جستجوی استاد راهنمای دانشگاهی

Stefan Monnierمشاهده پروفایل

دانشیار

• Type Theory
• Dependent Types
• Functional Programming
• +۶ مورد دیگر

Stefan Monnier is an Associate Professor in the Department of Computer Science and Operations Research at the University of Montreal's Faculty of Arts and Science. His office is located in room 2341 of the André-Aisenstadt building. He is an active researcher in programming language theory with numerous publications and ongoing research projects. Dr. Monnier's primary research interests focus on pushing the limits of strongly typed programming languages to make them applicable in circumstances where they're currently inapplicable. He's particularly interested in designing advanced type systems that enable secure low-level code development, including automatic memory management. His work explores how programmers can explain program correctness to compilers through type annotations. He's the creator of the Typer language, which combines elements of ML, Scheme, and Coq. His recent publications (2021-2025) show a consistent focus on dependent types, type theory, and programming language implementation. The research trends indicate a growing interest in practical applications of advanced type systems, with several papers addressing memory management challenges through type-theoretic approaches. His work bridges theoretical foundations with practical implementation concerns. Dr. Monnier has supervised numerous graduate students, including 14 Master's students and 2 PhD students over the past two decades. His research is supported by multiple grants from NSERC and FRQNT, including the ongoing "Typer: An exocompiler to program with dependent types" project (2018-2026) and previous iterations dating back to 2012. Typer: An exocompiler to program with dependent types (2018-2026, NSERC) Fondements logiques pour la meta-programmation (2021-2024, FRQNT) Supplément COVID-19 CRSNG_Typer (2020-2021, NSERC) TYPER: A LISP APPROACH TO DEPENDENT TYPES (2012-2019, NSERC) He is a member of the LTP (Laboratoire de Traitement Parallèle) and collaborates with the Logic and Computation group at McGill University. His work has significant connections to the Emacs community, where he previously served as a maintainer and contributed several packages now bundled with Emacs.

Ponnie Clarkمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Blockchain
• Data Science
• +۶ مورد دیگر

Dr. Ponnie Clark is a Multi-Discipline Adjunct Senior Lecturer at the University of Western Australia (UWA), affiliated with the School of Physics, Maths and Computing and the Department of Computer Science and Software Engineering. Prior to UWA, she held an academic position at Curtin University for two decades, focusing on research and teaching in Information Systems and Computer Science. She has supervised 12 PhD and 2 Master’s students, and published over 100 peer-reviewed articles. Education: PhD in Information Systems, Curtin University (Thesis: A methodology for multi-site distributed software development) Master of Computer Science (Visualisation Amortised Analysis) Bachelor of Mathematics Her research spans multidisciplinary areas including Artificial Intelligence, Blockchain technology, Data Science, and Ontology Engineering. Key projects include developing blockchain frameworks for financial systems, peer-to-peer energy trading, and flood risk management. She has contributed to UN Sustainable Development Goals related to environmental sustainability and digital solutions. Her recent articles focus on blockchain scalability, decentralized finance, flood risk modeling, and social politics analysis through knowledge graphs. She has led grants such as the Blockchain research group and projects funded by industry partners like I Synergy Group Limited and Alphabright Digital Pty Ltd. Dr. Clark has been involved in collaborative research across disciplines, including energy systems, disaster management, and social media analytics. Her work emphasizes practical applications of technology to solve real-world challenges in areas like energy trading, financial inclusion, and environmental resilience.

André Schlichtingمشاهده پروفایل

استاد

• Stochastic Processes
• Partial Differential Equations
• Optimal Transport
• +۴ مورد دیگر

André Schlichting is a full professor at the University of Ulm, leading the Institute of Applied Analysis. His research focuses on stochastic processes, partial differential equations (PDEs), and optimal transport, with a particular emphasis on gradient flows, nonlocal dynamics, and mathematical physics. He investigates systems such as stochastic particle models, McKean-Vlasov equations, and metric graphs, often exploring their long-time behavior and discretization techniques. Collaborations include work on diffusive transport distances, porous medium equations, and manifold analysis. His recent contributions address topics like convergence of particle systems to continuous models, gradient flows on graphs with reservoirs, and stationary solutions on high-dimensional spheres. His work bridges theoretical analysis with applications in statistical mechanics and numerical methods. Education and affiliations are not explicitly detailed in the provided texts, but his current role highlights academic leadership in applied analysis. Key collaborations involve researchers such as Daniel Matthes, Eva-Maria Rott, and Jan-Frederik Pietschmann. His research group at the University of Ulm is expanding, focusing on advanced analytical techniques and their implications in interdisciplinary fields.

• Control of Complex Systems
• Graph Theoretic Algorithms
• Electric Power Systems
• +۵ مورد دیگر

Dr. Aleksandar I. Zecevic is a Professor of Electrical and Computer Engineering at Santa Clara University, serving as Associate Dean for Graduate Studies. His dual focus spans technical research in complex systems control and interdisciplinary work bridging science, religion, and aesthetics. He has authored over 40 technical papers and multiple influential books. His technical research explores control theory, graph algorithms, and electric power systems. His 2010 book Control of Complex Systems remains foundational in this field. Parallel work in science-religion interfaces includes books examining environmental ethics and the limits of scientific knowledge. Recent publications like Reflections of an Infinite Beauty (2025) integrate ecological science with theological perspectives. Zecevic pioneered courses like Chaos Theory and the Limits of Knowledge , uniquely counting as both engineering electives and core humanities requirements. His global initiatives include establishing the Jesuit Engineering Schools' Ethics and Spirituality task force, fostering international academic collaboration through conferences and hybrid course offerings across three continents. His aesthetic explorations manifest in a trilogy analyzing beauty's role across disciplines. These works emphasize interdisciplinary dialogue, encouraging students to synthesize technical rigor with humanistic inquiry.

• Human-Computer Interaction
• Cognitive Workload
• Human Factors
• +۳ مورد دیگر

Dr. Spencer Castro is an Assistant Professor in Management of Complex Systems at UC Merced, investigating cognitive aspects of human-machine systems. His research program integrates human factors, neuroscience, and visualization to address challenges in: Cognitive workload measurement during multitasking Neural correlates of environmental exposure Visualization design for complex information Human-automation interaction in driving contexts Methodologically, he employs neurophysiological measures (EEG), virtual reality, and behavioral experiments. Recent studies examine how natural environments influence reward processing and attention, design principles for uncertainty visualization, and cognitive costs of intermittent multitasking. Applied work includes VR evacuation training for wildfires and COVID-19 forecast communication.

• Algebraic Combinatorics
• Spectral Properties
• Probability Theory
• +۳ مورد دیگر

Nadia Lafrenière is an Assistant Professor with a limited-term appointment at Concordia University . She holds a Ph.D. in Combinatorial Mathematics from Université du Québec à Montréal (UQAM), under the supervision of Franco Saliola. Previously, she was a John Wesley Young Research Instructor at Dartmouth College. Her research focuses on algebraic combinatorics , particularly spectral properties of combinatorial structures, card shuffling dynamics, and dynamical algebraic systems. She is also deeply engaged in mathematics outreach, designing activities for diverse audiences through radio shows, walking tours, and educational workshops. Education : Ph.D. in Combinatorial Mathematics (2019), UQAM M.Sc. in Complexité palindromique des mots et des arbres (2016), UQAM Research Interests : Her work explores spectral properties of random walks, combinatorial actions on permutations, and homomesy in dynamical systems. She has contributed to studies on interval-closed sets, toggle groups, and Markov chain mixing times. Her methods bridge algebraic combinatorics with probability theory, often involving eigenvalue analysis and poset theory. Outreach Contributions : Lafrenière co-hosted the French radio show L’oeuf ou la poule , presenting monthly math segments for four years. She co-created Maths in the city walking tours in Montreal, Chicago, and Trois-Rivières, using urban landscapes to illustrate mathematical concepts. For schools, she offers workshops on Braess’s paradox, magic tricks, and mathematical problem-solving, tailored for K-12 audiences. Grants & Collaborations : Supported by the Institut des Sciences Mathématiques, she delivers talks on applied combinatorics (e.g., traffic modeling, relationship optimization). Her collaborations span North America, with co-authors like Joel Brewster Lewis, Jessica Striker, and Rosa Orellana. Labs & Future Work : Her research continues to investigate combinatorial dynamics, particularly homomesy phenomena and eigensystem analysis of shuffle operators. She actively promotes mathematics through accessible public engagement initiatives.

• Inverse Problems
• Variational Methods
• Total Variation Regularization
• +۷ مورد دیگر

Jose A. Iglesias Martínez is an Assistant Professor in the Department of Applied Mathematics at the University of Twente, affiliated with the Mathematics of Imaging and AI (MIA) group within the Faculty of Electrical Engineering, Mathematics and Computer Science. He conducts research at the intersection of variational analysis, inverse problems, shape analysis, and machine learning, with a geometric perspective on regularization and optimization. University: University of Twente School: Faculty of Electrical Engineering, Mathematics and Computer Science Department: Department of Applied Mathematics Research Group: Mathematics of Imaging & AI (MIA) His research focuses on analytic and variational methods for inverse problems in imaging, particularly involving total variation and related regularization models. The geometric structure of function domains and convex geometry in minimization problems plays a central role in his work. He applies these methods to imaging, shape analysis, and machine learning, with strong mathematical foundations in PDEs, functional analysis, and optimization. The recent publications reflect a consistent focus on variational regularization, extremal point analysis, sparse optimization, and PDE-constrained problems. Key themes include total variation, TGV, nonlocal perimeters, and shape optimization, with applications in image denoising, optical flow, and fluid dynamics. His work spans both theoretical convergence analysis and algorithmic development, often with computational implementations. Scientific Awards and Recognition: Member of the Editorial Board, Numerical Functional Analysis and Optimization (since August 2023) He actively teaches in applied mathematics, including courses such as Analysis 3, Mathematics behind Data-Driven Methods, and interdisciplinary topics like Art, Mathematics and Technology. He has supervised tutorials and projects in calculus, linear algebra, and modeling. His academic training includes dual MSc degrees in Mathematics and Telecommunication Engineering from Madrid, a PhD (2015) and habilitation (2021) in Mathematics from the University of Vienna, followed by postdoctoral work at the Radon Institute in Linz. He has no listed students yet, but is actively publishing and contributing to the mathematical imaging community. He is involved in editorial work and maintains a strong publication record in top-tier applied mathematics journals.

• Coding Theory
• Distributed Storage Systems
• Matroid Theory
• +۳ مورد دیگر

Thomas Westerbäck is a Senior Lecturer at Mälardalen University, affiliated with the School of Education, Culture and Communication and the Division of Mathematics and Physics. His research focuses on coding theory, distributed storage systems, matroid theory, combinatorics, information theory, and algebraic coding. He has contributed to advancements in locally repairable codes, matroid structures in storage systems, and applications of combinatorial methods in coding. His publications highlight theoretical bounds, code constructions, and practical implementations in storage efficiency and error resilience. Key research themes include matroid minors in coding, alphabet-dependent code bounds, and polymatroidal frameworks for heterogeneous storage systems. His work bridges abstract mathematical concepts (e.g., Möbius inversion in lattices) with applied coding challenges in distributed environments. Publications span topics from binary matroid flats to exact regenerating codes, demonstrating a blend of theoretical depth and practical storage solutions. His research emphasizes resilience against data erasures and optimizing storage efficiency through combinatorial and algebraic innovations.

• Cloud Computing
• Distributed Systems
• High-Performance Computing
• +۵ مورد دیگر

Raja Appuswamy is an Assistant Professor in the Department of Data Science at EURECOM, focusing on data management on modern hardware and molecular information storage. His research integrates cutting-edge storage technologies with bioinformatics, particularly in DNA-based data storage. He teaches courses in cloud computing, distributed systems, and databases. His work spans Optimizing storage hierarchies with DNA archival systems Hardware-conscious algorithms for GPUs and heterogeneous architectures Error correction in molecular storage Long-term database preservation strategies Key research interests include Cross-architecture data joins (e.g., OneJoin, XJoin) Cold storage innovation with CMOSS and OligoArchive Integration of bio-inspired storage with traditional computing and explores interdisciplinary challenges at the intersection of computer science and molecular biology. Publications emphasize scalable solutions for DNA storage density, error tolerance, and archival systems. Awards highlight contributions to DNA-based image encoding, storage reliability, and page cache checksums. Current projects focus on enzymatic DNA ligations for storage density improvement, motif-based error correction, and hardware acceleration for knowledge graphs. His work targets both theoretical advancements and practical implementations in storage systems.

Anton Bravermanمشاهده پروفایل

دانشیار

• Stochastic Modeling
• Applied Probability
• Queueing Theory
• +۳ مورد دیگر

Anton Braverman is an Associate Professor of Operations at the Kellogg School of Management, Northwestern University, serving since 2017. His research focuses on stochastic modeling, applied probability, and their applications in ridesharing systems and healthcare operations. He holds a PhD in Operations Research from Cornell University (2017), an MS in Operations Research from Cornell (2015), and a BS in Mathematics and Statistics from the University of Toronto (2012). Braverman's research explores advanced analytical techniques for stochastic systems, including diffusion approximations, queueing networks, and steady-state analysis. His work bridges theoretical probability with practical applications in operations management and engineering systems. Notable contributions include studies on the Join-the-Shortest-Queue model in heavy traffic regimes and optimal routing strategies for ridesharing platforms. He has received the 2017 Best Publication Award from the Applied Probability Society of INFORMS. His teaching includes advanced courses such as Queueing Networks: Models, Algorithms and Emerging Applications and Stochastic Processes I , emphasizing stochastic analysis and dynamic programming techniques. Key Awards: 2017 Best Publication Award (INFORMS Applied Probability Society) Research Focus: Stochastic processes, queueing theory, applied probability applications Teaching: Specializes in stochastic modeling and operations management courses

• Commercial Law
• Corporate Law
• Law and Economics
• +۳ مورد دیگر

Jonny Hardman is a Senior Lecturer in the School of Law at the University of Edinburgh, affiliated with the Edinburgh Centre for Commercial Law. He holds a PhD in Law from the University of Glasgow (2020), an LLM (2017), and an LLB (2008). He is actively involved in the Law Society of Scotland as Convenor of the Banking, Company and Insolvency Law Policy Sub-Committee. His research focuses on commercial law, particularly rights in security (real and personal), company law, and law and economics. He has published extensively on secured transactions, corporate debt, and theoretical company law, with recent work addressing corporate legal personality, creditor protections, and regulatory reforms. His empirical studies include analyses of Scottish liquidations, corporate transparency, and director interconnectivity in UK publicly traded companies. Jonny has contributed to datasets such as company-director graphs and insolvency filings, reflecting his commitment to empirical legal research. He frequently participates in academic conferences, delivers invited talks, and serves as an editorial contributor to journals like the European Business Law Review . He currently supervises PhD students in areas spanning his research interests and is engaged in policy debates through his roles in academic and professional networks.

• Physics
• Computer Science
• Mathematics
• +۷ مورد دیگر

Cristopher Moore is a Professor at the Santa Fe Institute, where he explores interdisciplinary research at the intersection of physics, computer science, and mathematics. His work focuses on phase transitions in computational problems, social network analysis, quantum algorithms, and algorithmic fairness in consequential decisions like criminal justice risk assessment. Organized decarbonization workshops for New Mexico's energy transition Developed topological classifications for n-body orbital dynamics Co-founded Journal of Weird-Ass Shit and Journal of Unpublished Results Research interests span from transparency in AI to quantum computing and social network theory . His recent work analyzes phase transitions in machine learning and the computational limits of pattern detection in noisy data. Key article trends include quantum computation ( Graph Isomorphism , Quantum Walks ), complex systems ( Sandpiles , Cellular Automata ), and algorithmic justice frameworks. All work emphasizes both theoretical barriers and practical policy implications.

• Complex Systems
• Dynamical Systems
• Network Science
• +۱۳ مورد دیگر

Luc Berthouze is a Professor of Complex Systems (Informatics) at the School of Engineering and Informatics, University of Sussex, where he leads interdisciplinary research at the intersection of mathematics, engineering, and neurophysiology. He also holds an honorary appointment at the Great Ormond Street Hospital Institute of Child Health, University College London. His educational background includes a PhD in Applied Mathematics and Computer Science from the University of Evry Val d’Essonne (1996), followed by research positions at the Electrotechnical Laboratory (ETL) and the National Institute of Advanced Industrial Science and Technology (AIST) in Japan, before joining the University of Sussex faculty in 2006. His research focuses on dynamical processes on networks, including neuronal synchronization, epidemic spreading, and fault detection in IT infrastructures. He develops mathematical models and analysis methods rooted in dynamical systems, random processes, graph theory, control theory, and signal processing. Applications span neuroscience (EEG, EMG, MEG), robotics, and large-scale network management. The trend in his recent publications reveals a strong emphasis on network dynamics across biological and technological systems. Key themes include functional connectivity inference, metastability in oscillatory networks, epidemic modeling on structured networks, and scalable observability in microservices. His work increasingly bridges neuroscience and computer engineering, exemplified by projects like 'Rethinking large-scale network management through the lens of neuroscience.' Mathematical Neuroscience Mathematical Epidemiology Time Series Analysis Network Analysis Synchronization and Criticality Motor Control and Coordination He has received substantial research funding from EPSRC, Innovate UK, Moogsoft, Aviva, and the Leverhulme Trust, supporting work on network controllability, data-driven decision making, and Bayesian inference. His grants often involve collaborations with industry partners, indicating applied and translational impact. He supervises research projects and teaches courses such as 'Intelligence in Animals and Machines,' contributing to both undergraduate and postgraduate education. His lab and research team work on developing scalable mathematical frameworks for network analysis, with applications in healthcare, robotics, and IT infrastructure.

• Generative AI
• Large Language Models
• Computer Vision
• +۴ مورد دیگر

Manuel Brack is an Applied Research Scientist at Adobe Firefly in San Jose, USA, with an adjunct research affiliation at hessian.AI in Darmstadt, Germany. His academic foundation includes a Ph.D. from TU Darmstadt's Machine Learning Lab where he specialized in generative AI systems. He co-founded the Occiglot research collective focused on open-source LLM development. Brack's research centers on large-scale generative models at the intersection of natural language processing and computer vision. His work addresses critical challenges in monosemanticity measurement , multilingual data curation , AI safety frameworks , and text-to-image generation ethics . He has pioneered techniques for bias mitigation in multilingual systems and developed novel architectures for memory-efficient language modeling. His publications reveal significant trends toward neuro-symbolic integration (DeiSAM), privacy-preserving mechanisms (CLIP identity attacks), and community-driven open science (Occiglot/Community OSCAR). The research consistently bridges theoretical innovation with practical deployment considerations for generative systems. Best Runner-Up Paper Award RBFM Workshop at NeurIPS 2024 (LlavaGuard) Best Paper Award at DPFM 2024 (Homoglyphs research) Brack actively contributes to open-source initiatives including Community OSCAR (345+ TiB multilingual dataset) and Occiglot (open LLM development). His work emphasizes ethical deployment through frameworks like Fair Diffusion for bias attenuation and Safe Latent Diffusion for content moderation, demonstrating commitment to responsible AI advancement.

Shu-Ching Chenمشاهده پروفایل

استاد

• Data Science
• Multimedia Big Data
• Disaster Information Management
• +۸ مورد دیگر

Shu-Ching Chen is a Professor and Executive Director of the Data Science and Analytics Innovation Center (dSAIC) at the University of Missouri-Kansas City (UMKC), School of Science and Engineering. He holds a Ph.D. in Electrical and Computer Engineering from Purdue University and has extensive experience in data science, multimedia big data, disaster information management, AI/ML, and AR/VR. He leads a multi-university research center focused on data analytics, AI, and societal impact. Research Interests: His research spans data science , multimedia big data , disaster informatics , AI/ML , and spatial computing . He has pioneered work in multimodal data fusion, multimedia retrieval, and intelligent systems for disaster response and climate modeling. His recent work emphasizes AI-driven solutions for public health, transportation, and environmental resilience. The 15 most recent publications reflect a strong trend in AI for societal challenges , including pandemic response, disaster management, climate modeling, and transportation analytics. His work integrates deep learning , graph neural networks , remote sensing , and multimodal data fusion , often applied to real-world problems in public safety, healthcare, and infrastructure. There is a clear emphasis on practical software systems (e.g., hurricane loss modeling) and interdisciplinary collaboration . Scientific Honors: IEEE Fellow (2016), AAAS Fellow (2016), AIMBE Fellow (2025), AAIA Fellow (2021), SIRI Fellow (2009) ACM Distinguished Scientist (2011) Best Paper Awards (2006, 2016, 2022) IEEE TCMC Impact Award (2024), Service Award (2019) FIU Top Scholar (2011, 2012), Eminent Scholar (2014–2019) Research Leadership and Grants: Dr. Chen has secured major funding from NSF, NIH, DHS, DOE, NOAA, DoD, and industry partners (Microsoft, IBM). He is PI of a U.S. Department of Education Center of Excellence in AI-Empowered Spatial Computing and leads the dSAIC. His grants focus on AI for disaster management, pandemic response, transportation, and secure computing. He actively mentors students and collaborates across institutions. Labs and Centers: He leads the Data Science and Analytics Innovation Center (dSAIC) , a University of Missouri System-wide center. He is also PI of the AI-Empowered Spatial Computing Center of Excellence funded by the U.S. Department of Education.