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Nada AminView profile

Assistant Professor

• Programming Languages
• Artificial Intelligence
• Metaprogramming
• +4 more

Nada Amin is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS), where she leads the metareflection lab . Her research combines programming languages (PL) and artificial intelligence (AI), focusing on neuro-symbolic systems that are correct by construction, with applications in program synthesis, verification, and precision medicine. Harvard John A. Paulson School of Engineering and Applied Sciences (2019–Present) University Lecturer in Programming Languages at University of Cambridge (2017–2019) Doctoral and postdoctoral work at EPFL (2011–2017) Her research spans three core themes: Safer : Type systems and formal verification (Coq, Dafny, Frama-C) Faster : Multi-stage programming and interpreter collapsing techniques Easier : Neuro-symbolic AI for program manipulation and biological reasoning Key publications include: 2025: Modular Imperative with LLMs (LMPL), Multi-stage Relational Programming (PLDI) 2024: Persimmon for extensible variant types (OOPSLA) 2023: LURK for recursive knowledge (ICFP), Dolorem language growth pattern (ECOOP) Scientific honors include: Teaching Assistant Team Award (EPFL, 2015) Michigan Cambridge Research Initiative Grant (2018) ArsDigita Prize (1999) She has served on program committees for GPCE (co-chair), ICFP, PLDI, and organized workshops in metaprogramming and logic programming. Her teaching portfolio includes graduate courses on neurosymbolic programming, program synthesis, and advanced PL theory.

Boudewijn van DongenView profile

Professor

• Process Mining
• Conformance Checking
• Business Process Management
• +3 more

Dr. Boudewijn van Dongen is a Full Professor at Eindhoven University of Technology's Department of Mathematics and Computer Science, chairing the Process Analytics group. His research focuses on conformance checking and process mining , particularly through alignment-based techniques. Develops ProM and CPN Tools frameworks Collaborates with Philips Research and Vanderlande Leads IEEE Task Force on Process Mining Research Interests : Specializes in aligning observed data with process models, addressing multi-case dependencies , privacy-aware mining , and stream processing . Project Highlights : • Strategic partnership with Philips on health data science • Logistics optimization with Vanderlande Industries • Foundational work in alignment theory and behavioral diagnostics

• Privacy
• Malware Analysis
• Systems Security
• +7 more

Dr. Oluwafemi Olukoya is a Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on privacy, malware analysis, systems security, and cybercrime, with an emphasis on mobile systems security, sustainable malware analysis, and cyber-attack attribution. He is particularly interested in interdisciplinary projects addressing socio-technical dynamics in cybersecurity. Research interests include malware detection engineering, digital forensics, and integrating legal/regulatory frameworks with software development. He is actively involved in PhD supervision and has secured research funding, including participation in the NIO New Deal Cyber Bid project (AIDE_NICYBER2025). Dr. Olukoya has published extensively in top-tier venues, leveraging machine learning for cybersecurity challenges such as concept drift in malware classification and vulnerability detection. He received the Queen's Merit Award for Fellowship in 2022. His work bridges technical cybersecurity solutions with real-world regulatory and societal implications, contributing to both academic and applied domains.

Stephanos MavromoustakosView profile

Associate Professor

• Web Engineering
• Human-Computer Interaction
• Information Security
• +3 more

Dr. Stephanos Mavromoustakos serves as Acting Associate Dean and Chair of Cybersecurity, Computer Science, and Software Engineering at Indiana Tech's Talwar College of Engineering and Computer Sciences. He holds an Associate Professor rank and directs the Cybersecurity Center. His academic journey includes a PhD in Computer Science from the University of Cyprus (2006) and multiple degrees from Nova Southeastern University. With over 25 years of experience, Dr. Mavromoustakos has taught at institutions like the University of Windsor and European University Cyprus. His professional leadership spans roles as department head, consultant, and CEO of Cyprosoft. He currently advises programs in Cybersecurity (BS/MS), Computer Science (BS), and Software Engineering (BS). Research interests focus on Web Engineering , Human-Computer Interaction , Information Security , and Artificial Intelligence . Notable work includes cybersecurity project development, adaptive e-Learning systems, and biometric authentication technologies. He has published extensively in journals like UPGRADE and conferences such as IEEE and ACM. Grants include NSF S-STEM ($1M), Google Cloud Platform grants, and EU projects like MEDFORIST ($2.5M). His funded projects address data security, mobile commerce, and educational technology innovations. He teaches a wide range of undergraduate and graduate courses, emphasizing practical skills in software engineering, database systems, and cybersecurity.

Enwu LiuView profile

Academic

• Epidemiology
• Clinical Trials
• Mathematical Statistics
• +9 more

Dr. Enwu Liu is a Senior Research Fellow at the Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne. He is a key member of the Bone Health and Fractures Research Program and holds an Adjunct Senior Lecturer position at the College of Medicine and Public Health, Flinders University. He also serves as a Research Fellow in Rehabilitation Aged & Extended Care at Flinders University and as an Associate Lecturer at the Research Unit for Schizophrenia Epidemiology, UNSW. His research is centered on epidemiology, clinical trials, and advanced biostatistical methods. Bachelor of Medicine, North China University of Science and Technology Bachelor of Science in Mathematics, Beijing Normal University Master of Science in Epidemiology and Biostatistics, Peking Union Medical College PhD in Epidemiology, University of California, Los Angeles (UCLA) Dr. Liu's research focuses on epidemiology, clinical trials, mathematical statistics, and biostatistics , with a strong emphasis on developing predictive models for clinical decision-making. He specializes in survival analysis using counting processes and Bayesian methodologies. His applied research spans osteoporosis, fracture risk assessment (including FRAX®), sarcopenia, dementia, and medication use in residential aged care . He has extensive experience managing large datasets using Oracle, SQL Server, and Access, and has developed custom data collection software. His recent publications highlight a consistent focus on aged care, geriatric pharmacology, and musculoskeletal health . Themes include medication appropriateness, quality of life in nursing homes, cost analysis of dementia care, and improvements to fracture risk prediction tools. His work often involves large cohort and cross-sectional studies, with frequent collaboration across Australian and international institutions. Dr. Liu is actively involved in academic service, serving on the review panels of prestigious journals including: Journal of the American College of Cardiology American Journal of Physical Medicine & Rehabilitation Drugs & Aging Statistical Applications in Genetics and Molecular Biology Biomedical and Environmental Sciences Drug and Alcohol Review Journal of Substance Abuse Treatment He has contributed to major clinical and epidemiological studies, particularly in rehabilitation post-hip fracture and medication management in dementia. While not formally HDR-accredited, his role in multi-center trials and data leadership positions indicates significant mentorship and collaborative supervision. Dr. Liu leads or contributes to projects involving data infrastructure, predictive analytics, and policy-relevant health outcomes research. Dr. Liu is affiliated with research teams at ACU’s Mary Mackillop Institute, Flinders University’s College of Medicine and Public Health, and UNSW’s schizophrenia epidemiology unit. His interdisciplinary collaborations span public health, geriatrics, biostatistics, and health economics, contributing to national and international efforts in improving care for older adults.

Kerstin SchneiderView profile

Professor

• Databases
• Data Engineering
• Workflow Management Systems
• +4 more

Kerstin Schneider is a full-time Professor for Databases at Harz University of Applied Sciences since 2007. She operates within the Faculty of Automation and Informatics , focusing on database theory, data engineering, and AI-driven educational technology solutions. Current research emphasizes adaptive e-learning platforms (ALEA system) Interdisciplinary projects in AI Engineering (AiEng 2021-2025) Custom database applications for SMEs (TEA project) Her research interests span: Reliable workflow execution in distributed systems Multimedia information systems Semantic data recommendations Temporal XML database models Recent publications (2021–2017) highlight trends in: Academic database foundations Intelligent e-learning architectures Life science data analytics Historical data visualization Scientific Awards : 2004 Research and Innovation Award of the Rhine-Neckar Triangle Foundation She contributes to academic governance through roles in: Steering Committee for Fundamentals of Databases Examination Board AI Engineering Senate Deputy at Harz University Equal Opportunities Officer for AI Department Key collaborative projects include: APRICOTS (CORBA-based workflow systems) GEIST (Intelligent History Information Systems) Team projects with international universities Open Educational Resources (OER) development

Jan HiddersView profile

Lecturer

• Graph Data Management
• Workflow Modelling
• Conceptual Data Models
• +2 more

Jan Hidders is a Lecturer in the School of Computing and Mathematical Sciences at Birkbeck, University of London. He joined Birkbeck in 2020 after holding academic positions at Vrije Universiteit Brussel (Associate Professor, 2016–2020), Delft University of Technology (Assistant Professor, 2008–2016), and the University of Antwerp (Postdoctoral Researcher, 2001–2008). He holds a PhD from Eindhoven University of Technology (2001) and has industry teaching experience at Avans Hogeschool (1996–2001). Research Focus Hidders' research centers on three interconnected domains: Graph Data Management : Design of graph query languages, schema definition systems (PG-Schema), and theoretical properties like expressive power and optimization. He contributes to ISO standardization for SQL/GQL graph extensions. Workflow Modelling : Hierarchical decomposition of Petri nets for workflow correctness verification and orchestration of multi-actor systems. Conceptual Data Models : Formal semantics of ORM2 and its relationship to graph data models, including ORM-native DBMS design. His recent publications emphasize graph database schemas , threshold query optimization , and scalable data processing , reflecting his involvement in LDBC benchmarks and ISO standards. No awards are documented. Academic Roles Programme Director for MSc Computer Science at Birkbeck. Researcher at Birkbeck Knowledge Lab and Birkbeck Institute for Data Analytics. ACM and IEEE member since 2003 and 2006, respectively. He teaches Software Engineering II and Computer Systems , with 1 recorded PhD supervision.

• Information Security
• Software Vulnerability Analysis
• Digital Forensics
• +3 more

Hanno Langweg serves as Associate Professor in the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU), based at the Gjøvik campus. His academic activities are deeply integrated with the COINS Research School of Computer and Information Security, a national consortium led by NTNU that unites Norway's major universities in cybersecurity research and doctoral training. Langweg's research centers on practical security vulnerabilities and compliance frameworks. His work extensively analyzes difficult-to-detect code patterns for SQL injection and cross-site scripting, develops forensic readiness methodologies, and bridges legal requirements like GDPR with technical security implementations. Recent publications demonstrate consistent focus on automated vulnerability injection, stakeholder-driven security requirements, and forensic evidence collection systems. His publication trends reveal sustained contributions across three interconnected domains: vulnerability pattern analysis (particularly in PHP and browser contexts), forensic readiness tooling (including kernel tracing and event reconstruction), and regulatory compliance engineering (GDPR, Common Criteria). This triad reflects both technical depth in code-level security and strategic understanding of legal-technical intersections. Langweg actively contributes to the academic community through conference organization (Sicherheit series) and teaching courses including Software Security (IIKG2001) and Information Security Summer School (IMT6003). His collaborations span multiple institutions with frequent co-authorship patterns involving Schuckert, Katt, and Liao. Within the COINS Research School framework, Langweg supports Norway's cybersecurity research ecosystem that now exceeds 100 doctoral students. His work directly enables the school's mission of integrating national research groups, enhancing student mobility, and establishing international recognition in information security.

• Numerical Optimization Algorithms
• Operations Research
• Parallel Computing
• +4 more

Jonathan Eckstein is a Distinguished Professor in the Department of Management Science and Information Systems at Rutgers Business School, Rutgers University. He holds a PhD in Operations Research from MIT (1989) and has an extensive academic and professional background including teaching at Harvard Business School and research at Thinking Machines, Inc. Professor Eckstein's primary research focuses on numerical optimization algorithms for both continuous and discrete problems, with particular emphasis on implementation on parallel computing platforms. His specialized areas include augmented Lagrangian/proximal methods, branch-and-bound algorithms, and stochastic programming. He has also made significant contributions to risk-averse optimization modeling and applying operations research techniques to information systems management. His publication record includes the notable 2018 book "Introductory Relational Database Design for Business, with Microsoft Access," which demonstrates his expertise in database theory and practical applications for business contexts. This work reflects his broader interest in making complex database concepts accessible to business students and professionals. Scientific Recognition: Elected Fellow of INFORMS (Institute for Operations Research and Management Science) in 2014 Professor Eckstein has successfully supervised seven doctoral students to completion between 2002 and 2020, demonstrating his commitment to academic mentorship. At Rutgers, he led the establishment of the undergraduate major in Business Analytics and Information Technology (BAIT), showcasing his leadership in curriculum development and his vision for integrating analytical methods with business education. His research has practical applications in business analytics, risk management, and information systems design, bridging theoretical optimization methods with real-world business challenges.

Gary AlvarezView profile

Part-Time Lecturer

• Information Technology
• Robotics
• STEM Education
• +3 more

Gary Alvarez is a part-time faculty member in the School of Art & Creative Media at Millikin University. He holds a Doctorate in Information Technology from Capella University, along with an MA in IT Management (Webster University) and a BS in Computer Science (St. John’s University). Certified in Security+ (CompTIA), he has extensive IT experience spanning Wall Street, aerospace, and robotics. His career includes systems design at NYSE/AMEX, IT management roles at Northrop Grumman and ATOS, and certifications like CCNE/CCNA (Cisco) and MCSE/MCSD (Microsoft). Research interests span IT security, project management, SQL databases, and robotics/AI. As a STEM advocate, he builds advanced robotics (e.g., Star Wars droids with GPS/Wi-Fi capabilities) for outreach at conventions, hospitals, and educational events. His droids, including a semi-automated R2-D2, engage audiences in STEM through interactive demonstrations and public appearances. Volunteer efforts include robotics advocacy for children and presentations on AI/robotics. Travels extensively across the U.S. and Europe, blending professional expertise with passion for technology and creative projects.

Ke YiView profile

Professor

• Database theory and systems
• Query processing
• Data security and privacy
• +7 more

Ke Yi is a Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), where he also serves as Director of the MSc Program in Big Data Technology. His research spans database theory and systems, query processing, data security and privacy, parallel and distributed algorithms, and computational geometry, with a focus on bridging theoretical guarantees with practical implementations. Dr. Yi earned his B.Eng. in Computer Science and Technology from Tsinghua University (1997-2001) and his Ph.D. in Computer Science from Duke University (2001-2006), advised by Professors Pankaj K. Agarwal and Lars Arge. Before joining HKUST in 2007, he was a Research Specialist at AT&T Labs-Research (2006-2007). His research interests center on database theory and systems with particular emphasis on query processing techniques, data security and privacy mechanisms, and efficient algorithms for big data. Yi's work consistently demonstrates the rich interdependence between theoretical foundations and practical implementations, favoring simple algorithms with elegant analyses that provide valuable insights for real-world applications. His research group has developed several notable system prototypes including Quorion (query optimization), DPSQL (differentially private SQL), SparkSQL+ (next-generation query planning), SecYan (secure query processing), CROWN/Cquirrel (continuous query processing), and XDB (online aggregation). Yi's publication record shows a clear evolution toward privacy-preserving database technologies, particularly differential privacy, with an increasing focus on practical implementations that maintain theoretical guarantees. His recent work has centered on query processing under differential privacy, secure multi-party computation for databases, and efficient algorithms for big data analytics, demonstrating consistent contributions to both theoretical foundations and practical systems. ACM SIGMOD Best Paper Award (2016, 2022) ACM PODS Test-of-Time Award (2022) ACM Distinguished Member (2021) Multiple ACM SIGMOD Best Paper Honorable Mentions Google Faculty Research Award (2010) HKUST School of Engineering Young Investigator Research Award (2012) Professor Yi has supervised numerous Ph.D. and MPhil students, many of whom have gone on to prestigious academic and industry positions at institutions including Nanyang Technological University, University of Waterloo, EPFL, Alibaba Cloud, and Google. His research has been generously supported by Hong Kong RGC, Alibaba, Huawei, ByteDance, Microsoft, and Google. In addition to his research leadership, Yi has made significant contributions to the academic community through editorial roles (ACM TODS, IEEE TKDE), program committee chairs (PODS 2026, ICDT 2021), and numerous service roles in top database conferences. His laboratory, the HKUST Database Lab (hkustDB), has become a leading center for database research in Asia, developing innovative prototypes that bridge theoretical database research with practical implementations. The lab maintains active GitHub repositories for their open-source projects and collaborates extensively with both academic and industry partners worldwide.

Peter PopovView profile

Researcher

• Software Reliability
• System Dependability
• Software Fault-Tolerance
• +5 more

Peter Popov is a Reader at the Centre for Software Reliability (CSR) , City St George's, University of London , where he has been employed since 1997. He specializes in software dependability , fault tolerance , and stochastic modeling of critical infrastructures. Before his current position, he was an Associate Professor at the Bulgarian Academy of Sciences (1990-1997) and a Research Fellow at City St George's. Peter's academic journey began with a PhD in Computer Science from the Kiev National University of Technologies and Design (1989), following his BEng in Computer Engineering from the National Technical University of Ukraine (KPI, 1982). He has worked as a visiting scientist at renowned institutions including the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign , LAAS-CNRS in Toulouse, and Duke University . His research interests span Software reliability assessment System dependability Software fault-tolerance Performance evaluation Interdependencies of critical infrastructures He has contributed extensively to projects such as ReSIST , IRRIIS , DISPO , and AFTER , focusing on the dependability of composite systems and critical infrastructure resilience. Key publication trends reveal expertise in Stochastic modeling of autonomous vehicle safety Software diversity for fault tolerance Interdependency analysis in critical systems Bayesian reliability assessment Performance evaluation of distributed protocols Security implications in cyber-physical systems Peter has supervised several PhD students , including those researching autonomous vehicle resilience , safety assurance with ML components , and adaptable web services . His professional activities include serving on program committees for ISSRE , SAFECOMP , and EDCC conferences, as well as editorial contributions to CEUR Workshop Proceedings . He is proficient in Bulgarian , English , and Russian , with peer-review capabilities in all three languages.

• Database Systems
• Natural Language Processing
• Data Mining
• +4 more

Hongrae Lee is a researcher specializing in database systems, natural language processing, and data mining. His work bridges structured data management with language models, focusing on tasks like natural language to SQL translation, similarity joins, and efficient data processing. His research interests include: Database query optimization and similarity search Language model applications for text generation and hallucination correction Web data curation and structured data ecosystems Cloud storage optimization and distributed systems Hongrae Lee's recent publications (2022-2023) highlight trends in large language models (LLMs) for dialogue applications, attributed text generation, and acronym disambiguation with weak supervision. Earlier work (2016-2007) established foundational techniques in database scalability, LSH-based similarity estimation, and geographical data thinning. He has collaborated extensively with researchers at institutions like Google, Seoul National University, and University of British Columbia on projects such as WebTables, LaMDA, and Google Fusion Tables. His contributions span both theoretical advancements (e.g., variance-aware query optimization) and practical systems (e.g., CloudRAMSort, T5-based disambiguation).

Dmitriy TraytelView profile

Associate Professor

• Interactive Theorem Proving
• Runtime Verification
• Logic
• +11 more

Dmitriy Traytel is an Associate Professor at the Department of Computer Science (DIKU), Faculty of Science, University of Copenhagen, where he leads the Software, Data, People & Society (SDPS) section. He earned his PhD from TU München under the supervision of Tobias Nipkow in 2015 and previously held a senior researcher position at ETH Zürich's Information Security Group. His primary research interests include interactive theorem proving, runtime verification, logic, automata, decision procedures, and coinduction. He works extensively with the Isabelle/HOL proof assistant, developing foundational theories and verified tools. His work bridges theoretical logic and practical system verification, focusing on correctness, expressiveness, and scalability. The recent publications highlight a strong focus on verified runtime monitoring, formalization of logical systems, and efficient query evaluation. Key themes include the development of formally verified monitoring tools (e.g., TimelyMon, WhyMon, VeriMon), foundational work on corecursion and datatypes in Isabelle, and translations of logical formalisms into executable and verifiable code. Several publications have received distinguished paper awards, indicating high impact in the programming languages and verification communities. Distinguished Paper Award, POPL 2025: 'Barendregt Convenes with Knaster and Tarski: Strong Rule Induction for Syntax with Bindings' Distinguished Paper Award, POPL 2023: 'Admissible Types-to-PERs Relativization in Higher-Order Logic' Distinguished Paper Award, ATVA 2018: 'Optimal Proofs for Linear Temporal Logic on Lasso Words' Best Student Paper Award, FSCD 2016: 'Formal Languages, Formally and Coinductively' Traytel has (co)supervised numerous PhD, MSc, and BSc students, many of whose projects contribute directly to his research agenda in verified systems and formal methods. He is actively involved in the academic community, serving on program committees for POPL, ITP, RV, and CPP, and has chaired conferences such as CPP 2023 and 2024. His tools, including TimelyMon, VeriMon, and WhyMon, are practical outcomes of his research, enabling scalable, explainable, and trustworthy runtime verification. He leads a research group focused on trustworthy stream processing, distributed streaming computations, and explainable monitoring. His work often involves collaboration with researchers at ETH Zürich and other institutions, particularly in the areas of security and monitoring.

Todor IvanovView profile

Researcher

• Big Data
• Distributed Systems
• Benchmarking
• +4 more

Todor Ivanov is a Senior Researcher at the Database and Information Systems (DBIS) Group, Goethe University Frankfurt. His research focuses on Big Data systems, distributed computing, and performance optimization. He holds a Doctorate in Natural Sciences (Computer Science) from Goethe University (2019), a Master's in Distributed Software Systems from TU Darmstadt (2014), and a Bachelor's in Computational Engineering from TU Darmstadt (2009). His work includes designing and evaluating benchmarks for Big Data platforms (e.g., CoreBigBench, ABench), analyzing SQL-on-Hadoop performance, and exploring Spark Structured Streaming. He has published extensively at venues like DBTest, ICPE, and WBDB, contributing to advancements in distributed system benchmarking and cloud resource management. Teaching activities include leading hands-on Big Data labs and workshops since 2014, emphasizing practical training in Hadoop, Spark, and data-intensive applications.