جست‌وجوی استاد و پژوهشگر

Eray Tüzünمشاهده پروفایل

دانشیار

• Software Analytics
• Mining Software Repositories
• Empirical Software Engineering
• +۳ مورد دیگر

Eray Tüzün is an Associate Professor of Computer Engineering at Bilkent University, leading the BILSEN Research Group focused on Software Engineering and Data Analytics. He holds a PhD in Information Systems alongside bachelor's and master's degrees in Computer Science. With over 20 years of industry experience, he previously worked at Havelsan, Microsoft, Howard Hughes Medical Institute, and CWRU Genomics Center in roles spanning software engineering and project management. His research emphasizes software analytics, mining software repositories, and empirical software engineering practices. He actively contributes to the International Software Engineering Research Network (ISERN) and holds certifications such as PSM, PSPO, and MCSD. Tüzün advocates for bridging educational and industrial gaps through interactive teaching methods and serious games for developer training. His work on code review practices, traceability tools (e.g., RSTrace+), and bus factor estimation (BFSig, Bus Factor Explorer) highlights practical software engineering challenges. He explores applications of AI in code generation evaluation and process improvement. Tüzün's BILSEN Lab engages in projects like BugCraft (bug reproduction) and SmartDelta (automated QA). He maintains active IEEE and ACM SIGSOFT memberships and contributes to conferences like ICSSP and ICGSE. His educational initiatives include board games (ToolStackers) and plugins for teaching DevOps and version control. Current research trends focus on LLM applications in code review, data cleaning impacts, and developer productivity metrics. Grants and collaborations involve industrial partnerships and national/international research networks. He supervises graduate students in masters/PhD programs and undergraduate volunteers, emphasizing empirical studies and tool development. Future work includes expanding AI-driven software analytics and educational technology integration.

Sasha Vassarمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence in Education
• Cognitive Load Theory
• Educational Technology
• +۳ مورد دیگر

Dr. Sasha Vassar is a Senior Lecturer at the School of Computer Science and Engineering, University of New South Wales (UNSW), specializing in the intersection of Artificial Intelligence and Education. She holds a PhD from UNSW and has transitioned from industry roles in engineering process optimization and critical infrastructure software development to academia, driven by her passion for innovative pedagogical approaches. Her research focuses on leveraging Large Language Models (LLMs) to enhance programming education, particularly through compiler-integrated conversational AI systems like the Debugging C Compiler (DCC) . This work, which integrates cognitive load theory principles, has received recognition including the 2024 Australian Financial Review Higher Education Award for Teaching and Learning Excellence. Sasha is a UNSW Nexus Fellow and Associate Fellow of the Higher Education Academy, emphasizing her contributions to educational innovation. Her publications (15 most recent listed) span AI-driven education tools, cognitive load theory applications in VR, and compiler-based error explanation systems. Beyond research, she advocates for scalable support of at-risk students in introductory programming courses and has pioneered adaptive learning resources in engineering design education through earlier work on virtual workshops. Awards: 2024 Australian Financial Review Award, 2022 UNSW Teaching Excellence Award Grants/Research: Focuses on LLM integration in compilers, cognitive load theory, and scalable educational technologies Labs/Teams: Leads the DCC project team and collaborates with UNSW's educational technology initiatives

Hong Jin Kangمشاهده پروفایل

مدرس

• Active Learning
• Artificial Intelligence
• Software Engineering
• +۳ مورد دیگر

Hong Jin Kang is a Lecturer in the School of Computer Science at The University of Sydney. His research focuses on improving developer productivity through human-centric AI tools, particularly in Active Learning and AI-driven Software Engineering. Prior roles include postdoctoral fellowships at UCLA and a PhD at Singapore Management University (SMU), advised by Prof. David Lo. His work spans vulnerability detection, program transformation, and software supply chain security, with practical industry impact through CVE discoveries and deployed techniques. Education : PhD in Computer Science, Singapore Management University (2023) Postdoctoral Research, University of California, Los Angeles (UCLA) Research Interests : Active Learning for code analysis and vulnerability management AI and ML integration into software development tools Cybersecurity in software supply chains Program transformation and legacy code modernization His publications address challenges in automated tool development, including vulnerability identification (e.g., CHRONOS), code model compression, and adversarial specification mining. Recent work emphasizes sustainability in AI and human-in-the-loop systems. Awards : Distinguished Reviewer Award for ICSE 2025 Advising & Grants : Actively mentoring PhD students in software engineering and AI, with a focus on curiosity-driven candidates. His research is supported by collaborations with industry and academic institutions. Labs/Teams : Pioneers tools like Coccinelle4J for Java program transformation and collaborates on projects such as VulCurator and HERMES for vulnerability analysis.

Dakuo Wangمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Human-AI Collaboration
• Natural Language Processing
• +۳ مورد دیگر

Dakuo Wang is an Associate Professor at Northeastern University, jointly appointed at Khoury College of Computer Sciences and the College of Arts, Media and Design. He is also a Visiting Associate Professor at Stanford University and previously held roles at IBM Research, MIT-IBM Watson AI Lab, and Stanford Institute for Human-Centered AI. His research focuses on the intersection of human-computer interaction (HCI) and artificial intelligence (AI), specifically on human-AI collaboration, natural language processing, and healthcare applications. He aims to democratize AI access for individuals and organizations through collaborative systems. Recent publications highlight his work on AI in healthcare (e.g., sepsis prediction, mental health analytics), collaborative writing tools, and feature engineering with human-AI synergy. His lab, NEU HAI, develops human-centered AI solutions for real-world challenges. Dakuo Wang has received recognition as an ACM Distinguished Speaker and contributes to organizing committees in ACM and CHI venues. His work bridges data science, NLP, and healthcare informatics, emphasizing user experience and ethical AI deployment.

Kostadin Damevskiمشاهده پروفایل

دانشیار

• Software Engineering
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Kostadin Damevski is an Associate Professor at Virginia Commonwealth University's Department of Computer Science, part of the College of Engineering. He leads the Software Improvement (SWIM) Lab, focusing on leveraging machine learning and NLP to enhance software engineering practices. His research spans developer productivity tools, cybersecurity, and open source sustainability, with notable projects in emotion analysis in developer communication and conversational interfaces for software tasks. Research Highlights: Emotions in Software Engineering : Analyzing toxic communication in open source projects to improve collaboration quality. Conversational Interfaces : Designing chat-based tools for bug reporting, documentation search, and end-user programming of cyber-physical systems. Vulnerability Data Quality : Enhancing accuracy of software vulnerability databases through empirical studies. Key Contributions: Created annotated datasets for GitHub issue threads and Gitter chat studies. Developed StackInTheFlow recommendation system for Stack Overflow navigation. Recipient of the 2005 Best Paper Award at IPDPS. Advisement & Collaboration: Actively mentoring PhD and undergraduate researchers, including Mia Mohammad Imran and Viral Sheth. Seeks industry partnerships to advance applied research in secure coding and developer tooling. Labs/Teams: SWIM Lab (Software Improvement Lab) focuses on practical solutions for software engineering challenges through empirical research and tool development.

Konstantinos Skianisمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۱۴ مورد دیگر

Konstantinos Skianis is an Assistant Professor in the Department of Computer Science and Engineering at the University of Ioannina, Greece. His research lies at the intersection of Natural Language Processing, Machine Learning, Deep Learning, and Data Science, with a focus on representations, regularization, robustness, and reasoning (the 4R's). He has founded the Greek AI company BLUAI and co-founded PrimeHost AI, a startup automating guest conversations using AI. Ph.D. in Computer Science, École Polytechnique, France (2015–2019) M.Sc. in Math, Computer Vision, and Machine Learning, École Normale Supérieure de Cachan, France (2014–2015) B.Sc. in Computer Science, Athens University of Economics and Business, Greece (2013) His research interests span Natural Language Processing , Graph Neural Networks , Large Language Models , Representation Learning , and Medical AI . He investigates robustness in neural networks, reasoning with LLMs via pseudo-code, and multilingual applications in low-resource settings like Greek. His recent work includes digital twins for industrial optimization, climate adaptation in ports using ML, and mental health detection via LLMs. The 15 most recent publications reflect a strong trend in graph-based learning , LLM reasoning , representation robustness , and applied AI in healthcare and industry . His work combines theoretical rigor with practical applications, often involving real-world datasets from public health, industrial processes, and climate science. Best Paper Award, TextGraphs at NAACL 2018 OpenAI Researcher Access Program (2024) He has advised or collaborated on several student projects and competitions, including the Data Mining Cup and Kaggle challenges. He has secured research support through the OpenAI credits program. He has taught courses in Linear Algebra, Data Mining, and advanced labs on graph and text learning at ENS Paris-Saclay. He is actively involved in the research community as an Area Chair for ARR (2025) and reviewer for top-tier venues like NeurIPS, ICLR, ACL, and EMNLP. He leads or contributes to open-source tools such as GoWvis (text visualization), GraKeL (graph kernel library), and SpreadViz (influence spread visualization). His work on Greek NLP resources has helped advance language technology for low-resource languages.

Christina Pöpperمشاهده پروفایل

دانشیار

• Cyber Security
• Privacy
• Information and Communication Security
• +۱۲ مورد دیگر

Christina Pöpper is an Associate Professor of Computer Science and Program Head of Computer Science at New York University Abu Dhabi (NYUAD), with a Global Network Associate Professor appointment at the Courant Institute of Mathematical Sciences, NYU. She leads the Cyber Security & Privacy (CSP) Lab and is Co-PI of the Center for Cyber Security at NYUAD. Education: PhD in Computer Science, ETH Zurich MSc (Diploma) in Computer Science, ETH Zurich Her research focuses on cyber security and privacy, with emphasis on wireless and mobile communication security, including 5G/nextG networks, secure localization, and privacy-preserving mechanisms. Her work combines systems and security approaches to build secure systems where cryptography alone is insufficient. She has published extensively in top venues such as USENIX Security, NDSS, IEEE S&P, and ACM CCS. The recent research articles highlight a strong trend in mobile and wireless security, including 5G vulnerabilities, SMS timing attacks, GPS spoofing, and privacy risks in AI-generated code and LLMs. Her group also investigates disinformation, browser fingerprinting, and secure communication protocols. Scientific Awards: Runner-up, Andreas Pfitzmann Best Student Paper Award (PETS 2024) Christina Pöpper has advised numerous PhD and master’s students, including Shujaat Mirza and Zacharia Kornas. She has led significant research grants through the Center for Cyber Security and the CSP Lab. She serves on the steering committees of NDSS and ACM WiSec and has chaired TPCs for NDSS and ACNS. She previously served as Assistant Professor at Ruhr-University Bochum and worked at the European Space Agency. Labs and Teams: Cyber Security & Privacy (CSP) Lab, NYUAD Center for Cyber Security, NYUAD Co-organizer of the WiseML workshop on Wireless Security and Machine Learning

Jens Kleesiekمشاهده پروفایل

استاد

• Medical Imaging
• Artificial Intelligence
• Oncology
• +۷ مورد دیگر

Jens Kleesiek is a Professor of Translational Image-guided Oncology at the Institute for AI in Medicine (IKIM) in Germany, affiliated with Heinrich Heine University Düsseldorf. He studied medicine in Heidelberg and bioinformatics in Hamburg, earning a Ph.D. in computer science in 2012, followed by radiology certification and habilitation in medical informatics. Research Interests: Self-supervised and weakly supervised learning for clinical pattern recognition Multimodal data integration to enhance decision-making Medical image analysis, segmentation, and body composition modeling AI applications in oncology, radiology, and pathology Explainable AI for real-world clinical implementation Publications highlight his work on diseases like cancer and liver fibrosis, focusing on deep learning, federated learning, and image analysis. Studies include virtual contrast enhancement in MRI, GAN-based data synthesis, and AI in radiology. Education spans medicine (Heidelberg) and bioinformatics (Hamburg), with a Ph.D. in computer science. His clinical training includes radiology and medical informatics at the German Cancer Research Center (DKFZ) and University Hospital Heidelberg. Collaborations involve institutions like the German Cancer Research Center, University Hospital Heidelberg, and Heinrich Heine University Düsseldorf. He co-authored works on medical imaging, language models, and AI ethics in healthcare.

Arjun Guhaمشاهده پروفایل

دانشیار

• Programming Languages
• Program Synthesis
• Low-Resource Programming Languages
• +۳ مورد دیگر

Arjun Guha is an Associate Professor of Computer Science at Northeastern University's Khoury College of Computer Sciences, where he also serves as Area Chair for Software. Additionally, he holds a Visiting Professor position at Roblox Research. His work spans programming languages, program synthesis, and the intersection of large language models with computer science education. Guha's research primarily focuses on two areas: program synthesis for "low-resource" programming languages (those not widely supported by existing AI code generation tools but widely used in specialized fields like scientific computing), and understanding how large language models impact computer science education. His work has led to significant contributions in benchmarking neural code generation across multiple languages and examining how students interact with AI-generated code. He is particularly interested in knowledge transfer between high-resource and low-resource programming languages for code LLMs. Guha's recent publications show a strong trend toward examining the practical applications and limitations of large language models in programming contexts. His work spans from developing benchmarks like MultiPL-E for cross-language code generation evaluation to studying how beginners understand and interact with AI-generated code. There's a clear emphasis on making code generation more accessible for specialized domains and understanding the educational implications of these technologies. Guha actively mentors students at all levels, leading a research group within Northeastern's Programming Research Laboratory. His current advisees include several PhD students and postdocs working on topics related to code LLMs, program synthesis, and computer science education. His research has been generously supported by the National Science Foundation, Department of Energy, Office of Naval Research, and industry partners including Google, JPMorgan Chase, MathWorks, Meta, Oracle, and Roblox. Guha is an active contributor to the programming languages research community, serving on program committees for major conferences including PLDI, POPL, SPLASH, and OOPSLA. He has chaired workshops and sessions at these conferences and continues to be an influential voice in the programming languages community.

Akash Lalمشاهده پروفایل

پژوهشگر

• Programming Languages
• Concurrency
• Verification
• +۴ مورد دیگر

Akash Lal is a Partner Researcher at Microsoft Research India, focusing on programming languages, concurrency, verification, and AI applications in software engineering. His work bridges formal methods with practical tools like Coyote and Corral for concurrent system reliability. PhD from University of Wisconsin-Madison (2009), advised by Thomas Reps Key research areas: LLM-driven memory safety (2024-2025) Concurrency testing frameworks (Coyote, P#) Smart contracts verification (Celestial) CodeQL-based resource leak detection His 15 most recent publications span 2021-2025, emphasizing LLM integration for verification, concurrency analysis, and systems research. Notable trends include industrial-strength concurrency testing (TACAS 2023), ML-driven documentation (ASE 2023), and Rust/ML pipeline safety (ICSE 2025). Major awards: CAV Award (2023) for context-bounded analysis EASST Best Paper (2023) for Coyote ACM Distinguished Paper (OOPSLA 2021) ACM Distinguished Artifact (OOPSLA 2020) Best Paper (FMCAD 2020) Advisees: 18 researchers including Ankush Das (CMU), Samvid Dharanikota (CMU), and Nausheen Mohammed (Leuven)

Arie van Deursenمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Trustworthy AI
• +۵ مورد دیگر

Arie van Deursen is a Professor in Software Engineering at Delft University of Technology, where he is affiliated with the Department of Software Technology within the Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS). He previously served as head of department from 2016-2023 and currently chairs the Software Engineering Research Group (SERG). He is also the scientific co-director of AI4SE, a five-year collaboration between JetBrains and TU Delft investigating novel uses of artificial intelligence in software engineering. His research interests span software engineering, software testing, trustworthy AI, language models for code, software architecture, human aspects of software engineering, and digitalization in the financial and public sector. His work often bridges academic research with practical industry applications, as evidenced by his co-founding of two companies: The Software Improvement Group (2000) and PerfectXL (2010/2013). Recent publications show a strong focus on large language models in software engineering contexts, with multiple 2024-2025 papers examining code generation quality, multilingual code comments, contamination-free code datasets, and license infringement concerns in LLM training data. His research demonstrates significant engagement with the challenges and opportunities presented by AI in software development. AIWare 2024 ACM SIGSOFT Distinguished Paper Award ASE 2024 ACM SIGSOFT Distinguished Paper Award CAIN 2023 Distinguished Paper Award CSCW 2015 ACM SIGCHI Best Paper Award EASE 2016 Best Paper Award Professor van Deursen has (co-)supervised 50 PhD students and 100 MSc students throughout his career. He serves on the Advisory Council on IT Assessment (AcICT) of the Dutch government (since 2022) and the Advisory Board of ING Bank Netherlands (since 2024). He was elected as a fellow of the Netherlands Academy of Engineering (NAE) in 2023. He leads the AI4SE lab, which hosts 10 PhD students, dozens of MSc and BSc students, and collaborates with many TU Delft faculty members and JetBrains specialists. The lab focuses on investigating novel uses of artificial intelligence in software engineering, with particular attention to practical applications and industry relevance.

Mario Fritzمشاهده پروفایل

استاد

• Trustworthy Artificial Intelligence
• Machine Learning
• Information Security
• +۴ مورد دیگر

Mario Fritz is a faculty member at the CISPA Helmholtz Center for Information Security , an honorary professor at Saarland University , and a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS) . Previously, he led a research group at the Max Planck Institute for Informatics (2011-2018) and conducted postdoctoral work at UC Berkeley. His research focuses on trustworthy artificial intelligence at the intersection of information security and machine learning , with recent projects including the EU-funded ELSA - European Lighthouse on Secure and Safe AI . Current roles: CISPA faculty (2018-present), Saarland University professor (2019-present) Past roles: Max Planck senior researcher (2011-2018), ICSI/UC Berkeley postdoc (2008-2010) Education: PhD from TU Darmstadt, computer science studies at FAU Erlangen-Nuremberg His research spans AI security , privacy-preserving machine learning , computer vision , and deep generative models . Recent work includes LLM-based code deobfuscation , certified malware detectors , and membership inference attacks on DocVQA models . He serves as Associate Editor for the journal IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and leads projects like Trustworthy Federated Data Analytics , PriSyn (synthetic health data with privacy), and ImageTox (automated toxicity detection). As of 2025, he has published over 100 scientific articles , including 80 in top venues like ICML, ICLR, and CVPR.

• Electronic health records
• Epidemiology
• Statistics
• +۹ مورد دیگر

Dr. Alvina Lai is an Associate Professor at the Institute of Health Informatics, University College London (UCL). Her work focuses on transdisciplinary research at the interface of large-scale electronic health records and genetic information, moving beyond siloed approaches to address complex health challenges through data-driven insights. Dr. Lai's research spans multiple domains within health sciences, with particular emphasis on: Electronic health records analysis and integration Epidemiological studies of multimorbidity patterns Statistical methods for causal inference in observational data Genetic information integration with clinical data Policy-relevant health services research Her scholarly work demonstrates a strong focus on cancer research, mental health applications of AI, and the impact of global health crises like the COVID-19 pandemic on healthcare systems. Recent publications reveal growing interest in applying advanced computational methods, including deep learning and large language models, to healthcare challenges. Her research consistently emphasizes real-world evidence generation and improving health outcomes through data-driven approaches. Dr. Lai has received recognition for her contributions to significant improvements in health outcomes and the quality of patient care. Her work has been cited extensively across numerous high-impact publications in journals including The Lancet, Nature Medicine, and BMJ. As an educator, Dr. Lai teaches across multiple domains including Genetics, Data Science, Statistics, and Advanced Data Science and Statistics, preparing the next generation of health informatics professionals with both theoretical knowledge and practical skills.

Sumon Biswasمشاهده پروفایل

استادیار

• Software Engineering
• Artificial Intelligence
• Responsible AI
• +۴ مورد دیگر

Sumon Biswas is a tenure-track Assistant Professor in the Department of Computer and Data Sciences at Case Western Reserve University's Case School of Engineering. His research focuses on the intersection of Software Engineering and Artificial Intelligence, particularly in modeling, verification, and design of responsible AI systems. He employs both formal and empirical methods to ensure algorithmic fairness and safety in ML pipelines. Education: Postdoctoral Researcher at Carnegie Mellon University PhD in Computer Science from Iowa State University MS in Computer Science from Iowa State University Research Focus: His work centers on Responsible AI Engineering with specific emphasis on fairness verification of neural networks, compositional fairness in ML pipelines, long-term fairness analysis, and safety assurance of predictive systems. He develops techniques using formal verification, causal reasoning, and empirical analysis to address bias in data transformers and ensemble learning. Publication Trends: Biswas consistently publishes in top-tier software engineering venues (ICSE, FSE, ASE) with research evolving from empirical fairness studies to formal verification techniques and pipeline architecture design. Recent works focus on LLM-assisted technical debt management and long-term fairness guarantees in ML systems. Awards & Honors: Research Excellence Award from Iowa State University (2022) ACM SIGSOFT CAPS Grants (2021, 2022) Publication Award from ISU Computer Science Department Distinguished Reviewer for ACM TOSEM Academic Service: Actively serves on program committees for ICSE (2024-2025), ASE (2023-2024), and reviews for IEEE TSE, IEEE Software, and EMSE. Mentors students through ICSE SMeW and judges research competitions. Research Group: Leads a lab focused on Responsible AI Engineering and seeks motivated PhD/MS students to work on verification and design of fair/safe AI systems.

Enea Zaffanellaمشاهده پروفایل

دانشیار

• Static Analysis
• Software Verification
• Abstract Interpretation
• +۷ مورد دیگر

Enea Zaffanella is an Associate Professor in Computer Science at the University of Parma , where he has been affiliated since 2000. His academic career spans from Fellow Researcher to Assistant Professor, culminating in his current role since 2006. He teaches courses in Programming Methodologies , Compilers , and Foundations of Computer Science at both undergraduate and graduate levels. PhD in Computer Science from School of Computing, University of Leeds (2002) Laurea in Computer Science from University of Pisa (1993) Research focuses on Static Analysis and Software Verification using Abstract Interpretation . Key contributions include theoretical frameworks for Constraint Logic Programs analysis, Convex Polyhedra abstractions, and Widening Operators design. His work bridges formal theory with practical implementations like the Parma Polyhedra Library (PPL) and its successor PPLite . Recent publications explore Hybrid Systems verification, Data Science linting tools (Pyra), and EVM Bytecode analysis. He received the Radhia Cousot Young Researcher Best Paper Award in 2019. Collaborations include academic Research Projects (PRIN, ESPRIT) and industrial partnerships through BUGSENG srl .