جستجوی استاد راهنمای دانشگاهی

Hamid Bagheri is an Associate Professor in the School of Computing at the University of Nebraska-Lincoln, where he conducts cutting-edge research at the intersection of software engineering, security, and formal methods. He also serves as a faculty associate of the Institute for Software Research (ISR) at the University of California, Irvine, and co-directs the ESQuaReD Lab (Engineering Software Quality and Reliability through Deductive methods), which focuses on ensuring software quality through rigorous engineering approaches. Dr. Bagheri's research spans several critical areas including security of mobile devices and IoT systems, scaling formal verification with machine learning, software analysis and testing, dependable cyber-physical systems, and automated program repair and fault localization. His work combines theoretical rigor with practical applications, addressing real-world challenges in software security and reliability through publications in top venues like ICSE, FSE, ASE, and IEEE/ACM Transactions journals. His recent publications demonstrate a strong trend toward integrating machine learning techniques with formal methods to enhance software analysis capabilities. This includes leveraging transformers for database design optimization, neuro-symbolic approaches for certified software synthesis, and large language models for repairing formal specifications, reflecting his ability to adapt to emerging technologies while maintaining a strong theoretical foundation. Dr. Bagheri has received numerous prestigious awards including the EPSCoR FIRST Award, the NSF CISE Career Research Initiation Initiative Award, multiple Distinguished Paper Awards from top conferences, and teaching honors such as the SoC Student Choice Outstanding Teaching Award (2023-2024) and the CSE Outstanding Teaching Award (2020-2021). As an advisor, Dr. Bagheri has mentored several successful PhD students including Mohannad Alhanahnah (who joined Chalmers University as a tenure-track faculty member in Fall 2024) and Clay Stevens (who joined Iowa State University as a tenure-track faculty member in Fall 2023). His research has been supported by significant grants from NSF and other funding agencies, with recent work focusing on automated specification repair, security analysis of IoT systems, and verification of network protocols. The ESQuaReD Lab, which Dr. Bagheri co-directs, brings together a multidisciplinary team focused on addressing critical challenges in software quality and reliability through innovative engineering approaches and formal analysis techniques, with applications spanning mobile security, IoT systems, and cyber-physical systems.

• Software Engineering
• Security of Mobile Devices and IoT Systems
• Formal Methods
• +۴ مورد دیگر

Abhishek Tiwari is an Associate Professor of Software Engineering at the Maersk Mc-Kinney Moller Institute, University of Southern Denmark, where he was promoted from Assistant Professor in September 2025. His academic journey includes positions as Senior Researcher at Software Institute, USI Lugano (2024), Senior Researcher at University of Passau (2022-2023), and Research Fellow at National University of Singapore (2020-2021). He completed his PhD in Software Engineering at University of Potsdam, Germany in 2019 under supervision of Prof. Dr.-Ing Christian Hammer. His research focuses on the intersection of programming languages and software engineering, with particular expertise in static program analysis, language-based security, information flow control, and automated program repair. His work has significant practical applications in Android security and privacy, addressing critical challenges in information flow analysis, vulnerability detection, and program repair. His research has evolved from foundational work on Android security mechanisms like PendingIntent analysis and anti-theft frameworks to more recent sophisticated approaches for information flow security repair and multilingual program analysis. Trends in his publication record show a consistent focus on Android security challenges, with increasing sophistication in analysis techniques from 2017 through 2025. His work spans theoretical foundations of program analysis while maintaining strong practical relevance to real-world Android security issues. Recent publications demonstrate growing interest in multilingual program analysis and formal specification challenges. ACM SIGSOFT Distinguished Paper Award (MOBILESoft 2023) Dr. Tiwari actively contributes to the academic community through program committee service for major conferences including ASE (2024, 2025), ICSE (2025), and ISSTA (2022-2024). His teaching portfolio includes Advanced Software Engineering Methodologies (2024), Automated Program Repair (2022-2023), and Mobile Security (2022) courses. He has received research funding from prestigious sources including Deutsche Forschungsgemeinschaft (DFG), German Federal Ministry of Education and Research, and EIT Digital for projects related to programming principles for privacy, SmartPriv, and SMAPPER.

• Static Program Analysis
• Android Security
• Language-Based Security
• +۴ مورد دیگر

Iftekhar Ahmed is an Associate Professor in Informatics at the Donald Bren School of Information and Computer Science, University of California, Irvine. His research focuses on software engineering, particularly combining software testing, static analysis, and machine learning to develop better tools and techniques for software quality assurance. His educational background includes: PhD in Computer Science (2018) from Oregon State University, advised by Carlos Jensen BSc in Computer Science & Engineering (2007) from Shahjalal University of Science and Technology Dr. Ahmed's research interests center on software testing, static analysis, and the application of machine learning to software engineering problems. He has made significant contributions to mutation analysis, particularly in scaling this technique for real-world software systems. His work often bridges theoretical advances with practical applications, focusing on how to make software testing more effective and efficient for developers. He leads the STAIRS (Software Engineering & Testing Using Artificial Intelligence for Reliable Software) research group at UCI, where his team explores innovative approaches to software reliability through AI and machine learning. His recent publications demonstrate a strong trend toward integrating artificial intelligence with traditional software engineering practices. There's a clear focus on applying machine learning to code analysis, commit message generation, and bug detection, while maintaining rigorous empirical validation through studies of real-world software projects and developer practices. His work spans multiple domains including web accessibility, quantum computing, and Jupyter notebooks, showing both depth in core software engineering topics and breadth across application areas. Dr. Ahmed has received several prestigious awards: IBM Ph.D. Fellowship for academic year 2016-2017 Graduate School tuition relief Scholarship for academic year 2016-2017 IBM Ph.D. Fellowship for academic year 2017-2018 Actively involved in the academic community, Dr. Ahmed serves on program committees for major software engineering conferences including ASE, ICSE, and ESEC/FSE. He is currently accepting PhD students into his research group and emphasizes mentorship and professional development. His research has been supported by various grants that enable his team to explore innovative approaches to software testing and analysis. Dr. Ahmed leads the STAIRS research group at UCI, which focuses on developing AI-powered techniques for software testing and reliability. The group collaborates with industry partners and academic institutions to ensure their research addresses real-world challenges in software development. Current projects include improving mutation testing scalability, analyzing code smells in emerging domains like quantum computing, and developing tools for accessibility testing.

• Software Engineering
• Software Testing
• Machine Learning for Software Engineering
• +۳ مورد دیگر

Xiaolou Yang is a Professor at the Lariccia School of Accounting & Finance, Youngstown State University, specializing in corporate finance, banking, and risk management. His expertise includes credit analysis, financial liquidity, and executive compensation, with teaching responsibilities in Financial Analysis, Business Finance, and Corporate Finance. His educational background comprises: Ph.D. in Financial Economics, University of Texas at Austin (2006) M.S. in Financial Economics, University of Texas at Austin (2002) M.A., Jilin University (1999) Dr. Yang's research examines critical finance domains through empirical methodologies, including CEO compensation structures, banking risk incentives, and portfolio optimization models. His work frequently analyzes Chinese financial firms and U.S. labor market data, bridging theoretical frameworks with real-world applications in corporate governance and monetary policy. Publication trends reveal sustained focus on banking risk (2009-2017) and evolving exploration of default risk measurement (2020-2024), consistently appearing in journals like the International Review of Economics and Finance and the Quarterly Review of Economics and Finance. His interdisciplinary approach connects finance with labor economics, particularly regarding technology's impact on wage premiums. Major recognitions include: Four YSU Research Professorships (2009-2015) YSU Distinguished Professorship Award in Scholarship (2013-2014) U.S. Department of Education Grant (2009) Best Paper Award at The Global Commerce Forum (2009) Dr. Yang has secured continuous research funding through YSU Faculty Development Awards and University Research Council Grants, supporting empirical investigations into financial markets and corporate behavior. His journal reviewing activities for publications including the Journal of Banking and Finance demonstrate active engagement with the academic community.

• Financial Economics
• Corporate Finance
• Banking
• +۴ مورد دیگر

Timo Jakobi is a Professor at Nuremberg Institute of Technology's Faculty of Digital Transformation, teaching Usable Security and Privacy, Human-Centered Design, and Computer Science/Programming. He actively supervises Bachelor theses and serves on conference committees for major academic events. His research focuses on: Explainability of AI systems Deep Fake Detection methodologies Usable Security and Privacy frameworks AI for Usability Engineering applications AI compliance with data protection regulations Data Literacy enhancement tools Human-Centered Design principles Current projects involve developing browser-based deepfake detection systems and real-time fact-checking applications for social networks, emphasizing empirical user studies showing 47.16% baseline detection rates. Jakobi chairs workshops for GROUP 2025 and Communities & Technologies 2025 conferences while reviewing for ACM publications and the iCOM Journal of Interactive Media.

• Explainability of AI
• Deep Fake Detection
• Usable Security and Privacy
• +۴ مورد دیگر

Alexander Rakhlin is a Professor in the Department of Brain & Cognitive Sciences at MIT, with affiliations at the MIT Statistics and Data Science Center and the Institute for Data, Systems, and Society (IDSS). His research bridges statistics, machine learning, and optimization, focusing on online prediction, reinforcement learning, and decision-making under uncertainty. He holds a PhD from MIT (2006) and previously served as an associate professor at the University of Pennsylvania (Wharton School) before joining MIT in 2023. Research interests include reinforcement learning, neural networks, overparametrized models, and the theoretical foundations of machine learning. His work addresses challenges in sequential decision-making, robust algorithms, and the statistical complexity of modern models like large language systems. Notable contributions include the Decision-Estimation Coefficient framework for RL analysis and insights into benign overfitting in overparametrized settings. Recent publications emphasize algorithmic advances in online learning, privacy-aware contextual bandits, and structural watermarking for language models. His theoretical work often unifies principles from information theory, optimization, and statistical learning. Lab affiliations include the Laboratory for Information and Decision Systems (LIDS), where he collaborates on interdisciplinary projects. No specific awards or grants are explicitly listed in the provided texts.

• Machine Learning
• Statistics
• Optimization
• +۳ مورد دیگر

Robert Gerwarth is Full Professor of Modern History at University College Dublin's School of History. A DPhil graduate from Oxford and FRHistS, he specializes in German/Central European political culture (1871-1945), memory studies, and violence history. Research examines: Post-WWI imperial collapse and violence Paramilitarism in interwar Europe Transnational dimensions of WWII resistance/collaboration Historiography of peace treaties Awards include Royal Irish Academy membership, Guggenheim Fellowship, Fraenkel Prize, and ERC grants. Administrative roles encompassed Director of Computer Engineering program and Associate Chair for Graduate Studies.

• Modern European History
• Political Violence
• Memory Studies
• +۲ مورد دیگر

Gunnar Stevens is a Professor and Divisional Director of IT-Security and Consumer Computing at the University of Siegen's Faculty of Information Systems and New Media. His research focuses on consumer-oriented technologies, including privacy-enhancing systems, human-robot interaction, sustainable mobility solutions, and explainable AI. Dr. Stevens' work bridges technical security with human-centered design, investigating topics such as algorithmic accountability in dating apps, digital sovereignty, privacy norm formation through gamification, and sustainable transportation behavior. Recent projects examine the digitalization of household administrative labor and renewable energy preferences for EV charging. He has led numerous projects on usable security policies, smart home intelligibility, and trust in voice assistants. His methodological approach combines design studies, living lab implementations, and empirical evaluations of technology adoption.

• Consumer Informatics
• Human-Computer Interaction
• Privacy
• +۳ مورد دیگر

Margarita Esau is a Researcher at the University of Siegen, affiliated with Faculty III (Business Informatics and New Media) and the Department of Business Informatics focusing on IT Security and Consumer Informatics. She holds an M.A. in Human-Computer Interaction (HCI) from the University of Siegen, complementing her Bachelor’s degree in Media Technology from Deggendorf University of Applied Sciences. Her research emphasizes sustainable practices, voice interaction, and user experience design, particularly in food consumption and smart living technologies. Key projects include investigations into smart refrigerators, vegan practice transformation via ICT, and voice interaction’s role in fostering embodied knowledge. She contributes to initiatives like the “Smart Live” and “Ganesha” projects, exploring how technology can enhance consumer empowerment and ethical decision-making. Esau’s publications span topics such as digital taste mediation, human-centered digital sovereignty, and multimodal interaction design. Her work often bridges technical innovation with societal impact, addressing issues like AI transparency and sustainable household practices. Collaborations include designing smart mirrors for morning routines and exploring networks of practices to inform interconnected system design. Her research also extends to prison resocialization through psychosocial ICT and the ethical implications of AI in everyday technologies. Esau’s interdisciplinary approach integrates empirical studies, design thinking, and critical theory to advance HCI’s role in shaping responsible technological futures.

• Food Consumption
• Voice Interaction
• User Experience (UX) Design
• +۴ مورد دیگر

Jim Alves-Foss is a University Distinguished Professor of Computer Science at University of Idaho's College of Engineering, specializing in high-assurance computing systems and cybersecurity. Core research domains: Formal methods for security verification Operating system security architectures Critical infrastructure protection Automated security policy enforcement Publications demonstrate consistent focus on: Rigorous verification of security properties (e.g., seL4 kernel) Development of automated analysis tools (fuzzing, SDN policy) Industrial control system protection frameworks Adversary-aware risk assessment methodologies

• Cybersecurity
• Formal Verification
• Operating Systems
• +۲ مورد دیگر

Imran Asif is an Assistant Professor of Teaching in the Department of Computer Science and Engineering at the University at Buffalo (SUNY), part of the School of Engineering and Applied Sciences. He holds a PhD in Computer Science and Engineering from UB (2022), an MS in Software Engineering from IIT, University of Dhaka (2014), and a BS in Information Technology from the same institution (2012). His research focuses on optimizing software resource utilization via code smell refactoring in cloud environments, blending cloud computing, software engineering, and machine learning. He has received grants including a 2024 NSF EAGER award ($350k) and a 2022 Best Presenter Award at ESSE. Teaching is central to his work, emphasizing student success through mentorship and curriculum design. He has taught courses like Operating Systems and Software Engineering at UB and CSUSM, advising over 200 students. His academic experience includes roles as an ABET committee member (CSUSM), research assistant at UB, and intern at IBM T.J. Watson. Key honors include the CSE Chair’s Fellowship (2017) and academic excellence gold medal (2015). His publications span 23 refereed articles and conference proceedings, addressing topics like code smell analysis, cloud resource optimization, and fault-tolerant middleware. He actively participates in workshops on teaching strategies and has presented at venues including IEEE CloudCom and ENASE. Imran leads efforts in integrating teaching and research, advocating for sustainable software practices and student-centered learning. His lab focuses on empirical studies of code refactoring impacts and cloud resource efficiency, with ongoing projects funded by NSF and industry collaborations.

• Software Engineering
• Cloud Computing
• Machine Learning
• +۳ مورد دیگر

Adnan Ashraf serves as a Senior University Lecturer at the Department of Information Technology within the Faculty of Science and Engineering at Åbo Akademi University, Finland. His academic profile spans software engineering, cybersecurity, and applied artificial intelligence with emphasis on maritime systems and drone technologies, supported by extensive EU and national research funding. His research portfolio demonstrates deep expertise in: Security frameworks for DevOps and IoT networks Machine learning applications in performance testing Edge computing optimization for resource-constrained environments Swarm robotics navigation and collision avoidance Systematic literature reviews in emerging software domains Analysis of his 46+ publications (2010-2024) reveals consistent focus on bridging theoretical models with practical implementations. Key trends include security enhancement through SDN controllers, reinforcement learning for UAV path planning, and systematic reviews of API documentation practices. His work consistently addresses real-world challenges in critical infrastructure domains with strong empirical validation. Dr. Ashraf has secured significant research funding including EU Horizon 2020 VeriDevOps (as Principal Investigator) and MegaM@Rt2 projects (as Co-Investigator), demonstrating leadership in collaborative research addressing DevOps security and runtime modeling challenges. While specific student supervision details aren't publicly enumerated, his project leadership indicates active mentorship in software engineering research.

• Software Engineering
• Cybersecurity
• Machine Learning
• +۷ مورد دیگر

Jens Binder is an Associate Professor in the Department of Psychology at Nottingham Trent University's School of Social Sciences. He serves as course leader for the MSc Cyberpsychology and Deputy Director of Doctoral Programmes for Social Sciences. His teaching focuses on Social Psychology and Cyberpsychology with an emphasis on quantitative methods. He regularly supervises PhD, Masters, and undergraduate research projects. Jens holds a doctoral degree from the University of Tuebingen and has held post-doctoral positions at the Universities of Sussex and Manchester. His research investigates online social behaviors, cyberpsychology, and human-media interaction, with core focuses on online social competence, safety, and wellbeing. Recent projects explore radicalization pathways, video call engagement, and smartphone distraction. His research employs experimental and survey-based methodologies, with particular interest in social network analysis and multi-level modeling. Collaborations include the Ministry of Justice (via CT-ARC) and DSTL. He has advised national research councils and edited the Palgrave Studies in Cyberpsychology book series (11 titles). Jens currently supervises three PhD projects examining AI-generated speech recall, music's impact on pseudo-profound statements, and short-video attention processes. Completed PhD projects include studies of Facebook networks and radicalization among extremists. He has delivered international summer schools on cyberpsychology and serves on Ofcom's 'Making Sense of Media' group. His work bridges evolutionary psychology with digital technologies, addressing societal challenges through empirical and theoretical innovation.

• Cyberpsychology
• Online Social Interaction
• Human-Computer Interaction
• +۷ مورد دیگر

Raffi Khatchadourian is an Associate Professor in the Department of Computer Science at Hunter College, part of the City University of New York (CUNY). He specializes in Software Engineering, Programming Languages, and Machine Learning Systems. His research focuses on automated software evolution, secure software engineering, and refactoring techniques, with notable contributions to tools like Hybridize Functions and μAkka. He holds a PhD in Computer Science from Ohio State University, along with MS and BS degrees in Computer Science from Ohio State and Monmouth University, respectively. His educational background includes advanced training in Computer Science, with a focus on theoretical and practical aspects of software systems. His research interests span automated refactoring, program analysis, and the integration of machine learning with traditional software engineering practices. Key projects include frameworks for assessing safety in deep learning systems (ReLESS) and mutation testing for actor concurrency in Akka (μAkka). Khatchadourian’s work emphasizes tool development and open-source contributions, such as the Common-Eclipse-Refactoring-Framework and Hybridize-Functions-Refactoring. He has secured grants from the National Science Foundation (NSF) for combating technical debt in machine learning systems and improving imperative-to-graph execution migration. His teaching spans graduate courses on programming languages and software engineering, reflecting his commitment to advancing computational education. His lab, Ponder Lab, focuses on empirical studies and tool creation for software evolution and concurrency. Notable grants include SHF-funded projects addressing practical analyses and safe transformations in deep learning programs. His research has addressed challenges in migrating deep learning code to graph execution, optimizing Java 8 streams, and analyzing legacy software refactoring.

• Software Engineering
• Programming Languages
• Machine Learning Systems
• +۳ مورد دیگر

Filippo Lanubile is a Full Professor and Head of the Department of Computer Science at the University of Bari, where he leads the Collaborative Development Research Group (Collab). He co-founded the academic spin-off PeoplewareAI and previously served as a research associate at the University of Maryland. His research spans human factors in software engineering, AI-augmented development, engineering of AI-enabled systems, and health informatics. Major publications focus on MLOps, emotion recognition in development, and LLM applications in software engineering. Lanubile received the NASA Group Achievement Award (1996), IBM Eclipse Innovation Award (2006), IBM Faculty Award (2008), and Microsoft Research Software Engineering Innovation Foundation Award (2011). He chaired the ACM/IEEE ESEM symposium (2020-2021) and IEEE Software Advisory Board (2020-2023).

• Software Engineering
• Artificial Intelligence
• Human-Computer Interaction
• +۲ مورد دیگر