a renowned mathematician affiliated with the University of Memphis as the Jabie Hardin Chair of Excellence in Combinatorics and the University of Cambridge as a Fellow of Trinity College and Honorary Professor at the Centre for Mathematical Sciences. His work spans combinatorics, probability theory, and graph theory, with significant contributions to percolation and random graphs. Dr. Rer. Nat. (Budapest, 1967) Ph.D. (Cambridge, 1972) Sc.D. (Cambridge, 1984) Bollobás pioneered extremal graph theory, random graphs, and probabilistic combinatorics. He introduced novel graph polynomials and advanced bootstrap percolation models, impacting both theoretical mathematics and statistical physics. His research includes inhomogeneous random graphs and cellular automata in random environments. His selected publications reveal a focus on percolation thresholds, graph invariants, and stochastic processes. Notably, he derived sharp thresholds for bootstrap percolation and defined critical probabilities for Voronoi percolation. Senior Whitehead Prize (2007) Fellow of the Royal Society (2011) Foreign Member, Hungarian Academy of Sciences (1990) Foreign Member, Polish Academy of Sciences (2013) Honorary Doctorate, Adam Mickiewicz University (2013) Szechenyi Prize (2017) Bollobás has supervised over 50 Ph.D. students and authored over 450 publications, including 10 books. He co-founded the journal Combinatorics, Probability and Computing and served on eight editorial boards. He organized numerous conferences, including Bill Tutte and Paul Erdős events.

Wenjing ZhangView profile

Assistant Professor

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +6 more

Dr. Wenjing Zhang is an Assistant Professor in the School of Computer Science at the University of Guelph, Canada. She holds a Ph.D. in Computer Science from the University of Guelph (2024) and was a visiting research scholar at the University of Arizona's Department of Electrical and Computer Engineering (2016–2018). Her research focuses on cybersecurity in AI/ML, including security threats to models, privacy-preserving techniques, and data privacy in generative AI. She leads projects on robust defenses against adversarial attacks, secure federated learning, and privacy-preserving prompt engineering for LLMs. Dr. Zhang’s research areas include: Security in AI/ML (e.g., poisoning, evasion, prompt injection attacks) Model Privacy (protection of internal parameters) Data Privacy (synthetic data generation, privacy-preserving prompt engineering) She has secured a five-year NSERC Discovery Grant (2025–2030) for her research on enhancing security, privacy, and fairness in generative AI. Her work has been published in top-tier venues such as NeurIPS, IEEE Transactions on Information Forensics and Security, and IEEE Transactions on Communications. She is a recipient of the 2022 Westin Scholar Award and serves on technical committees for IEEE conferences including CNS 2025 and ICC 2025. Her team collaborates on interdisciplinary projects involving federated learning, information theory, and reinforcement learning. She actively seeks partnerships in security, privacy, and generative AI applications.

Vlatko VedralView profile

Professor

• Quantum physics
• Quantum information
• Quantum computation
• +1 more

Vlatko Vedral is a Professor of Quantum Information Science at the University of Oxford and a Professor of Physics at the National University of Singapore. He is a Governing Body Fellow at Wolfson College, Oxford, and a Principal Investigator at the Centre for Quantum Technologies (CQT), Singapore. His research spans quantum physics, quantum information, quantum computation, and many-body physics, with foundational work on quantum entang *ul> for education and leadership roles. PhD in physics at Imperial College of Science, Technology and Medicine (1995-1998), supervised by Prof. Sir Peter L. Knight, FRS. Thesis: 'Quantum Information Theory of Entanglement', summarized in a review article in Rev. Mod. Phys. (2002). BSc in physics (first class) at Imperial College, specializing in Theoretical Physics (1992-1995). Vedral has organized major international conferences such as Foundations of Physics (Leeds 2007), Theoretical Quantum Computation (Singapore 2009), and Quantum Discord (2011). He has led international collaborations, including a group of 15 Oxford academics at the James Martin School on 'Bio-inspired quantum technologies' and served on editorial boards for Annalen der Physik and JPhysA . With over 300 publications, including 44 in Physical Review Letters , 13 in Nature , and six invited reviews on quantum information, Vedral's work has been cited over 25,000 times, with an h-index of 62. He has delivered around 300 invited lectures globally, including plenary talks at major conferences and public lectures for Café Scientifique (2007) and New Scientist (2016). Scientific Awards and Honors Fellowship at the UK Institute of Physics (2017) Chair Professorship at Tsinghua University, Beijing (2015) Award 'Srpski Krivak', Belgrade (2015) Marko Jaric Award, Serbian Institute of Physics (2010) World Scientific Medal and Prize, Singapore IOP (2009) Royal Society Wolfson Research Merit Award (2007-2009) Finalist, John Archibald Wheeler competition, Princeton (2002) Abdus Salam Award, Imperial College (1997) Overseas Research Student Award, Imperial College (1995-1998) University of London Knight Trust Scholarship (1996-1998) He has supervised 40 PhD students and numerous undergraduates across the UK, US, and Singapore through programs like SURF Caltech exchange and Stanford University collaborations. His leadership roles in academia highlight his influence in advancing quantum information theory and fostering global research initiatives.

Horacio Rostro GonzálezView profile

Associate Professor

• Artificial Intelligence
• Computational Neuroscience
• Neuromorphic Computing
• +7 more

Horacio Rostro González is an Associate Professor in the Department of Industrial Engineering at IQS School of Engineering, Universitat Ramon Llull (URL), Barcelona, Spain. He is an active researcher with a strong international academic background and current affiliations in both research and teaching. Education: PhD in Control and Signal and Image Processing (2011, INRIA & University of Nice – Sophia Antipolis, France) Master’s in Electrical Engineering (2007, University of Guanajuato, Mexico) Electronic Engineer (2003, National Technological Institute of Mexico) His research interests lie at the intersection of Artificial Intelligence, Computational Neuroscience, Neuromorphic Computing, Embedded Systems, and Robotics. He applies advanced techniques in neural networks, machine learning, and control systems to solve complex engineering problems in robotics, biomedicine, and industrial design. His work emphasizes biomimetic approaches, such as spiking neural networks for robot locomotion and AI-driven analysis of physiological signals like ECG and facial expressions. The recent publications highlight a strong trend in interdisciplinary research, combining AI with photonics, robotics, cardiovascular diagnostics, and emotional recognition in children. His work spans from theoretical algorithm development to practical industrial applications, particularly in additive manufacturing and smart systems. Scientific Projects: Offshore Wind Farms: Data analysis using machine learning and power generation prediction (2023–2024) GEPI: Grup Enginyeria de Productes Industrials (2022–2025) He is actively involved in research grants and collaborative projects, with no mention of formal student advising in the provided text. He is a member of the GEPI (Industrial Products Engineering Group), which focuses on additive manufacturing, reverse engineering, and material characterization. His scientific output is robust, with consistent publication activity from 2005 to 2025, including numerous articles in indexed journals.

Dennis W. DukeView profile

Professor

• History of Ancient Astronomy
• Computational Ancient Astronomy
• High Performance Computing
• +5 more

Dennis W. Duke is a Professor of Physics at Florida State University, with a joint appointment in the Department of Physics within the College of Arts and Sciences and the School of Computational Science. He has been a faculty member at FSU since 1979, progressing from Visiting Assistant Professor to his current position as full Professor since 1992. Duke has also held significant leadership roles including Director of the Supercomputer Computations Research Institute (1993-1998) and Director of the San Diego Supercomputer Center (1996-1997). Duke's research spans multiple disciplines with primary focuses on the history of ancient astronomy, computational physics, and high-performance computing. His work in ancient astronomy includes detailed studies of Greek, Babylonian, and Indian astronomical traditions, with particular emphasis on planetary models, trigonometry development, and historical measurement techniques. In computational science, he has made contributions to nonlinear dynamics, chaos theory, and statistical process control. Duke is particularly known for creating educational computer animations of ancient planetary models that have become valuable teaching tools. Analysis of Duke's 15 most recent publications reveals a consistent focus on historical astronomy, with approximately 75% of his work dedicated to analyzing ancient astronomical systems from Greek, Babylonian, and Indian traditions. The remaining publications address statistical methods and computational techniques. His historical research often combines mathematical analysis with historical scholarship to reconstruct and understand ancient astronomical practices. COFRS Award, The Florida State University (1985) COFRS Award, The Florida State University (1986) Developing Scholar Award, The Florida State University (1986) President's Continuing Education Award, The Florida State University (1992) Duke has been extensively involved in professional service, serving on numerous national committees related to supercomputing and organizing major conferences including multiple Supercomputing 'XY events from 1988 through 2006. His teaching extends beyond traditional academic boundaries through public outreach initiatives such as chairing the Board of Directors for Tallahassee Free-Net, Inc. and serving on the Leon County Schools Information Services Advisory Board. He has supervised numerous graduate students in computational physics and the history of science, though specific student names are not documented in the provided materials. Duke maintains an active research laboratory focused on computational ancient astronomy, where he develops mathematical models and computer animations to visualize historical astronomical concepts. His team has created an extensive collection of interactive animations demonstrating planetary models from Ptolemy through Copernicus, which are widely used in educational settings. Current projects include further analysis of ancient star catalogs and the development of more sophisticated visualization tools for historical astronomical concepts.

Marta VeljkovicView profile

Lecturer

• Social Inequalities
• Social Stratification
• Intragenerational Mobility
• +7 more

Marta Veljkovic is a Lecturer and permanent academic member affiliated with Sorbonne University, specializing in sociology with a focus on social inequalities, intragenerational mobility, class and wage trajectories, and gender disparities. She earned her Doctorate in Sociology from Sciences Po, Paris, and the National Institute of Demographic Studies (Ined) between 2018 and 2022, following a Master of Research in Sociology from Sciences Po (2016–2018) and a Bachelor of Sociology from the University of Belgrade (2012–2016). Her academic work is deeply rooted in quantitative methods and large-scale empirical analysis of career dynamics in France. Her research interests include: Social inequalities and stratification Intragenerational mobility and career trajectories Class and wage mobility Gender inequalities in professional advancement The subjective experience of social mobility Quantitative methodologies in sociological research The trends in her recent publications reveal a consistent focus on analyzing long-term shifts in career mobility in France, particularly how class barriers persist despite increased fluidity in job transitions. Using data from the Formation-Qualification Professionnelle survey and EGP class schema, she investigates both absolute and relative mobility, demonstrating that while career paths have become more complex, structural class boundaries remain resilient. Her work bridges academic sociology and public discourse through contributions to outlets like Pour l’Éco and the Observatory of Inequalities. She holds active responsibilities in the academic community, including: Co-organizer of the Gemass scientific seminar Member of the editorial board of the Sociological Year Marta Veljkovic teaches a wide range of courses at undergraduate and graduate levels, including Theory and Concepts (L1), Sociological Tradition – Contemporary French Sociology (L2), Statistics and Computer Science (L3), and advanced methods such as Quantitative Methods I, Sociology of Inequalities, and Advanced Methodology at the M1 level. She has presented her research at numerous international conferences, including the ISA RC28 Spring Meeting, ECSR, and LIVES colloquia. Although no specific students or scientific awards are listed, her extensive publication record and institutional engagement underscore her growing influence in the field of sociological inequality research. She is involved in collaborative research with scholars such as Nicolas Duvoux, Richard Nennstiel, and Ettore Recchi, and her work is supported by access to major French social science data infrastructures like ELIPSS and Insee surveys. She does not appear to lead a lab, but her participation in Gemass—a joint research unit between CNRS, Sorbonne University, and Sciences Po—positions her within a vibrant interdisciplinary network focused on social mechanisms, networks, and inequalities.

• High-Performance Computing
• Reconfigurable Computing
• FPGA Acceleration
• +7 more

Carlos Alvarez Martinez is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture at the Barcelona School of Informatics (FIB). He is a key member of the Programming Models (PM) research group and collaborates closely with the Barcelona Supercomputing Center (BSC). His research focuses on high-performance computing, FPGA acceleration, task-based programming models like OmpSs, and hardware-software co-design for heterogeneous systems. His research interests center on advancing parallel computing through innovative programming models and hardware acceleration. He investigates efficient task scheduling, resource management in multicore and FPGA-based systems, and runtime support for dataflow models. His work enables high-performance execution of complex applications in domains such as scientific computing and cyber-physical systems. He actively contributes to European initiatives like TEXTAROSSA and AXIOM, aiming to develop next-generation exascale supercomputing technologies. The trend in his recent publications shows a strong focus on leveraging FPGAs for HPC, optimizing SpMV operations, improving task scheduling with hardware support, and developing frameworks for multi-FPGA clusters. His work consistently bridges theoretical models with practical implementations, emphasizing performance, scalability, and energy efficiency in heterogeneous computing environments. Scientific Awards: Premi UPC al Compromís Social 2019 Premi Disseny per al Reciclatge 2013 Alvarez Martinez has advised or collaborated with several doctoral students, including Jaume Bosch, Xubin Tan, and Fahimeh Yazdanpanah. He has been involved in numerous competitive R&D projects, often related to high-performance computing and parallel programming models. His work includes significant contributions to educational innovation, particularly in active learning methodologies and formative assessment using interactive systems. He leads and participates in research labs and teams focused on programming models and computer architecture, notably the PM group at UPC/BSC. These teams develop runtime systems, compilers, and hardware accelerators to push the boundaries of parallel computing efficiency and programmability.

John F. RudgeView profile

Professor

• Geophysics
• Geochemistry
• Petrology
• +4 more

John F. Rudge is a Professor of Geodynamics at the Bullard Laboratories, Department of Earth Sciences, University of Cambridge , and a Fellow and Dean of Trinity College. His research focuses on the dynamics of Earth's interior, including mantle convection, magma transport, and the geochemical evolution of the planet. He employs continuum mechanics, numerical analysis, and statistical methods to address questions about melt dynamics, mantle heterogeneity, and the surface expressions of geophysical processes. Education includes a mathematics undergraduate degree and a PhD in Earth Sciences and Applied Mathematics from Cambridge. He held postdoctoral positions at institutions like ETH Zürich, Yale, and Columbia University before joining Cambridge as a University Lecturer in 2010 and being promoted to Professor in 2022. Key research areas include: Transport of melts and volatiles in the mantle Evolution of Earth's interior from accretion to present Dynamic topography and inner core dynamics Publications span over two decades, with recent works addressing melt rheology, mantle mineralogy, and interdisciplinary applications in oncology. He has supervised numerous PhD students and postdocs, including David Rees Jones, Isarapong Eksinchol, and Laura Alisic. Awards include the Philip Leverhulme Prize and Geological Society President’s Award. Teaching responsibilities include courses on geophysics, magma dynamics, and supervisions in Natural Sciences at Trinity College. Research tools include finite element modeling and collaborations with computational scientists.

Mark BravermanView profile

Professor

• Complexity theory
• Information theory
• Theory of real computation
• +3 more

Mark Braverman is a Professor in the Department of Computer Science at Princeton University. His research spans computational complexity, information theory, and algorithmic mechanism design, with a focus on theoretical foundations and their interdisciplinary applications. Fields of Interest: Complexity theory, Information theory, Theory of real computation, Machine learning, Algorithms, Algorithmic mechanism design Braverman's recent work, including the paper Communication and information complexity (2022), explores the intersection of computational complexity and information theory, advancing understanding of communication efficiency in distributed systems. His research also emphasizes optimization-friendly mechanisms in game theory and algorithm design. Teaching: Spring 2022: COS445 (Economics and computation), Fall 2021: COS521 (Advanced algorithm design) Service: Program committee chair for ITCS'22 (Innovations in Theoretical Computer Science, 2022)