جست‌وجوی استاد و پژوهشگر

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.

Dana Schmalzمشاهده پروفایل

پژوهشگر ارشد

• international refugee law
• European refugee law
• fundamental rights
• +۷ مورد دیگر

Dana Schmalz is a Senior Research Fellow at the Max Planck Institute for Comparative Public Law and International Law in Heidelberg, Germany, and currently serves as a visiting scholar at Columbia Law School and a fellow at the Columbia Center for Contemporary Critical Thought in New York. Funded by an Alexander von Humboldt Foundation fellowship, she pursues a two-year research project examining critical dimensions of international refugee law and migration governance. Her academic profile combines rigorous legal scholarship with theoretical engagement across international law, political philosophy, and migration studies. Dr. Schmalz earned her doctorate from the University of Frankfurt in 2017 with a dissertation on democratic theoretical issues in refugee law, followed by an LL.M. in Comparative Legal Thought from Cardozo Law School, New York, in the same year. She serves as an editor for the journal Kritische Justiz (KJ), contributing to critical legal scholarship in German-speaking academic circles. Her research centers on the intersection of refugee law and democratic theory, examining how political rights are secured for those without voice in democratic processes. She critically investigates population discourse and its relationship to migration governance, exploring how demographic projections shape perceptions of 'too many' migrants. Her scholarship analyzes the language and power structures of international law, examining how legal frameworks construct hierarchies and influence global governance. She has published extensively on European border policies, refugee protection mechanisms, and the theoretical foundations of international legal order. Dr. Schmalz's recent publications reveal a sophisticated analysis of migration governance that connects historical developments with contemporary challenges. She examines how legal concepts like responsibility-sharing function in practice, investigating the role of geopolitical proximity in determining state obligations toward refugees. Her work consistently combines rigorous legal analysis with critical theoretical perspectives, often challenging conventional understandings of migration governance. She explores the internalization of borders in demographic thinking and the ways legal frameworks contribute to perceptions of migration as 'excess.' Alexander von Humboldt Foundation fellowship supporting current research As an active contributor to Völkerrechtsblog, Dr. Schmalz helps shape digital infrastructure for global legal scholarship. Her work connects with broader academic communities through collaborations with scholars across international law and refugee studies. While specific grant details beyond the Humboldt fellowship aren't provided in the source material, her extensive publication record indicates sustained research activity across multiple funding cycles. She engages with practical policy challenges through her analysis of European migration governance and refugee protection mechanisms. Dr. Schmalz's research connects with multiple academic networks through her work at the Max Planck Institute, Columbia University, and her editorial role at Kritische Justiz. Her scholarship bridges German and Anglo-American academic traditions, contributing to transnational legal discourse on migration and refugee protection. She participates in interdisciplinary conversations that connect legal scholarship with political theory, philosophy, and migration studies.

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.

Jonathan Ragan-Kelleyمشاهده پروفایل

استادیار

• High-Efficiency Visual Computing
• Compilers
• Computer Architecture
• +۳ مورد دیگر

Jonathan Ragan-Kelley is the Esther and Harold E. Edgerton Assistant Professor of Electrical Engineering & Computer Science at MIT and an Assistant Professor of EECS at UC Berkeley. He leads the Visual Computing group at CSAIL, focusing on high-efficiency visual computing, compilers, and architectures for image processing, machine learning, and 3D rendering. His research bridges systems, compilers, and hardware design, emphasizing scalable solutions for computational challenges. Education: PhD in Computer Science from MIT (2014), postdoc at Stanford University, and visiting researcher at Google. He co-created the Halide language and has developed multiple domain-specific languages (DSLs) and compiler systems. Research interests include compiler optimization, scheduling languages (e.g., Exo), and efficient computing frameworks. He has received awards such as the NSF CAREER Award and ACM SIGGRAPH’s Significant New Researcher Award. Awards: ACM SIGGRAPH Award, NSF CAREER, Intel Outstanding Researcher Award Key Contributions: Halide compiler framework, Exo scheduling language, machine learning acceleration techniques Labs/Teams: Visual Computing at MIT CSAIL

• Machine Learning
• Data Mining
• Bioinformatics
• +۱۱ مورد دیگر

Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.

• Automating Software Engineering Tasks
• Mining Software Repositories
• Program Analysis
• +۹ مورد دیگر

Univ.-Prof. Martin Pinzger is a Professor at the Department of Informatics Systems, Alpen-Adria-Universität Klagenfurt. He serves as Head of Department and Member of the Senate, actively contributing to academic governance. Research Focus: Automating Software Engineering Tasks, Mining Software Repositories, Program Analysis, Software Evolution and Visualization Recent Work: Developing tools for API evolution analysis, cybersecurity AI (CAI), robotics benchmarking (RobotPerf), and dependency validation His research combines empirical studies with tool development for software maintenance and security. Current projects address challenges in REST API breaking changes, cloud security certifications, and robotic system performance evaluation. Publications since 2023 demonstrate continued engagement with topics spanning AI-driven code segmentation, microservice API evolution, and cybersecurity tool development. Key trends include cross-disciplinary applications of NLP to software engineering and security-focused tool creation. Contact: martin.pinzger@aau.at

• Human Activity Recognition
• Wearable Sensors
• Machine Learning
• +۴ مورد دیگر

Thomas Ploetz is an Adjunct Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. His work focuses on sensor-based human activity recognition, wearable computing, and computational behavior analysis with applications in healthcare, smart homes, and education. He leads interdisciplinary research integrating machine learning, IoT systems, and human-centered design. His research interests include improving activity recognition robustness through synthetic data generation, developing explainable AI for time-series analysis, and advancing healthcare technologies like diabetic foot ulcer monitoring systems. He explores ethical implications of wearable sensing in diverse populations, including underrepresented groups. Key contributions include IMUTube (virtual sensor data generation), ProxiCycle (cyclist safety monitoring), and DISCOVER (smart home activity recognition framework). His work addresses challenges in data scarcity, domain adaptation, and long-term system maintenance in pervasive computing environments. He has been awarded grants such as the GVU/IPaT Research and Engagement Grant (2020), and his research appears in top venues like UbiComp and ISWC. He actively contributes to the wearable computing community through conference organization and editorial work.

FENG Menglingمشاهده پروفایل

دانشیار

• Biostatistics
• Healthcare Data Analytics
• Medical AI
• +۳ مورد دیگر

**FENG Mengling** is an Associate Professor at the National University of Singapore (NUS) and holds primary affiliation with the Saw Swee Hock School of Public Health. She serves as the Domain Leader for the Biostatistics, Modelling, AI and Data Analytics (B.MAD) Domain and Director of the AI for Public Health (AI4PH) Program. Her academic credentials include a Senior Post-doc from Harvard-MIT Health Science Technology Division, a PhD from Nanyang Technological University (2009), and a Bachelor's degree (2003) from NTU. Research & Teaching: Her research focuses on causal inference for evidence-based medicine, generative models for medical time-series analysis, and healthcare data analytics. She teaches courses on big data technologies for healthcare problems and healthcare data analytics. Professional Roles & Awards: She has led the Biomedical and Healthcare Analytics Lab at the Institute for Infocomm Research (2014–2015) and currently serves as an Affiliate Scientist at Harvard-MIT. Notable accolades include the MIT Teaching & Learning Laboratory Kaufman Teaching Certificate and recognition as a finalist in MIT’s 2013 Innovation Showcase. Her work has been featured in prominent media outlets like The Straits Times and Channel NewsAsia, highlighting breakthroughs such as AI nurses and Singlish-speaking healthcare assistants. Publications & Impact: Over 50 peer-reviewed publications span AI-driven clinical decision support, medical imaging analysis, and predictive modeling in critical care. Key contributions include frameworks like MedDreamer (reinforcement learning for EHR analysis) and DivScore (LLM-generated text detection). Her research bridges causal inference, generative AI, and scalable healthcare solutions. Labs & Initiatives: As a leader in NUS’s Public Health AI Innovation Center (launching early 2025), she drives initiatives like FxMammo (AI for breast cancer screening) and the Biomedical and Healthcare Analytics Lab. Her work emphasizes ethical AI deployment and cross-disciplinary collaboration in healthcare.

Eric TOTELمشاهده پروفایل

استاد

• Network Security
• Intrusion Detection Systems
• Data Confidentiality
• +۳ مورد دیگر

Eric TOTEL is a Professor at Telecom SudParis, specializing in cybersecurity and network security. His research focuses on intrusion detection systems, graph-based anomaly detection, machine learning applications in security, and data confidentiality in distributed systems. He has contributed to projects such as DAMS (DDoS mitigation using deep reinforcement learning), Sec2Graph (novelty detection on graph-structured data), and DAEMON (dynamic autoencoder-based anomaly detection). His work emphasizes scalable solutions for multi-step attack detection and privacy-preserving infrastructure for encrypted DNS logs. Key contributions include developing correlation engines for distributed systems, formalizing invariant-based attack detection in web applications, and exploring static analysis for information flow control. He has authored over 50 peer-reviewed publications and served on program committees for conferences like RAID, CRiSIS, and EuroS&P. His HDR (2012) formalized error-detection techniques applied to intrusion detection. Advising and grants: He collaborates on projects funded by French national research agencies and has mentored students in cybersecurity, AI for defense (CAID conferences), and cloud infrastructure security. His research often bridges theoretical models and practical implementations, with tools like STARLORD for 3D graph visualization of security data.

Saman Amarasingheمشاهده پروفایل

استاد

• Programming Languages
• Compilers
• Domain-Specific Languages
• +۷ مورد دیگر

Professor Saman Amarasinghe is a full Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), and Principal Investigator at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Commit compiler research group, which focuses on programming languages and compilers that maximize application performance on modern computing platforms. His work spans multiple academic departments and research centers, with strong affiliations to both MIT's School of Engineering and CSAIL. Professor Amarasinghe's research interests center around high-performance domain-specific languages and compiler technology . His work combines language design with sophisticated compilation techniques to deliver unprecedented performance for targeted application domains. His research spans multiple areas including image processing (Halide), sparse tensor algebra (TACO), graph analytics (GraphIt), stream computations (StreamIt), and bioinformatics (Seq). A significant thread throughout his work is the application of machine learning for compiler optimizations, from Meta optimization in 2003 to the OpenTuner autotuner framework. Analysis of Professor Amarasinghe's recent publications reveals a strong focus on sparse computing , compiler vectorization , and domain-specific language implementation . His work consistently bridges theoretical compiler concepts with practical performance gains across diverse application domains. The progression from earlier work on StreamIt and Halide to more recent projects like GraphIt and TACO shows an evolution toward more specialized, high-performance DSLs targeting specific computational patterns. His 2020-2025 publications particularly emphasize sparse tensor operations, GPU acceleration, and machine learning integration with compiler technology. ACM Fellow (2019) Professor Amarasinghe has made significant contributions to academic entrepreneurship and student development. He founded Determina, Inc. (acquired by VMware) based on security research from his MIT lab and co-founded Lanka Internet Services, Ltd., Sri Lanka's first ISP. As faculty director of MIT Global Startup Labs, his programs across 17 countries have helped create over 20 successful startups. His teaching includes the popular Performance Engineering of Software Systems (6.172) course with Professor Charles Leiserson, as well as innovative project-based courses like the Open Source Software Project Lab and Bring Your Own Software Project Lab. His educational approach emphasizes hands-on experience with compiler and language design concepts. Professor Amarasinghe leads the Commit compiler research group at MIT CSAIL, which has produced numerous influential domain-specific languages and compilers including Halide, TACO, Simit, StreamIt, and GraphIt. The lab maintains strong industry connections through projects like OpenTuner and Determina, and collaborates with researchers worldwide on compiler technology. The group's work spans both theoretical compiler research and practical implementation, with a consistent focus on bridging the performance gap between high-level programming abstractions and hardware capabilities.

Hager Aliمشاهده پروفایل

پژوهشگر

• Political Science
• Middle Eastern Studies
• Civil-Military Relations
• +۵ مورد دیگر

Hager Ali is a Doctoral Researcher at the German Institute for Global and Area Studies (GIGA), specifically affiliated with the GIGA Institute for Middle East Studies, and is part of the GIGA Doctoral Programme since October 2018. Ali also serves as an External Editor for the thematic series "Autocracies with Adjectives" at the Loop, ECPR's Political Science Blog since March 2022, and maintains a career as a freelance journalist since 2010. German Institute for Global and Area Studies (GIGA) - Institute for Middle East Studies (2018-present) Central European University (CEU) - Teaching appointments (2025) Institute for Peace Research and Security Policy (IFSH) - Teaching appointments (2024) Ali's research focuses on civil-military relations in the Middle East and North Africa, military coups and civilian control of armies, authoritarian regimes and institutions, regime stability and destabilization, party systems and cleavages, and German military deployments in the MENA-Region and Sahel. The doctoral dissertation examines "Praetorianism in the MENA-Region: Military Governance and Civilian Control across Regime Types." Ali's work demonstrates sophisticated methodological approaches to understanding authoritarian resilience and military politics through comparative analysis. Ali's publication record reveals a consistent focus on Sudanese politics and civil-military relations, with increasing attention to contemporary conflicts since 2022. The research combines theoretical insights from comparative authoritarianism studies with detailed empirical analysis of military institutions and political transitions in the Middle East and North Africa. Recent work shows a growing emphasis on practical policy implications, particularly regarding peace negotiations in Sudan and the role of civilian actors in conflict resolution. Shortlisted for the Best Blog Award of 2022 by ECPR's Political Science Blog "The Loop" (2023) Ali actively contributes to academic discourse through conference presentations, policy papers, and media commentary. Recent engagements include serving as a discussant at the ECPR General Conference 2024 on "Trends in autocratic practices and behaviors" and scheduled lectures on the Sudan conflict in 2025. Ali has established collaborations with institutions like the German Institute for International and Security Affairs (SWP) and maintains strong connections with academic networks across Europe. As a member of the GIGA research programme on Accountability and Participation, Ali contributes to interdisciplinary research on governance and political institutions. Ali participates in multiple ECPR Standing Groups including Political Methodology, Political Violence, Comparative Political Institutions, and Critical Peace and Conflict Studies, demonstrating engagement with diverse methodological and theoretical approaches to political science research.

Srinivas Narayanaمشاهده پروفایل

استادیار

• Computer Networks
• Formal Verification
• Compilers
• +۴ مورد دیگر

Srinivas Narayana is an Assistant Professor in the Department of Computer Science at Rutgers University, specializing in programmable networking, formal verification, and systems research. He holds a PhD from Princeton University and a B.Tech from IIT Madras, with postdoctoral work at MIT. His research focuses on building safe, high-performance networks through optimizing compilers, verified programming, and distributed system monitoring. He has received NSF grants, the CGO 2022 Distinguished Paper Award, and the 2017 SIGCOMM Best Paper Award. Education: PhD and MA in Computer Science, Princeton University (2016) B.Tech in Computer Science, IIT Madras (2010) Postdoctoral Research, MIT (2018) Research Interests: His work bridges networking and systems with a focus on compilers, formal methods, and programmable hardware. Notable projects include K2 compiler for eBPF, the eBPF verifier soundness work, and congestion control mechanisms like CCP. He explores parallel packet processing, privacy-preserving analytics, and load balancing strategies. Grants & Awards: NSF Awards #2422076, #1910796, #2019302 eBPF Foundation Grant Facebook Networking Research Award Network Programming Initiative (NPI) Funding Lab & Teams: Leads the NetSys group at Rutgers, collaborating with teams on projects like the eBPF verifier, verified packet processing, and network monitoring tools like Marple. His lab emphasizes open-source contributions and industry collaboration.

Adam Jardineمشاهده پروفایل

دانشیار

• Phonology
• Computational Linguistics
• Formal Language Theory
• +۴ مورد دیگر

Adam Jardine is an Associate Professor in the Department of Linguistics at Rutgers University. He serves as the Graduate Program Director, overseeing the academic affairs of the department's graduate programs. His research focuses on computational and mathematical approaches to phonological theory, with emphases on formal language theory, learnability, and the application of computational models to phonological phenomena. Jardine holds a PhD in Linguistics from the University of Delaware (2016) and has held prior academic positions. His research integrates theoretical linguistics with computational methods, exploring topics such as autosegmental phonology, subregular complexity classes, and phonological learning algorithms. He has organized major conferences including AMP 2024 and workshops on subregular phonology. Jardine leads NSF-funded research on subregular inference of morpho-phonology (Award #2416184), collaborating with Jane Chandlee and Jeffrey Heinz. Key contributions include work on autosegmental representations, computational phonology, and the logical foundations of phonological theory. He has advised numerous PhD students, including Huteng Dai (2024) and Hyunjung Joo (expected 2026), whose research spans phonological learning and computational modeling. Jardine teaches advanced courses such as Phonology Seminar and maintains active participation in interdisciplinary projects linking linguistics to computer science and artificial intelligence. Recent work includes contributions to The Cambridge Handbook of Phonology (2026) and co-organizing sessions on computational learnability at the LSA Annual Meeting. His lab focuses on formal phonology, with ongoing projects exploring the computational boundaries of phonological systems and the application of recursive schemes to linguistic analysis.

Karin H. Jamesمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Developmental Psychology
• Brain and Behavior
• +۵ مورد دیگر

Karin H. James is a Professor and Director of Graduate Studies in the Department of Psychological and Brain Sciences at Indiana University Bloomington's College of Arts and Sciences, actively reviewing applications for Fall 2026 admissions. Her research program investigates how self-generated actions shape cognitive development through neural mechanisms. Her educational background includes: Post Doctoral Fellow, Vanderbilt University (2001-2003) Ph.D. in Psychology, University of Western Ontario (2001) M.A. in Psychology, University of Western Ontario (1998) B.S. in Psychology, University of Toronto (1996) B.A. in History, University of Toronto (1991) Dr. James' research centers on the neural correlates of learning, specifically how motor experience influences visual recognition across domains including object recognition, reading acquisition, language learning, and mathematical understanding. Using fMRI and behavioral methods, she examines how sensorimotor interactions during symbol production (e.g., handwriting) reconfigure brain networks for perception. Her work demonstrates that visual-motor contingency during learning creates lasting neural changes that enhance recognition, with implications for educational practices in literacy and numeracy development. Analysis of her 2018-2021 publications reveals consistent focus on embodied cognition principles, particularly how action-perception loops establish neural representations. Key themes include gesture-based learning in mathematics, visual-motor integration in symbol recognition, and category formation mechanisms, bridging cognitive neuroscience with developmental psychology through innovative methodologies like the MRItab neuroimaging tool. Dr. James maintains active membership in the Society for Research in Child Development (2007-present), Cognitive Neuroscience Society (2004-present), and Vision Sciences Society (2000-present), reflecting her interdisciplinary approach to learning mechanisms. As Director of Graduate Studies, she oversees program development and student mentorship while leading the Cognition and Action Neuroimaging Lab. Her research infrastructure includes specialized neuroimaging tools developed for naturalistic experimental paradigms, supporting investigations into how embodied experiences shape cognitive architecture from childhood through adulthood. The Cognition and Action Neuroimaging Lab develops cutting-edge methodologies like the MRItab touchscreen system to study naturalistic learning during fMRI scanning, focusing on how sensorimotor experiences during symbol production reconfigure neural networks for perception and recognition across developmental stages.