جست‌وجوی استاد و پژوهشگر

Daohong Qiuمشاهده پروفایل

دانشیار

• Geotechnical Engineering
• Underground Engineering
• Structural Health Monitoring
• +۲ مورد دیگر

Daohong Qiu is an Associate Professor and Master Tutor at the Geotechnical and Structural Engineering Center, School of Civil Engineering, Shandong University. His research focuses on advanced geological prediction in tunnels, surrounding rock stability, structural health monitoring for urban rail transit, and TBM tunneling performance optimization. Position: Associate Professor Affiliation: School of Civil Engineering, Shandong University Email: qiudh@sdu.edu.cn Research Interests: He specializes in geotechnical engineering challenges related to underground construction, including rock burst prediction , disaster control , and machine learning applications in geological modeling. His work emphasizes integrating advanced computational methods like quantum genetic algorithms and RBF neural networks with field data to improve tunnel safety and efficiency. Publication Trends: His recent 2019 studies address subsea tunnel risk assessment , rock burst prediction in underground caverns, and machine learning-driven surrounding rock classification . Earlier works (2014-2015) explore SVM/GA-SVM for geological disaster forecasting, while pre-2010 papers focus on optimization theory and stress field analysis. Patent Contributions: He holds multiple invention patents for geological prediction devices, including three-dimensional geological network modeling , seismic signal detection , and concrete elevation control systems .

Hemanta K. Majiمشاهده پروفایل

دانشیار

• Cryptography
• Information Theory
• Secure Computation
• +۴ مورد دیگر

Hemanta K. Maji is an Associate Professor in the Department of Computer Science at Purdue University, affiliated with the College of Science. He joined Purdue in 2015 as an Assistant Professor and was promoted to Associate Professor in 2023. His research focuses on cryptography, information theory, secure computation, and quantum computing, with a particular emphasis on leakage-resilient secret sharing, secure multiparty computation, and cryptographic protocol design. Education: Ph.D. in Computer Science from the University of Illinois Urbana-Champaign (2011), B.Tech. from IIT Kanpur (2004). Postdoctoral research included positions at UCLA’s Center for Encrypted Functionalities (2013–2015) and as a Computing Innovation Fellow (2011–2013). Research interests span theoretical cryptography, including secure computation, information-theoretic cryptography, and algorithmic aspects of privacy-preserving techniques. He has received grants from NSF, IARPA, MITRE, and the Ross–Lynn Research Scholar Grant. His work has led to advancements in leakage-resilient schemes, non-malleable codes, and cryptographic complexity analysis. Key contributions include leakage-resilient Shamir’s secret sharing, secure non-interactive simulation, and geometric approaches to secure two-party computation. His articles address foundational questions in cryptographic protocol design, emphasizing practical applications such as low-latency MPC and collusion-resistant secret sharing. Awards include the 2019 Excellence in Research at Purdue Award and the 2004 Best B.Tech. Project Award from IIT Kanpur. He mentors Ph.D. and master’s students, including Mingyuan Wang, Hai H. Nguyen, and Hamidreza Amini Khorasgani. Teaching includes courses on cryptography, theoretical computer science, and mathematical toolkits for computer science. Labs/Teams: Leads research in secure computation and cryptography, collaborating with institutions like UCLA and MITRE. Active in organizing workshops and guest lectures on cryptographic complexity and information theory.

Martin Savageمشاهده پروفایل

استاد

• Nuclear Physics
• Lattice Quantum Chromodynamics (QCD)
• Quantum Computing Applications in Nuclear Physics
• +۳ مورد دیگر

Martin Savage is a Professor of Physics at the University of Washington, affiliated with the Institute for Nuclear Theory (INT) within the College of Arts and Sciences. His primary role involves advancing theoretical nuclear physics through computational and quantum methods. He holds a PhD from the California Institute of Technology (1990), and earlier degrees from the University of Auckland (B.Sc. 1983, M.Sc. 1984). Research: Savage focuses on applying quantum computing and quantum information theory to solve Grand Challenge problems in nuclear physics, such as finite-density systems and non-equilibrium dynamics. As a founding member of the NPLQCD collaboration (2004), he pioneered lattice QCD techniques for light nuclei and few-baryon systems. His current work addresses computational limitations posed by classical exascale challenges, exploring quantum algorithms for field theories and collaborations with technology firms. Teaching: He has taught advanced courses including Quantum Field Theory, Nuclear Physics, Quantum Mechanics, and Electrodynamics at both undergraduate and graduate levels. Over seven PhD theses have been supervised, with additional students in progress. Collaborations & Affiliations: Savage collaborates with national laboratories (e.g., DOE, NSF), technology companies, and international research groups. Key affiliations include the Hyak computing cluster, SciDAC, and Teragrid initiatives. His research emphasizes interdisciplinary efforts to bridge quantum computing and fundamental physics. Labs & Teams: Central to his work is the Institute for Nuclear Theory (INT), a hub for collaborative research in nuclear and particle physics. His team actively engages in developing quantum algorithms for Minkowski space calculations and finite-density systems.

Kate Smithمشاهده پروفایل

استادیار

• Quantum Computing
• Computer Architecture
• Quantum Compilation
• +۵ مورد دیگر

Kate Smith is an Assistant Professor of Computer Science at Northwestern University, affiliated with the McCormick School of Engineering. She holds a PhD in Electrical Engineering from Southern Methodist University (SMU), along with MS and BS degrees in Electrical Engineering and Mathematics from the same institution. Her research focuses on quantum computing, specifically in system architecture, optimized compilation, error mitigation, and security. Prior to joining Northwestern in 2024, she worked at Infleqtion managing the Superstaq compiler team and as a postdoctoral scholar at the University of Chicago under the CQE/IBM program. She has contributed to over 25 peer-reviewed publications and served on technical committees for major conferences like MICRO, ISCA, and DAC. Education: SMU (PhD 2019, MS 2015, BS in EE/Math 2014). Professional experience includes roles at EPFL (Switzerland), Texas Instruments, and the Darwin Deason Institute for Cyber Security. Her honors include the 2022 HPCA Best Paper Award, MIT EECS Rising Star (2021), and the IEEE TC-MVL Early Career Award (2021). Research interests span quantum compilation, distributed systems, qudit processing, and quantum security. She co-organized the 2023 CCC Workshop on Next Steps in Quantum Computing and chaired the 2022 ISMVL conference. Her work emphasizes bridging hardware-software gaps to enable scalable quantum systems. Grants and collaborations include the EPiQC group at the University of Chicago and projects funded by the Swiss NSF. Teaching experience includes courses on quantum computing fundamentals and digital design at SMU, University of Chicago, and adjunct roles.

Fuchun Guoمشاهده پروفایل

دانشیار

• Public-Key Cryptography
• Security Proofs
• Cryptography
• +۳ مورد دیگر

Dr. Fuchun Guo is an Associate Professor at the University of Wollongong's School of Computing and Information Technology, affiliated with the Faculty of Engineering and Information Sciences. He holds an ARC Future Fellowship (2023-2026) and previously an ARC DECRA Fellowship (2017-2019). His research focuses on public-key cryptography, security proofs, and cryptographic definitions, with contributions to provable security, post-quantum cryptography, and privacy-preserving technologies. Guo earned his PhD in 2013 from the University of Wollongong, supervised by Yi Mu and Willy Susilo. His work extends to cryptographic primitives, security reductions, and formal definitions. Notable publications include books like Introduction to Cryptographic Definitions (Springer, 2025) and foundational research in CRYPTO, EUROCRYPT, and ASIACRYPT venues. His research interests span modern cryptography's theoretical underpinnings, including security models, cryptographic protocols, and practical implementations. Recent work emphasizes post-quantum security, privacy-preserving systems (e.g., cloud-assisted IoT, mobile social networks), and lattice-based cryptography. Awards: ARC Future Fellowship, ARC DECRA Fellowship Grants: ARC grants for "Provably Secure Cryptography Techniques" and "Secure Crowdsourcing Classification" Supervision: Actively supervising PhD students in cloud security, cryptographic proofs, and anonymous signature schemes. Guo’s lab explores cutting-edge cryptography for real-world applications, with a focus on bridging theoretical advancements and practical deployments in cybersecurity domains.

• Cryptography
• Elliptic Curves
• Post-Quantum Cryptography
• +۱ مورد دیگر

Dr. Huseyin Hisil is a Lecturer in Cryptography at the University of Wollongong's School of Computing and Information Technology. His research focuses on asymmetric cryptosystems, including elliptic curve cryptography, hyperelliptic curves, and post-quantum cryptographic solutions. He develops optimized algorithms for multiprecision arithmetic and finite field operations. Recent publications address efficient elliptic curve arithmetic using Karatsuba-friendly primes and Jacobian coordinate systems for genus 2 curves. His work bridges computational number theory with practical cryptographic implementations, enhancing security and performance in next-generation systems. He teaches undergraduate courses including Introduction to Web Technology (CSIT128) and Web Development (CSIT884), while coordinating Professional Capstone Projects (CSIT998). His research group explores cryptographic pairings and algebraic structures relevant to quantum-resistant protocols.

Risi Kondorمشاهده پروفایل

دانشیار

• Machine Learning
• Graph Theory
• Quantum Mechanics
• +۶ مورد دیگر

Risi Kondor is an Associate Professor in the Departments of Statistics and Computer Science at the University of Chicago. His research focuses on machine learning, group theory applications, and equivariant neural networks. He develops algorithms respecting geometric and physical symmetries, with contributions to graph learning, quantum mechanics modeling, and multiresolution matrix factorization. Key projects include the development of Covariant Compositional Networks (CCNs) for graph-structured data and N-body networks for molecular simulations. He has created software tools like GraphFlow, SnOB (FFT for symmetric groups), and Mondrian for high-performance computing. His work bridges algebraic methods (e.g., Fourier analysis on permutation groups) with machine learning, addressing challenges in multi-object tracking, computer vision, and materials science. Risi Kondor holds grants including a DARPA Young Faculty Award ($500K, 2016–2018) and NSF funding for non-commutative harmonic analysis in machine learning. His research emphasizes theoretical foundations and practical applications, advancing areas like equivariant architectures, multiscale analysis, and symmetry-aware machine learning systems.

Michelle Mazurekمشاهده پروفایل

دانشیار

• Cybersecurity
• Human-Centered Security and Privacy
• Human-Computer Interaction (HCI)
• +۴ مورد دیگر

Michelle Mazurek is an Associate Professor of Computer Science and Director of the Maryland Cybersecurity Center (MC2) at the University of Maryland, College Park. She holds affiliations with UMIACS and is an Affiliate Associate Professor in Electrical and Computer Engineering (ECE). Her research focuses on human-centered security and privacy, emphasizing real-world user behavior analysis to improve security practices for professionals and end-users. Key areas include developer security education, end-user security behaviors, and encrypted messaging adoption. Education: Ph.D., Electrical and Computer Engineering, Carnegie Mellon University (2014) B.S., Computer Science, University of Maryland (2004) Research Interests: Dr. Mazurek investigates how humans interact with security systems, particularly in professional (e.g., sysadmins, developers) and everyday contexts. Her work addresses challenges like insecure code propagation from online forums, privacy implications of digital tracking, and safety needs of marginalized groups (e.g., sex workers). She leads the Security, Privacy, People (SP2) lab and collaborates with industry partners to design usable security tools. Awards: NSF CAREER Award (2020) CyLab Distinguished Alumni Award (2022) Best Paper Awards at USENIX Security (2018) and NSA (2016) Advising & Leadership: Mazurek advises multiple PhD students in computer science and oversees MC2’s postdoctoral fellowship program. Her leadership expands MC2’s interdisciplinary research in areas like blockchain security, post-quantum cryptography, and human-centered approaches. She also fosters collaborations with industry leaders such as Cisco and Northrop Grumman. Labs & Teams: Directs the SP2 lab within MC2 and participates in the Human-Computer Interaction Lab (HCIL). Past affiliations include CyLab and the Parallel Data Lab (PDL).

Tim Kacprowskiمشاهده پروفایل

استاد

• Medical Informatics
• Data Science
• Network Medicine
• +۹ مورد دیگر

Summary Prof. Tim Kacprowski is a Professor and Head of Data Science in Biomedicine at the Peter L. Reichertz Institute for Medical Informatics (PLRI), jointly affiliated with TU Braunschweig and Hannover Medical School. His research focuses on integrating computational methods with biomedical challenges, particularly in network medicine, federated learning, and alternative splicing analysis. Key projects include the development of the NeDRex platform for drug repurposing and the FeatureCloud framework for privacy-preserving federated learning in healthcare. Research Interests His work spans multiple domains: Network Medicine: Leveraging molecular networks for disease module identification and drug discovery. Data Science: Advanced analytics for biomedical data, including ECG monitoring, microbiome studies, and flow cytometry. Federated Learning: Developing decentralized AI systems to protect patient data while enabling collaborative research. Alternative Splicing: Investigating splicing patterns in diseases like cancer and kidney disorders. Publications Trends Recent publications emphasize tools for drug repurposing (NeDRex-Web), ethical AI in clinical decision-making, and microbiome dynamics in chronic diseases. His work bridges computational methods with clinical applications, addressing challenges in precision medicine and healthcare technology. Labs & Collaborations As head of the Data Science group at PLRI, he leads interdisciplinary teams advancing biomedical informatics. Collaborations span institutions in Germany and internationally, focusing on translational research and AI-driven healthcare solutions.

Ping Zhongمشاهده پروفایل

دانشیار

• Free Probability
• Random Matrix Theory
• Operator Algebras
• +۲ مورد دیگر

Ping Zhong is an Associate Professor in the Department of Mathematics at the University of Houston. He holds a Ph.D. from Indiana University Bloomington and joined UH in 2024 after serving as Assistant Professor at the University of Wyoming (2018-2024) and completing postdoctoral work at the University of Waterloo. His research explores free probability theory, operator algebras, and applications of random matrix theory to high-dimensional statistics. Educational background includes: Ph.D. Mathematics, Indiana University (2014) M.S. Mathematics, Peking University (2008) B.S. Applied Mathematics, Huazhong University of Science and Technology (2005) Research focuses on fundamental mathematical structures in probability with emerging applications in quantum physics and statistical learning. Current investigations bridge theoretical frameworks with computational approaches for high-dimensional data analysis. Recent publications demonstrate consistent contributions to spectral analysis of random operators and convergence properties in non-commutative probability spaces. Works frequently appear in premier mathematics journals including Transactions of the AMS and Journal of the European Mathematical Society.

Arjan Durresiمشاهده پروفایل

استاد

• Trust engineering
• Trustworthy AI
• AI control and alignment
• +۳ مورد دیگر

Arjan Durresi is a Professor of Computer Science at Indiana University's Luddy School of Informatics, Computing, and Engineering. His research develops trustworthy AI systems, cybersecurity frameworks, and quantum computing applications with $1.8M+ NSF/USDA funding. Research focuses on trust engineering for decision systems, AI alignment, causal inference models, and resilient network architectures. Current projects develop trustworthy AI for medical diagnostics and environmental resource management. Recent publications address quantum algorithms for trust systems, 5G security, and responsible AI frameworks. Research emphasizes measurement-based trust models across human-machine interactions. Ranked top 0.01% scholar in software-defined networking Top 2% scientist worldwide (Stanford List 2021-2024) Trustee Teaching Award (2012) He leads NSF projects on resilient AI systems for 5G networks and directs quantum computing courses. Current work establishes standards for trustworthy AI in healthcare diagnostics.

Vladimir Podolskiiمشاهده پروفایل

دانشیار

• Computational complexity
• logical foundations of computer science
• tropical geometry

Vladimir Podolskii serves as an Associate Professor in the Department of Computer Science at Tufts University's School of Engineering and holds a concurrent appointment in the Department of Mathematics within the School of Arts and Sciences. His academic work bridges theoretical computer science and mathematical foundations, with a focus on computational boundaries and algebraic structures. He completed his doctoral studies at Lomonosov Moscow State University in Moscow, Russia, receiving his PhD in 2009. Podolskii's research program centers on computational complexity theory, examining the inherent difficulty of computational problems through decision trees and threshold functions. His investigations extend to the logical underpinnings of computation and the application of tropical geometry in optimization contexts. This interdisciplinary approach connects discrete mathematics with theoretical computer science frameworks. His 2022 publications reveal concentrated exploration of decision tree efficiency for threshold functions and classification systems for ontology-mediated queries, demonstrating methodological rigor in analyzing computational limits and knowledge representation structures. Podolskii actively mentors graduate researchers through Dissertation Research courses while teaching core curriculum including Algorithms and specialized seminars on computational complexity toolkits, shaping the next generation of theoretical computer scientists.

• Space Logistics
• Orbital Debris Remediation
• Quantum Computing
• +۱ مورد دیگر

Hao Chen is an Assistant Professor at Stevens Institute of Technology, directing the Complex Engineering Systems Lab. His research integrates space logistics, quantum computing, and orbital debris management to advance space exploration infrastructure. He develops optimization frameworks for lunar resource utilization and multi-mission campaigns, with NASA/NSF-funded projects on debris remediation commercialization. Chen's work has been recognized by the Luigi G. Napolitano Award and features in journals like the Journal of Spacecraft and Rockets. NSF Engineering Research Initiation Award (2023) Luigi G. Napolitano Award (2019) He mentors graduate students in quantum computing applications and space systems optimization, contributing to AIAA technical committees on space logistics.

Ali Ghorbaniمشاهده پروفایل

استاد

• Cybersecurity
• Machine Learning
• Network Security
• +۳ مورد دیگر

Dr. Ali Ghorbani is a Professor and Tier 1 Canada Research Chair in Cybersecurity at the University of New Brunswick's Faculty of Computer Science, where he served as Dean from 2008-2017. He is the founding Director of the Canadian Institute for Cybersecurity (CIC), established in 2016. Ghorbani holds BSc (Tehran), MSc (George Washington University), and PhD (UNB) degrees. With 42+ years in academia, his research spans cybersecurity, machine learning, adaptive systems, and critical infrastructure protection. Ghorbani has co-founded three cybersecurity startups (Sentrant Security, EyesOver Technologies, Cydarien Security) and co-invented four awarded patents. He has published 300+ peer-reviewed papers and supervised 250+ researchers. Professional roles include co-founding the National Cybersecurity Consortium (NCC) and serving as co-editor-in-chief of Computational Intelligence journal. Awards & Honors: Canada Research Chair in Cybersecurity Startup Canada Senior Entrepreneur Award (2017) RBC Top 25 Canadian Immigrants (2019) CAIAC Lifetime Achievement Award (2024)

Qianwen Guoمشاهده پروفایل

استادیار

• Public transit
• Transportation resilience
• Shared mobility
• +۴ مورد دیگر

Dr. Qianwen (Vivian) Guo is an Assistant Professor in the Department of Civil and Environmental Engineering at the FAMU-FSU College of Engineering. Her research focuses on optimizing transportation systems, particularly in public transit, shared mobility, and infrastructure resilience. She holds a joint Ph.D. in Civil Engineering from Cornell University and Management Science and Engineering from Huazhong University of Science and Technology. Dr. Guo's work spans interdisciplinary areas including quantum computing applications in transportation, disaster evacuation planning using autonomous vehicles, and equity-aware transit policies. She has received funding from NSF, FDOT, and USDOT, and leads the FAMU-FSU ITE student chapter. Her research has been recognized by the Society of Asian Scientists & Engineers in 2021. Education: Ph.D., Joint Program: Civil Engineering (Cornell University) Ph.D., Management Science and Engineering (Huazhong University) M.S., Transportation Engineering (Huazhong University) B.S., Transportation Engineering (Southwest Jiaotong University) Her recent publications emphasize quantum-driven frameworks for transportation resilience, machine learning for safety analysis, and real-time transit optimization strategies. Dr. Guo collaborates actively on projects addressing demand uncertainty, sustainable policies, and equitable infrastructure investments.