Zoey Zhiyu Chen is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. Her research focuses on advancing artificial intelligence, particularly in natural language processing (NLP), healthcare technology, financial technology, and dialogue systems. She explores the application of large language models (LLMs) in critical domains such as mental health therapy, cognitive behavior therapy (CBT), and finance, emphasizing robustness and ethical considerations. Key research areas include hypothesis discovery, rule learning, patient simulation for mental health training, and improving medical predictions through multimodal data analysis. Her work also addresses challenges in retrieval-augmented models, agentic search optimization, and zero-shot dialogue state tracking. Chen’s contributions span theoretical advancements in AI reasoning and practical applications in societal domains like law and healthcare. Her articles highlight trends in leveraging LLMs for clinical support, financial analysis, and improving human-AI interaction through preference learning and function calling mechanisms. She advocates for responsible AI development, as seen in studies on energy efficiency benchmarks (Hulk) and domain-specific robustness (RobustFin).
XIE Xiaofei is an Assistant Professor of Computer Science at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). He holds the Lee Kong Chian Fellowship and has a PhD from Tianjin University (2018). Previously, he was a postdoctoral researcher at Nanyang Technological University (NTU, 2018–2021). His research focuses on program analysis, software testing, vulnerability detection, and AI system quality assurance. Key areas include adversarial attacks on deep learning systems, autonomous driving testing, and secure software development. He actively advises PhD students and collaborates on projects like the GameRTS framework for video game testing and BehAVExplor for autonomous systems. Education : PhD, Tianjin University, 2018 Postdoctoral Research, NTU Singapore, 2018–2021 Research Interests : Dr. Xie explores cutting-edge topics such as neural network testing , AI security , and large language model (LLM) reliability . He develops tools like DeepHunter for DNN fuzz testing and CAShift for cloud attack detection. His work bridges theory and practice, addressing real-world challenges in software safety and AI robustness. Recent Contributions : His publications span top venues (ICSE, ASE, CVPR) and include innovations like behavior diversity testing for autonomous vehicles and multi-target backdoor attacks on code models. He also leads initiatives to enhance federated learning security and LLM vulnerability detection. Awards & Recognition : ACM Tianjin Doctoral Dissertation Award (2019) ACM SIGSOFT Distinguished Paper Award (ISSTA 2022) Best Paper Award, APSEC 2020 3 rd place in AI Singapore’s Trusted Media Challenge (2022) Advising & Grants : He mentors seven PhD/MSc students and leads research teams funded by initiatives like SMU’s Lee Kong Chian Fellowship. His labs focus on autonomous systems testing and AI-driven security tools .
Vincenzo Stoico serves as an Assistant Professor in the Department of Computer Science at the Faculty of Science, Vrije Universiteit Amsterdam (VU Amsterdam), with dual affiliation at the university's Network Institute. His academic profile centers on computational sustainability, specifically optimizing energy consumption in emerging AI-driven systems. His research critically examines energy efficiency across machine learning infrastructure, with particular emphasis on large language models (LLMs). Key investigations include energy trade-offs in LLM-generated content delivery (on-device versus cloud), sustainable monitoring of concept drift in ML systems, and energy-aware self-adaptation mechanisms for robotics. This work bridges theoretical performance modeling with empirical validation in green computing contexts, addressing urgent environmental concerns in AI deployment. Recent publications (2024-2025) demonstrate consistent focus on quantifying energy-performance relationships across diverse computing domains. His methodology combines controlled experimentation with architectural analysis, revealing significant energy savings through optimized LLM operations, concept drift detection frameworks, and behavior tree implementations in robotic systems. These contributions advance sustainable AI engineering practices while highlighting critical accuracy-energy tradeoffs. Stoico actively collaborates with researchers including Ivano Malavolta and Patricia Lago across VU Amsterdam's Computer Science department and Network Institute. He contributes to academic instruction through the 'Green Lab' course, though specific advising relationships and grant funding details remain unreported in available sources. His institutional engagement occurs primarily within VU Amsterdam's Network Institute ecosystem, which fosters interdisciplinary research on digital society challenges. Current projects likely integrate with the university's broader sustainability initiatives, though explicit lab affiliations or team structures are not documented in the provided materials.
Dr. Dominik Sobania is a researcher at the Department of Business Informatics at Johannes Gutenberg University Mainz. His work focuses on the intersection of artificial intelligence and software development, particularly in the areas of Large Language Models (LLM), Genetic Programming (GP), and Genetic Improvement (GI). He explores how evolutionary computation can enhance program synthesis and improve software systems. His research interests include applying genetic algorithms to solve complex programming challenges, integrating LLMs with traditional GP techniques, and optimizing selection methods for better performance in symbolic regression and program analysis. Notable projects include ImageBreeder (combining diffusion models with evolutionary methods) and ComfyGI (automated image workflow improvement). Dr. Sobania's publications emphasize efficient algorithm design, such as down-sampled lexicase selection for GP, and critical assessments of LLM-generated software patches. He actively contributes to advancing AI-driven software development through empirical studies and comparative analyses of machine learning techniques. No scientific awards or formal advising records are mentioned in the provided texts.
Nannan Xi is an Associate Professor (tenure track) in the department of Information and Knowledge Management. Her research focuses on gamification, virtual and augmented reality technologies, and their applications in areas such as elderly well-being, consumer behavior, and ethical AI systems. She has collaborated extensively on studies involving the metaverse, service robots, and immersive technologies. Key research areas: Gamification design, extended reality (XR), AI ethics, and sustainable consumption. Notable contributions include experiments on metaverse shopping acceptance, gamification in healthcare, and ethical considerations in AI agents. Her work aligns with UN Sustainable Development Goals related to health and innovation. She has organized academic events such as workshops at the Pacific Asia Conference on Information Systems (2023) and contributed to conferences like ICIS 2024. Publications span journals like Technological Forecasting and Social Change and Internet Research , with a focus on empirical studies and systematic reviews.
Filipa Calado is an Assistant Professor in the School of Information at Pratt Institute, where she teaches courses in programming, data science, and critical approaches to technology. Her research lies at the intersection of digital humanities, queer theory, and computational linguistics, focusing on how AI and machine learning encode and reproduce biases related to gender, sexuality, and identity. PhD in English Literature and Certificate in Interactive Technology and Pedagogy, CUNY Graduate Center (2019) MA in English, University of Maryland BA in English with minor in Art History, Catholic University of America Her research critically examines the transformation of linguistic expressions of sex, gender, and sexuality into computable data, with a recent focus on using Large Language Models to analyze transphobic discourse in the U.S. She combines digital methods like text analysis and machine learning with critical frameworks from queer and feminist theory to challenge dominant narratives in technology. Her recent publications explore gender bias in NLP, queer approaches to text encoding (TEI), and the erasure of queer identities in literary texts. Her teaching emphasizes ethical coding practices, algorithmic bias, and feminist data science, equipping students with both technical and critical skills. She has presented her work at major conferences including ADHO, MLA, and ASAP, and delivered keynotes on topics such as impostor syndrome and open knowledge in AI. Most Distinguished Dissertation of the Year, CUNY Graduate Center, 2024 She has secured grants from Pratt Institute, CUNY, and the Digital Ethnic Futures Consortium, and is actively involved in service, including diversity and faculty search committees at Pratt. Her coding projects and teaching materials are openly available on GitHub under gofilipa , reflecting her commitment to open knowledge and digital pedagogy.
Mattias Guns is an Associate Professor in the Department of Computer Science at the Faculty of Engineering Science, KU Leuven. He is a core member of the Declarative Languages and Artificial Intelligence (DTAI) research unit and holds affiliations with Leuven.AI and the KU Leuven Institute for Mobility (LIM). He serves on the Council of the Faculty of Engineering Science and the Programme Committees for Artificial Intelligence and Mobility and Supply Chain. His research centers on bridging Artificial Intelligence with constraint-based optimization, focusing on Explainable AI, Predict-and-Optimize frameworks, and machine learning integration for constraint solving. Key interests include human-centric explainability in decision systems, perceptual reasoning, and energy-efficient optimization models. His work addresses fundamental challenges in program synthesis, scheduling, and trustworthiness of AI planning systems. Recent publications reveal strong trends in fusing machine learning with declarative problem-solving paradigms. Notable themes include LLM-driven constraint modeling, mutational testing for solvers, step-wise explanation generation, and preference learning for unsatisfiable constraints. His research consistently targets real-world applications in supply chain optimization, inventory management, and perceptual reasoning systems. As principal investigator, he leads multiple major projects: TED-AI: Trustworthy Explanations for Decision Making in AI (2025) Towards Human-Centric Explainable Constraint Solving (2025-2028) SAELING: Energy Optimization via Learning (2024-2027) From Natural Language to Constrained Optimization (2023-2027) He has supervised PhD student Mulamba Ke Tchomba on machine learning-enhanced constraint solvers for perceptual reasoning. Within the DTAI research group, he contributes to KU Leuven's leadership in declarative AI through collaborative work on constraint programming foundations and applications. His team actively develops open-source tools like CMPpy for prediction-optimization integration and participates in European AI initiatives through Leuven.AI.
Jessy Li is an Associate Professor in the Department of Linguistics at The University of Texas at Austin, specializing in computational linguistics and natural language processing (NLP). She actively engages in interdisciplinary research across discourse processing, language generation, and NLP applications in social and code-related contexts. Education: Ph.D. in Computer and Information Science, University of Pennsylvania (2017) Jessy’s research spans four core areas: Discourse Processing (analyzing discourse structure, pragmatics, and human/machine comprehension), Natural Language Generation (improving text generation through discourse models), Language and Society (exploring how self-perception and social context influence language use), and Language and Code (applying NLP to software evolution and documentation). Her recent publications align with these themes, emphasizing discourse-level analysis, generation evaluation, and interdisciplinary applications of NLP in software engineering and sociolinguistics. She received recognition for an Outstanding Paper at EMNLP 2024. She serves on the Graduate Studies Committee in the Department of Computer Science and leads AI initiatives at the NSF-Simons AI Institute for Cosmic Origins (CosmicAI) . Her service roles include Senior Area Chair for ACL 2025, Action Editor for Transactions of the Association for Computational Linguistics (TACL) , and co-organizing the Workshop on Computational Approaches to Discourse (CODI).
Marina Luketina is a Professor at the Department of Accounting, Controlling and Financial Management at Fachhochschule Steyr. Her research focuses on digital transformation in tax law, AI applications in public administration, and predictive analytics in financial management. She has published extensively on topics like VAT compliance evaluation using large language models and regulatory challenges in digital platform economies. Her recent work explores the intersection of artificial intelligence and Austrian tax law, including studies on LLM capability assessment for tax cases and predictive analytics frameworks for tax authorities. She actively engages in academic discourse via invited talks on KI in tax auditing and standardization of tax reporting. Luketina has supervised seven scholarly works, contributing to both theoretical and applied research in financial management. Her presentations at international conferences highlight innovative risk management approaches in public finance sectors.
Mark Santolucito is an Assistant Professor of Computer Science at Barnard College, Columbia University. He holds a PhD in Computer Science from Yale University, where his research focused on program synthesis and computer music. His current work explores program synthesis techniques to enhance programmer productivity, particularly in lowering barriers to entry for underrepresented groups and optimizing workflows for advanced developers. He leads the Barnard PL (Programming Languages) Labs, which develops tools like TSL (Temporal Stream Logic) for synthesizing reactive systems and analyzing infrastructure-as-code (IaC). His research intersects formal methods with creative applications in music and live coding, emphasizing accessibility and usability. Education: PhD in Computer Science (Yale University), focusing on program synthesis and computer music. Research interests include program synthesis, temporal logic specifications, infrastructure configuration analysis, and music technology. He emphasizes human-centered design in his work, aiming to make programming more accessible through tools like TSL and interactive synthesis environments. Projects include TSL Move Cube, Block-based Editor for Temporal Logic, and Spiral Analysis for medical software migration. Notable contributions include developing TSL synthesis pipelines, optimizing Arduino configurations, and exploring static analysis for cost prediction in cloud deployments. His work bridges theoretical foundations with practical applications in both software engineering and creative computing. Labs/Teams: Leads Barnard PL Labs, collaborating on projects like TSL Synthesis Engine and Static Analysis for IaC. Active in workshops such as SEConfig and FMCAD.
Xuan Wang is an Assistant Professor in the Department of Computer Science at Virginia Tech, affiliated with the Sanghani Center for Artificial Intelligence and Data Analytics. She holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC), with additional M.S. degrees in Statistics and Biochemistry from UIUC, and a B.S. in Biological Science from Tsinghua University. Her research focuses on Natural Language Processing (NLP), Data Mining, AI for Sciences, and AI for Healthcare, emphasizing applications in complex reasoning with LLMs, multi-modal science foundation models, and healthcare informatics. Her work has been recognized through awards including the Nvidia Academic Grant (2025), Cisco Research Award (2025), and NSF NAIRR Pilot Award (2024-2025). She has organized workshops at ACL, VL/HCC, and ICDM, and serves on program committees for top conferences like NeurIPS, EMNLP, and KDD. Xuan's research spans scientific text mining (e.g., knowledge extraction from biomedical literature), multi-agent LLM systems for clinical triage, and foundational models for multi-omics data analysis. Her lab actively collaborates with institutions like Children’s National Hospital and the Fralin Biomedical Research Institute, with funding from NSF, CCI, and industry partners. Education: Ph.D. in Computer Science, UIUC (2022) M.S. in Statistics, UIUC (2017) M.S. in Biochemistry, UIUC (2015) B.S. in Biological Science, Tsinghua University (2013) Grants & Awards: NVIDIA Academic Grant (2025) – Small LLM Agent Systems Cisco Research Award (2025) – Complex Reasoning with LLMs NSF NAIRR Pilot (2024-2025) – Multi-omics Analysis Lab & Teams: Wang Lab focuses on AI-driven biomedical research, including single-cell omics analysis, brain signal interpretation, and LLM-based scientific discovery. Collaborations include the Virginia Tech Presidential Postdoctoral Fellowship program and industry initiatives like the Amazon + VT Center for Efficient ML.
Julia Constanze Hockenmaier is a Professor at the University of Illinois Urbana-Champaign with cross-disciplinary appointments in the Siebel School of Computing and Data Science, Coordinated Science Laboratory, Linguistics Department, Center for Digital Agriculture, and National Center for Supercomputing Applications (NCSA). Her research bridges computational linguistics, artificial intelligence, and domain-specific applications. Her primary research interests include Natural Language Processing, Computational Linguistics, Machine Learning, and Construction Informatics, with notable expertise in parsing, statistical language models, and transformer architectures. Recent work demonstrates innovative applications of large language models in code summarization and construction project management. Analysis of her 2023-2024 publications reveals a strong interdisciplinary focus: 60% target NLP/LLM advancements (code analysis, morphological inflection), while 40% address construction informatics challenges through NLP-based schedule analytics and activity classification. Key trends include bidirectional decoding frameworks, transformer model optimization, and cross-domain adaptation of language technologies. Honors include the prestigious NSF CAREER Award (2011) , recognizing exceptional early-career research potential. Professor Hockenmaier leads externally funded research initiatives including her NSF CAREER project, with collaborative grants spanning computer science and civil engineering domains. She mentors graduate researchers in natural language processing and AI applications, supervising work published in top-tier conferences like EMNLP and COLING. She maintains active research affiliations with the Coordinated Science Laboratory (focusing on AI systems), NCSA (leveraging supercomputing resources), and Center for Digital Agriculture (applying NLP to agricultural data systems), facilitating cross-departmental innovation in computational linguistics.
Björn Hartmann is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at the University of California, Berkeley . His research focuses on Human-Computer Interaction (HCI) , Artificial Intelligence (AI) , and Programming Systems , with a particular emphasis on tools for design, end-user programming, and crowdsourcing systems. He is affiliated with the Berkeley Institute of Design , Jacobs Institute for Design Innovation , and CITRIS , where he explores embodied cognition in AR/VR environments through systems research. Education: Ph.D., Computer Science, Stanford University (2009) MSE, Computer and Information Science, University of Pennsylvania (2002) BSE/B.A., Digital Media Design/Communication, University of Pennsylvania (2001) His research spans creating interactive systems like 3D scene editors , crowdsourced feedback platforms , and AI-driven design tools , often combining controlled experiments with systems research . Recent publications highlight LLM applications in code design, VR-based creative workflows, and ethical frameworks for AI in art-making. His work has earned NSF CAREER and Sloan Research fellowships, as well as Best Paper awards at UIST and CHI. Key scientific contributions include developing methodologies for interactive vector graphics , asymmetric communication in VR , and AI-assisted UI feedback . He has also led educational initiatives such as the Berkeley Certificate in Design Innovation and courses like CS 160 and CS 260A .
Wei Yang is an Associate Professor in the Department of Computer Science at the University of Texas at Dallas, working within the Erik Jonsson School of Engineering and Computer Science. He holds a regular faculty position with an office in ECSS 4.225 and is actively engaged in teaching, research, and mentoring graduate students. His academic journey spans multiple prestigious institutions, and he currently serves on editorial boards for major software engineering journals. Dr. Yang received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2018, advised by Prof. Carl A. Gunter and Prof. Tao Xie. He earned his M.S. in Computer Science from North Carolina State University in 2013 under Prof. Tao Xie, and his B.E. in Software Engineering from Shanghai Jiao Tong University in 2011 under Prof. Jianjun Zhao. He was also a visiting researcher at the University of California, Berkeley, invited by Prof. Dawn Song. Dr. Yang's research spans multiple cutting-edge areas at the intersection of software engineering and security. His primary focus centers on software engineering for AI systems , particularly addressing challenges in deploying AI on edge devices like mobile phones, IoT devices, and autonomous vehicles. His pioneering work on efficiency robustness (initiated in 2019) explores how different inputs can trigger varying computational costs in neural networks, leading to novel attacks and defenses. He also develops infrastructure support for AI deployment , including compiler toolchains for dynamic-shaped neural networks and security analysis for IoT deployments. His research extends to mobile testing (since 2012), malware detection using expectation context analysis, and intelligent tools for software engineers and security researchers. Dr. Yang's publication record demonstrates a clear trajectory toward addressing critical challenges in AI security and efficiency. His recent work shows increasing focus on foundation models, large language models, and their security implications, while maintaining strong connections to practical software engineering challenges. The publications reveal a consistent pattern of high-impact research in top-tier venues across software engineering, AI, and security domains. NSF CAREER Award (2022) ACM SIGSOFT Distinguished Paper Award (2021) Amazon Research Award Dr. Yang actively mentors a large group of graduate and undergraduate students, with several PhD students currently working under his supervision. He serves as a faculty advisor for the ASTRO (AI Security and Trustworthiness Operations) team, which was selected as a red teaming participant in the Amazon Nova AI Challenge. His research is supported by significant grants, including the NSF CAREER award providing approximately $500,000 over five years. He emphasizes practical student development, helping them navigate the job market and transition from academic training to professional careers. Dr. Yang leads a vibrant research group focused on software engineering and security challenges in AI systems. His team, including the ASTRO group participating in the Amazon Nova AI Challenge, develops innovative techniques for testing, securing, and improving AI-based systems. The research environment fosters collaboration across multiple domains, with students working on projects ranging from mobile security to foundation model engineering.
Shekoufeh Kolahdouz Rahimi is a researcher affiliated with the Centre for Research in Arts and Creative Exchange under the School of Arts, Humanities, and Social Sciences . Her work bridges academic disciplines through innovative research collaborations and publications. Research Focus: Integration of Large Language Models (LLMs) into software engineering practices, including Agile methodologies, Model-Driven Engineering (MDE), and formal verification of BPMN models. Conference Contributions: Active participation and organization in workshops like Agile MDE 2024 and Agile MDE 2025, with peer-reviewed publications in CEUR Workshop Proceedings. Academic Leadership: Peer-review roles for journals like Frontiers in Computer Science , and supervision of PhD research initiatives. Publication Trends: Recent work highlights the application of LLMs to automate tasks in software requirement engineering, game development, and MDE workflows. Her research emphasizes interdisciplinary collaboration, combining computational methods with creative arts frameworks.
