جست‌وجوی استاد و پژوهشگر

• Speech Processing
• Automatic Speech Recognition
• Speaker Recognition
• +۳ مورد دیگر

Tanel Alumäe is an Associate Professor of Speech Processing at Tallinn University of Technology's School of Information Technologies, Department of Software Science. With over 15 years of academic experience, he has held various research and teaching positions at the university since 2006, progressing from Research Fellow to Tenured Associate Professor. His work focuses on speech and language technologies with a particular emphasis on Estonian language applications. PhD in Information and Communication Technology (2006), Tallinn University of Technology Research Master's Degree in Informatics (2002), Tallinn Technical University MSc studies at Tallinn Technical University (1999-2002) and Universität Erlangen-Nürnberg, Germany (1999-2000) Diploma in Computer and Systems Engineering (1994-1999), Tallinn Technical University Alumäe's research spans automatic speech recognition, speaker recognition, natural language processing, and computational linguistics with a focus on Estonian language technology. His work addresses challenges in multilingual speech processing, deep learning applications for speech technologies, and developing practical systems for real-world applications including broadcast media processing and accessibility solutions. He has made significant contributions to low-resource language processing and specialized applications for children's speech and emotion recognition. His recent publications demonstrate a strong focus on cutting-edge speech processing techniques including deepfake detection, multi-speaker systems, speech-to-speech translation, and applying large language models to speech applications. The research shows a consistent pattern of addressing both theoretical challenges in speech processing and practical implementations for Estonian language technology. Award 'Keeletegu 2019' from the Ministry of Education and Research Award 'Keeletegu 2011' from Estonian Ministry of Education and Research 3rd award at the Tallinn University of Technology contest for applied scientific projects (2011) Boris Tamm stipend (2007) First prize at the national contest of students' scientific works (2007) Ustus Agur stipend of Estonian Information Technology and Telecommunications Association (2005) Alumäe has supervised postdoctoral researchers including Rena Nemoto (2012-2015) on pronunciation modeling for speech recognition. He serves in editorial and review capacities for major journals including Nature, Computer Speech & Language, and IEEE Transactions. His administrative roles include Secretary of the Northern European Association for Language Technology Board and membership on the Department of Software Science Council at TalTech. His research group at Tallinn University of Technology actively participates in international challenges (IWSLT, Interspeech, Odyssey) and collaborates with institutions worldwide. The team has developed open-source platforms for Estonian speech transcription and created systems for automatic closed captioning of Estonian broadcasts, demonstrating strong practical applications of their research.

Brooke Foucault Wellesمشاهده پروفایل

استاد

• Network Science
• Social Networks
• Computational Social Science
• +۷ مورد دیگر

Brooke Foucault Welles is a Professor in the College of Arts, Media and Design at Northeastern University, where she also serves as Interim Dean and Director of the Network Science PhD program. Her research focuses on how social networks and communication technologies shape power dynamics, particularly in contexts of marginalization and social justice. PhD in Media, Technology and Society from Northwestern University MS and BS in Communication from Cornell University Her research spans multiple domains including: Network science of AI and social systems Digital activism and social movement dynamics Health information and (mis)information flows Open source community structures Race/ethnicity in digital contexts Computational social science methodologies Recent publications focus on attention dynamics in social networks, hate speech protection mechanisms, and open-source software sustainability. Her work has been supported by grants from the NSF, NULab, and Chan Zuckerberg Initiative. Awards include: McGannon Book Award (2021) for #HashtagActivism Best Paper Honorable Mention at CSCW 2019 She leads the Communication Media and Marginalization Lab (CoMM Lab) which includes PhD students and postdocs from diverse disciplines. Her advising approach emphasizes interdisciplinary collaboration and methodological training in both quantitative and qualitative approaches.

Pasquale Davide Schiavoneمشاهده پروفایل

مدرس

• Embedded Systems
• RISC-V Architecture
• Ultra-low-power Computing
• +۵ مورد دیگر

Pasquale Davide Schiavone holds multiple research and teaching positions at École Polytechnique Fédérale de Lausanne (EPFL), serving as a Lecturer at the School of Computer and Communication Sciences (IC) and as a Scientist at both the Embedded Systems Laboratory (ESL) within the School of Engineering (STI) and PAT Administration. His interdisciplinary work bridges computer architecture, embedded systems design, and biomedical applications, with office located at ELG 136 in Lausanne, Switzerland. Dr. Schiavone's research focuses on ultra-low-power computing systems, particularly RISC-V architectures and TinyML applications for edge devices. His work develops open-source hardware platforms like X-HEEP and HEEPOCRATES that enable energy-efficient AI at the edge, with applications spanning biomedical monitoring, neural interfaces, and wearable computing. He explores innovative hardware-software co-design approaches to overcome energy constraints in resource-limited environments. His recent publications reveal a consistent research trajectory centered on open, configurable computing platforms for specialized applications. The work spans from fundamental RISC-V architecture improvements (ARCANE, e-GPU) to application-specific implementations for biomedical contexts (BiomedBench, neural interfaces). A strong emphasis on energy efficiency permeates all his research, whether through novel arithmetic approaches (Posit), system architecture (near-memory computing), or specialized accelerators (Strela, Quadrilatero). Lecturer, School of Computer and Communication Sciences (IC) Scientist, Embedded Systems Laboratory (ESL), School of Engineering (STI) Scientist, PAT Administration, School of Engineering (STI) Dr. Schiavone teaches courses on hardware compilation, presenting algorithms and methods for transforming hardware description languages into optimized circuit implementations. His Embedded Systems Laboratory work places him at the forefront of developing practical, open-source solutions for next-generation computing challenges in energy-constrained environments.

Johannes Kinderمشاهده پروفایل

استاد

• Software Security
• Program Analysis
• Machine Learning
• +۳ مورد دیگر

Prof. Dr. Johannes Kinder is a Professor and Chair of Programming Languages and Artificial Intelligence at the Institute of Informatics , Ludwig Maximilian University of Munich. His research focuses on software security through program analysis and machine learning, particularly targeting malware detection , vulnerability analysis , and reverse engineering . He has held faculty positions at Royal Holloway, University of London, and Bundeswehr University Munich. Research Interests include: Securing software systems via program and machine learning techniques Detection of software vulnerabilities and malware Preventing exploitation through binary analysis Applications of formal methods in systems security Recent Publications highlight advancements in binary function embedding , malware detection in npm , and speculative execution attack modeling . His work appears in top venues like USENIX Security and IEEE S&P . Education : Diplom from TU Munich (2005), Doctorate from TU Darmstadt (2010). Professional Roles : General Chair, ACM CCS 2019 Doctoral Symposium Chair, ESSoS 2016 Program Committee member for NDSS 2026, IEEE S&P 2022-2025

• Model-Based Testing
• Automata Theory
• Machine Learning
• +۵ مورد دیگر

Bernhard Aichernig is an Associate Professor at the Institute of Software Engineering and Artificial Intelligence. His work bridges formal methods, model-based testing, and artificial intelligence, with a focus on automata learning, digital twins, and AI-assisted programming. Institution: Institute of Software Engineering and Artificial Intelligence Key Research Areas: Model-Based Testing, Automata Learning, AI-Driven Verification His research explores the integration of machine learning into formal verification, enabling scalable testing of complex systems like IoT devices and reinforcement learning agents. Recent projects include AI-Augmented DevOps frameworks (AIDOaRT) and digital twin validation (LearnTwins). Notable scientific awards include multiple best paper recognitions at SEFM (2020, 2021) and the TAYSIR Competition first place (2023). His publications emphasize hybrid approaches combining genetic programming, SMT solving, and neural networks for system modeling. 2025 : AI-assisted programming, timed automata via domain knowledge 2024 : Stochastic environment modeling, Git system learning 2023 : Reinforcement learning under partial observability, digital twins for VPN servers He actively contributes to testing frameworks like AALpy and investigates explainable AI for fault diagnosis in cyber-physical systems.

• Refugee Studies
• Migration Studies
• Youth Studies
• +۹ مورد دیگر

Dr. Caitlin Nunn serves as Reader in Migration and Youth Studies at Manchester Metropolitan University, where she leads research at the intersection of refugee studies and youth studies with emphasis on refugee-background young people's lived experiences. Her work is anchored in the Manchester Centre for Youth Studies (MCYS) and involves significant collaboration with institutions including Manchester Museum and UKRI-funded initiatives. Her educational background demonstrates interdisciplinary training: PhD in Australian Studies (2013), University of Melbourne MA Preliminary (Honours equivalent) in Development Studies (2005), La Trobe University Graduate Diploma in Development Studies (2004), La Trobe University Bachelor of Dramatic Arts in Theatre Production (2000), Victorian College of the Arts, University of Melbourne Professional qualifications include Associate Fellowship of the Higher Education Academy, Certificate II in Applied Language (Vietnamese), and CELTA certification. Nunn's research program critically examines refugee youth resettlement through innovative participatory and arts-based methodologies. Key thematic strands include: Rural and regional refugee resettlement dynamics Environmental dimensions of migration and belonging Transnational generational relations among refugee families Decolonizing research approaches with displaced communities Creative interventions for refugee wellbeing Her work consistently centers youth agency while challenging conventional integration paradigms. Analysis of her 15 most recent publications reveals evolving methodological sophistication with increasing emphasis on environmental migration, cross-disciplinary collaboration (particularly with ecological sciences), and co-produced knowledge generation. The research demonstrates consistent focus on rural resettlement contexts and creative methodological innovations while expanding into digital engagement (e.g., AI/data co-production projects). Current research leadership includes: NERC Future of UK Treescapes Grant (£2m, 2021-2024): 'Voices of the future' exploring children's relationships with treescapes UKRI Citizen Science Collaboration (£343k, 2021-2023): 'Ancient History, Contemporary Belonging' with refugee-background youth EPSRC Public Engagement Scheme (£197k, 2022-2025): 'PEAs in Pods' for AI/data research co-production She actively supervises doctoral research including Nobila Bano's work on intergenerational knowledge transfer and Ahmad Al Shahma's research on equitable ESOL policies. Nunn maintains significant professional engagement as Member of the Editorial Board for Qualitative Research , Member of the AHRC Peer Review College, and Member of the UKRI Interdisciplinary Assessment College, demonstrating her disciplinary leadership beyond direct research outputs.

• Information Access
• Information Retrieval
• Natural Language Processing
• +۳ مورد دیگر

Tetsuya Sakai is a Professor at the School of Fundamental Science and Engineering within Waseda University's Faculty of Science and Engineering. His work focuses on information access, retrieval, and natural language processing, with a particular emphasis on evaluation frameworks for search systems. Affiliations: Waseda University (Faculty of Science and Engineering, School of Fundamental Science and Engineering) Academic Rank: Professor Research Interests : Dr. Sakai's research spans four key areas: (1) Information Access —designing systems for direct and immediate information delivery, (2) Search Evaluation —developing metrics like Height-Biased Gain and hierarchical intent-based diversity measures, (3) Fairness in IR —pioneering frameworks for group fairness in conversational search, and (4) Statistical Reform —advocating Bayesian methods and robust experimental design. His work also addresses privacy inconsistencies in mobile apps and cognitive biases in LLMs. Scientific Awards : Notable recognitions include induction into the SIGIR Academy (2023) , ACM Distinguished Member (2018) , ACM Senior Member (2016) , and multiple DEIM/FIT/CSS Best Paper Awards . He has received teaching honors like the Waseda Presidential Teaching Award (2016) and WASEDA e-Teaching Award (2018) . Article Trends : Recent publications highlight: Advancements in LLM-assisted relevance assessments and hallucination diagnostics for tool-augmented models Conversational search fairness through multi-level evaluation frameworks and group diversity metrics Innovations in 3D medical reconstruction from clinical data and multimodal uncertainty modeling Statistical rigor via randomization tests , credible intervals , and topic set design Privacy analysis in mobile app descriptions and cognitive bias studies in search interaction

• Corpus Linguistics
• Educational Technology
• Natural Language Processing
• +۵ مورد دیگر

Laurence Anthony is a Professor at Waseda University's School of Creative Science and Engineering, specifically affiliated with the Center for English Language Education in Science and Engineering (CELESE). He has held this position since 2009, having previously served as an Associate Professor at the same institution from 2004-2009. His academic journey began with a BSc from The University of Manchester (1991), followed by an MA (1997) and PhD (2002) from The University of Birmingham. Anthony's research centers on corpus linguistics, educational technology, and natural language processing applications in foreign language teaching. He is renowned for developing AntConc, a widely used freeware corpus analysis toolkit, along with numerous other educational software tools including AntWordProfiler, FireAnt, and ProtAnt. His work bridges linguistic theory with practical classroom applications, particularly in data-driven learning approaches for English as a Foreign Language contexts. His publication record is extensive with over 50 papers, 12,115 Google Scholar citations, and an h-index of 45. His recent work increasingly explores the intersection of corpus linguistics and artificial intelligence, examining how language models can enhance language teaching and analysis. Anthony's research has evolved from foundational corpus tool development to sophisticated applications in vocabulary profiling, writing analysis, and AI-assisted language learning. Among his notable recognitions are the Waseda University 6th e-Teaching Award (2018), the National Prize of the Japan Association of English Corpus Studies (2012), and the L'Oreal Art and Science of Color Gold Prize (2005). He serves on multiple editorial boards including for Studies in Corpus Linguistics, Journal of Asia TEFL, and Corpus Linguistics Research Journal. Anthony actively contributes to the academic community through numerous presentations at international conferences, recent ones including talks on AI integration with corpus methods at Corpus Linguistics 2025 and the LSP-Num Conference. His professional activities demonstrate ongoing engagement with both theoretical developments and practical applications in language education technology.

Robert Nowakمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Science
• +۳ مورد دیگر

Robert Nowak holds dual distinguished professorships as the Keith and Jane Morgan Nosbusch Professor in Electrical and Computer Engineering and the Grace Wahba Professor of Data Science at the University of Wisconsin–Madison. Based at the Discovery Building (330 N Orchard Street), he leads interdisciplinary research at the Wisconsin Institute for Discovery, bridging engineering with data science applications. His academic foundation includes: BS, MS, and PhD from the University of Wisconsin–Madison Post-doctoral Fellowship at Rice University Nowak's research program spans artificial intelligence, machine learning, and optimization with dual emphases on AI-driven health applications and systems optimization. His work integrates theoretical rigor with practical implementations, particularly in large language model fine-tuning, active learning frameworks, and neural network theory. Recent publications demonstrate strong focus on improving model efficiency, humor comprehension in AI systems, and theoretical bounds for retrieval-augmented generation. Analysis of his 15 most recent publications reveals dominant trends in large language model advancement (particularly humor understanding and task diversity), theoretical neural network analysis (including sparse architectures and multi-task learning), and novel active learning methodologies for open-world scenarios. His work consistently bridges theoretical machine learning with real-world applications in health and recommendation systems. While specific named awards aren't documented in the source material, his appointment to two endowed chairs (Nosbusch and Wahba professorships) represents exceptional institutional recognition of his scholarly impact. Nowak advises graduate students in the Electrical and Computer Engineering department and secures significant research funding, including NSF grants such as CIF: Small: Advanced Understanding and Applications of Deep Learning. His group operates within the collaborative ecosystem of the Wisconsin Institute for Discovery, fostering cross-disciplinary projects that integrate AI with health sciences and engineering systems. Current projects indicate strong momentum in human-AI collaboration frameworks and optimization of language model training pipelines.

Zhibin Chenمشاهده پروفایل

استادیار

• Transportation Network Modeling and Optimization
• Intelligent Transportation Systems
• Discrete Optimization
• +۴ مورد دیگر

Zhibin Chen is an Assistant Professor of Engineering at NYU Shanghai and concurrently a Global Network Assistant Professor within the broader New York University system. Since January 2019 he has led research and teaching activities at the Division of Engineering and Computer Science in Shanghai, while maintaining university-wide collaborations through his Global Network appointment. Education Ph.D. in Transportation Engineering, University of Florida (2017) Research Interests Dr. Chen’s scholarship centres on Transportation Network Modeling and Optimization , Intelligent Transportation Systems , and Discrete Optimization . He integrates operations research, data science, and engineering to address emerging challenges in electric mobility, autonomous vehicles, and large-scale urban networks. Recent thrusts include: Data-driven analytics of electric-vehicle charging behaviour under usage heterogeneity. Optimization of charging and swapping infrastructure for electric buses and trucks. Network-level deployment and control strategies for connected and automated vehicles. Day-to-day traffic dynamics and equilibrium models with elastic demand. Pricing, policy, and incentive design for sustainable transportation systems. Scientific Awards Stella Dafermos Best Paper Award – awarded at the 95th Transportation Research Board Annual Meeting. Ryuichi Kitamura Paper Award – also conferred at the 95th TRB Annual Meeting. Editorial & Professional Service Dr. Chen currently serves on the Editorial Advisory Board of Transportation Research Part C: Emerging Technologies , shaping the editorial direction of the leading journal in his field. Grants & Collaborations While specific grant identifiers are not disclosed in the provided text, Dr. Chen’s extensive publication record in top-tier journals ( Transportation Science , Transportation Research Parts B, C, D , IEEE ITS , Applied Energy ) and his editorial role indicate sustained research funding and active collaboration with international partners across North America and China. Laboratories & Teams Operating within the Division of Engineering and Computer Science at NYU Shanghai , Dr. Chen leads a research group focused on next-generation mobility analytics, leveraging the university’s interdisciplinary ecosystem and NYU’s Global Network resources to advance smart and sustainable transportation.

Silverio Juan Martinez Fernandezمشاهده پروفایل

استاد

• Empirical Software Engineering
• Environmental Sustainability of AI
• Green AI
• +۳ مورد دیگر

Silverio Juan Martinez Fernandez is a Professor at the Universitat Politècnica de Catalunya (UPC) , affiliated with the Barcelona School of Informatics (FIB) and the Department of Service and Information Systems Engineering . He is a core member of the inSSIDE and GESSI research groups. His expertise spans Empirical Software Engineering , Green AI , MLOps , and Software Analytics . Education: Bachelor's in Computer Engineering PhD from UPC in Software Engineering Master's in Computing Research Interests: Focuses on sustainable AI practices, energy-efficient ML systems, and MLOps education. He investigates architectural design for green AI, energy labeling tools for ML models, and agile software development methodologies. His work bridges theoretical research and industrial applications, emphasizing data-driven decision-making. Grants & Collaborations: Leads projects like Green AI-Based Systems Architecture and Q-Rapids , funded by national and EU programs. Collaborates with institutions like Softeam and industry partners to apply software analytics in real-world scenarios. Labs & Teams: Coordinates the inSSIDE group, focusing on integrated software and data engineering. Active in organizing conferences like GREENS and ESEM , and co-develops tools like Skuld for technical debt management.

Haiyang Aiمشاهده پروفایل

دانشیار

• Corpus Linguistics
• Natural Language Processing
• Second Language Acquisition
• +۳ مورد دیگر

Haiyang Ai is an Associate Professor in the Literacy and Second Language Studies program at the University of Cincinnati's School of Education. His research applies corpus linguistics and natural language processing to investigate second language acquisition, writing complexity, and bilingual language processing. His educational background includes a Ph.D. in Applied Linguistics from The Pennsylvania State University (2015), an M.A. in Linguistics & Applied Linguistics from the University of Chinese Academy of Sciences (2006), and a B.A. in English from Shaanxi Normal University (2003). Dr. Ai's research spans corpus linguistics, natural language processing, second language acquisition, and computer-assisted language learning. He specializes in compiling and analyzing native and learner corpora to develop intelligent language learning systems and investigate lexical/syntactic complexity in L2 writing. His methodological approach integrates computational tools with theoretical linguistics to address practical language teaching challenges. His recent publications (2019-2023) demonstrate consistent focus on lexical bundles in professional communication, automating complexity measurement in Chinese, speech perception mechanisms in bilinguals, and grammatical puzzles in English learning. These works bridge corpus-based analysis with psycholinguistic experimentation across diverse subfields including morphosyntax, pragmatic competence, and cognitive processing in L2 acquisition. Dr. Ai has secured multiple University of Cincinnati grants including three CECH Faculty Development Grants ($2500 in 2017-2018 for verb-noun collocation research, $2000 in 2016-2017 for corrective feedback tools) and the NCFDD Faculty Success Program ($3250 in 2017), all supporting his development of computational language learning resources.

Jie M. Zhangمشاهده پروفایل

استادیار

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

Jie M. Zhang is an Assistant Professor in the Department of Informatics at King's College London, specializing in the intersection of software engineering and artificial intelligence. Her research focuses on two main directions: AI for Software Engineering (leveraging AI technologies to automate software tasks) and Software Engineering for AI (applying SE principles to enhance AI system trustworthiness). Her educational background includes a PhD in Computer Science from Peking University, where she was supervised by Professors Lu Zhang and Dan Hao. Prior to joining King's College London, she was a Research Fellow at University College London working with Professor Mark Harman and Professor Federica Sarro. Dr. Zhang's research interests center on software testing, machine learning trustworthiness, fairness testing, bias mitigation in AI systems, and program analysis. Her work particularly examines how large language models can be utilized for code generation, test case creation, and program repair, while also developing techniques to detect and fix issues within AI models. Her recent publications demonstrate strong trends in evaluating and enhancing the trustworthiness of AI-generated code, with specific emphasis on fairness testing across various domains including autonomous driving systems, machine translation, and decision-making software. Her research increasingly focuses on the efficiency of generated code and detecting hallucinations in large language models. 2025 ACM Sigsoft Early Career Researcher Award for pioneering contributions to software engineering for AI IEEE TSE 2024 Best Paper Award for 'Stealthy Backdoor Attack for Code Models' FSE 2025 Distinguished Paper Award Royal Society International Exchange Grant recipient NMES Enterprise & Engagement Partnerships Fund recipient Dr. Zhang has served in numerous leadership roles across major software engineering conferences including as General Chair for AIware 2025, Area Chair for ASE 2025, and Steering Committee Member for ICST. She has advised multiple PhD students and received significant research funding for her work on LLMs and software engineering. Her research group collaborates with industry partners including Huawei and Facebook, and she leads projects such as ITEA GENIUS and ITEA GreenCode. She is actively involved with King's College London research hubs including the Trusted Autonomous Systems Hub, Security Hub, and Software Systems group, where her work contributes to developing trustworthy AI systems across multiple domains.

Allison Sullivanمشاهده پروفایل

استادیار

• Software Engineering
• Formal Methods
• Programming Languages
• +۳ مورد دیگر

Allison Sullivan is an Assistant Professor of Computer Science at the University of Texas at Arlington (UTA), where she also serves as the Undergraduate Software Engineering Program Director. She is a member of the Software Engineering Research Center (SERC) at UTA and serves as faculty advisor for UTA's Society of Women Engineers (SWE) club. Dr. Sullivan received her PhD in Software Verification, Validation and Testing (SVVAT) from the University of Texas at Austin in 2017 under Sarfraz Khurshid. Her educational background includes: PhD in Software Verification, Validation and Testing, University of Texas at Austin (2017) M.S. in Software Engineering, University of Texas at Austin (2014) B.S. in Software Engineering, University of Texas at Dallas (2012) Dr. Sullivan's research focuses on two primary areas: Automated Software Engineering : Test/Oracle Generation, Automated Bug Localization and Repair, Mutation Testing, and Regression Testing Formal Methods and Programming Languages : Abstractions, Finite Model Finders, Program Synthesis, and SAT/SMT Solvers She leads the SCOPE lab which focuses on 'showing the correctness of all program executions' and has published extensively on Alloy modeling language applications. Her recent publications demonstrate a strong focus on applying formal methods to software engineering problems, with a growing emphasis on the intersection of large language models and software development practices. Her work spans theoretical foundations, tool development, and empirical studies of how developers use modeling languages. Her scientific achievements have been recognized with: NSF CAREER Award (2024) UTA CSE department Rising Star Research Award (2024) UTA College of Engineering Outstanding Early Career Faculty Award (2025) NSF grant for building an educational tool for software modeling ($400k) Dr. Sullivan has successfully advised two PhD students to completion: Dr. Ana Jovanovic (defended November 2024) and Dr. Anahita Samadi (defended February 2025). She actively mentors undergraduate researchers and has secured significant research funding including the NSF CAREER grant. Her service includes committee roles for major conferences including ASE, ISSRE, and FormaliSE. She leads the SCOPE lab at UTA, which brings together graduate and undergraduate researchers to develop techniques for improving software verification and validation, with particular emphasis on making formal methods more accessible to practitioners.

Juan Zhaiمشاهده پروفایل

استادیار

• Software-AI Synergy
• Software Dependability
• Formal Specification Synthesis
• +۴ مورد دیگر

Juan Zhai is an Assistant Professor in the Manning College of Information & Computer Sciences (CICS) at University of Massachusetts Amherst, where she co-directs the Laboratory for Advanced Software Engineering Research (LASER) and participates in the UMass NLP group. Her academic career spans over 7 years of active service including program committee roles at top-tier conferences like ICSE, FSE, and ASE. Her research focuses on Software-AI Synergy with core areas including: Formal Specification Synthesis for precise software behavior definition Comment Generation and Maintenance using LLMs Trustworthy AI through bias detection and framework testing Deep Learning Infrastructure Reliability Recent work demonstrates strong emphasis on practical tools for AI safety and software dependability. Her publication trends show consistent output in top software engineering venues (ASE, ICSE, FSE) with increasing focus on AI/ML conferences (ACL, CVPR, ICLR). Key themes include metamorphic testing for deep learning frameworks, bias analysis in LLMs, and formal methods for specification synthesis. She actively serves the community through: Program committees for 13 major conferences Reviewing for 5 top journals including TOSEM and TSE 40+ total reviews across SE and AI venues Juan mentors PhD students including Gehao Zhang (research focus: Software Engineering, AI Safety) and teaches graduate courses like CS520 (Theory and Practice of Software Engineering) and CS692P (Hot Topics in SE Research). She leads the LASER lab which develops tools like C2S, CPC, and DevMuT for software reasoning and AI infrastructure testing.