Prof. Dr. Anke Holler is a Professor of German Linguistics at the University of Göttingen , specializing in formal grammar, discourse analysis, and computational linguistics. Her career spans roles in academic administration, including Vice President for Appointments since 2021, and leadership in DFG and Leibniz Association committees. Education : University of Tübingen, University of Leipzig, and University of Massachusetts, Amherst Current Projects : GRK 2636 'Form-Meaning Mismatches', DFG 'Structuring Literature' (SPP 2207), VW Foundation 'Uncertain Attribution' Research Interests focus on the intersection of grammar theory, experimental linguistics, and computational modeling. She explores discourse structures, narrative perspective, and constraint-based parsing, with applications in digital humanities and text mining. Her recent work involves neural networks for speaker attribution and computational analysis of literary reflexivity. Collaborations include the Carl Friedrich Lehmann-Haupt Doctoral Program and the 'Textstrukturen' center. She serves as editor for the Zeitschrift für Sprachwissenschaft and coordinates interdisciplinary initiatives.
Rainer Sinn is a University Professor (on leave) at Leipzig University, specializing in Applied Algebra within mathematics. His research centers on real algebraic geometry, convex optimization, and sums of squares, with significant contributions to spectrahedra, amplituhedra, and nonnegativity certificates. His primary research interests include real algebraic geometry (focusing on nonnegative polynomials and quadratic forms), convex algebraic geometry (studying convex hulls of algebraic varieties), and combinatorial applications in optimization. He explores geometric structures like amplituhedra in theoretical physics and investigates algebraic solutions to optimization problems. Recent publications (2022-2025) demonstrate a cohesive focus on algebraic approaches to optimization, with recurring themes in nonnegativity certificates, tropical geometry, and combinatorial aspects of algebraic varieties. His German-language works also address the philosophy and public understanding of mathematics, highlighting interdisciplinary impact. No scientific awards were documented in the provided sources. Details regarding academic advising, research grants, laboratories, or collaborative teams were not specified in the available information.
Shari Trewin is a prominent researcher in accessibility and human-computer interaction at IBM Research with over 25 years of scholarly contributions. Her work focuses on making digital technologies accessible to people with disabilities, particularly in web and mobile contexts. She has published extensively in top-tier venues including ACM SIGACCESS conferences (ASSETS, W4A) and journals, CHI, and other leading HCI publications. Dr. Trewin's research interests span web accessibility, mobile accessibility for users with physical and cognitive disabilities, inclusive design methodologies, and the application of artificial intelligence to improve accessibility. Her work addresses both theoretical foundations and practical implementations of accessible technologies, with particular emphasis on user-centered design approaches and evaluation methodologies. She has made significant contributions to understanding how people with disabilities interact with digital interfaces and how to design systems that accommodate diverse user needs. Her publication record shows a clear progression from foundational work on input devices and keyboard accessibility in the 1990s to contemporary research on AI fairness for people with disabilities. Recent publications demonstrate her leadership in addressing emerging challenges at the intersection of AI and accessibility, particularly around algorithmic fairness and inclusive design practices for AI systems. Among her notable contributions are editorial work for ACM Transactions on Accessible Computing and co-editing conference proceedings for the ASSETS conference. She has collaborated extensively with leading researchers in the field including Vicki L. Hanson, Gregg Vanderheiden, and Calvin Swart. Dr. Trewin has advised junior researchers including Jessica J. Tran, and her work has influenced both academic research and industry practices in accessibility. Her research has practical implications for web developers, designers, and policy makers working to create more inclusive digital experiences.
Prof. Yu-Seop Kim is a Professor at the School of Software, Hallym University, Chuncheon-si, Republic of Korea. He holds a B.Eng. in Computer Science from Sogang University (1992), and M.Eng. (1994) and D.Eng. (2000) in Computer Engineering from Seoul National University. His academic work is centered on the integration of artificial intelligence with biomedical applications. B.Eng., Department of Computer Science, Sogang University, 1992 M.Eng., Computer Engineering, Seoul National University, 1994 D.Eng., Computer Engineering, Seoul National University, 2000 His research interests lie at the intersection of bioinformatics, computational intelligence, natural language processing, and deep learning , with a strong emphasis on medical applications. He actively explores how AI can assist in clinical diagnostics and healthcare documentation. The recent trend in his publications demonstrates a focus on AI-driven medical image analysis and automated clinical text generation . His work leverages convolutional neural networks and language models to interpret brain CT scans, detect aortic dissection, and augment medical reports for cerebrovascular diseases. These efforts reflect a consistent effort to bridge machine learning with real-world clinical challenges. While no scientific awards are listed in the provided text, his collaborative research output suggests active engagement in academic and clinical partnerships. Prof. Kim has advised multiple researchers and co-authored numerous publications, particularly in journals like Applied Sciences and Journal of Clinical Medicine . Although specific grant information is not mentioned, his research likely involves funding for AI in healthcare. He collaborates with colleagues such as Byoung-Doo Oh, Chulho Kim, and Bitnarae Kim, indicating a multidisciplinary team approach. His work appears to be conducted within a research group or lab focused on AI for medical imaging and language processing , potentially involving students and clinical collaborators from affiliated institutions like Chuncheon Sacred Heart Hospital. This environment supports translational research from algorithm development to clinical validation.
Koushik Sen is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on developing software tools and methodologies to enhance programmer productivity and software quality, with expertise in Software Engineering, Programming Languages, and Formal Methods. Education: B.Tech from Indian Institute of Technology, Kanpur M.S. and Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Research Focus: Professor Sen pioneers automated testing techniques including concolic testing and DART (Directed Automated Random Testing). His work bridges formal methods with practical software development, emphasizing bug detection, program synthesis, and AI-driven software analysis tools. Recent innovations include machine learning approaches for code recommendation and fuzzing. Publication Trends: His recent publications (2019-2023) demonstrate strong emphasis on fuzzing techniques, program synthesis, and AI/ML applications in software engineering. Notable domains include smart contract security, automated testing, and developer tooling, with frequent collaborations in top-tier conferences. Awards and Honors: NSF CAREER Award (2008) Sloan Foundation Fellowship (2011) IFIP TC2 Manfred Paul Award (2010) Okawa Foundation Research Grant (2015) Multiple ACM SIGSOFT Distinguished Paper Awards UIUC Distinguished Alumni Educator Award (2014) Leadership: Active program committee member for premier conferences (PLDI, ICSE, ISSTA) and keynote speaker. His research is supported by NSF, Okawa Foundation, and Sloan Foundation.
Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.
Prof. Dr. Leo van Waveren is a Professor and Head of the Technical Didactics Group at RheinMain University of Applied Sciences (RPTU/Koziel). His academic career includes postdoctoral research at the University of Stuttgart (2018-2022) and academic roles at Technische Universität Kaiserslautern. He holds a PhD (Dr. phil.) from the University of Stuttgart (2017) and a Diplom in Technikpädagogik (Dipl.-Gwl. Informatik/Chemie). His research focuses on vocational education, ICT integration in teaching, and the impact of artificial intelligence (AI) on education. Recent work emphasizes AI's role in curriculum design and teacher training. He actively participates in national and international conferences, delivering keynote speeches on topics like generative AI in classrooms and digital transformation in vocational training. Research interests include technical competency modeling, dual education systems, and educational technology. Notable contributions include studies on vocational competencies in automation technology and teacher job satisfaction across career pathways. His work bridges theoretical frameworks with practical applications, such as smartphone-based experiments in STEM education. Prof. van Waveren leads the RPTU's efforts in digital education initiatives, including the DIGILL@Praxis program. He collaborates with regional and international institutions to advance educational innovation. Despite no listed awards, his extensive publication record and active speaking engagements underscore his scholarly impact.
Sebastian Köhler is an Associate Professor of Philosophy at the Frankfurt School of Finance & Management. He teaches in the BSc in Management, Philosophy & Economics, BSc in Computational Business Analytics, and the Master of Applied Data Science. He previously served as a Wissenschaftlicher Mitarbeiter at Universität Duisburg-Essen and a Lecturer at Princeton University. His research focuses on meta-ethical questions surrounding normativity, the philosophies of Mind and Language, and the ethics of emerging information technologies. University of Edinburgh (PhD) University of Bielefeld London School of Economics University of Cambridge Köhler’s research bridges philosophical theory with practical applications in data science and AI ethics. His work on conceptual engineering addresses the challenges of defining and implementing ethical frameworks in technological contexts, particularly in autonomous systems and machine learning. Recent publications explore expressivist accounts of moral disagreement, the functional role of normative concepts, and the ethical implications of robot moral status. His scholarly contributions span leading journals such as The Journal of Philosophy, Ethics, Australasian Journal of Philosophy, Philosophical Studies, The Philosophical Quarterly, Erkenntnis, and Ratio. While no explicit scientific awards are documented, his interdisciplinary research has shaped discourse on responsible AI and the intersection of ethics and computational analytics.
Professor Tania Avgustinova is a Professor of Slavic and Computational Linguistics in the Department of Language Science and Technology at Saarland University. She serves as Principle Investigator for SFB 1102 - Project C4 (INCOMSLAV) and is Head of the Slavic Lab. Her academic career spans several decades with significant contributions to Slavic linguistics, computational approaches to language, and cross-linguistic studies. Her academic qualifications include: Habilitation venia legendi in General Linguistics (2003) PhD in Slavic and Computational Linguistics (1997) Diploma in Slavistics (1987) Professor Avgustinova's research focuses on the intersection of Slavic linguistics and computational methods. She specializes in microsyntax, intercomprehension among Slavic languages, language contact phenomena, and the processing of non-compositional expressions. Her work examines how speakers of one Slavic language can understand related languages without formal instruction, with particular attention to cognitive and linguistic factors. She has developed computational models to analyze linguistic distances and asymmetries between Slavic languages, contributing significantly to understanding receptive multilingualism. Her recent publications reveal a strong focus on experimental approaches to studying cross-linguistic comprehension using web-based platforms, eye-tracking, and speech processing techniques. There's a clear trend toward investigating microsyntactic units and non-compositional expressions across Slavic languages, with applications in language technology and education. Her work bridges theoretical linguistics with practical applications in natural language processing and language learning. Principle Investigator of SFB 1102 Project C4 (INCOMSLAV) Head of Slavic Lab at Saarland University Contributor to Russian National Corpus project Developer of INCOMSLAV platform for measuring linguistic distances As Principle Investigator of SFB 1102 Project C4 (INCOMSLAV), Professor Avgustinova leads research on mutual intelligibility and surprisal in Slavic intercomprehension. She has been instrumental in developing the INCOMSLAV platform for measuring linguistic distances and asymmetries in receptive multilingualism. Her leadership extends to international collaborations, including contributions to the Russian National Corpus project where she provides expertise on grammatical phenomena at the borderline between lexicon and syntax. Professor Avgustinova heads the Slavic Lab at Saarland University, which focuses on applied, experimental, and computational linguistics. The lab conducts research on Russian corpus grammar, word embedding models, sense frequencies, and microsyntax. Her work has established important connections between theoretical linguistics and practical language technology applications, particularly in the Slavic language context.
Claire Le Goues is a Professor of Computer Science at Carnegie Mellon University, primarily affiliated with the Software and Societal Systems Department (S3D) within the School of Computer Science (SCS). She serves as the Associate Department Head for Faculty within S3D and leads the squaresLab research group. Le Goues also co-directs the REUSE@CMU summer program and teaches software engineering and program analysis at undergraduate, master's, and PhD levels. Her research spans software engineering and programming languages, with a particular focus on how to construct, maintain, evolve, improve/debug, and assure high-quality software systems. Le Goues has made significant contributions to automated program repair, program analysis, and defect detection. Her work often bridges theoretical foundations with practical applications, addressing real-world challenges in software development and maintenance. Le Goues' recent publications demonstrate a clear trend toward integrating large language models and generative AI with traditional software engineering techniques. Her research examines how these technologies can enhance program repair (BatFix, AdverIntent-Agent), vulnerability detection (Interpretable Vulnerability Detection Reports), and testing (LWDIFF for WebAssembly). This represents an evolution from her earlier foundational work in program repair (GenProg) toward leveraging contemporary AI advancements. She has mentored numerous students through her squaresLab research group and has been instrumental in developing educational programs that prepare the next generation of software engineers. Le Goues is also known for her advocacy for double-blind review processes in academic conferences, having implemented this approach when co-chairing the Symposium for Search-Based Software Engineering in 2014.
Baishakhi Ray is an Associate Professor of Computer Science at Columbia University, working at the intersection of AI, Software Engineering, and Security. She received her Ph.D. from the University of Texas, Austin, and has established herself as a leading researcher in applying artificial intelligence to software engineering challenges. Her educational background includes a Ph.D. from the University of Texas, Austin, which provided the foundation for her research career at the forefront of AI and software engineering. Dr. Ray's research focuses on leveraging artificial intelligence to solve fundamental challenges in software engineering and security. Her work spans multiple areas including code generation with large language models, vulnerability detection, software testing, and program analysis. She has pioneered approaches that combine deep learning with traditional software engineering techniques to create more robust, secure, and efficient software development processes. Her research has practical implications for improving code quality, enhancing software security, and accelerating development cycles through AI assistance. Her recent work demonstrates a strong emphasis on semantic-aware code generation, execution reasoning, and addressing hallucinations in code language models. She has also made significant contributions to evaluating the functionality and security of AI-generated code, identifying critical challenges in the practical adoption of AI for software development. Dr. Ray has received numerous prestigious awards recognizing her contributions to the field: IEEE TCSE Rising Star NSF CAREER award IBM faculty award VMware Faculty award Distinguished Paper awards at FSE'17, ASE'22, and ISSTA'23 ICSME Most Influential Paper award Publications featured in CACM Research Highlights As an Amazon Visiting Academic and active participant in major software engineering conferences, Dr. Ray has established herself as a thought leader in AI for software engineering. Her research has been widely covered in trade media, indicating its relevance and impact on industry practices. She has mentored numerous students through their research and has been instrumental in shaping the next generation of researchers in this interdisciplinary field. Her work demonstrates a consistent focus on bridging theoretical advances with practical applications, ensuring that her research has tangible benefits for the software development community. The trajectory of her publications shows an evolving research agenda that has successfully adapted to the rapidly changing landscape of AI and its applications to software engineering.
Dr. Antonio Mastropaolo is an Assistant Professor of Computer Science at William & Mary, USA. His research lies at the intersection of Artificial Intelligence, Natural Language Processing, and Software Engineering, with a strong emphasis on the automation of SE-related practices. He promotes explainability, efficiency, and optimization from both model-centric and output-centric perspectives. His research interests focus on the reliability and efficiency of AI systems for software engineering. He investigates robustness and adaptability of foundation models like GitHub Copilot, as well as documentation and summarization of code components. His work addresses critical challenges in AI-driven software development including transparency, scalability, and developer productivity. His publication portfolio shows a strong trend toward neurosymbolic approaches that combine neural learning with symbolic reasoning. Recent articles explore quantization of large code models, code summarization optimization, and resource-efficient AI for software engineering. His work spans both theoretical foundations and practical applications, with emphasis on empirical validation of AI techniques in real-world SE contexts. Distinguished Reviewer Award for service on FSE'25 program committees Distinguished Reviewer Award at ASE 2024 Distinguished Paper Award for 'Unveiling ChatGPT's Usage in Open Source Projects' at MSR'24 Distinguished Paper Award for 'How do Hugging Face Models Document Datasets, Bias, and Licenses?' at ICPC'24 Dr. Mastropaolo advises PhD students, with Saima recently starting her PhD journey with a publication in FORGE 2025. He received an NSF Grant (#2451058) in April 2025 for research on efficient and responsible AI for software engineering. His service includes committee membership for major conferences including ASE, ICSE, ICSME, and FSE across multiple tracks including Research Papers, NIER, and Tool Demonstrations.
Dongdong She is an Assistant Professor in the Department of Computer Science and Engineering at The Hong Kong University of Science and Technology (HKUST). His research focuses on the intersection of security and machine learning, applying data-driven approaches to solve security problems. He has established himself as a prominent researcher in software security and fuzzing techniques with publications in top conferences including IEEE S&P, CCS, and USENIX Security. Dr. She received his Ph.D. from Columbia University's Department of Computer Science, where he worked with Professors Suman Jana and Baishakhi Ray. Prior to Columbia, he conducted research with Zhiyun Qian on Android Security at the University of California, Riverside. He completed his undergraduate studies at Huazhong University of Science and Technology. His research spans two main areas: LLM Security, which investigates the security of large language models and LLM-powered systems, and LLM for Traditional Security, which leverages LLMs to solve traditional security problems such as program analysis and vulnerability discovery. His work often combines machine learning techniques with traditional security approaches to develop innovative solutions for software security challenges. Dr. She's publication record shows a consistent evolution from foundational work in neural network-assisted fuzzing (NEUZZ) toward more advanced applications in LLM security and program analysis, demonstrating both theoretical rigor and practical impact with techniques adopted by the security community. Among his notable achievements: Distinguished Paper Award at ISSTA 2025 Distinguished Paper Award at IEEE S&P 2025 Best Paper Award Runner-Up at CCS 2022 Second Place in SBFT 2024 Fuzzing Competition Finalist in 2019 NYU CSAW Applied Research Competition Dr. She currently advises several Ph.D. students including Yuchong Xie, Shuangjie Yao, and Qiao Zhang, who began their studies in Fall 2024. He serves on program committees for major conferences including ASE 2025, where he is a PC Member for the Research Papers track. His research is supported by grants enabling his team to pursue innovative approaches at the intersection of machine learning and security. His research group maintains active collaborations with institutions worldwide and contributes to open-source security tools that are widely used in both academia and industry, with a particular focus on developing advanced techniques for software security analysis through the application of machine learning.
Daye Nam is an Assistant Professor in the Department of Informatics at the University of California, Irvine, where they design, build, and evaluate AI tools for developers using natural language processing techniques. Their work sits at the intersection of software engineering, artificial intelligence, and human-computer interaction, with a strong focus on creating useful and usable tools that make software development more accessible, efficient, and enjoyable. Education PhD in Software Engineering, Carnegie Mellon University (2018-2024) MS in Computer Science, University of Southern California (2016-2018) BS in Computer Science, Yonsei University (2012-2016) Research Interests Dr. Nam's research focuses on designing, building, and evaluating AI tools for programmers at all levels, with an emphasis on making these tools both useful and usable. Their work spans several key areas including machine learning for software engineering (ML4SE), developer experience, and human-AI interaction. They employ a user-centered approach that involves conducting empirical studies to understand programmers' needs, building and training machine learning models based on those insights, creating tools for programmers, and evaluating them using human-computer interaction methods. Their research has particular relevance to AI-powered developer tools, API documentation and discovery, and educational applications of AI for programming students. Publications and Research Trends Dr. Nam's recent publications demonstrate a clear trajectory toward understanding and improving how developers interact with AI systems. Their work increasingly focuses on empirical studies of developer-AI interaction, particularly with large language models for code generation and understanding. There's a strong emphasis on understanding trust in AI systems among developers, measuring the actual impact of AI on development speed, and designing tools that balance automation with user control. Their research methodology often combines log analysis, user studies, and the development of novel AI-powered tools that address specific developer pain points. Scientific Awards and Honors Best Tool Paper Award at ASE ACM Student Research Competition 2nd Place SIGSOFT CAPS Student Travel Award for FSE ACM SIGSOFT NSF Travel Award NSF Travel Award for ICSE SIGSOFT Best Research Award from University of Southern California Teaching and Service Dr. Nam teaches SWE 233: Intelligent User Interfaces at UC Irvine, guiding students through the design and evaluation of AI-powered interfaces for software development. They have previously served as a Teaching Assistant and Co-Instructor for Foundations of Software Engineering at Carnegie Mellon University. In terms of service, they've been on program committees for major software engineering conferences including ICSE, ASE, and FSE, and have reviewed papers for journals like TOSEM and Empirical Software Engineering. They've also been active in student support programs, organizing and mentoring for graduate applicant support initiatives.
Sarah Fakhoury is a Senior Researcher in the Research in Software Engineering (RiSE) group at Microsoft Research, Redmond. Her work bridges formal methods, empirical software engineering, machine learning, and human-computer interaction to optimize developer cognitive effort in AI-assisted programming tools. Her research focuses on trustworthy AI for code generation , leveraging formal verification to ensure correctness in LLM-generated outputs. Key areas include program comprehension, source code readability, and empirical evaluation of developer-AI interaction. She develops tools like 3DGen for provably correct binary parsers and NL2Fix for natural language-based code repair. Her publications reveal strong trends in formal methods integration with AI (60% of recent work), empirical developer studies (30%), and readability/metrics innovation (10%). Keywords cluster around program verification, LLM evaluation, and cognitive load measurement. ACM/SIGSOFT Distinguished Paper Award (ICPC 2018) Fakhoury actively contributes to the academic community as PC member for ASE, ICSE, and ESEC/FSE. She co-organizes workshops like Muslims in ML at NeurIPS and mentors through SMeW. Her RiSE group collaboration with Shuvendu Lahiri and Madanlal Musuvathi drives Microsoft's trustworthy AI4Code initiatives, focusing on verifiable developer tools.
