جست‌وجوی استاد و پژوهشگر

Marshall Ballمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Cryptography
• Computational Complexity
• +۷ مورد دیگر

Marshall Ball is an Assistant Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences . His research focuses on theoretical computer science, with an emphasis on cryptography and computational complexity. He holds a PhD from Columbia University and has held postdoctoral and visiting positions at the University of Washington and the FACT Center at IDC Herzliya. Ball's work spans foundational areas including non-malleable codes , secure computation , cryptographic protocols , and optical computing applications in blockchain . His contributions address critical challenges in privacy, security, and algorithmic efficiency. Notable projects include topology-hiding computation, zero-knowledge proofs, and experimental photonic computing for cryptocurrency. Recent research trends highlight advancements in randomness extraction , secure aggregation protocols , and fine-grained computational complexity . His work often bridges theoretical insights with practical implementations, such as garbled circuits and photonic hardware solutions. Ball has advised students at both undergraduate and graduate levels, including collaborations with Yevgeniy Dodis on cryptographic topics. He serves on program committees for major conferences like CRYPTO, EUROCRYPT, and TCC, reflecting his leadership in the field. His academic journey includes a NSF/CRA Computing Innovation Fellowship and extensive collaborations across institutions. Current efforts prioritize mentoring underrepresented students and advancing interdisciplinary research in cryptography and computational theory.

Robbert Krebbersمشاهده پروفایل

دانشیار

• Semantics
• Separation logic
• Theorem proving
• +۷ مورد دیگر

Robbert Krebbers is an associate professor at the Department of Software Science at Radboud University Nijmegen, Netherlands. He is a leading researcher in program verification, specializing in separation logic and the Coq proof assistant. Krebbers is a core contributor to the Iris framework, a higher-order concurrent separation logic framework implemented in Coq. His research spans theoretical foundations of programming languages and practical applications to real-world languages including C, Rust, and Scala. His research interests focus on scaling program verification techniques to challenging programming paradigms like concurrency, higher-order functions, and modules. Krebbers' work bridges formal methods with practical language implementation, particularly in verifying memory safety and concurrency properties. He has made significant contributions to Rust verification through the RustBelt project and has pioneered techniques for verifying concurrent data structures and message-passing systems. Krebbers' recent publications demonstrate a strong trend toward verifying complex concurrency patterns, developing automated proof techniques, and applying separation logic to practical programming language features. His work frequently appears in top-tier programming languages conferences including POPL, PLDI, and ICFP, with several papers receiving distinguished paper awards. The research spans from foundational logic development to practical verification tools for real-world programming languages. As a principal investigator in the Iris project, Krebbers has secured significant research funding including the ERC Consolidator Grant for the RustBelt project. His work has influenced both academic research and industrial practice, particularly in the Rust programming language ecosystem. The Iris framework he helped develop has been adopted by numerous verification projects worldwide. Krebbers has advised PhD students including Ike Mulder (focusing on proof automation for concurrent separation logic) and Jules Jacobs (working on guarantees by construction). He has been actively involved in the programming languages research community, serving on program committees for major conferences and organizing workshops on formal methods and verification. He leads research in the Department of Software Science at Radboud University, where his group focuses on developing foundational techniques for program verification. The group collaborates closely with the Logic and Semantics Group and Foundations of Programming Group across institutions, contributing to a vibrant research ecosystem around formal methods and programming languages.

Yangming Liمشاهده پروفایل

پژوهشگر ارشد

• Image Analysis
• Machine Learning
• Mathematical Modeling
• +۷ مورد دیگر

Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.

Michael Weharمشاهده پروفایل

مدرس

• Computer Science
• Algorithms
• Computational Art
• +۷ مورد دیگر

Michael Wehar is a Lecturer in Computer Science at Bryn Mawr College , where he teaches Introductory Programming Courses. He is a multidisciplinary researcher and developer with a focus on algorithms , computational art , and software engineering , creating innovative tools that bridge computer science with creative applications. Co-founder of AlgoArt.org , a platform for algorithmic art creation and exhibition Creator of Word of The Hour , a multilingual vocabulary learning platform Co-developer of Treegle Dictionary , a structured definition platform His research spans computational complexity, human-computer interaction, and educational technology. Key areas include: Algorithmic Art : Generative design systems, interactive visualizations Pattern Matching : Matrix algorithms, Voronoi diagrams, formal verification Language Technology : Multilingual dictionaries, crowdsourced translation systems Educational Tools : Git repository analysis, interactive learning platforms His 15 most recent publications focus on topics ranging from 2D pattern matching to ETH-based complexity lower bounds , with significant contributions to automata theory and algorithm design. He has received academic recognition including the Best Faculty Poster at CCSC:EA 2022 and Honorable Mention at IFoRE 2022 . As a dedicated mentor, he has guided over 100 students across institutions like Swarthmore College, Temple University, and University at Buffalo in projects spanning: Web development frameworks AI applications in art and education Mobile productivity tools Language learning platforms Game development projects

André Nusserمشاهده پروفایل

پژوهشگر

• Algorithms and Complexity
• Computational Geometry
• Dynamic Time Warping
• +۴ مورد دیگر

André Nusser is a CNRS Researcher affiliated with the COATI group at Inria Center, Université Côte d’Azur. His work focuses on algorithms, computational geometry, and metric space analysis, particularly around the Fréchet distance, dynamic time warping, and graph hyperbolicity. Current Position: CNRS Researcher at Inria Université Côte d’Azur Primary Research Areas: Algorithms & Complexity, Computational Geometry, Metric Space Analysis Research Focus: André's research explores geometric optimization problems, trajectory analysis, and fine-grained complexity. Key contributions include advancements in dynamic time warping under translation, diameter computation for intersection graphs, and efficient algorithms for polygon partitioning. Awards & Recognition: Recipient of the Best Paper Award at VLDB 2014, Honorable Mention at AAAI 2014, and Best Poster Runner-up at SIGSPATIAL 2019. His work on Hausdorff distance and graph hyperbolicity has been invited to special issues. Academic Collaborations: Co-author on over 20 publications across conferences like STOC, SoCG, SODA, ESA, and journals including ACM Transactions on Algorithms and Discrete & Computational Geometry. Collaborates with leading researchers in computational geometry and algorithms. Software Advocacy: Actively promotes free software in research, highlighting tools like Ipe, Xournal++, Zotero, and Zulip for scientific productivity and collaboration.

Marco Leandro Carmosinoمشاهده پروفایل

استادیار مدعو

• Computational Complexity Theory
• Cryptography
• Theoretical Computer Science
• +۳ مورد دیگر

Marco Leandro Carmosino is an Adjunct Assistant Professor at Boston University and a Research Scientist at IBM Yorktown. He holds a PhD from the University of California, San Diego (2019), advised by Russell Impagliazzo. His research focuses on computational complexity theory , exploring the interplay between computational limitations, algorithmic structure, and applications to cryptography and privacy. Key areas include understanding how complexity lower bounds relate to learning algorithms, simulating natural phenomena computationally, and enforcing privacy in machine learning tasks. He has received the Best Paper Award at CCC 2016 for his work linking natural proofs to learning algorithms. His publications span topics from fine-grained derandomization to privacy-preserving learning techniques, with applications in both theoretical computer science and interdisciplinary fields like spectroscopy analysis. Affiliations: Boston University (Adjunct), IBM Yorktown (Research Scientist) Education: PhD in Computer Science, UC San Diego (2019) Key Awards: Best Paper at CCC 2016 His work bridges foundational complexity theory with practical algorithm design, emphasizing privacy and efficiency in computational systems. He contributes to both academic research and industry applications, leveraging theoretical insights to address real-world challenges in security and data analysis.

• Graph Algorithms
• Convex Optimization
• Fine-Grained Complexity Theory
• +۳ مورد دیگر

Rasmus Kyng is an Assistant Professor at the Department of Computer Science, ETH Zurich, since Fall 2019. He focuses on developing fast algorithms for graph problems, convex optimization, and structured linear equations, with a specialization in fine-grained complexity theory and dynamic graph algorithms. Assistant Professor, Department of Computer Science, ETH Zurich (2019–present) Postdoc, Theory of Computation Group, Harvard (2018–2019) Research Fellow, Simons Institute, UC Berkeley (Fall 2017) His research explores the intersection of graph algorithms, optimization, and complexity, particularly through dynamic graph data structures, sparsification, and applications in machine learning. Recent work includes breakthroughs in almost-linear time algorithms for network flow problems. Key trends in his publications include: Advancements in dynamic graph algorithms and data structures Applications of convex optimization to graph theory Connections between matrix theory and algorithmic efficiency Derandomization techniques for algorithmic speed Expander graph-based oblivious routing Sparsification and low-congestion vertex sparsifiers Scientific recognition includes the FOCS Best Paper Award (2022), ICBS Frontiers of Science Award (2022), and the Machtey Award for Best Student Paper (2017). His group mentors PhD candidates and postdocs, including Ming Ding , Federico Soldà , Simon Meierhans , Aurelio Sulser , and Wuwei Yuan . Research is supported by Swiss National Science Foundation grants (project no. 200021 204787, starting grant no. TMSGI2 218022).

• Quantum Algorithms
• Quantum Complexity Theory
• Quantum Cryptography
• +۲ مورد دیگر

Nai-Hui Chia is an Assistant Professor in the Department of Computer Science at Rice University. His academic journey includes postdoctoral fellowships at the University of Maryland's Joint Center for Quantum Information and Computer Science (QuICS) and UT Austin, following a Ph.D. in Computer Science and Engineering at Penn State University under Dr. Sean Hallgren and undergraduate studies at National Taiwan University. Ph.D.: Computer Science and Engineering, Penn State University, 2018 Bachelor's: National Taiwan University Chia's research focuses on quantum algorithms, quantum complexity theory, and quantum cryptography. He investigates quantum computing's capabilities, limits, and its transformative potential for computer science, particularly in computational tasks with quantum advantages, quantum depth verification, and circuit complexity. His work bridges theoretical exploration with practical applications in quantum machine learning and cryptographic security. Recent publications highlight advancements in adversarially robust quantum state learning (FOCS 2025), quantum depth verification (COLT 2025, TQC 2024), and quantum-inspired classical algorithms for low-rank matrix problems. His research also addresses fundamental questions in quantum complexity theory, including black-box simulation barriers and impossibility results for quantum zero-knowledge protocols. Honors include the NSF CAREER Award (2024), DOE Quantum Testbed Pathfinder (2023), Google Research Scholar (2023), and an NSF grant (2022). He serves on program committees for TQC 2024, CCC'23, and Crypto 2022, and organizes quantum computing sessions at IOS 2024 and QuantIPS 2023. Chia advises students like Yu-Ching Shen and Chia-Ying Lin, and has mentored postdocs including Jianqiang Li and Daniel Liang. His teaching includes courses on cryptography and quantum computing at Rice and Indiana University, and guest lectures at UT Austin. Outside academia, he enjoys sports and history.

Ivy K. Y. Wooمشاهده پروفایل

پژوهشگر

• Cryptography
• Privacy
• Computer Security
• +۲ مورد دیگر

Ivy K. Y. Woo is a Doctoral Researcher (academic rank: Researcher) in the Department of Mathematics and Systems Analysis at Aalto University, Finland. She is an active member of the Algebra and Discrete Mathematics research group, collaborating with principal investigators including Russell W.F. Lai and Chris Brzuska on advanced cryptographic protocols. Her research concentrates on Cryptography and Privacy with specialized expertise in lattice-based constructions, anonymous transaction systems, and steganography. Key focus areas include traitor tracing mechanisms, functional encryption schemes, Learning With Errors (LWE) assumptions, and privacy-preserving technologies for blockchain applications. Her methodology integrates theoretical cryptography with practical security implementations, particularly addressing leakage resilience and sustainable anonymity systems. Analysis of her 10 recent publications (2022-2025) reveals dominant research trajectories in lattice-based cryptography (70% of output), with significant contributions to traitor tracing, attribute-based encryption, and obfuscation techniques. Parallel streams explore blockchain privacy (20%) through transaction graph analysis and sustainable ring signatures, plus steganographic methods (10%) for natural data hiding. Her work consistently targets high-impact venues including ASIACRYPT, CRYPTO, and Privacy Enhancing Technologies Symposium. Scientific recognition includes: The Andreas Pfitzmann - PETS Best Student Paper Award Runner Up 2022 for "On Defeating Graph Analysis of Anonymous Transactions" Woo maintains active research partnerships within Aalto's cryptography ecosystem, particularly through the Algebra and Discrete Mathematics group. Her current projects involve lattice-based solutions for multi-authority attribute encryption and leakage-resilient cryptographic primitives, with several 2025 publications indicating ongoing high-productivity collaboration with international research teams.

• Responsible AI
• Computer Vision
• Robotics
• +۳ مورد دیگر

Jindong Gu is a Senior Researcher at the University of Oxford and a member of the Torr Vision Group. He concurrently serves as a Faculty Researcher at Google. He holds a PhD from the University of Munich (2022) and focuses on building Responsible AI systems. His research emphasizes interpretability, robustness, privacy, and safety in visual perception, foundation models, robotic policies, and their integration into general intelligence systems. His research interests include adversarial machine learning, vision-language models, AI safety, and multimodal systems. Recent work explores vulnerabilities in large models, including jailbreak attacks, privacy threats, and robustness improvements through techniques like latent editing and model distillation. No scientific awards are explicitly listed for Dr. Gu in the provided text. His advisory roles and grants remain unspecified, though his work is supported through affiliations with Oxford and Google. He collaborates with the Torr Vision Group, a leading research collective in computer vision and AI at Oxford.

• Big data analytics
• Scalable intelligent information systems
• Cloud data analytics
• +۶ مورد دیگر

Yanlei Diao is a Professor of Computer Science at Ecole Polytechnique in France and also holds a professorship at the University of Massachusetts Amherst. She joined Ecole Polytechnique in September 2015 and leads the CEDAR research team focusing on Rich Data Exploration at Cloud Scale. Her work bridges theoretical computer science with practical big data systems that address real-world challenges in data analytics. Professor Diao's research spans big data analytics, scalable intelligent information systems, and cloud data processing infrastructure. Her work emphasizes practical solutions for explainable anomaly detection, interactive data exploration, and uncertain data management. She has pioneered systems like UDAO (a next-generation optimizer for cloud analytics), EXAD (explainable anomaly detection), AIDEme (interactive data exploration), and GESALL (genomic scalable analysis) that have influenced both academia and industry. Her recent publications reveal a strong focus on making big data analytics more explainable, efficient, and accessible. She has developed frameworks for unsupervised anomaly detection across heterogeneous domains, created benchmarks like Exathlon for evaluating explainable anomaly detection systems, and advanced human-in-the-loop approaches for interactive database exploration. Her work consistently bridges theoretical foundations with practical implementations in distributed systems. Selected Awards: ERC Consolidator Award (2017-2023) for "Charting a New Horizon of Big and Fast Data Analysis through Integrated Algorithm Design" CRA-W Borg Early Career Award (2013) NSF CAREER Award (2008) IBM Innovation Award on Scalable Data Analytics (2010) Professor Diao actively mentors PhD and Master's students, with former students now holding positions at top technology companies including Google, Facebook, Amazon, Netflix, and Huawei. Her research is supported by diverse funding sources including the European Research Council, National Science Foundation, ANR, and industry partners like Google, IBM, and Alibaba. She serves as PC Co-Chair of PVLDB 2025-2026 and has delivered keynotes at major industry events including Amazon Machine Learning Workshop (2024), SWIFT AI Forum (2023), and Berlin Institute for the Foundations of Learning and Data (2022). Her CEDAR research team at Inria/LIX develops cutting-edge technologies for big data analytics, with current projects focusing on foundation models for big data, explainable AI for anomaly detection, and genomic data analysis at scale. The team maintains strong collaborations with industry partners including Alibaba Cloud, where joint work has led to significant publications at top database conferences.

Sebastian Forsterمشاهده پروفایل

استاد

• Algorithms
• Graph Theory
• Network Science
• +۴ مورد دیگر

Sebastian Forster is a Professor at the University of Salzburg , affiliated with the Faculty of Digital & Analytical Sciences and the Department of Computer Science . He leads the Big Data Algorithms Group and holds administrative roles as Vice Dean, Local Coordinator of CIVIS Hub 5, and coordinator of the supplementary study programme "Informatikkompetenz für alle." His research focuses on algorithms for big and fast data, particularly distributed and dynamic algorithms applied to graphs, networks, and complexity theory. Education: Ph.D. in Computer Science (University of Vienna, 2011–2015) M.Sc. in Computational Intelligence (Vienna University of Technology, 2008–2011) B.Sc. in Computer Science (University of Passau, 2005–2008) Research Agenda: Forster's work addresses the theoretical and mathematical challenges of prior-free algorithms in the post-Moore's Law era. His key contributions include advancements in graph algorithms , fine-grained complexity , and network science . Recent publications span distributed Laplacian methods , dynamic graph sparsifiers , and approximate shortest path solutions , reflecting a blend of algorithmic design and hardness analysis. Scientific Awards: Heinz Zemanek Award (OCG) Award of Excellence (BMWFW) Academic Service: He has served on program committees for ICALP 2025, STACS 2024, FOCS 2023 , and other major conferences. He actively reviews for journals like Journal of the ACM and funding bodies such as BSF and ISF . Co-organized the Dagstuhl Seminar 22461 on Dynamic Graph Algorithms.

• Computational Complexity Theory
• Algorithms
• Cryptography
• +۳ مورد دیگر

Rahul Santhanam is a Professor of Computer Science at the University of Oxford and a Tutorial Fellow at Magdalen College. His research focuses on theoretical computer science, particularly computational complexity theory and its connections to learning , cryptography , logic , and game theory . PhD in Computer Science, University of Chicago (2005) Postdoctoral research: Simon Fraser University, University of Toronto Lecturer at University of Edinburgh (2008), promoted to Reader (2013) Joined University of Oxford in 2016 His research spans computational complexity , exact algorithms , fine-grained complexity , and circuit complexity . Key themes include bounded rationality and probabilistic computation . His publications from 2004–2010 reflect a long-term focus on lower bounds , probabilistic time hierarchies , satisfiability algorithms , and probabilistically checkable proofs (PCPs) . Trends include intersections between cryptographic complexity and theoretical foundations . Scientific awards include the ERC Consolidator Grant ALUnif (2013–2019), supporting work on algorithms and lower bounds . Current student: Matthew Gray Past students: Levente Bodnar Thomas Orton Ninad Rajgopal

Johann Gamperمشاهده پروفایل

استاد

• Temporal Databases
• Time Series Analysis
• Query Processing
• +۳ مورد دیگر

Johann Gamper is a Professor and Head of the Database Systems Group at the Free University of Bozen-Bolzano. He previously served as Vice-Rector for Research (2018-2024) and Vice-Dean for Studies (2014-2018). Education: Information not provided in text. Research Focus: His core research investigates data-intensive systems with emphasis on temporal data management, time series analysis, and efficient query processing. His team combines fundamental research with system development for applications in governance, tourism, healthcare, and agriculture. Current projects include temporal RDBMS extensions, time series motif mining, and multidimensional range count approximation. Publication Trends: Recent work addresses temporal query optimization, time series classification, and generative approaches for e-commerce data. His research demonstrates consistent innovation in temporal data handling and database performance optimization. Teaching: Currently teaches Database Management Systems and advises PhD students in temporal database research.

• Computer Vision
• Machine Learning
• Quantum Methods
• +۳ مورد دیگر

Davi Geiger is an Associate Professor of Computer Science and Neural Science at New York University (NYU), affiliated with the Courant Institute of Mathematical Sciences. He is part of the Computer Vision Group within the College of Arts and Science and the Graduate School of Arts and Science. His research focuses on advancing computer vision and machine learning, with a recent emphasis on integrating quantum methods and group theory. His work explores topics such as stereo vision, human perception modeling, quantum entropy, and medical imaging analysis. Key research areas include quantum algorithms applied to vision problems, geometric constraints in stereo matching, and entropy-based models in physics. Notable contributions include studies on cyclopean stereo vision, spin entropy dynamics, and regularization techniques for image classification. Dr. Geiger has led projects funded by grants such as the NSF's 'Geometry-and Symmetry-Driven Computer Vision Methods for High-Throughput Automated Microscopic Imaging.' His work bridges theoretical physics and computational methods, with applications in medical imaging and developmental biology. Scientific achievements include pioneering methods for cell division detection in embryo development and developing adaptive confusion energy models for natural data distribution. His research often combines interdisciplinary approaches, leveraging insights from neuroscience and quantum computing to solve complex vision problems.