جست‌وجوی استاد و پژوهشگر

Lionel Smithمشاهده پروفایل

استاد

• Comparative Law
• Trust Law
• Fiduciary Obligations
• +۳ مورد دیگر

Lionel Smith is a Professor of Comparative Law at the University of Cambridge's Faculty of Law, appointed in 2024. His career includes roles at the University of Alberta (1991-1996), Oxford University (1996-2000), McGill University (2000-2022), and the University of Cambridge (2022-2024). He holds degrees from the University of Western Ontario, Cambridge University, Oxford University, and the Université de Montréal. LL.B. from University of Western Ontario and Université de Montréal LL.M., LL.D. from Cambridge D.Phil., M.A., D.C.L. from Oxford His research focuses on comparative private law, particularly fiduciary obligations, trust law, and unjust enrichment. He emphasizes understanding unselfish behavior through legal frameworks across civil and common law systems. Recent work includes The Law of Loyalty (Oxford UP, 2023) and contributions to Comparative Property Law: Global Perspectives (2017). Publications span 9 journal articles, 2 books, 2 book chapters, and 2 edited volumes. His 2024 Cambridge Law Journal article on 'knowing receipt' explores cross-jurisdictional trust recovery mechanisms, while his 2020 University of Toronto Law Journal paper redefines parenthood through fiduciary principles. Awards: Titular Member of the International Academy of Comparative Law, Member of American/European Law Institutes Leadership: President of World Society of Mixed Jurisdiction Jurists, non-practicing member of Alberta Bar Teaching includes the 'Comparative Law' course at Cambridge and co-authoring leading textbooks like Waters’ Law of Trusts in Canada . His research programs investigate trust law evolution in civil law systems and fiduciary duty foundations.

Riyadh Baghdadiمشاهده پروفایل

استادیار

• Compilers
• Programming Languages
• Deep Learning
• +۹ مورد دیگر

Riyadh Baghdadi is an Assistant Professor of Computer Science at New York University Abu Dhabi and a Global Network Assistant Professor at the Tandon School of Engineering, NYU. He is also a Research Affiliate at MIT, where he previously completed a postdoctoral fellowship. His academic journey includes a PhD and Master’s from Sorbonne University (INRIA/UPMC) and an engineering degree from Ecole Supérieure d’Informatique in Algiers. Assistant Professor, NYU Abu Dhabi Global Network Assistant Professor, Tandon School of Engineering, NYU Research Affiliate, MIT His research lies at the intersection of compilers, programming languages, and applied machine learning, with a focus on developing advanced compiler techniques for deep learning, high-performance computing, and data-parallel algorithms. He is the lead developer of the Tiramisu compiler , a polyhedral compiler designed to optimize dense and sparse deep learning workloads across diverse architectures including CPUs, GPUs, and FPGAs. Riyadh’s recent publications demonstrate a strong trend toward integrating machine learning into compiler optimization—particularly in cost modeling, loop scheduling, and automatic code generation. His work addresses critical challenges in optimizing sparse neural networks and enabling efficient execution on resource-constrained platforms like smartphones and autonomous vehicles. Outstanding Paper Award, MLSys 2021 He has mentored 18 students and taught core courses such as Computer Systems Organization and Machine Learning at NYUAD. His service to the academic community includes program committee roles at MLSys, IPDPS, ECOOP, and PACT, as well as organizing workshops on polyhedral compilation and machine learning for hardware-software co-design. Riyadh actively contributes to open-source projects and collaborates with industry leaders including Google, Facebook, NVIDIA, and Intel. He leads the development of Tiramisu and collaborates on DSLs like GraphIt and Halide, focusing on performance portability and automation in compiler design.

Aryeh Kontorovichمشاهده پروفایل

استاد

• Theoretical Machine Learning
• Probability
• Statistics
• +۴ مورد دیگر

Aryeh Kontorovich is a Professor in the Computer Science Department at Ben-Gurion University. His research primarily focuses on theoretical machine learning, with expertise in probability, statistics, Markov chains, and metric spaces. His research interests span theoretical machine learning, with particular emphasis on: Probability theory and concentration inequalities Statistical learning theory Markov chains and mixing time estimation Metric space learning Kernel methods Sample compression schemes Professor Kontorovich's recent publications (2021-2025) demonstrate a continued focus on theoretical foundations of machine learning. His work shows strong trends in statistical estimation for Markov processes, distribution learning, metric space analysis, and sample compression. Many papers explore the intersection of probability theory and machine learning, particularly examining concentration inequalities, minimax optimality, and theoretical guarantees for learning algorithms. His research consistently bridges abstract mathematical theory with practical machine learning applications. Scientific awards and recognitions: Distinguished contribution award at MLG 2007 for "A Universal Kernel for Learning Regular Languages" Professor Kontorovich has advised numerous students and collaborated extensively with researchers in theoretical machine learning. His work spans both theoretical foundations and practical applications, with significant contributions to understanding the mathematical limits of learning algorithms. While specific grant information isn't provided in the source material, his extensive publication record in top venues suggests successful funding for his research programs. He maintains active collaborations with researchers worldwide, including prominent names like L. Gottlieb, D. Berend, and S. Hanneke.

Alina Eneمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithms
• Optimization
• +۳ مورد دیگر

Alina Ene is an Associate Professor in the Department of Computer Science at Boston University. Her research focuses on theoretical computer science, algorithms, optimization, and theoretical machine learning, with recent work emphasizing submodular optimization and convex/non-convex optimization techniques. Key research areas include Submodular optimization Convex and non-convex optimization Stochastic gradient methods Streaming and parallel algorithms Her recent publications analyze high-probability convergence in optimization, adaptive gradient methods, and submodular function applications in machine learning and network design. Current advisees include PhD students Fabian Spaeh and Ta Duy Nguyen. She has previously mentored students and postdocs such as Andrew Suh, Adrian Vladu, and Rafael da Ponte Barbosa. Alina has taught courses like CS 237 (Probability in Computing), CS 531 (Advanced Optimization Algorithms), and CS 591 E2 (Convex Optimization Algorithms) at Boston University, and earlier courses at the University of Warwick.

Lida Derevninaمشاهده پروفایل

پژوهشگر

• Plant Pathology
• Plant Immunity
• Molecular Plant Pathology
• +۳ مورد دیگر

Dr. Lida Derevnina is a leading researcher in plant-pathogen interactions and immune receptor networks at the University of Cambridge , affiliated with the School of Biological Sciences and the Department of Plant Sciences . She heads the Crop Pathogen Immunity Group at the Crop Science Centre , focusing on NLR (Nucleotide-binding and Leucine-Rich Repeat) immune receptor networks and pathogen effector functions to engineer durable crop resistance. Education: PhD in Plant Pathology and Phytopathology, University of Sydney (2008-2012) BSc in Agricultural Science, University of Sydney (2004-2007) Her research explores how pathogens evade plant immunity through effector proteins and how NLR networks can be bioengineered to counteract these mechanisms. This work bridges molecular biology, evolutionary genetics, and sustainable agriculture, with recent advances in resistosome activation models and effectorome characterization. Scientific awards include the Crop Science Centre Fellowship (2022) , Marie Skłodowska-Curie Individual Fellowship (2016) , and the Jeanie Borlaug Laube Women in Triticum Award (2012) . Her work has been published in top journals such as Science , Nature , and PLoS Biology . Dr. Derevnina is actively engaged in public outreach, including the Talking Biotech podcast and a YouTube video explaining plant immunity . Her laboratory collaborates on projects related to global food security and pathogen genomics.

Nathaniel Nucciمشاهده پروفایل

دانشیار

• Protein Biophysics
• Spectroscopy
• Drug Development
• +۱۱ مورد دیگر

Nathaniel Nucci is an Associate Professor at Rowan University's College of Science & Mathematics, jointly appointed in the Department of Biological & Biomedical Sciences and Physics & Astronomy. His research bridges biophysics, structural biology, and nanotechnology to understand protein behavior in confined environments. Education Ph.D., Biochemistry and Molecular Biophysics, University of Pennsylvania M.S., Biochemistry and Molecular Biology, University of New Hampshire B.S., Biochemistry and Molecular Biology, University of New Hampshire Research Interests Dr. Nucci's lab focuses on: Protein biophysics in crowded/confining environments Reverse micelle technology for biomolecular studies Hydration dynamics of proteins (NMR-based methods) Structural biology of disease-related proteins (PHDs, p53) Drug delivery systems for protein therapeutics Nanoparticle synthesis with protein conjugation Research Trends His recent publications demonstrate expertise in using reverse micelles to study: Protein structural stability under confinement Hydration dynamics of therapeutic proteins Microenvironmental effects on phase-separating proteins Conformational changes in GPCRs Interfacial interactions in biomolecular systems Scientific Awards Gary J. Hunter Excellence in Mentoring Award (2024) College of Science and Mathematics Excellence in Academic Student Support (2022) Teaching Philosophy Emphasizes applied and experiential learning, integrating recent scientific discoveries into classroom practice and promoting hands-on scientific investigation.

Giuseppe Mingioneمشاهده پروفایل

استاد

• Calculus of Variations
• Elliptic Partial Differential Equations
• Parabolic Partial Differential Equations
• +۳ مورد دیگر

Giuseppe Mingione is a Full Professor in the Department of Mathematical, Physical and Computer Sciences at the University of Parma, Italy. His research spans Calculus of Variations , Elliptic and Parabolic PDEs , and Nonlinear Potential Theory . He has delivered over 25 lecture series and 180+ invited talks globally. PhD in Mathematics, University of Naples Federico II (1999) Degree in Mathematics, University of Naples Federico II (1994) His work focuses on regularity theory for nonlinear PDEs, nonuniform ellipticity, and geometric analysis. Publications include breakthroughs in Schauder estimates, double phase problems, and nonlocal systems. He has received the Amerio Prize (2016) , Caccioppoli Prize (2010) , and Stampacchia Medal (2006) . Recent articles address nonuniform ellipticity, nonlocal PDEs, and gradient regularity. Awards include the Order of the Merit of the Italian Republic (2017) and Von Staudt Chair (2004) . He serves on editorial boards for international journals.

• Mathematical Analysis
• Partial Differential Equations
• Calculus of Variations
• +۳ مورد دیگر

Cristiana De Filippis serves as Associate Professor in the Department of Mathematical, Physical and Computer Sciences at the University of Parma. Her academic journey includes a Bachelor's degree from the University of Torino (2014), Master's from University of Milano-Bicocca (2016), and PhD from the University of Oxford (2020), followed by a tenure-track position at Parma since 2021 and habilitation to full professorship in 2023. Her research focuses on Mathematical Analysis , particularly Regularity Theory for elliptic and parabolic partial differential equations and the Calculus of Variations . She investigates fundamental properties of solutions to nonlinear PDEs, including sharp growth conditions, nonuniform ellipticity, and double-phase functionals. Her work bridges abstract mathematical theory with applications in physics and engineering through rigorous analysis of solution behavior. Analysis of her 15 most recent publications reveals a concentrated research program in nonuniformly elliptic systems (2022-2025), double-phase variational problems (2023-2024), and gradient regularity under irregular coefficients (2020-2024). Key contributions include establishing sharp growth rates in Schauder theory and developing novel techniques for nearly linear growth conditions. European Mathematical Society Prize 2024 Bartolozzi Prize 2023 (Italian Mathematical Union) Iapichino Prize 2020 (Accademia dei Lincei) Premio per la Cultura Mediterranea 2023 G-Research Prize 2019 Forbes 100 Most Successful Italian Women 2023 European Mathematical Society Young Academy (inaugural cohort) Professor De Filippis has delivered invited lectures at prestigious institutions including Erwin Schrödinger Institute (2025), Charles University Prague (2024), and Accademia Nazionale dei Lincei (2022). She teaches Mathematical Analysis courses for Computer Science, Geological Sciences, and Management Engineering programs at undergraduate level. Her research is supported through multiple international collaborations, particularly with Giuseppe Mingione at Parma and researchers at European institutions.

Martin Peiferمشاهده پروفایل

استاد

• Computational Cancer Genomics
• Tumor Evolution
• Genome Instability
• +۵ مورد دیگر

Professor Martin Peifer is a computational cancer genomics researcher at the University of Cologne, where he leads the Department of Translational Genomics. He serves as Principal Investigator of the Peifer Lab, which focuses on developing computational methods to analyze cancer genome sequencing data. His work is deeply integrated with the Center for Data and Simulation Science and he is an active member of the International Cancer Genome Consortium and the Pan-Cancer Analysis of Whole Genomes project. Peifer's research interests center on computational approaches to understanding cancer biology, with particular emphasis on tumor evolution and genome instability mechanisms. His lab develops methods to analyze somatic genome alterations including point mutations, copy number changes, and rearrangements. They also create computational tools for integrative genome analyses, tumor evolution reconstruction, and single-cell sequencing data analysis (both RNA and DNA). His interdisciplinary team applies high-performance computing and machine learning to interpret complex cancer sequencing data, aiming to better understand tumorigenesis, clonal evolution, and therapy resistance. Analysis of Peifer's extensive publication record reveals a strong focus on neuroblastoma and lung cancer genomics, with particular attention to tumor evolution patterns and genomic instability mechanisms. His work spans multiple cancer types but maintains consistent themes of computational methodology development and application to understand cancer progression and treatment resistance. The publications demonstrate increasing sophistication in analyzing intra-tumor heterogeneity and clonal dynamics over time. Peifer leads an active research group including postdoctoral fellows (Joel Kaufmann, Dr. Stephanie Pabel, Agnieszka Rumińska) and PhD students (Magdalena Seiffert, Justinas Valiulis). His lab is involved in the Collaborative Research Center 1399 focused on Mechanisms of Drug Sensitivity and Resistance in Small Cell Lung Cancer, indicating significant grant funding and collaborative research efforts. The Peifer Lab operates at the intersection of computational biology and cancer research, maintaining an interdisciplinary approach that combines bioinformatics, machine learning, and high-performance computing to address complex questions in cancer genomics. Their work has significant implications for understanding cancer evolution and developing more effective treatment strategies.

Ares Llop Nayaمشاهده پروفایل

مدرس

• Romance Linguistics
• Historical Linguistics
• Microsyntactic Variation
• +۶ مورد دیگر

Dr Ares Llop Naya serves as an Affiliated Catalan Lecturer in the Department of Spanish and Portuguese within the Faculty of Modern and Medieval Languages and Linguistics at the University of Cambridge, while holding the Batista i Roca Fellowship at Fitzwilliam College. Her academic profile bridges theoretical linguistics with practical language pedagogy, focusing on Romance language structures and their educational applications. Her research centers on microsyntactic variation in Romance languages, particularly examining negation systems and syntactic phenomena in Catalan and Pyrenean varieties through both diachronic and synchronic lenses. She actively integrates theoretical insights into language teaching methodology, specializing in Catalan and Spanish pedagogy for both native and foreign language contexts. This dual focus manifests in her publications spanning syntactic theory, dialectology, and innovative language teaching approaches that address contemporary classroom challenges. Analysis of her publication trajectory reveals consistent thematic concentration on Romance microvariation, with recent work expanding into pedagogical applications. Her scholarship demonstrates methodological diversity—combining linguistic atlas data, corpus analysis, and classroom research—while maintaining core focus on Catalan syntax and its implications for language acquisition theory. The Pyrenean linguistic corridor remains a persistent geographical anchor across her diachronic and synchronic investigations. Dr Llop Naya's scholarly contributions have been recognized through prestigious awards: Joan Solà International Prize in Catalan Philology for her monograph on Romance negation systems Her teaching portfolio encompasses advanced courses in Romance linguistics, historical syntax, and sociolinguistics, with particular emphasis on translating theoretical frameworks into practical classroom resources. While specific grant funding details aren't publicly documented, her work consistently addresses real-world language education challenges through collaborative projects like the Microvariació.cat initiative. She contributes to the Microvariació.cat project—a digital repository documenting syntactic variation across Catalan dialects—which serves as both research infrastructure and pedagogical resource. This initiative exemplifies her commitment to creating practical applications from theoretical linguistic research while preserving endangered dialectal features of the Romance continuum.

Robin Neumayerمشاهده پروفایل

استادیار

• Calculus of Variations
• Partial Differential Equations (PDE)
• Geometric Analysis
• +۳ مورد دیگر

Robin Neumayer is an Assistant Professor in the Department of Mathematical Sciences at Carnegie Mellon University. Her research focuses on the intersection of calculus of variations, partial differential equations (PDE), and geometric analysis, with a particular emphasis on stability and regularity in geometric inequalities. Education: Ph.D. in Mathematics, University of Texas at Austin, supervised by Alessio Figalli and Francesco Maggi. Her work explores problems related to Sobolev inequalities, isoperimetric problems, scalar curvature, and free boundary phenomena. Recent publications highlight collaborations with leading researchers and address topics such as quantitative stability, anisotropic geometries, and nonlinear PDE. Scientific Awards and Fellowships: NSF Grant DMS-2155054 (2022-2025) RTG Postdoctoral Fellow at Northwestern University (2017-18, 2019-21) Institute for Advanced Study member (2018-19) She teaches courses such as Introduction to Differential Equations and maintains active research collaborations with institutions like the Center for Nonlinear Analysis.

• Embedded Systems
• Hardware-Software Co-Design
• Invasive Computing
• +۷ مورد دیگر

Prof. Dr.-Ing. Jürgen Teich is a full Professor and Chair for Hardware-Software Co-Design at the Department of Computer Science, Friedrich Alexander University Erlangen-Nuremberg (FAU). He serves as Head of Department Computer Science and Vice Dean of the Technical Faculty since August 2024, and has been Speaker of the FAU Research Center Embedded System Initiative (FAU ESI) since 2023. His educational background includes: Diploma degree in Electrical Engineering, University of Kaiserslautern (1989) Dr.-Ing. degree in Electrical Engineering, University of Saarland (1993) Habilitation (PD Dr.-Ing.) entitled "Synthesis and Optimization of Digital Hardware/Software Systems" (1996) Prof. Teich's research focuses on Embedded Systems , Invasive Computing , Hardware-Software Co-Design , and Reconfigurable Computing . His work spans from theoretical foundations to practical implementations, with particular emphasis on resource-constrained systems, many-core architectures, and energy-efficient computing. He has pioneered research in invasive computing paradigms that enable more efficient use of many-core processors by allowing applications to dynamically claim resources. His recent publications reveal a strong trend toward energy-efficient AI deployment on embedded devices , security of embedded systems , and novel memory technologies . There's a clear focus on practical implementations of machine learning on microcontrollers (TinyML), hardware acceleration for data processing, and innovative approaches to power management in self-powered systems. Among his notable scientific awards are: IEEE Fellow (since 2018) Member of Academia Europaea, Section Informatics (since 2011) Member of the National Academy of Science and Engineering (acatech) (since 2018) Member of the German Society of Humboldtians (since 2021) Prof. Teich has been Principal Investigator for numerous DFG-funded projects including SFB/Transregio 89 "Invasive Computing" (2010-2022), SFB 694, and multiple priority programs. He has coordinated large collaborative research efforts across Germany and internationally, with significant funding from DFG and other sources. His research group has produced influential work in embedded systems design and co-design methodologies. He leads the Hardware-Software Co-Design research group at FAU, which focuses on innovative approaches to embedded system design, invasive computing architectures, and efficient implementation of machine learning on resource-constrained devices. The group maintains strong collaborations with industry partners including Intel, Xilinx, and automotive companies.

Peng Gaoمشاهده پروفایل

استاد

• River Morphodynamics
• Geospatial Analysis (GIS, Remote Sensing, UAV)
• Urban Planning
• +۳ مورد دیگر

Peng Gao is a Professor in the Department of Geography and the Environment at Syracuse University, affiliated with the Maxwell School of Citizenship and Public Affairs. His work bridges river geomorphology and urban geospatial analysis, leveraging GIS, remote sensing, and UAV technologies to address environmental and social challenges. Education: Ph.D., Physical Geography, State University of New York at Buffalo (2003) M.S., Physical Geography, Lanzhou University (1993) B.S., Solid Mechanics, Lanzhou University (1990) Professor Gao specializes in river morphodynamics—particularly in the Qinghai-Tibet Plateau—and geospatial applications for urban planning. His research examines braided/meandering river systems, peatland hydrology, and how urban built environments influence social inequities and public health outcomes through spatial analysis. His 2020-2024 publications reveal a dual focus: (1) fluvial processes in high-altitude regions (e.g., neck cutoff dynamics, braided river discharge estimation using Landsat), and (2) urban applications (e.g., green building design, lead poisoning exposure mapping). This reflects a strategic integration of field geomorphology with computational geospatial modeling. Professor Gao actively mentors through SOURCE undergraduate research grants and PhD committees. Current funded projects include peatland mapping in the Andean Altiplano, I-81 Viaduct impact analysis in Syracuse, and studies on urban built environments affecting childhood lead poisoning. His work utilizes UAVs for BVLOS operations and collaborates with Syracuse CoE on urban environmental simulations, emphasizing technical innovation in geospatial data acquisition and analysis.

Richard Danceمشاهده پروفایل

استاد

• Old English Language and Literature
• Middle English Language and Literature
• Germanic Philology
• +۷ مورد دیگر

Professor Richard Dance serves as Professor of Early English in the Department of Anglo-Saxon, Norse & Celtic (ASNC) at the University of Cambridge's Faculty of English, and is a Fellow of St Catharine's College where he holds positions as Director of Studies in ASNC, Praelector, and Clerk to the Governors of the Skerne Exhibition Foundation. He joined the university in 1997 after completing his studies at Oxford and has established himself as Cambridge's principal teacher of Old English, lecturing and supervising extensively in Old English language and literature and Germanic Philology for the ASNC Tripos. His research focuses on Old and Middle English language and literature with particular emphasis on Germanic Philology, English etymology, and the profound influence of Old Norse on early English vocabulary and poetic style. His work spans critical editions of texts like 'Ancrene Wisse' and 'Sir Gawain and the Green Knight', extensive etymological surveys of Scandinavian loanwords, and investigations into medieval multilingualism. His publications reveal consistent engagement with historical semantics, dialectology, and the linguistic interface between Norse and English during critical transition periods. Dance's scholarly impact is evidenced by major awards including the British Academy's 2021 Sir Israel Gollancz Prize for 'The Gersum Project' and the University of Cambridge Pilkington Prize for Excellence in Teaching (2023). His editorial roles include Secretary of the Early English Text Society, Council membership in The Philological Society, and etymological consultancy for the Oxford English Dictionary. As Principal Investigator for The Gersum Project (2016-2020), he led significant research into Scandinavian influence on English vocabulary, securing substantial academic recognition. British Academy's 2021 Sir Israel Gollancz Prize University of Cambridge Pilkington Prize for Excellence in Teaching (2023) Editorial Secretary, Early English Text Society Consultant for Oxford English Dictionary (etymology) International Advisory Committee member, Dictionary of Old English Dance maintains active leadership in academic communities through roles on the British Academy/Royal Historical Society Joint Committee on Anglo-Saxon Charters and as Series Editor for Exeter Medieval Texts and Studies (2008-24). His supervisory work encompasses graduate students in Old English language and literature, while his departmental responsibilities include Director of Studies for multiple Cambridge colleges. Current projects indicate ongoing research into medieval multilingualism, with several forthcoming publications addressing etymological methodology and lexical evolution in the medieval period.

• Theoretical Deep Learning
• Uncertainty Quantification
• Reliable Artificial Intelligence
• +۱۰ مورد دیگر

Gianni Franchi is an assistant professor at ENSTA Paris , affiliated with the Computer Science and Systems Engineering Unit (U2IS) . His work focuses on theoretical deep learning , with a strong emphasis on uncertainty quantification, robustness, and explainability in machine learning models. Current affiliation: ENSTA Paris (U2IS) Academic rank: Assistant Professor Key collaborators: David Filliat, Emanuel Aldea, Andrei Bursuc, Antoine Manzanera His research spans uncertainty quantification , explainable AI , and reliable machine learning . He investigates methods like Bayesian neural networks, ensemble approaches, and deterministic uncertainty models. His work also addresses domain adaptation , self-supervised learning , and autonomous systems , particularly in trajectory forecasting and semantic segmentation for autonomous driving. Recent publications analyze probabilistic modeling for robustness, symmetry-aware Bayesian methods , and multi-modal datasets like InfraParis. He develops frameworks like Torch-Uncertainty and benchmarks such as MUAD for uncertainty types in autonomous driving. Key themes: Uncertainty Quantification Deep Learning Theory Autonomous Systems Explainable AI Dataset Creation Bayesian Methods