Find Academic Supervisors

Chang LouView profile

Assistant Professor

• Distributed Systems
• Cloud Computing
• Runtime Assurance
• +4 more

Chang Lou is an Assistant Professor in the Department of Computer Science at the University of Virginia, part of the School of Engineering and Applied Science. His research focuses on enhancing cloud system reliability through runtime assurance, distributed systems, and formal verification techniques. He holds a Ph.D. from Johns Hopkins University (2023) and a B.S. from Shanghai Jiao Tong University (2016), with a gap year at IPADS under Prof. Haibo Chen. Education: Ph.D., Computer Science, Johns Hopkins University, 2023 (Advisor: Prof. Peng Huang) B.S., Computer Science, Shanghai Jiao Tong University, 2016 Research Interests: Dr. Lou’s work addresses complex failures in distributed systems, including runtime failure detection, formal verification for ML models, and anomaly mitigation in cloud infrastructure. His lab develops tools like OKLib and RESIN , deployed in production environments such as Microsoft Azure. Recent efforts include NSF-funded research on low-effort runtime assurance for cloud systems. Awards: Recipient of the NSF CAREER Award (2024), NSDI’20 Best Paper Award, and the ACM SIGOPS Dissertation Honorable Mention (2023). Teaching: Teaches CS4740: Cloud Computing and CS6501: Cloud System Reliability at UVA. Courses emphasize distributed systems principles, reliability techniques, and practical project-based learning. Service: Serves on program committees for NSDI’26, SOSP’25, EuroSys’25, and others. Co-organizes workshops on Formal Methods in Networked Systems (SIGCOMM 2024).

Arnold Arz von StraussenburgView profile

Researcher

• Internet of Things (IoT)
• Artificial Intelligence
• Data Integration
• +3 more

Arnold Arz von Straussenburg is a Researcher at the University of Koblenz, Department of Business Information Systems and Smart Data, since October 2022. He holds an M.Sc. and has experience in academic and corporate roles, including student assistantships at the Universities of Koblenz-Landau and Münster, and working as a tutor and working student in software development and verification training. His research focuses on IoT data platforms, AI integration, sensor technologies, and educational technology. He has contributed to projects like responsible LLM-based grading systems, CO2-based occupancy detection in smart spaces, and hybrid architectures for AI-driven chatbots. His work emphasizes practical applications of technology in education and urban environments. Education includes studies in Business Information Systems, reflected in his roles as a member of the Board of Symposium Oeconomicum Münster eV and his academic publications. His research spans IoT interoperability, AI literacy, and high-availability data architectures. Key research trends in his articles include leveraging IoT for smart city solutions, integrating large language models ethically in education and chatbots, and optimizing sensor-based systems for workplace efficiency. He has developed frameworks for evaluating conversational agents and improving data model flexibility in IoT systems. Arnold has no listed scientific awards but has been actively involved in collaborative projects and academic organizations. His work on automated application deployment and sensor-based smart parking demonstrates his interest in both theoretical and applied computing.

Evangelos E. MiliosView profile

Professor

• Visual Text Analytics
• Modelling and Mining of Networked Information Spaces
• Text Mining
• +8 more

Evangelos E. Milios is a Professor in the Faculty of Computer Science at Dalhousie University , Halifax, Nova Scotia. He has been a faculty member since 1998 and leads the MALNIS (Machine Learning and Networked Information Spaces) research group. He is affiliated with the Institute of Big Data Analytics and served as Scientific Director of DeepSense , an innovation hub for ocean data analytics. Education: PhD in Electrical Engineering and Computer Science, MIT (1986) SM & EE, MIT (1983) Dipl. Eng. in Electrical Engineering, NTUA, Greece (1980) His research focuses on visual text analytics, text mining, graph mining, social network analysis, and machine learning . He has made significant contributions to modeling and mining of networked information spaces, with applications in data science and AI. The recent publications reflect a strong trend in data mining, robotics, pattern recognition, and semantic analysis , particularly in log analysis, pose estimation, and information retrieval. His work bridges theoretical algorithms with practical applications in robotics and web technologies. Scientific Awards and Honors: Distinguished Research Professor (2017–2022) Killam Chair in Computer Science (2006–2011) Senior Member, IEEE Professional Engineer, Ontario (1998–2024) He has served in key administrative roles including Associate Dean, Research (2008–2017) and Director of the Graduate Program (1999–2002) . He has supervised numerous graduate students and taught a wide range of courses in AI, machine learning, data science, and networking. His research is supported by major grants and collaborations, including NSERC and industry partnerships. Research Labs and Teams: MALNIS – Focuses on machine learning and networked information spaces. DeepSense – Ocean data analytics and AI innovation. Institute of Big Data Analytics – Cross-disciplinary big data research.

Stefan ĆirkovićView profile

Lecturer

• Cybersecurity
• Artificial Intelligence
• Computer Vision
• +3 more

Stefan Ćirković is a Teaching Associate in the Department of Information Technologies at the Faculty of Technical Sciences in Čačak, University of Kragujevac. His office is located at Saint Sava 65, 32102 Čačak, Serbia. He is currently pursuing doctoral studies in Information Technology (2023–present) at the same institution. Education: Doctoral Academic Studies – Information Technology, University of Kragujevac (2023–present) Master of Academic Studies – Information Technology, University of Kragujevac (2022–2023) Basic Academic Studies – Information Technology, University of Kragujevac (2018–2022) Specialized Program – Cybersecurity, University of Kragujevac (2024) Research Focus: Ćirković specializes in applied artificial intelligence with significant work in cybersecurity, medical imaging, and network systems. His research demonstrates strong emphasis on practical AI implementations including real-time object detection (YOLO algorithm), anomaly detection in networks, federated learning for healthcare applications, and cybersecurity techniques using large language models. His work frequently intersects with medical informatics, particularly in diagnostic applications. Publication Trends: Recent publications (2024–2025) show predominant focus on AI/ML applications across multiple domains. Cybersecurity remains a core theme with novel approaches to web application security and network anomaly detection. Medical applications feature prominently, especially in diagnostic imaging (kidney stones, skin cancer) and hemodialysis optimization. Methodologically, there's consistent exploration of deep learning architectures and real-time systems. Laboratory Affiliations: Associated with the Computer Science Laboratory at the Faculty of Technical Sciences in Čačak. Utilizes institutional resources including the Moodle e-learning platform and Microsoft 365/Teams infrastructure for academic activities.

Gerard de MeloView profile

Professor

• Artificial Intelligence
• Large Language Models
• Natural Language Processing
• +9 more

Gerard de Melo is a Professor and Chair of Artificial Intelligence and Intelligent Systems at the Hasso Plattner Institute (University of Potsdam, Germany). He also serves as a member of the Cognitive Sciences Research Focus and ELLIS Unit Potsdam . Research Interests: AI and Machine Learning Natural Language Processing Cross-modal AI (vision-language models, knowledge graphs) Societal and philosophical aspects of AI Medical Informatics Recommendation Systems Graph Neural Networks Recent Article Trends: His work spans generative AI (Vector Grimoire), medical LLM evaluation (CliMedBench), distributed training (Efficient Parallelization), and social media analysis (WallStreetBets) with a focus on multimodal systems and ethical AI . Scientific Recognition: Rutgers CSGSS Best Professor Award (Teaching, Advising) Best Paper Awards: NeurIPS Workshop, CIKM, EACL LANTERN IEEE TCSE Distinguished Paper Award First Runner-Up ACM WebSci Advising & Grants: Mentored students like Rohan Sawahn (Better World Award) and Maximilian Schall (IEEE TCSE Award). Secured major funding including German BMBF Grant for AI Service Center Berlin-Brandenburg and DARPA SocialSim Program support.

Andreas ZendlerView profile

Professor

• Software Engineering for Cloud Computing
• Data Science / Data Mining
• Empirical Computer Science Didactics
• +9 more

Andreas Zendler serves as Director of the Institute of Computer Science at Ludwigsburg University of Education (2023-25), where he has held various leadership positions since 2004, including Head of the Department of Computer Science (2018-2022) and Director of the Institute of Mathematics and Computer Science (2011-2016). As a Professor (C4) for Computer Science and its Didactics since 2004, he has shaped computer science education programs including the BA/MA degree program in Computer Science. Zendler earned dual doctorates - a Dr. rer. nat. in Practical Computer Science from the University of Potsdam (1997) and a Dr. phil. in Experimental Psychology from the University of Regensburg (1988), followed by his habilitation in Practical Computer Science at the University of Potsdam (2000). His academic journey spans computer science, psychology, and education, creating a unique interdisciplinary foundation for his research. His research focuses on empirical computer science didactics, software engineering for cloud computing, data science, and the digitalization of teaching through SaaS models. He investigates content and process concepts for computer science teaching, compares learning effectiveness of instructional methods, and develops research methodologies specifically for educational contexts with small sample sizes. His work bridges theoretical frameworks with practical classroom applications across STEM disciplines. Zendler's publication record reveals a consistent trajectory from foundational software engineering research toward educational applications. His recent work (2018-2025) demonstrates increasing specialization in computer science education, particularly in competence measurement frameworks like cpm.4.CSE/IRT and their adaptations for small sample sizes. His upcoming publications indicate a strategic pivot toward AI applications, exploring how LLMs and GPTs can transform both data science practices and software engineering education. Throughout his career, Zendler has served as a dissertation reviewer for Practical Computer Science since 2001 and contributed to numerous research projects funded by organizations including the European Union, the Bavarian Research Foundation, and the German Aerospace Center (DLR). His project leadership spans bioinformatics, educational informatics, and software engineering domains. His methodological expertise includes experimental design for teaching research (particularly 3-factorial designs with repeated measurements), statistical evaluation approaches for small samples, and handling missing data in educational contexts. This technical proficiency supports his broader mission to establish rigorous empirical foundations for computer science education.

• Computer Science Education
• Intelligent Tutoring Systems
• Deep Reinforcement Learning
• +3 more

Tiffany Barnes is a Researcher at the North Carolina State University , affiliated with the Department of Computer Science within the College of Engineering . Her work focuses on Computer Science Education , Intelligent Tutoring Systems , and Deep Reinforcement Learning applications in pedagogy. Research interests include: Metacognitive interventions in learning Programming education tools Game-based learning environments Cultural barriers in CS education AI-driven educational technologies Recent publications explore LLM-powered educational tools , Parsons Problems for code comprehension, and Reinforcement Learning in adaptive tutors. Trends highlight student motivation , code trace analysis , and personalized feedback systems . Collaborations span institutions like Duke University , University of North Carolina , and Georgia Tech . Key projects include Jigsaw (problem decomposition) and MerryQuery (LLM for education).

• Program Analysis
• Program Synthesis
• Program Transformation
• +5 more

Kihong Heo is an Associate Professor at the School of Computing, Korea Advanced Institute of Science and Technology (KAIST), where he leads the Programming Systems Laboratory. He received his Ph.D. in Computer Science & Engineering from Seoul National University and previously served as an Assistant Professor at KAIST (2017-2019) and a Post-doctoral Researcher at the University of Pennsylvania (2009-2017). His research focuses on developing program reasoning systems for safe and reliable software, with three main thrusts: AI-based program analysis systems for detecting deep semantic software bugs, general-purpose program simplification systems for secure and efficient software, and scalable program synthesis systems for automatic software generation and repair. His work bridges formal methods, programming languages, and machine learning to address critical challenges in software reliability and security. Prof. Heo's recent publications demonstrate a strong trend toward integrating machine learning techniques with traditional program analysis and verification methods. His research spans compiler correctness (particularly for JavaScript engines), mobile security verification, and automated program transformation. The work on 'Safeguarding Mobile GUI Agent via Logic-based Action Verification' (MobiCom 2025) and 'Optimization-Directed Compiler Fuzzing for Continuous Translation Validation' (PLDI 2025) exemplifies his focus on practical verification techniques for real-world systems. ACM SIGSOFT Distinguished Paper Award (FSE 2025) Amazon Research Award (2024) The Soo-Young Lee Teaching Innovation Award, KAIST (2024) Prize for Excellence in Teaching, KAIST (2024) Best Artifact Award, ICSE (2022) ACM SIGPLAN Distinguished Paper Award, PLDI (2019) Prof. Heo actively mentors graduate students, currently advising three Ph.D. students (Yeonhee Ryou, Taeeun Kim, Sujin Jang) and four Master's students. He serves on program committees for major conferences including ICSE, PLDI, OOPSLA, and SAS, and is an Associate Editor for ACM Transactions on Software Engineering and Methodology (TOSEM). His Programming Systems Laboratory develops tools like Sparrow, a state-of-the-art static analyzer for C programs that applies abstract interpretation techniques to verify the absence of fatal bugs.

• Software Engineering
• Information Security Management
• Enterprise Architecture
• +3 more

Prof. Dr. Carsten Lucke is a faculty member at the Technische Hochschule Mittelhessen within the Department of Mathematics, Natural Sciences and Data Processing (MND), specifically affiliated with the Department of Business Informatics . He serves as Head of the M.Sc. Business Informatics program, Chair of the Master's program committee, and Chair of the admissions committee. His primary research focuses on software engineering , enterprise architecture (with emphasis on TOGAF, NAF, ADMBw), and IT security . Research Interests: Software Architecture, Architectural Modeling, Knowledge Management in Software Projects, Enterprise Architecture Frameworks Expertise: Stakeholder-Oriented Enterprise Modeling, ERP System Integration, PHP-based Software Development His publications reflect systematic approaches to integrating security management with enterprise architecture and addressing architectural complexity in large-scale systems. While no scientific awards are documented in the provided texts, his contributions span multiple empirical studies, conceptual frameworks, and practical implementations in software engineering.

Mohammad ZubairView profile

Professor

• High performance computing
• digital libraries
• autonomic computing
• +3 more

Mohammad Zubair is a Professor in the Department of Computer Science at Old Dominion University's College of Sciences, specializing in high performance computing, digital libraries, autonomic computing, and peer-to-peer networks. His work bridges scientific computing and information systems with significant federal and industry collaborations. Education: Ph.D. in Computer Science, Indian Institute of Technology Delhi (1987) B.S. in Electrical & Electronics Engineering, University of Delhi (1981) Research Interests: Dr. Zubair's expertise centers on high performance computing for scientific applications and large scale data analytics. He develops scalable algorithms for scientific simulations (e.g., particle physics, structural health monitoring) and advances digital library systems through autonomic computing, collaborative classification, and metadata extraction. His work integrates parallel computing with domain-specific challenges in finance, physics, and digital preservation. Publication Trends: His recent publications (2017-2008) show strong focus on GPU-accelerated scientific computing (particle colliders, structural monitoring) and computational finance (binomial option pricing), while maintaining digital library research in collaborative classification and metadata extraction. The work increasingly addresses energy efficiency in cloud systems and leverages parallel architectures from multicore CPUs to GPUs. Scientific Awards: IBM Faculty Award (2006) IBM Faculty Award (2005) Grants and Funding: Dr. Zubair has secured over $800,000 in research funding since 2004, including projects like 'Design AMD Impl Scalable Opt Kernels for Lrg Sc Simu USN Fund3d On Emer' ($24,890, 2017-2018), 'Graduate Research Award Program On Public Sector Aviation Issues' ($35,519, 2016-2018), and foundational digital library work with the Library of Congress and NASA. He frequently collaborates with K. J. Maly and R. Mukkamala on autonomic systems and metadata extraction. Labs and Teams: His research integrates with ODU's high performance computing initiatives and digital library projects, particularly through collaborations with federal agencies (NASA, Library of Congress) and industry partners like IBM. Current work focuses on GPU-accelerated scientific simulations and energy-efficient cloud management systems.

• Machine Learning
• Computer Vision
• Data Compression
• +3 more

Don Adjeroh is a Professor and Associate Chair in the Lane Department of Computer Science and Electrical Engineering at West Virginia University (WVU). He serves as Graduate Coordinator for CS programs and has led multiple NSF-sponsored projects including Bridges in Digital Health and Multi-Scale Integrative Approach to Digital Health . His research focuses span Machine Learning , Bioinformatics , Computational Biology , and Data Compression . NSF CAREER Award recipient Collaborated with institutions like University of Central Florida and University of Canterbury Led workshops on Plant Image Analysis and Long Non-Coding RNA research His academic contributions include co-authoring the book The Burrows Wheeler Transform: Data Compression, Suffix Arrays, and Pattern Matching (Springer, 2008) and developing software tools like BSMP and PSA for data compression and protein family modeling. He teaches graduate-level courses in String Algorithms and Information Theory , with former projects involving undergraduate and graduate students in DNA microarray image processing and 3D video compression research. Key Scientific Awards : NSF CAREER Award NASA-WV Space Consortium funding WV-EPSCOR grants NSF-CITeR support DOD-ONR/DHS/DOJ/NIJ/NHPRC sponsorships

Thomas P. JensenView profile

Professor

• Program Analysis
• Static Program Analysis
• Abstract Interpretation
• +7 more

Thomas P. Jensen is a Directeur de recherche (Research Director) at INRIA, the French National Institute for Computer Science and Applied Mathematics. He has been affiliated with INRIA since 2010, previously serving as a researcher at CNRS (French National Center for Scientific Research) from 1993 to 2010. Currently, he leads the Epicure project-team on semantic analysis and compilation for secure execution platforms, and since 2022, he serves as director of the Laboratoire d'Excellence CominLabs. His educational background includes a Cand. scient. in Computing and Mathematics from the University of Copenhagen (1990), a PhD from Imperial College, University of London (1992), and a Habilitation à diriger des recherches from Université Rennes 1 (1999). Dr. Jensen's research focuses on program analysis and software security , with particular expertise in static program analysis based on abstract interpretation theory, language-based security, information flow control, and Software Fault Isolation. His work bridges theoretical foundations with practical applications in secure compilation and program verification. His recent publications reveal a strong trend toward formally verified security mechanisms, particularly in the integration of Software Fault Isolation with verified compilers like CompCert. His research spans both theoretical aspects of program semantics and practical security applications, with increasing emphasis on algebraic data types, relational analysis, and formal verification of program transformations. Best paper award at GPCE 2018 for Verification of High-Level Transformations with Inductive Refinement Types Editor of Strategic research and innovation roadmap for the SPARTA cybersecurity competence network (2022) Dr. Jensen has been actively involved in numerous research projects including JavaSec, Decert, Seccloud, AJACS, Anastasec, SPARTA, and SecurEval. His leadership roles in research teams and projects demonstrate his significant contributions to both academic research and practical cybersecurity applications. His work often involves collaboration with researchers across Europe on large-scale cybersecurity initiatives. He maintains strong connections with the international programming languages and security research communities, regularly publishing in top venues including PLDI, POPL, ICFP, SAS, ESOP, and CSF, and participating in program committees for major conferences in these fields.

Ryan MarcusView profile

Assistant Professor

• Machine Learning
• Query Optimization
• Index Structures
• +4 more

Ryan Marcus is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research focuses on integrating machine learning into data management systems to create adaptive tools that optimize hardware utilization, invent novel processing strategies, and interpret user intentions. Currently based in Office 407, Amy Gutmann Hall, he actively explores query optimization, index structures, intelligent clouds, programming language runtimes, program synthesis for data processing, and reinforcement learning applications to systems challenges. Key research themes include machine learning for databases , learned query optimization , intelligent cloud systems , and blockchain adaptability . Scientific achievements include the Best Paper Award at SIGMOD '21 for the Bao system and the development of AutoSteer, a cross-database learned query optimizer. Notable PhD advisees co-advised include Peizhi Wu (with Zack Ives), Jeffrey Tao (with Andrew Head), and Zixuan Yi (with Zack Ives). His recent work, presented at venues like VLDB and SIGMOD, emphasizes scalable LLM-augmented data systems (ScaleLLM), robust cardinality estimation, and adaptive Byzantine fault-tolerant consensus (BFTBrain). For full system evaluations, he created testing environments such as BFTGym. Contact: rcmarcus@seas.upenn.edu

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.

Thomas LaTozaView profile

Associate Professor

• Software Engineering
• Human-Computer Interaction
• Crowdsourced Programming Environments
• +8 more

Thomas LaToza is an Associate Professor in the Department of Computer Science at the George Mason University, School of Computing . His research focuses on the intersection of software engineering and human-computer interaction, exploring how developers interact with code and designing novel software development methodologies. He has pioneered crowdsourced programming environments and microtask programming frameworks, with notable projects including RulePad for checkable design rules and Hypothesizer for hypothesis-based debugging. His work emphasizes empirical studies of developer behavior, tool impact analysis, and integration of AI assistants in software development. His recent publications highlight trends in AI-driven development , debugging mental models , and developer information seeking . He has received recognition including the George Mason University Teacher of Distinction award and has served on the editorial board of Empirical Software Engineering . His advising includes PhD students Ruochen Wang, Mainul Hossain, and alumni Abdulaziz Alaboudi, Emad Aghayi, David Samudio, Sahar Mehrpour, and Maryam Arab. He co-founded the OurCode developer tools startup, translating academic research into industry applications.