جستجوی استاد راهنمای دانشگاهی

Paul Larsonمشاهده پروفایل

استاد

• Set Theory
• Topology
• Model Theory
• +۳ مورد دیگر

Paul Larson is a Professor of Mathematics at Miami University. His research focuses on set theory, topology, and model theory, with particular expertise in forcing axioms, descriptive set theory, and infinitary logic. He holds a Ph.D. in Mathematics from the University of California, Berkeley. His work bridges foundational mathematical logic with applications in topology and combinatorics. Key contributions include studies on canonical models under fragments of the Axiom of Choice, polar forcings, and cardinal characteristics. Larson has collaborated extensively with leading researchers such as Saharon Shelah and Jindřich Zapletal. His publications span prestigious journals like the Annals of Pure and Applied Logic and Transactions of the American Mathematical Society. Beyond research, he contributes to the academic community through editorial work and expository writings on historical developments in determinacy theory. Education: Ph.D., Mathematics, University of California, Berkeley Research interests emphasize foundational questions in set theory with applications to topology and model theory. His recent work explores advanced forcing techniques, square principles in Pmax extensions, and combinatorial properties of cardinal invariants. Publications reflect interdisciplinary engagement, including crystal structure prediction in high-pressure chemistry and operator theory in functional analysis. Despite an extensive publication record, no specific scientific awards are documented here. His advising and grant activities remain unspecified in the provided texts. Collaborations span international institutions, reflecting his role as a central figure in contemporary set theory research.

• Artificial Intelligence
• Digital Economy
• E-Commerce
• +۷ مورد دیگر

Kartik Hosanagar is the John C. Hower Professor of Technology and Digital Business and a Professor of Marketing at The Wharton School, University of Pennsylvania. He is also the Co-Director of the Wharton Human-AI Initiative. His work spans the Department of Operations, Information and Decisions, focusing on the intersection of technology, business, and society. Education: PhD in Management Science and Information Systems, Carnegie Mellon University MPhil in Management Science, Carnegie Mellon University Masters in Information Systems, Birla Institute of Technology and Sciences (BITS, Pilani), India Bachelors in Electronics Engineering, Birla Institute of Technology and Sciences (BITS, Pilani), India Research Interests: Kartik’s research focuses on the digital economy, particularly the impact of AI, algorithms, and analytics on consumers, businesses, and society. His work explores internet marketing, e-commerce, digital media, information diffusion, platform economics, and the ethical implications of AI. He investigates how technology transforms business models and consumer behavior in online environments. Publication Trends: His recent research combines machine learning, causal inference, and behavioral modeling to study digital platforms, user behavior, and AI applications in marketing and operations. The articles reflect a strong focus on empirical analysis of social media, search engines, and platform design, with applications in advertising, content sharing, and product adoption. Scientific Awards: Best Information Systems paper published in Management Science, 2013-2016 Finalist for Best Information Systems paper in Management Science (2012-2015) Recognized as one of the world’s top 40 business professors under 40 Eleven-time recipient of teaching excellence awards at Wharton MBA Excellence in Teaching Award, 2007 “Goes above and beyond the call of duty” award (multiple years) Advising and Grants: While no formal PhD students are listed, Kartik has supervised independent studies and mentored numerous students through research projects. His entrepreneurial ventures, such as Yodle and Jumpcut Media, reflect real-world applications of his research. He has also secured significant industry engagement through consulting and executive education with major firms like Google, American Express, and Citi. His work is supported by academic recognition, editorial roles, and media outreach. Labs and Teams: Kartik co-directs the Wharton Human-AI Initiative, a research hub exploring the integration of human and artificial intelligence in business. He also leads research groups focused on digital platforms and AI ethics, collaborating with scholars across disciplines to advance understanding of algorithmic decision-making and its societal impact.

• Theoretical Computer Science
• Error-Correcting Codes
• Approximation Algorithms
• +۵ مورد دیگر

Venkatesan Guruswami is a Chancellor's Professor in the Department of EECS and a Senior Scientist at the Simons Institute for the Theory of Computing at UC Berkeley . He also holds a Professor position in the Department of Mathematics . His academic journey began with a B.Tech in Computer Science from the Indian Institute of Technology, Madras (1997) , followed by a Ph.D. in Computer Science from the Massachusetts Institute of Technology (2001) . After a Miller Research Fellowship at UC Berkeley (2001–02), he held faculty roles at the University of Washington and Carnegie Mellon University before returning to UC Berkeley in January 2022. Education : B.Tech, IIT Madras (1997) Ph.D., MIT (2001) Professional Affiliations : Chancellor's Professor, UC Berkeley (EECS) Senior Scientist & Interim Director, Simons Institute Professor, UC Berkeley (Mathematics) Guruswami's research spans multiple domains within Theoretical Computer Science , focusing on Error-Correcting Codes , Approximation Algorithms , Randomness in Computing , Probabilistically Checkable Proofs , and Computational Complexity . His groundbreaking work in List Decoding has enabled codes with minimal redundancy for correcting worst-case errors, while recent advancements include Polar Codes , Deletion-Correcting Codes , and Constraint Satisfaction Problems . He has also contributed to Quantum Coding Theory , Locally Recoverable Codes , and Approximation Hardness in various computational contexts. His publications reflect a deep engagement with interdisciplinary topics. Key trends include: Quantum Information Theory : Quantum LDPC codes, transversal gates, and quantum storage. Algebraic Coding : Reed-Solomon codes, AG codes, and polynomial-based constructions. Computational Complexity : Hardness of approximation, CSPs, and parameterized intractability. Data Transmission : Polar codes, deletion channels, and feedback mechanisms. Algorithmic Techniques : Spectral methods, semirandom models, and Lasserre hierarchy applications. Guruswami has received numerous accolades, including the Simons Investigator Award , Presburger Award , Packard Fellowship , Sloan Research Fellowship , ACM Doctoral Dissertation Award , and the IEEE Information Theory Society Paper Award . He is an ACM Fellow (2017) and IEEE Fellow (2019) , with recent honors like the Guggenheim Fellowship (2023) and AMS Fellow (2023) . As an advisor, he has mentored over 25 PhD and postdoctoral researchers , including Atri Rudra , Prasad Raghavendra , and Peter Manohar , whose work has won awards like the Edmund M. Clarke Doctoral Dissertation Award and CRA Outstanding Undergraduate Researcher Award . His research is supported by grants from the National Science Foundation , Packard Foundation , and Sloan Foundation . He also serves as Editor-in-Chief of the Journal of the ACM and holds leadership roles in IEEE and arXiv moderation. Guruswami is actively involved in Simons Institute programs and co-organized workshops on Coded Computation and Information Theory . His work bridges theoretical advancements with practical applications in Cloud Storage , Quantum Computing , and Group Testing , including pandemic-era contributions like AC-DC: Amplification Curve Diagnostics for SARS-CoV-2 .

• Formal Verification
• Software Testing
• Programming Languages
• +۱۲ مورد دیگر

Alastair F. Donaldson is a Professor and Director of Research in the Department of Computing at Imperial College London, where he leads the FastPL research group. His work bridges formal methods, software testing, and programming languages, with a focus on enhancing the reliability of high-performance and parallel software systems. He has held key roles including Director of Research (since 2023) and previously served as Lecturer (2011–2014), Senior Lecturer (2014–2017), and Reader (2017–2020) before being promoted to Professor in 2020. His research interests include formal verification, compiler testing, GPU programming, concurrency, and fuzzing. He has made significant contributions to the verification of GPU kernels, metamorphic testing of graphics drivers, and the development of tools like GPUVerify and GraphicsFuzz. His work combines theoretical rigor with practical impact, demonstrated by the acquisition of his startup GraphicsFuzz by Google in 2018 and his subsequent roles as Senior Software Engineer and Visiting Researcher at Google. His recent publications reflect a sustained focus on compiler and system reliability, with trends in fuzzing, formal specification, and automated testing of complex systems such as WebGPU, CXL cache coherence, and large language models for code generation. His work increasingly integrates empirical validation with formal techniques to uncover subtle bugs in real-world systems. Scientific awards and recognitions include: 2017 BCS Roger Needham Award EPSRC Early Career Fellowship Fellow of the British Computer Society Best Paper awards at EuroSys 2024, MET 2021, IISWC 2019, IWOCL 2019, and ICST 2016 Best Industry Paper at ICST 2024 ACM SIGSOFT Distinguished Paper at ISSTA 2023 ACM SIGPLAN Most Influential OOPSLA Paper Award (2012 paper), awarded in 2022 Best Student Paper at PPoPP 2014 He has advised numerous PhD students and leads a vibrant research group. He has secured significant research funding and collaborates extensively with industry and academia. His service includes leadership roles such as General Chair of PLDI 2020, PC Chair of ECOOP 2019, and Steering Committee Chair of PLDI (2022–2025). He also serves on the advisory board of PACM-PL and on program committees for top venues including POPL, OOPSLA, PLDI, ICSE, and ISSTA. He leads the FastPL research group, which focuses on the design and implementation of programming tools and techniques for reliable software. The group conducts cutting-edge research in compiler testing, formal methods, and high-performance systems, fostering collaboration across academia and industry.

Omkant Pandeyمشاهده پروفایل

دانشیار

• Cryptography (Applied and Theory)
• Security
• Privacy
• +۳ مورد دیگر

Omkant Pandey is an Associate Professor at Stony Brook University, affiliated with the National Security Institute. He holds a PhD from the University of California, Los Angeles (UCLA). His research focuses on Cryptography, Security, and Privacy, with a particular emphasis on post-quantum cryptographic systems, secure computation protocols, and privacy-preserving technologies. Education: PhD in Computer Science, University of California Los Angeles (UCLA) Research interests span theoretical and applied cryptography, including the design of secure multi-party computation protocols, non-malleable codes, and post-quantum cryptographic primitives. His work also addresses challenges in privacy-preserving data sharing and IoT security in enterprise environments. His recent publications highlight advancements in post-quantum secure computation, zero-knowledge proofs, and cryptographic protocol design. Notable projects include grants such as the CAREER award for concurrent security against quantum adversaries and foundational work on black-box constructions in cryptography. Advising and grants: He advises graduate students in cryptography and has secured grants focusing on post-quantum security and cryptographic protocol development. His work is supported by initiatives like the National Science Foundation’s SaTC and CAREER programs. Labs/Teams: His affiliation with the National Security Institute reflects his contributions to applied security research and collaboration with interdisciplinary teams.

Benjamin J. Delawareمشاهده پروفایل

استادیار

• Programming Languages
• Software Engineering
• Formal Verification
• +۳ مورد دیگر

Benjamin J. Delaware is an Assistant Professor of Computer Science at Purdue University. His research focuses on programming languages, formal verification, and tools for ensuring software correctness using mechanized theorem provers. He holds a Ph.D. from The University of Texas at Austin (2013), an MSc from Washington University in St. Louis (2007), and a B.S. from Truman State University (2005). His work emphasizes practical formal methods, including static enforcement of privacy policies, compiler design for oblivious computation, and automated verification techniques. Key contributions include tools like Taypsi, KestRel, and HACCLE. His research bridges theory and practice, addressing challenges in software security, correctness, and efficiency. Publications span top venues like POPL, PLDI, and OOPSLA, reflecting a strong focus on foundational programming language concepts. Collaborations with researchers like Suresh Jagannathan and Qianchuan Ye drive advancements in automated reasoning and secure computation.

Stuart Kurtzمشاهده پروفایل

استاد

• Theoretical Computer Science
• Computational Complexity Theory
• Type Theory
• +۴ مورد دیگر

Stuart Kurtz is a Professor in Computer Science and the College at the University of Chicago, and serves as Master of the Physical Sciences Collegiate Division. He holds the endowed position of George and Elizabeth Yovovich Professor. His research focuses on theoretical computer science, including computational complexity theory, randomness in computation, type theory, and formal logic. He has contributed to foundational areas such as the Berman-Hartmanis Isomorphism Conjecture and the computational properties of random sets. Kurtz is affiliated with the Theoretical Computer Science and Programming Languages Groups at the University of Chicago. He has been recognized with the 2009 Quantrell Award for teaching excellence. His service roles include Director of Undergraduate Studies and Department Chair in Computer Science. He actively mentors Ph.D. students and has advised multiple graduates in complexity theory and related fields. His academic background includes a Mathematics Ph.D. from the University of Illinois, supervised by Carl Jockusch. He approaches type theory as an intersection of formal logic and functional programming. Research interests span measure-theoretic randomness, computational logic, and complexity class separations. His recent work explores connections between theoretical computer science and interdisciplinary fields like physics and statistics. In teaching, Kurtz has instructed courses such as Formal Language Theory, Discrete Mathematics, and Honors Intro Programming. His service contributions include roles in the Computation Institute and Toyota Technological Institute at Chicago. His lab affiliations and collaborative work reflect a strong commitment to advancing theoretical foundations in computer science.

• Trustworthy Machine Learning
• Algorithmic Fairness
• Data Privacy
• +۷ مورد دیگر

Hui Wang is a Professor and Associate Chair for PhD Studies and Research in the Department of Computer Science at the Charles V. Schaefer, Jr. School of Engineering and Science, Stevens Institute of Technology. She also serves as the Director of the Data Science PhD Program and holds leadership roles in multiple institutional committees, including the Doctoral Committee, Faculty Mentoring Program, and Strategic Planning initiatives at both departmental and university levels. Research Interests: Dr. Wang's research focuses on building trustworthy machine learning systems by integrating privacy, fairness, and accountability . Her work aims to fortify ML models against privacy attacks, eliminate algorithmic biases, and ensure auditable decision-making. She explores intersections between machine learning, data mining, and cybersecurity, with applications across domains requiring ethical and secure AI deployment. Recent Research Trends: Her recent publications and funded projects reflect a strong emphasis on privacy-preserving machine learning , fairness-aware systems , and verifiable computing . Themes include securing graph embeddings, federated learning with fairness guarantees, and audit mechanisms for black-box models. Supported by NSF, Cisco, and Google, her work bridges theoretical rigor with practical system design. Scientific Awards: NSF CAREER Award, 2014 Advising and Grants: Dr. Wang actively mentors PhD students and hosts visiting scholars. She leads multiple NSF-funded projects, including Securing Network Embedding against Privacy Attacks and Privacy for All: Ensuring Fair Privacy Protection in Machine Learning . Her research is supported by substantial grants from the National Science Foundation, Cisco, and Google, reflecting her leadership in trustworthy AI. Labs and Teams: While not explicitly named, Dr. Wang leads a research group focused on trustworthy machine learning, advising students and collaborating with industry partners. She is deeply integrated into the Data Science PhD program and CS faculty leadership, shaping research and academic strategy at Stevens.

Sean Welleckمشاهده پروفایل

استادیار

• Large Language Models
• Mathematical Reasoning
• Code Generation
• +۴ مورد دیگر

Sean Welleck is an Assistant Professor at Carnegie Mellon University's School of Computer Science, specifically within the Language Technologies Institute (LTI). He leads the L3 Lab and serves as an advisor for the AI for Math Fund. His academic journey includes a PhD from New York University under Kyunghyun Cho and postdoctoral positions at the Allen Institute for Artificial Intelligence and the University of Washington with Yejin Choi. Dr. Welleck's educational background shows a strong foundation in computer science. He earned his PhD in Computer Science from New York University, where he worked under the mentorship of Kyunghyun Cho and Zheng Zhang. Prior to this, he completed his MSE and BSE in Computer Science from the University of Pennsylvania, demonstrating a long-standing commitment to the field. Dr. Welleck's research focuses on bridging informal and formal reasoning with AI, with particular emphasis on developing learning, inference, and evaluation algorithms for large language models. His work spans multiple cutting-edge areas including mathematical reasoning , code generation , inference algorithms , and AI reasoning agents . A significant portion of his recent work involves combining AI with formal methods for mathematics, where he has developed frameworks like Llemma (an open-source language model for mathematical reasoning) and meta-generation (for inference-time algorithms). His research is characterized by a strong theoretical foundation coupled with practical applications that push the boundaries of what AI systems can achieve in formal reasoning domains. Analysis of Dr. Welleck's recent publications reveals a clear research trajectory focused on enhancing language models' capabilities in formal reasoning and mathematical problem-solving. His work demonstrates an evolution from foundational research in neural text generation to increasingly sophisticated approaches that integrate formal methods with deep learning. Key trends include the development of inference-time algorithms that improve model performance without additional training, frameworks for mathematical reasoning that connect informal and formal proofs, and novel evaluation methodologies for language models. His publications consistently appear in top-tier conferences including NeurIPS, ICLR, ICML, and ACL, reflecting the high impact of his contributions to the field. Dr. Welleck's scientific achievements have been recognized with several prestigious awards: NAACL 2025 Best Paper Award ICLR 2025 Oral Presentation (Top 2%) ICLR 2025 Spotlight Presentation (Top 5%) NeurIPS 2021 Outstanding Paper Award (Top 0.1%) for MAUVE NVIDIA AI Labs Pioneering Research Award (2017 and 2018) As an educator and mentor, Dr. Welleck actively guides the next generation of AI researchers. He currently advises multiple PhD students including Pranjal Aggarwal, Weihua Du, Andre He, and Seungone Kim (some co-advised with other faculty), along with MS students Riyaz Ahuja, Jiewen Hu, Qinyue Tan, and Thomas Zhu, and undergraduate Tate Rowney. At CMU, he teaches advanced courses such as Neural Code Generation and Advanced NLP, and has previously taught at New York University and the University of Washington. His commitment to education extends to creating resources like the Thesis Review Podcast and developing tutorials on neural theorem proving that have been presented at major conferences. Dr. Welleck leads the L3 Lab at CMU, which focuses on the intersection of language, learning, and logic. The lab brings together students and researchers to tackle challenging problems in AI reasoning, with particular emphasis on mathematical reasoning and code generation. Recent initiatives include the development of Llemma, an open-source language model specialized for mathematical reasoning, and work on inference-time algorithms that enable language models to improve their performance through additional computation during inference rather than through additional training.

• computer security
• formal methods
• program logics
• +۳ مورد دیگر

Mads Dam is a Professor in Teleinformatics at the School of Computer Science and Communication at Kungliga Tekniska Högskolan (KTH), where he heads the Department of Theoretical Computer Science. His research focuses on computer security, formal methods, and program logics, with particular emphasis on the formal modeling and verification of low-level hardware and software execution platforms for security and application isolation. His educational background includes: PhD in Computer Science from the University of Edinburgh (1990) MSc in Computer Engineering from Aalborg University, Denmark BSc in Information Technology from Aalborg University, Denmark Mads Dam's research interests center on computer security, formal methods, and program logics. His current work focuses on the formal modeling and verification of low-level hardware and software execution platforms such as hypervisors and OS kernels and their underlying hardware. He has made significant contributions to information flow security, verification of microarchitectural systems, and network programming language security. His research bridges theoretical foundations with practical security applications. His recent publications show a strong trend toward verifying low-level systems, with a focus on information flow security for processors, network programming languages (particularly P4), and microarchitectural vulnerabilities. His work combines formal methods with practical security concerns, developing verification techniques that address real-world security challenges in hardware and software systems. The research spans theoretical foundations in temporal and epistemic logics to practical applications in network security and processor verification. His scientific awards and recognition include: Two framework grants from the Swedish Foundation for Strategic Research A junior individual grant from the Swedish Foundation for Strategic Research Project grants and a five-year research fellowship from the Swedish Research Council (VR) Project grants from Ericsson, Microsoft Research, US Air Force, and Vinnova (the Swedish Innovation Agency) Mads Dam has been a principal investigator on numerous research projects and has supervised many graduate students. He has been a partner in several European projects including HATS, S3MS, VerifiCard, LOMAPS, and UaESMC. His research has been supported by substantial grants from major funding bodies, reflecting the significance and impact of his work in computer security and formal methods. He is a founding member of several research centers at KTH, including Access, the CASTOR software research center, and the CDIS center for cyber defense and information security. These centers bring together researchers from multiple disciplines to address complex challenges in cybersecurity and software engineering.

• Type Theory
• Dependent Types
• Functional Programming
• +۱۱ مورد دیگر

Andreas Abel is a Senior Lecturer in the Division of Computing Science at the Department of Computer Science and Engineering, Chalmers University of Technology and the University of Gothenburg. He has previously served as an Assistant Professor at Ludwig-Maximilians-Universität (LMU) Munich and has been a visiting researcher at INRIA in Paris. His primary affiliations are with Chalmers and the University of Gothenburg, where he conducts research and teaches in programming languages and type theory. Chalmers University of Technology, Department of Computer Science and Engineering, Senior Lecturer University of Gothenburg, Division of Computing Science LMU Munich, Assistant Professor (former) INRIA Paris, Visiting Researcher Abel’s research lies at the intersection of type theory, functional programming, and formal verification. He is particularly known for his work on dependent types, normalization by evaluation, and the development of the Agda proof assistant. His interests include constructive logic, logical frameworks, modal and linear typing, program verification, and compiler construction. He leads the Modal Dependent Type Theory project funded by the Swedish Research Council (Vetenskapsrådet) and has contributed to several other major research initiatives in programming language theory. His recent publications reflect a strong focus on foundational aspects of type systems, including cubical type theory, decidability of conversion, and formalization of algebraic completeness. These works appear in top-tier venues such as ICFP, LICS, POPL, and TYPES, showcasing both theoretical depth and practical implementation in Agda. Distinguished Paper Award, ICFP 2019 Editor, Theoretical Pearls column, Journal of Functional Programming Member, IFIP WG 1.3 on Foundations of System Specification Abel actively supervises students and contributes to the research community through program committee memberships for major conferences including LICS, ICFP, and CPP. He is a senior developer of Agda and the maintainer of the BNFC (Backus-Naur Form Compiler) tool. His work bridges theoretical computer science with practical software development for formal methods. He is involved in several research groups and projects, including the Programming Logic Group at Chalmers and the international EUTYPES network. His role as principal investigator and core contributor in multiple funded projects highlights his leadership in the field of programming language foundations.

• Financial Mathematics
• Actuarial Mathematics
• Stochastic Processes
• +۷ مورد دیگر

Thorsten Schmidt is Professor of Mathematical Stochastics at the University of Freiburg, succeeding Prof. Ernst Eberlein in the summer semester of 2015. He also serves as Senior Financial Engineer at MathFinance. Previously, he held professorships at Chemnitz University of Technology (2008-2015), Technical University Munich (2008), and University of Leipzig (2004 onwards). From 2017-2019, he was a Research Fellow at the Freiburg Institute for Advanced Studies (FRIAS) in a joint research group with the University of Strasbourg and USIAS on the topic of Linking Finance and Insurance. His research focuses primarily on financial and actuarial mathematics, stochastic processes, and statistics, with recent work on machine learning methods and their applications in financial mathematics and AI regulation. In Freiburg, his goal with his young team is to tackle complex challenges with improved mathematical models and apply these methodologies to various fields. Key Research Areas: Financial mathematics and credit risks Pricing and hedging of derivative financial products Statistics of stochastic processes Energy markets and nonlinear filter theory Machine learning applications in finance and insurance His recent publications show a strong trend toward integrating machine learning with traditional mathematical finance, particularly in risk management, insurance-finance arbitrage, and robust financial modeling. His work increasingly addresses ethical considerations in AI applications within finance, reflecting his broader interest in responsible AI development. Notable Awards: IDA Award Finance (2015) FRIAS-USIAS Research Fellow (2017/2018) IDA Award Machine Learning and AI (2020) MAPFRE Research Grant (2020) Luis Bachelier Fellow (2021) As Editor-in-Chief of Statistics and Risk Modeling and Associate Editor for Mathematical Finance and International Journal of Theoretical and Applied Finance, Schmidt plays a significant role in academic publishing. He leads the CRC 'Small Data' research center with Harald Binder, focusing on medical problems where disease progression must be estimated with few data points per patient. His LeanAI project, funded by the Vector Foundation, explores the connection between machine learning and theorem-proving software LEAN, aiming to develop AI that can translate between mathematics and formal proof systems. His laboratory work centers around the application of stochastic methods combined with machine learning to solve problems in finance and insurance where data is limited ('Small Data' initiative), with significant funding from DFG (€12 million for CRC Small Data) and the Carl Zeiss Foundation.

Lisa Regazzoniمشاهده پروفایل

استاد

• Philosophy and Theory of History
• History of Science of History
• Material Culture Research
• +۵ مورد دیگر

Professor Lisa Regazzoni serves as Professor of Theory of History at Bielefeld University within the Faculty of History, Philosophy and Theology. She directs the Center for Theories in Historical Research and is a member of the Executive Board of the Bielefeld Graduate School in History and Sociology. Her academic leadership extends to multiple roles including Speaker for History: Politics - Image - Art and membership in Historical Cultures research groups. Regazzoni completed her academic education with a Magister Artium in Philosophy from the University of Bologna (1998), followed by doctoral studies in Philosophy at the University of Potsdam (2006), and habilitation in Modern History at Goethe University (2020). Her educational background spans philosophy and modern history across Italian and German institutions, reflecting her transnational scholarly perspective. Her research focuses on the intersection of history, temporality, and materiality, developing what she terms a 'critique of historical materiality.' Regazzoni explores how scholars construct knowledge through material and immaterial sources, particularly examining 18th and 19th century French historical research. Her concept of 'unwritten pasts' addresses methodological limitations in historical reconstruction, while her work on 'monument episteme' investigates how objects function as historical testimony. She has pioneered research on the Reinhart Koselleck Figure Collection, examining how material objects contribute to historical understanding beyond textual sources. Regazzoni's publication trajectory reveals a sophisticated evolution from literary catalog studies to contemporary investigations of historical materiality. Her recent work demonstrates increasing engagement with object-oriented ontology and the epistemic potential of material culture in historical research. The recurring themes across her publications include the relationship between visual evidence and historical knowledge, the transformation of historical methodology, and the theoretical implications of material objects in historical practice. Momentum funding from Volkswagen Foundation (2025) for 'Towards a Material-Oriented Theory of History' (€935,000 over 48 months) Nominated for the 1822 university prize for excellence in teaching at Goethe University (2014) As academic coordinator of the International Graduate School on Political Communication and director of the Center for Theories in Historical Research, Regazzoni has secured significant research funding and fostered interdisciplinary collaboration. She co-founded the Theory-Oriented Object Laboratory (TOOL) with Lara Keuck, creating innovative spaces for material-based historical inquiry. Her leadership extends to the 'Denkraum Theorie' study group, which facilitates theoretical exchange among humanities scholars. Regazzoni directs the Reinhart Koselleck Figure Collection at Bielefeld University, transforming the historian's personal artifact collection into a research laboratory for historical materiality. Through the Theory-Oriented Object Laboratory (TOOL), she has created interdisciplinary spaces where material objects become catalysts for theoretical reflection on history and medical knowledge. Her leadership in the 'Denkraum Theorie' study group fosters theoretical exchange across disciplines, while her digital humanities work maintains the online platform for Goethe University collections.

Le-Nam Tranمشاهده پروفایل

پژوهشگر

• Wireless Communications
• 5G/6G Network Optimization
• Signal Processing
• +۴ مورد دیگر

Dr. Le-Nam Tran is a researcher at the UCD School of Electrical & Electronic Engineering , University College Dublin. His work focuses on optimizing the last hop of 5G/6G wireless networks through mathematical programming, with emphasis on energy efficiency, interference management, and security against eavesdropping. Develops low-cost, low-complexity transmission techniques Projects supported by Science Foundation Ireland Career Development Award Author of over 80 peer-reviewed publications Research Keywords: Wireless Communications Network Security Signal Processing Energy-Efficient Systems Beamforming Optimization Interference Mitigation

Lenka Marekováمشاهده پروفایل

پژوهشگر

• Cryptography
• Information Security
• Secure Messaging Protocols
• +۳ مورد دیگر

Dr. Lenka Mareková is a postdoctoral researcher in the Applied Cryptography Group at ETH Zürich. Previously, she completed her PhD in the Information Security Group at Royal Holloway, University of London, supervised by Martin R. Albrecht. Affiliation: ETH Zürich - Department of Computer Science Cryptographic analysis of secure messaging platforms Research focus on end-to-end encryption and real-world security implementations Her research interests span cryptographic protocol analysis, secure communication systems, and privacy-preserving technologies. The 10 most recent publications in her DBLP profile reflect expertise in: Secure messaging protocols (Telegram, Delta Chat) Cloud storage security (MEGA analysis) E-voting systems (Zerovote, E-cclesia) Mobile security frameworks (Android policy enforcement) Mesh network vulnerabilities Current work appears to focus on practical cryptographic implementations and their real-world security implications. Contact via hi@lenka.sh or lenka.marekova@inf.ethz.ch .