جست‌وجوی استاد و پژوهشگر

James C. Kaufmanمشاهده پروفایل

استاد

• Creativity
• Creativity Assessment
• Creativity and Meaning
• +۳ مورد دیگر

James C. Kaufman is a Professor of Educational Psychology at the University of Connecticut. He holds a Ph.D. in Cognitive Psychology from Yale University (2001). His research focuses on creativity, including assessment, meaning-making, social justice applications, and interdisciplinary domains. Notable contributions include the Four-C Model of Creativity and co-founding journals like Psychology of Aesthetics, Creativity, and the Arts . He has authored over 50 books and 300 papers, including foundational works like Cambridge Handbook of Creativity . Education: Ph.D., Cognitive Psychology, Yale University, 2001. Research Interests: Creativity Assessment, Creativity and Meaning, Creativity & Social Equity, and Cross-Domain Creativity Applications. His work bridges psychological theory with practical educational strategies. Awards: Mensa Research Award, Torrance Award (NAGC), APA Berlyne & Farnsworth Awards, and leadership in APA Division 10. Advising & Collaborations: Advised students like K. Arrington and C. Connery. Active in mentoring through co-authored research. Collaborates globally on creativity measurement tools like the K-DOCS. Labs/Teams: Leads research teams at UConn focusing on creativity’s societal impact, including pandemic-related studies on creativity and well-being during crises.

Vivy Suhendraمشاهده پروفایل

دانشیار

• Software Assurance
• Security and Privacy
• Cybersecurity
• +۱ مورد دیگر

Vivy Suhendra serves as Associate Professor of Practice and Programme Director for Master Programmes at the National University of Singapore's School of Computing, while also holding the position of Assistant Dean for Graduate Studies. Previously, she led the Singapore Cybersecurity Consortium (SGCSC) as Executive Director from 2016 to 2022, driving collaborative cybersecurity research between academia, industry, and government agencies. Her career at NUS spans over two decades, beginning as a Research Assistant before advancing to her current leadership roles. Her academic credentials include: Ph.D. in Computer Science, National University of Singapore (2009). Thesis: "Memory Optimizations for Time-predictable Embedded Software". Advisors: Abhik Roychoudhury and Tulika Mitra. B.Comp. (Honors) in Computer Science, National University of Singapore (2004). Dr. Suhendra's research integrates Software Assurance, Cybersecurity, Security and Privacy, and Embedded Systems domains. Her work bridges theoretical foundations with practical applications, particularly in national cybersecurity ecosystem development, smart grid security protocols, denial-of-service mitigation techniques, and real-time embedded system optimization. This interdisciplinary approach enables innovative solutions for critical infrastructure protection and time-predictable software execution in multi-core environments. Her 14 selected publications (2004-2020) demonstrate an evolving research trajectory from foundational embedded systems timing analysis to applied cybersecurity solutions. Early work focused on memory optimization for predictable execution in multi-core embedded systems, which naturally transitioned into cybersecurity applications for smart grids, cloud environments, and national infrastructure. This progression highlights her ability to translate low-level system expertise into high-impact security frameworks for complex real-world systems. Scientific recognition includes: Microsoft Research Asia Fellowship (2006) Valedictorian at NUS School of Computing Ph.D. Commencement (2010) While specific graduate student advising details aren't provided, her leadership as SGCSC Executive Director involved extensive mentorship across academic-industry partnerships. She has also contributed significantly to the research community through roles including Conference Chair for ESEC/FSE 2022 and Workshops Committee Member for ICSE 2024. Dr. Suhendra established the Singapore Cybersecurity Consortium as a national platform for collaborative R&D during her directorship (2016-2022). Though no personal laboratory is specified, her research leadership manifests through cross-institutional teams focused on cybersecurity innovation, particularly in critical infrastructure protection and embedded systems security where she maintains active publication records.

Simon Mooreمشاهده پروفایل

استاد

• Computer Architecture
• Computer Security
• Hardware Security
• +۳ مورد دیگر

Simon Moore is a Professor of Computer Engineering at the University of Cambridge's Department of Computer Science and Technology. He leads the Computer Architecture research group, focusing on secure processors and subsystems, particularly the CHERI project. His work emphasizes formal verification, hardware-software co-design, and scalable security solutions. He is a Fellow and Director of Studies at Trinity Hall, overseeing undergraduate admissions and mentoring in Computer Science. Research Interests: Moore's primary focus is the CHERI secure processor architecture, integrating RISC-V cores with formal verification. His work spans secure hardware design, memory safety, and embedded systems. Notable contributions include the CHERI-RISC-V microarchitecture, CheriABI, and formal verification frameworks. Key Projects: CHERI, CheriBSD, Morello (ARM collaboration) Recent Achievements: Test of Time Award (IEEE Security & Privacy 2025), finalist for Bhattacharyya Award (2022) Grants: Innovate UK Digital Security by Design, DARPA Mission Oriented Resilient Clouds Publications: Over 200 papers on secure architectures, including influential work on CHERI's capability model, formal verification, and hardware security. Recent focus areas include temporal memory safety, embedded system security, and GPU-based capability systems. Labs/Teams: Directs the Computer Architecture Group and collaborates with industry partners like ARM and Microsoft on CHERI implementations.

Benno Liebchenمشاهده پروفایل

استاد

• Soft Matter Physics
• Active Matter
• Colloidal Systems
• +۳ مورد دیگر

Prof. Benno Liebchen holds a faculty position at the Technische Universität Darmstadt within the Institute for Condensed Matter Physics , part of the Faculty of Physics. He leads the Liebchen Group , dedicated to advancing research in the Theory of Soft Matter , focusing on active matter, colloidal systems, and non-equilibrium phenomena. His work explores collective behavior in self-propelled particles, phase transitions in active fluids, and adaptive strategies in smart materials. Research Interests include: Active matter dynamics and pattern formation Non-equilibrium statistical mechanics Biophysical systems and biomimetic design Computational modeling of soft matter Recent publications highlight breakthroughs in intelligent active particles , self-reverting vortices , and motility-induced phase coexistence . His lab develops tools like the AMEP Python package to analyze active systems. Teaching responsibilities include advanced modules in soft matter physics. Collaborative projects involve interdisciplinary approaches to microswimmer behavior and machine learning-driven optimization of collective systems. Contact: +49 6151 16-24509 / Office: S2|04 104

Leonid Reyzinمشاهده پروفایل

استاد

• Cryptography
• Secure Communication
• Biometric Security
• +۳ مورد دیگر

Leonid Reyzin is a Professor and Associate Chair of Academics at Boston University, specializing in cryptography. His research focuses on enabling secure computation, communication, and collaboration in untrusted environments, with contributions to biometric security, cryptographic protocols, and privacy-preserving technologies. He earned his Ph.D. from MIT and has advised on industry standards and received prestigious awards including the NSF CAREER Award and Boston University’s Neu Family Award for Excellence in Teaching. His work spans cryptographic primitives such as fuzzy extractors, verifiable random functions, and proofs of space, addressing challenges like secure key derivation from noisy data and blockchain security. He has collaborated on standards like RFC 9381 and contributed to cryptographic tools for distributed systems and privacy-enhancing technologies. Reyzin’s research emphasizes practical applications, including secure authentication systems, cryptographic protocols for blockchain and distributed ledgers, and mitigating threats like side-channel attacks. His recent work explores advancements in proofs of space, memory-hard functions, and asynchronous authenticated data structures, reflecting a balance between theoretical rigor and real-world applicability. Awards: NSF CAREER Award, Neu Family Award for Excellence in Teaching Key Contributions: Fuzzy extractors, VRFs, cryptographic standards development, blockchain security frameworks Education: Ph.D. in Computer Science, MIT

Jonathan Weareمشاهده پروفایل

استاد

• Monte Carlo sampling
• Multiscale analysis
• Randomized numerical linear algebra
• +۲ مورد دیگر

Jonathan Weare is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds affiliations with the Faculty of Arts and Science and the Graduate School of Arts and Science. His academic journey includes roles as an Associate Professor at the University of Chicago (2014–2019) and Assistant Professor (2011–2014), following postdoctoral work as a Courant Instructor at NYU. He earned his Ph.D. in Mathematics from UC Berkeley in 2007. His research focuses on stochastic algorithms and models, with applications in astrophysics, biophysics, computational chemistry, and climate science. Key areas include Monte Carlo methods, rare event simulation, and machine learning-driven scientific analysis. Collaborations with domain experts ensure his work addresses real-world challenges in diverse fields. Recent publications emphasize advancements in trajectory stratification, rare event prediction using machine learning, and efficient algorithms for high-dimensional problems. Notable contributions include the BAD-NEUS framework and AI-based solar system instability predictions. His group’s interdisciplinary approach bridges computational methods with scientific inquiry. Weare has advised numerous students and mentored postdocs, fostering talent in applied mathematics and computational science. His work on Mercury’s orbital dynamics and extreme weather prediction showcases the societal impact of his research. Current projects explore AI applications in weather modeling and rare event analysis, leveraging cutting-edge machine learning techniques. Labs/Teams: His research group at Courant develops stochastic algorithms and collaborates with interdisciplinary teams in computational chemistry, climate science, and astrophysics. Key collaborations include the University of Chicago and Columbia University.

Amir Asadiمشاهده پروفایل

دانشیار

• Scalable Manufacturing
• Multifunctional Composites
• Additive Manufacturing
• +۵ مورد دیگر

Amir Asadi is an Associate Professor in the Department of Engineering Technology and Industrial Distribution at Texas A&M University, holding the Corrie & Jim Furber '64 Faculty Fellow position. His research focuses on scalable manufacturing of multifunctional composites, structural energy systems, and advanced materials design. He leads the Polymer Composites Advanced Manufacturing (PCAM) Lab, which explores bottom-up fabrication techniques and additive manufacturing processes. Asadi holds a Ph.D. in Mechanical and Manufacturing Engineering from the University of Manitoba (2013), an M.S. in Mechanical Engineering from Iran University of Science & Technology (2006), and a B.S. in Mechanical Engineering from the same institution (2004). His work bridges molecular-level interactions with macroscale material performance, targeting applications in aerospace, e-mobility, and energy storage. Key research interests include structural battery/supercapacitor composites, additive manufacturing of polymer composites, and fast-rate manufacturing of thermoplastics. He has pioneered methods like supercritical CO₂-assisted atomization and cellulose nanocrystal-enabled interface tailoring to enhance composite performance. Asadi has received the NSF CAREER Award (2022) and has been an invited speaker at major conferences such as the Brazilian Conference on Composite Materials (2021) and Chalmers University’s “Materials for Tomorrow” event (2020). His lab’s innovations aim to revolutionize lightweight, multifunctional materials for industrial sectors. His research outputs include over 50 peer-reviewed articles, covering topics from nanocomposite interfaces to 3D-printed structural batteries. He collaborates with industry partners like the Air Force Research Lab and focuses on translating lab-scale innovations into scalable manufacturing solutions.

Zoran Budimlićمشاهده پروفایل

دانشیار

• High Performance Computing
• Parallel Computing
• Compiler Optimizations
• +۳ مورد دیگر

Zoran Budimlić is an Instructional Associate Professor in the Department of Computer Science and Engineering at Texas A&M University. He also serves as Director of Undergraduate Studies for Galveston. His roles include teaching and academic leadership in computer science education and high-performance computing. He holds a Ph.D. in Computer Science from Rice University (2001) and a B.S. in Computer Science and Engineering from the University of Belgrade (1994). His research interests focus on high-performance and parallel computing, compiler optimizations, programming languages, runtime systems, and high-level programming models. He emphasizes improving educational practices in computer science through innovative methods and curricula. His recent publications span parallel algorithms, task parallelism integration with MPI, and compiler optimizations for performance. Earlier work includes contributions to Java runtime optimization and static analysis techniques. Zoran Budimlić has no explicitly listed scientific awards or grants in the provided text, but his contributions to parallel computing and compiler design are notable. He advises students in these areas but no names are provided in the text.

Maryam Aliakbarpourمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Statistical Inference
• Learning Theory
• +۲ مورد دیگر

Maryam Aliakbarpour is the Michael B. Yuen and Sandra A. Tsai Assistant Professor in the Department of Computer Science at Rice University, affiliated with the Ken Kennedy Institute. She holds a Ph.D. and M.S. from MIT (2020 and 2015) and a B.S. from Sharif University of Technology (2013). Her research focuses on theoretical computer science, statistical inference, learning theory, differential privacy, and hypothesis testing, with an emphasis on algorithm design under computational and privacy constraints. She has held postdoctoral positions at Boston University, Northeastern University, and UMass Amherst, and participated in the Simons Institute's 2020 program on high-dimensional computation. Her work bridges foundational theory and practical applications, particularly in designing efficient algorithms for distribution testing, privacy-preserving machine learning, and hypothesis selection. Notable contributions include optimal algorithms for distribution testing under memory constraints and advancements in differential privacy for metalearning. She has received the Rising Stars in EECS (2018) and MIT’s Neekeyfar Award. Teaching includes graduate courses on learning theory and probabilistic methods, emphasizing algorithmic tools for modern computational challenges. Her publications span top conferences like COLT, NeurIPS, and ICML, addressing topics such as privacy-aware learning, efficient entropy estimation, and robust statistical methods. She advises on research projects requiring strong algorithmic foundations and mentors students in theoretical computer science and data privacy.

Grant Weddellمشاهده پروفایل

دانشیار

• Database Technology
• Real-Time Applications
• Schema Management
• +۵ مورد دیگر

Grant Weddell is an Associate Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. His research focuses on database technology for real-time applications, including large-scale schema management, information clustering, dependency theory, and query optimization for heterogeneous data sources. He teaches courses such as CS338 (Introductory Databases), CS348 (Advanced Databases), CS446 (Software Engineering), and CS848 (Advanced Database Systems). His research interests emphasize the interplay between description logics and database systems, particularly in optimizing query processing and managing complex schemas. Recent work explores path agreements, functional dependencies, and ontology-mediated querying to enhance data integration and schema management efficiency. Teaching responsibilities include foundational database courses (CS338/348), software engineering (CS446), and advanced topics in information integration (CS848). No scientific awards are explicitly listed, though his contributions to database theory and optimization are extensive.

Christian Plesslمشاهده پروفایل

استاد

• High-Performance Computing
• Reconfigurable Computing
• FPGA Acceleration
• +۳ مورد دیگر

Prof. Dr. Christian Plessl is a W3 Professor of High-Performance Computing at the Institute of Computer Science, University of Paderborn. He leads the Paderborn Center for Parallel Computing (PC²), a national HPC center within the NHR alliance. His roles include Director of PC², Board Member of the NHR association, and member of the Sonderforschungsbereich 901. Education: PhD (Dr. sc. ETH) in Computer Engineering, ETH Zürich (2006) MSc in Electrical Engineering, ETH Zürich (2001) Postdoc at ETH Zürich (2007–2011) Research Interests: Architecture and tools for high-performance parallel and reconfigurable computing, FPGA acceleration, quantum chemistry, scientific computing, adaptive systems, and energy-efficient HPC solutions. Key projects include EKI-App (FPGA-based neural networks), FPGA4XPCS (X-ray spectroscopy), and HighPerMeshes (unstructured grid frameworks). Publications: Over 100 peer-reviewed works, focusing on FPGA acceleration, HPC frameworks, and quantum computing. Recent trends emphasize energy-efficient neural networks, FPGA-based quantum computing, and scalable HPC algorithms. Awards: Best Paper Awards at HEART 2023, ReConFig 2012/2014 Paderborn University Research Awards (2018, 2009) SEW-EURODRIVE Student Award (2001) Grants & Projects: Principal investigator in DFG, BMBF, and EU-funded projects. Collaborates with AMD/Xilinx, Intel/Altera, and Fujitsu. Leads initiatives like PerficienCC (custom computing) and HighPerMeshes. Labs/Teams: Directs the High-Performance Computing group at PC², focusing on FPGA supercomputing and HPC infrastructure. Active in the NHR alliance for national HPC coordination.

• Federated Learning
• Optimization for Machine Learning
• Privacy-Preserving Machine Learning
• +۱۲ مورد دیگر

Sai Praneeth Karimireddy is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), with a courtesy appointment in the Ming Hsieh Department of Electrical and Computer Engineering. He previously held an SNSF postdoctoral fellowship at UC Berkeley under Michael I. Jordan and earned his PhD at EPFL advised by Martin Jaggi. He co-leads the Federated Learning and Data Quality working group at MONAI (NVIDIA) and collaborates with researchers at Apple Research. His research lies at the intersection of optimization, machine learning, statistics, and economics, with a strong focus on federated learning, privacy-preserving machine learning, data valuation, and AI for healthcare. He investigates how data quality, privacy, and incentives shape collaborative ML systems, especially in high-stakes domains like medicine. His work has been deployed at companies such as Meta, Google, OpenAI, and Owkin. His recent publications span top-tier venues including NeurIPS, ICML, ICLR, and JMLR, with influential contributions such as the SCAFFOLD algorithm for federated learning. His research shows a consistent trend toward building robust, private, and incentive-compatible collaborative learning systems, with increasing emphasis on real-world deployment in healthcare and decentralized data markets. 2023 SNSF Mobility Fellowship 2022 Patrick Denantes Memorial Prize for best thesis in computer science 2022 EPFL thesis distinction (top 8%) 2021 Chorafas Foundation Prize for exceptional applied research Capitol One Fellow (2025) He is actively mentoring PhD students and leads a research group focused on foundational and applied challenges in federated and privacy-preserving ML. He teaches graduate courses at USC, including CSCI 599 on Optimization for Machine Learning and CSCI 699 on Privacy-Preserving Machine Learning. He serves as an area chair for ICLR 2025 and co-organizes major workshops on incentives in data sharing and federated learning. His lab collaborates with institutions like NVIDIA, Apple, and Argonne National Laboratory, and he is building a research program centered on sustainable, equitable, and trustworthy AI ecosystems.

Shuvendu K. Lahiriمشاهده پروفایل

پژوهشگر

• Formal Verification
• Smart Contracts
• Program Synthesis
• +۵ مورد دیگر

Shuvendu K. Lahiri is a researcher at Microsoft Research, focusing on formal verification, program synthesis, and software testing. His work bridges artificial intelligence with formal methods, particularly in blockchain security and automated code generation. 2025 : Published LLM-Vectorizer (verified loop vectorizer) and neural synthesis for SMT-assisted proof-oriented programming 2024 : Explored LLM-based test-driven code generation and natural precondition inference 2023 : Developed resource management specifications and contributed to test generation with pre-trained models 2022 : Advanced Solidity type systems and merge conflict resolution using language models His research combines large language models with formal verification tools to improve software correctness. He actively contributes to conferences like ICSE, PLDI, and ISSTA as author and committee member.

Andreas Moshovosمشاهده پروفایل

استاد

• Computer Architecture
• Deep Learning Acceleration
• Energy-Efficient Computing
• +۲ مورد دیگر

Andreas Moshovos is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. He holds a Bachelor's and Master's degree from the University of Crete, and a PhD from the University of Wisconsin-Madison. Previously, he taught at Northwestern University, École Polytechnique Fédérale de Lausanne, University of Athens, and Hellenic Open University. His research focuses on designing optimized computing hardware for performance, energy efficiency, and cost. Primary interests include: Deep Learning acceleration through value-based optimization (exploiting sparsity/precision variability) Hardware specialization for neural networks Efficient data management systems High-performance processor/memory architecture His publications demonstrate consistent focus on hardware acceleration techniques for deep learning, particularly leveraging value sparsity, dynamic precision adaptation, and ineffectual computation elimination to boost performance and energy efficiency in neural network processing. Significant Awards: ACM SIGARCH Maurice-Wilkes Award (2010) 2× IEEE MICRO Top Picks Awards (2006, 2010) 2× IBM Faculty Awards (2008, 2009) NSF CAREER Award (2000) MICRO Hall of Fame Award He leads the NSERC COHESA Network on Machine Learning Hardware Acceleration (19 researchers across 7 universities) and advises multiple PhD/Master's students in computer architecture and deep learning acceleration. His lab develops specialized hardware for computational imaging, gene sequencing, and neural network optimization.

• Francophone Caribbean Literature
• South Asian Diaspora Studies
• AI in Language Education
• +۲ مورد دیگر

Dr. Shanaaz Mohammed is an Assistant Professor of French and French Program Coordinator at Marist College’s School of Liberal Arts. Originally from Trinidad, she specializes in Francophone Caribbean Studies with a focus on South Asian diaspora representations in literature. She integrates AI tools like ChatGPT to gamify and personalize language learning, emphasizing grammar through cultural context. Education: PhD, MA (Florida State University); BA (University of the West Indies, St. Augustine) Key Research: Coral poetics, Aapravasi Ghat heritage, Coolitude theory, and digital pedagogy Initiatives: Developed the Virtual Exchange Program (VExP) with Université de la Réunion for collaborative language instruction Her recent publications explore Khal Torabully’s poetry and Ernest Moutoussamy’s narratives. She advocates for ethical AI use in education, addressing algorithmic bias and cultural stereotyping, while promoting cautious optimism about technology’s role in enhancing human-centered language learning. Scientific Awards Northeast Modern Language Association (NeMLA) Postcolonial Essay Award (2022) She teaches all levels of French language, literature, and culture courses, emphasizing the global diversity of the French-speaking world beyond France.