Prof. Dr. Till Tantau is a Full Professor of Theoretical Computer Science at the University of Lübeck , Germany. He has served as Dean of Studies for the Faculty of Technology and Natural Sciences (MINT sections) since 2008 and chairs the Scientific Advisory Board of the German National Computer Science Competition from 2014. His research focuses on computational complexity, parameterized algorithms, and bioinformatics, particularly logspace problems and descriptive complexity theory. Born 1975 in Berlin, Germany 1994–1999: Studied at TU Berlin 1999–2005: Research Assistant at TU Berlin 2003: Doctorate in Natural Sciences (Dr. rer. nat.) at TU Berlin 2004: Research stay at ICSI, Berkeley with Richard Karp 2005: Appointed W2 Professor at University of Lübeck 2015: Promoted to W3 Professor (highest professorial rank in Germany) His research interests include: Computational Complexity: Logspace problems, weak cardinality theorems, and structural similarities across computation models Parameterized Algorithms: Color coding, kernelization techniques, and parallelization of fixed-parameter tractable problems Bioinformatics: Haplotyping problems under perfect phylogeny models Descriptive Complexity: Logical characterizations of computational problems Graph Algorithms: Shortest/longest paths in series-parallel graphs and smoothed analysis of binary search trees Publications since 2000 demonstrate expertise across theoretical computer science, with 15 recent works focusing on parameterized complexity, parallel algorithms, and applications in bioinformatics. He contributes to algorithmic metatheorems, color coding techniques, and kernelization for hitting set problems. Scientific Awards & Scholarships : 1992: Federal winner of German computer science competition 1993: Silver medal at International Olympiad in Informatics 1993: German National Academic Foundation scholarship 1994: Dr. Habbena Prize for Abitur excellence 1999: Erwin Stephan Prize, TU Berlin 2002: Best student paper award at MFCS 2003: GI Dissertation Prize nomination 2007–2021: Heliprof Teaching Awards (5x recipient) His teaching includes courses on algorithm design, complexity theory, LaTeX/TikZ, and theoretical computer science. He chairs the Scientific Advisory Board of the German National Computer Science Competition and participates in academic governance as Dean of Studies.
جستوجوی استاد و پژوهشگر
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نمایش ۳۱-۴۵ از ۶۷ استاد راهنما
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Leonardo Querzoni is a Professor at the Department of Computer, Automatic and Management Engineering Antonio Ruberti within Sapienza University of Rome . He currently serves as Deputy Rector for Digital Technologies and Cybersecurity (2024-2026) and President of the CYBER 4.0 Competence Center since 2022. His academic career spans over two decades with 120+ publications. PhD in Computer Engineering (2007) with thesis "Techniques for Efficient Event Routing" Coordinates Italy's first joint PhD program in Cybersecurity (Sapienza-Luiss) Co-author of Italy's National Cybersecurity Framework Research spans Cybersecurity , Stream Processing , Dependable Distributed Systems , and Big Data Scalability . Particular focus areas include: Malware analysis and incident forensics Secure distributed systems Binary code analysis using neural networks Security for cyber-physical systems IoT attack triage Stream processing optimization Key awards: 2023 DEBS 10 Years Time Award 2021 ASPLOS Distinguished Paper 2019 DIMVA Best Paper Runner-Up 2017 DEBS 10 Years Time Award Active in European projects EU-GUARDIAN, ATENA, and national initiatives like PRIN TENACE. Industry collaborations include: Presidenza del Consiglio dei Ministri Leonardo-Finmeccanica CISCO Research Banca MPS
Weihang Wangمشاهده پروفایل
استادیار
Weihang Wang is a WiSE Gabilan Assistant Professor in the Department of Computer Science at the University of Southern California. His research focuses on building testing and analysis techniques to improve the reliability, security, and efficiency of complex software systems, with particular expertise in WebAssembly technologies. Wang received his Ph.D. in Computer Science from Purdue University in 2018. His educational background has provided a strong foundation for his research in software engineering and security, leading to numerous publications and awards in the field. His research spans multiple critical areas including WebAssembly analysis , software security , and program analysis . Wang's work addresses fundamental challenges in detecting and mitigating vulnerabilities in modern web applications and systems. His research group develops innovative approaches to static and dynamic analysis, bug detection, and security testing, with a strong emphasis on practical applications and real-world impact. Recent projects include developing frameworks for Spring analysis, WebAssembly function identification, and business flow tampering detection. Wang's publication record shows a clear trajectory of increasing impact in WebAssembly research, with multiple papers at top-tier conferences like WWW, ICSE, and FSE. His work spans from foundational analysis techniques to practical tools that address security and performance challenges in web technologies, demonstrating both theoretical depth and practical applicability. His scientific achievements have been recognized with prestigious awards including: N2Women Rising Stars in Networking and Communications (2024) University at Buffalo Exceptional Scholar - Young Investigator Award (2022) NSF CAREER Award (2021) Facebook Testing and Verification Research Award (2019) Mozilla Research Award (2019) Maurice H. Halstead Memorial Research Award (2018) Best Poster Award at ACSAC'22 Wang has successfully advised numerous graduate and undergraduate students, many of whom have published first-author papers at top conferences and secured positions at leading technology companies. He actively serves the research community through program committee roles at major software engineering and security conferences. His work has been supported by competitive grants from NSF, Facebook, and Mozilla, demonstrating the significance and potential impact of his research directions. At USC, Wang leads a vibrant research group focused on software engineering, security, and systems. The group is currently working on cutting-edge projects related to static/dynamic bug detection, program analysis for WebAssembly, attack investigation and detection, compiler testing, and performance profiling of modern web applications.
Keyu Qiمشاهده پروفایل
پژوهشگر
Keyu Qi is a researcher affiliated with the Institute of Information Engineering at the Chinese Academy of Sciences and the School of Cyber Security at the University of Chinese Academy of Sciences, actively contributing to cybersecurity and AI-driven software analysis. Their institutional roles bridge theoretical research and practical security applications. Research focuses on: Cyber Security and software vulnerability detection Artificial Intelligence for binary program analysis Reverse engineering and malware analysis AI-based function similarity techniques Recent work demonstrates a strong trend toward applying machine learning to binary security problems, particularly in scaling similarity detection for real-world security practitioners. The 2025 ASE publication highlights evolving methodologies in program analysis using neural networks. No scientific awards were documented in the source materials. Information regarding student advising, grant funding, or laboratory teams was not provided in the available conference profile data.
Eran Yahav is a Professor in the Computer Science Department at the Technion, Israel Institute of Technology, and serves as CTO at Tabnine. His research bridges programming languages, software engineering, program analysis, and machine learning, focusing on program synthesis, verification, and code intelligence. Research Interests: His work spans program synthesis , abstract interpretation , verification of concurrent systems , binary analysis , and AI for code . He leads the PRIME project, which uses machine learning and static analysis to enable programming with millions of examples, improving code completion, search, and prediction. The recent publications reflect a strong trend toward integrating neural models with program analysis—using structured representations of code (e.g., AST paths) for property prediction, generating sequences from code (code2seq), learning distributed representations (code2vec), and interpreting neural networks via automata extraction. His work consistently appears in top-tier venues such as POPL, PLDI, ICSE, OOPSLA, and ICLR. Scientific Awards: Best paper award at ISSTA'07 Best paper award at ISSTA'06 Advising and Grants: He has advised numerous PhD and Master’s students, many of whom have published in premier conferences and now hold academic or industry positions. While specific grants are not mentioned, his sustained high-impact research and leadership in major projects (e.g., PRIME, Fender, SAFE) imply significant funding support. He has served on program committees for PLDI, POPL, CAV, OOPSLA, and VMCAI, reflecting his standing in the programming languages and verification communities. Labs and Teams: He leads a research group focused on program analysis and synthesis, with strong collaborations, particularly with Martin Vechev and others, on concurrency, synthesis, and machine learning for code. The group has developed influential tools such as PRIME, code2seq, code2vec, TRACY, and SAFE.
- Software Security
- Formal Methods
- Cryptography
- +۵ مورد دیگر
David Brumley is a Professor of Electrical and Computer Engineering at Carnegie Mellon University with a courtesy appointment in the Computer Science Department. He previously served as Director of CyLab, CMU's Security and Privacy Institute, from 2015 to 2017. His research focuses on developing systems that automatically check software for exploitable bugs using program analysis with security-specific properties. Brumley received his Ph.D. in Computer Science from Carnegie Mellon University, an MS in Computer Science from Stanford University, and a BA in Mathematics from the University of Northern Colorado. Before his academic career, he served as a Computer Security Officer for Stanford University from 1998-2002. Brumley's research focuses on software security techniques that provide users with guarantees. His work sits at the intersection of model checking, formal methods, compilers, and logic, all applied to security problems. He develops efficient symbolic execution, reasoning about bit-level arithmetic in finite fields, sound decompilation, and decision procedures. His research also extends to network security and applied cryptography, focusing on efficient protocols, signature schemes, and privacy-preserving cryptography. A key aspect of his work involves binary code analysis, which allows reasoning about the security of code that actually executes. Brumley's publication record shows a consistent progression from theoretical foundations in program analysis to practical security systems. His work spans symbolic execution, fuzzing, exploit generation, and binary analysis. The Mayhem Cyber Reasoning System, which won the DARPA Cyber Grand Challenge, represents the culmination of his research vision for automated vulnerability detection and patching. His publications demonstrate how theoretical advances in program analysis can be translated into real-world security tools. USENIX Security Best Paper Awards (2003, 2007) International Conference on Software Engineering Distinguished Paper Award (2014) NSF CAREER Award (2010) United States Presidential Early Career Award for Scientists and Engineers (PECASE) (2010) Sloan Foundation Award (2013) DARPA Cyber Grand Challenge Winner ($2,000,000) (2016) Brumley has mentored numerous PhD students who have gone on to successful careers in academia and industry, including co-founders of ForAllSecure. He served as faculty mentor for the CMU Hacking Team Plaid Parliament of Pwning (PPP), which has been ranked #1 internationally and won DefCon 2013. His research has been supported by significant grants including DARPA programs and the NSF CAREER award. He also runs PicoCTF, an annual computer security contest for high school students that has become one of the largest cybersecurity education initiatives of its kind. Brumley leads the development of security systems through both academic research and commercialization. He is the CEO of ForAllSecure, which commercializes the Mayhem system developed through his academic research. His work bridges the gap between theoretical security research and practical security tools used by industry, creating a pipeline from academic innovation to real-world impact.
Yuede Jiمشاهده پروفایل
استادیار
- High-Performance Computing (HPC)
- Security
- Graph AI
- +۵ مورد دیگر
Dr. Yuede (YJ) Ji is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), leading the Graph Lab. His research focuses on graph-centric security, learning, and computing, intersecting High-Performance Computing (HPC), Security, Graph AI, and Graph Analytics. Prior to UTA, he was an Assistant Professor at the University of North Texas (2021–2024). He earned his Ph.D. from George Washington University in 2021 under Dr. Howie Huang. His work has been recognized with awards such as the Best Paper Award at NPC 2014 and the Best Poster Award at SMCDC 2023. Education: Ph.D., Computer Engineering, George Washington University (2021) M.S., Computer Science & Technology, Jilin University (2015) B.E., Software Engineering, Jilin University (2012) Research Interests: Graph-centric security, HPC, Graph AI, malware detection, and privacy-preserving systems. His research is funded by NSF, DOE, and Google. Grants & Fundings: NSF SHF: Small: An Accelerated Computation Architecture (2024–2027) NSF CICI: Secure Containers in HPC Infrastructure (2023–2026) Students & Advising: Advising Ph.D., M.S., and undergraduate students. Current advisees include Chaoqun Li, Emmanuel Emakhue Oseghale, and Xiao Hu. Awards: Best Editor Award, IEEE OJCOMS (2024) CSE Junior Faculty Research Award Nominee (2024) Labs & Teams: The Graph Lab develops scalable solutions for HPC security and graph analytics. Recent projects include BINGO (EuroSys ‘25) and TANGO (SC ‘23).
DUAN Yueمشاهده پروفایل
استادیار
DUAN Yue is an Assistant Professor of Computer Science at the School of Computing and Information Systems, Singapore Management University (SMU). He holds a PhD from the University of California, Riverside (2019). His research focuses on cybersecurity, software security, AI security, and blockchain security, with an emphasis on vulnerabilities in smart contracts, malware analysis, and adversarial machine learning. Education: PhD, Computer Science, University of California, Riverside (2019); Postdoctoral training at Cornell University and University of Utah. Research Interests: Cybersecurity, software testing, blockchain security, AI security, mobile security, and system security. His work leverages program analysis and deep learning to address emerging security challenges. Awards: NSF CRII Award (2023), Excellence in Teaching Award (2023), ACM CCS Best Paper Honorable Mention (2022), RAID Best Paper Award (2019). Advising: Current students include Shengchen Duan (PhD, SMU) and visiting researchers. Notable former advisees include William Bodell (Increment Finance), Hua Xu (PhD, IIT), and others. Labs/Teams: Leads a research group focused on software and system security, with collaborations across academia and industry.
Elias Athanasopoulos is an Associate Professor in the Department of Computer Science at the University of Cyprus. His research focuses on systems security and privacy, with particular emphasis on web application security, hardware-based defenses, and privacy-preserving technologies. He holds a Ph.D. in Computer Science from the University of Crete (2011) and a BSc in Physics from the University of Athens (2005). Prior to joining the University of Cyprus, he served as an Assistant Professor at Vrije Universiteit Amsterdam. His work has been recognized with awards such as the Best Paper Award at IEEE European Symposium on Security and Privacy 2020 and the DCSR Best Paper Award at the 23rd USENIX Security Symposium. His research spans topics including fuzzing frameworks, adversarial machine learning, and network security. Elias has published extensively in top-tier conferences like IEEE Security & Privacy, ACM CCS, and Usenix Security. His contributions include practical solutions like Lethe for data breach detection and auth.js for advanced web authentication. He has also held fellowships at Columbia University (Marie Curie) and collaborated with organizations like Microsoft Research and FORTH. His teaching and research interests integrate theoretical and applied aspects of security, emphasizing real-world impact. Current projects explore vulnerabilities in modern systems and developing robust defenses against emerging threats.
Doowon Kimمشاهده پروفایل
استادیار
- Computer Security
- Cybersecurity
- Usable Security
- +۷ مورد دیگر
Doowon Kim is an Assistant Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. He holds a PhD in Computer Science from the University of Maryland, College Park (2020) and has held academic positions including research internships at Symantec Research Labs and Korea Electronics Technology Institute. Education: PhD, Computer Science, University of Maryland (2020) MS, Computer Science, University of Utah (2013) B.E., Computer Science and Engineering, Hankuk University of Foreign Studies (2011) Research Interests: Computer Security: Data-driven security, usable security, phishing analysis, and PKI abuse. Network Security: Internet measurement, TLS protocols, and threat intelligence sharing. Web Security: Client-side vulnerabilities, phishing kits, and anti-Asian racism in social media. AI in Security: Adversarial attacks on code generation models and poisoned AI suggestions. Recent Trends in Publications: Focus areas include phishing detection mechanisms, AI-driven code exploitation, and PKI security. Recent work explores visual similarity-based phishing detection, backdoor attacks on code completion models, and longitudinal studies of client-side vulnerabilities. Awards: NSA Best Scientific Cybersecurity Paper (2017) Ann G. Wylie Dissertation Fellowship (2019) Best Student Paper at WISA 2022 Advising & Grants: Advises 14+ graduate/undergraduate students (e.g., Kyungchan Lim, Fujiao Ji). Secured travel grants for students at Web Conf and NDSS. Recipient of NSF proposal review panelist roles. Labs & Teams: Actively leads research in cybersecurity with a focus on phishing, PKI, and AI ethics. Collaborates with global institutions like KAIST and Samsung Research.
- Computer Vision
- Machine Learning
- Human Analysis
- +۲ مورد دیگر
Dr. Zhixiang Chen is a Lecturer in Machine Learning at the Department of Computer Science, University of Sheffield, and an active member of both the Machine Learning and Computer Vision research groups. Previously, he held postdoctoral positions at Imperial College London and Tsinghua University. Education: PhD in Computer Science, Tsinghua University, China B.Eng. in Computer Engineering, Xi'an Jiaotong University, China His research focuses on advancing Computer Vision and Machine Learning through human analysis, object detection, and image retrieval systems. Recent work integrates deep learning for industrial applications like PCB inspection and autonomous systems, while earlier contributions pioneered video hashing techniques for efficient multimedia search. His methodology emphasizes robust feature representation and scalable algorithm design. Publication trends reveal a strategic shift from foundational hashing research (2016-2019) toward applied 3D vision and industrial automation (2020-2024), with increasing emphasis on uncertainty-aware models and sensor fusion for real-world deployment. Scientific Awards: China Society of Image and Graphics Doctoral Dissertation Award (2019) IEEE International Conference on Multimedia and Expo Best Paper Award (2018) China National Postdoctoral Program for Innovative Talents (2017) Dr. Chen currently leads the EPSRC-funded project 'VIM: towards a Vision based System for Inventory Management with Deep Learning' (2024-2026, £46,354) as Principal Investigator. He serves as a Grant Reviewer for UKRI-FLF and NSFC, Senior Program Committee Member for AAAI 2023, and regularly reviews for top-tier conferences (CVPR, ICCV, NeurIPS) and journals (IEEE T-PAMI, IEEE T-MM). As a core member of Sheffield's Machine Learning and Computer Vision research groups, he contributes to collaborative projects involving industrial partners and interdisciplinary teams focused on AI-driven solutions for healthcare, manufacturing, and autonomous systems.
Dr. Ameer Al-Nemrat is a Reader in Cyber Security at the School of Architecture, Computing and Engineering, University of East London. He serves as the director of the Cyber Security and AI Centre, and directs both the Professional Doctorate in Information Security and the MSc Information Security and Computer Forensics programmes. Additionally, he is the founder and director of the Chip Forensics Reverse Engineering & The Electronic Evidence Laboratories, collaborating with law and policy makers as well as national and international agencies on cyber security research projects. Dr. Al-Nemrat's educational background includes: PhD in Investigation into Cybercrime Trends and Analysis (2010) from University of East London PGCert in Learning and Teaching in Higher Education (2009) from University of East London PGCert Research (2008) from University of East London MSc in Computer Systems Auditing (2006) from London Metropolitan University BCS BIS (Informatics, Management, and Statistics) (1991) from Damascus University Dr. Al-Nemrat's research in cyber security and digital forensics has had considerable impact on public and industry practice at national and international levels. His expertise spans cyber criminal network analysis using data mining techniques, cybercrime prevention and detection, profiling cybercrime victims, and digital forensics including chip-off reverse engineering and steganography techniques. He has been directly involved in professionalizing police training across Africa, South America, and the Middle East, and has advised national defense organizations and international bodies on cyber security matters. His extensive publication record reveals a strong research trajectory focusing on the application of machine learning to cybersecurity challenges, particularly in intrusion detection systems, malware analysis, and the financial implications of data breaches and regulatory compliance. His work bridges theoretical security research with practical implementation, making significant contributions to both academic knowledge and real-world security practices. Dr. Al-Nemrat has served as guest editor for multiple special journal issues covering emerging security topics and has edited books on cybercrime and digital forensics. His scholarly influence extends through his numerous keynote addresses at international conferences, including engagements with the Albanian Ministry of Defence, Chatham House, and various global cybersecurity forums. As an educator, he teaches Digital Forensics (MSc) and Computer and Network Security (BSc) courses, preparing students for careers in information security through a blend of theoretical knowledge and practical application. His leadership in establishing specialized research laboratories has created valuable resources for advancing hardware-level digital forensics and electronic evidence analysis.
Kexin Peiمشاهده پروفایل
استادیار
Kexin Pei is an Assistant Professor in the Department of Computer Science at the University of Chicago. His research focuses on the intersection of Security, Software Engineering, and Machine Learning, emphasizing data-driven program analysis techniques to enhance software reliability and security. He holds a Ph.D. from Columbia University's Computer Science department. Affiliations: University of Chicago, Google DeepMind (Research Collaboration), Microsoft Research (Past Internship) Education: Ph.D., Computer Science, Columbia University Research interests include Machine Learning for Code, Program Analysis, Software Security, AI & Machine Learning Foundations, and Systems Architecture. His work has received awards such as the Neubauer Family Assistant Professorship (2024), Best Paper Award at SOSP (2017), and Distinguished Artifact Award (2017). Recent publications explore topics like neural program analysis, binary similarity, and cybersecurity evaluation through Capture-the-Flag challenges. He collaborates with industry partners like Google DeepMind to develop program analysis tools using large language models. Awards: Neubauer Family Assistant Professorship (2024) Google Conference Scholarship (2023) CSAW Applied Research Competition Runner-Up (2018) Grants/Projects: Leads research on adversarial testing for deep learning systems and neural program smoothing for fuzzing. Active in academic service, he serves on program committees for top venues like ICSE, CCS, and USENIX Security.
- Security
- Intrusion Detection
- Malware Detection
- +۲ مورد دیگر
Christopher Krügel is a Professor at Technische Universität Wien, affiliated with the Department of Automation Systems (E191-03). His research focuses on Security, Intrusion Detection, Malware Detection, Operating Systems, and Networks. He leads multiple research projects and has supervised 20+ graduate students. His research explores malware analysis techniques, web security vulnerabilities, hardware-level attacks (e.g., Rowhammer), and privacy threats in mobile/social systems. Recent work examines ARM TrustZone security and machine learning limitations in malware detection, while earlier publications cover P2P networks, phishing defenses, and binary analysis. Awards: Wirtschaftskammerpreis (2005) EIB Scientific Award for Excellent Diploma Thesis (2000) Grants & Projects: Includes SECoverer (web app security, 2008-2011), WOMBAT (malware observatory, 2008-2011), Pathfinder (malware analysis, 2007-2009), and 5+ other funded initiatives focusing on binary analysis, pervasive security, and malware detection. He leads the Automation Systems research group, collaborating with institutions like European Commission and Austrian Science Fund.
Svyatoslav Voloshynovskyy is a Professor at the University of Geneva's Faculty of Science, Department of Computer Science, with an impressive publication record of 195 works and supervision of 26 graduate projects. His extensive research portfolio demonstrates significant contributions to signal processing, information theory, and multimedia security, evolving into cutting-edge applications in medical imaging and privacy-preserving technologies. Dr. Voloshynovskyy's research interests span multiple interconnected domains at the intersection of theoretical foundations and practical applications. His work has progressed from foundational research in data hiding and watermarking to contemporary applications in privacy-preserving medical imaging. Key research areas include: Information-theoretic approaches to multimedia security Advanced signal processing techniques for image and data analysis Federated learning frameworks for healthcare applications Privacy-preserving machine learning methodologies Medical imaging analysis with focus on PET/CT technologies Theoretical foundations of data hiding and watermarking Analysis of his recent publications (2022-2024) reveals a strategic shift toward healthcare applications, particularly in medical imaging with privacy preservation. His work integrates federated learning, differential privacy, and deep learning to address critical challenges in healthcare data analysis while maintaining patient confidentiality. This represents a natural evolution from his earlier theoretical work in signal processing and information theory to impactful real-world healthcare applications requiring multi-institutional collaboration. With 26 supervised works documented in the University of Geneva archive, Dr. Voloshynskyy has demonstrated significant commitment to academic mentoring and research supervision. His publication pattern suggests successful grant acquisition for projects at the intersection of signal processing, machine learning, and healthcare applications, though specific funding details aren't provided in the available information. His research trajectory indicates leadership in a specialized research group focused on signal processing and machine learning applications, with recent emphasis on medical imaging. The consistent mention of multi-institutional collaborations in his latest publications suggests an active research team working on federated learning approaches for medical image analysis, likely involving partnerships with healthcare institutions across multiple countries.