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Adrian SampsonView profile

Associate Professor

• computer architecture
• programming languages
• compilers
• +5 more

Adrian Sampson is an Associate Professor in the Department of Computer Science at Cornell University, where he is part of the Computer Systems Laboratory and the programming languages group. He joined Cornell in 2016 as an Assistant Professor and was promoted to Associate Professor in 2022. Prior to Cornell, he was a Visiting Researcher at Microsoft Research (2015-2016). He received his Ph.D. from the University of Washington in 2015 under advisors Luis Ceze and Dan Grossman, with a dissertation on Hardware and Software for Approximate Computing. His research focuses on breaking down abstraction barriers and rethinking the hardware-software interface. He is particularly known for his work on approximate computing, which explores how computers can be more efficient by allowing them to make controlled mistakes. He leads the Capra research group at Cornell, which investigates programming languages and computer architecture. Sampson's recent publications demonstrate a strong focus on hardware acceleration, FPGA programming, compiler design, and programming language theory. His work often bridges the gap between high-level programming abstractions and low-level hardware implementation, with particular attention to predictability, verification, and energy efficiency. He has made significant contributions to geometry types for graphics programming, timeline types for modular hardware design, and virtual machines for FPGA programming. Among his notable recognitions are the IEEE TCCA Young Computer Architect Award (2021), NSF CAREER award (2019), and multiple Distinguished Artifact Awards at major conferences. He has advised numerous Ph.D. students who have gone on to positions at institutions like Wellesley College, Northwestern University, and Amazon. Sampson is actively involved in academic service, serving on program committees for major conferences including PLDI, ASPLOS, and ISCA. He has also held leadership roles such as ACM SIGARCH Board of Directors (2023-2025) and SIGPLAN Information Director. His teaching at Cornell includes courses on computer systems, programming languages, and advanced compilers.

• Semiotics
• Zoosemiotics
• Translation Studies
• +4 more

Astrid GUILLAUME serves as an HDR Lecturer in the Faculty of Letters, specializing in semiotics with a focus on human and non-human animal communication systems. Her academic position bridges theoretical linguistics, cultural studies, and animal ethics, examining how meaning transfers across species and temporal frameworks through verbal and non-verbal channels. Her research centers on three interconnected domains: theoretical semiotics (intertheoricity and transferogenesis), zoosemiotics (animal communication, sentience, and liminality), and cultural semiotics (semiotranslationology and semiomedievalistics). GUILLAUME challenges anthropocentric paradigms by developing frameworks for understanding non-human sign systems, advocating for terms like 'animal liminaire' in academic discourse. Her work integrates medieval studies with contemporary animal ethics to analyze evolving human-animal relationships. Recent publications (2021-2023) reveal consistent focus on practical applications of semiotic theory to animal welfare crises, particularly cetacean captivity, commercial fishing practices, and hunting ethics. She employs transdisciplinary methodologies to critique anthropocentric exploitation while proposing legislative reforms based on semiotic evidence of animal sentience and communication complexity. Though specific advisory roles remain undocumented, GUILLAUME's direction of academic collections ('Zoosémiotique' and 'Traditions et Croyance') and organization of doctoral seminars demonstrates scholarly leadership. Her editorial work bridges theoretical semiotics with real-world animal advocacy, positioning her as a key figure in emerging humanimalist scholarship.

James LOPARICSView profile

Professor

• All-day education
• Education for Sustainable Development
• General Educational Science
• +3 more

James LOPARICS serves as Professor for All-Day Education at the University of Teacher Education Vienna's Institute for Continuing Education, teaching primary/leisure education courses while providing school development consulting across Viennese all-day schools. His institutional role bridges academic research and practical school improvement through direct engagement with educators and policymakers. Academic Background Doctorate in Educational Science, University of Rostock (2018-2022) Master's in Education and Sustainability, University of Rostock (2015-2017) Bachelor's in Educational Science, FernUniversität Hagen (2011-2014) University Course in School Development Consulting, PH Vienna (2018-2020) Advanced Psychotherapy Training, Austrian Society for Behavioral Therapy (2020-present) Loparics' research centers on transformative education frameworks addressing epochal crises like climate change, with critical examinations of economization in school quality systems and innovative approaches to leisure pedagogy. His work consistently connects theoretical constructs with practical school contexts, particularly through Viennese case studies on interdisciplinary cooperation in all-day settings. His 15 most recent publications (2019-2024) reveal three dominant trends: (1) Development of indicators for sustainable development education (SDG 4.7) amid measurement challenges; (2) Critical analysis of quality management systems (QMS/SQR) as manifestations of educational economization; and (3) Theoretical advancement of leisure pedagogy as neglected policy domain. These threads converge in his signature focus on 'epochal key problems' as quality criteria for all-day schooling. He actively supervises master's theses in primary education programs and contributes to the ÖKOLOG-Team's environmental certification (Umweltzeichen) initiatives, developing sustainability curricula for Viennese schools while presenting research at international conferences including ECER and WERA Extended Education forums.

• Natural Language Processing
• Text Analysis
• Speech Processing
• +4 more

Mark Cieliebak is a Professor for Speech and Text Processing at the Zurich University of Applied Sciences (ZHAW), School of Engineering, where he leads the Natural Language Processing (NLP) research group at the Centre for Artificial Intelligence. He has held this position since December 2018, following prior roles as a lecturer at ZHAW (2012-2018). His educational background includes a Dr. sc. techn. in Computer Science from ETH Zürich (1999-2003) and a Diplom in Computer Science from Universität Dortmund (1990-1999). His research focuses on Natural Language Processing , Text and Speech Analysis , and Machine Learning applications . Primary domains include dialogue systems evaluation, low-resource speech recognition (particularly Swiss German), automated text generation assessment, and NLP for social media analysis. He also explores educational applications through flipped classroom methodologies and machine learning operations. Cieliebak's publications (2020-2024) demonstrate strong emphasis on NLP evaluation frameworks, speech corpus development for dialects, and generative AI validation. Recurring themes include robustness testing, bias mitigation in automated metrics, and multilingual processing challenges. His work frequently involves large-scale dataset creation and ensemble learning techniques. Scientific Awards: SwissNLP Award 2023 Best Paper Award - Honorable Mention at EMNLP 2020 ZHAW Teaching Award 2015 He leads numerous research projects including: Unified Model for Text Generation Evaluation End-to-End Swiss German Speech Translation Holistic Analysis of Misinformation in Social Networks Automated News Processing (AutoNews) Chatbot Development for Language Learners He co-founded SpinningBytes AG (2015-present) and maintains extensive industry collaborations. As head of the NLP research group, he oversees doctoral candidates and master's students, though specific advisee names aren't listed.

Yi MeiView profile

Associate Professor

• Evolutionary Computation
• Combinatorial Optimisation
• Genetic Programming
• +10 more

Dr. Yi Mei is an Associate Professor and Associate Dean (Research) at the Faculty of Engineering, Victoria University of Wellington, New Zealand. He serves as Programme Director for Computer Science & Computer Graphics within the School of Engineering and Computer Science and is affiliated with the Centre for Data Science and Artificial Intelligence (CDSAI). Dr. Mei's research focuses on evolutionary computation for combinatorial optimisation , with specific expertise in genetic programming , automatic algorithm design , explainable AI , and multi-objective optimisation . His work bridges theoretical foundations with practical applications in transportation, healthcare, manufacturing, and aquaculture. His publication portfolio shows a clear trajectory toward increasingly complex real-world applications of evolutionary computation, with recent work focusing on emergency medical dispatch optimization , dynamic transportation systems , and explainable AI for routing problems . The research demonstrates strong interdisciplinary connections between evolutionary computation, machine learning, and domain-specific optimization challenges. Victoria University of Wellington Ki te Pae - Research Excellence Award 2024 Multiple Best Paper Awards at ACM Genetic and Evolutionary Computation Conference (GECCO) from 2022-2024 HUMIES Silver Award at GECCO 2023 IEEE Transactions on Evolutionary Computation Outstanding Associate Editor (2024, 2025) Fellow of Engineering New Zealand and IEEE Senior Member Dr. Mei has successfully supervised numerous PhD and Master's students whose research has received recognition, including the ACM SIGEVO Dissertation Award. He leads significant research projects funded by MBIE Endeavour Smart Idea Fund ($1M NZD), New Zealand Royal Society Catalyst Leaders Fund ($150K NZD), and industry partners like Honda Research Institute Europe. His service contributions include editorial roles for top journals including IEEE Transactions on Evolutionary Computation and leadership positions in IEEE Computational Intelligence Society. Dr. Mei leads the Evolutionary Computation for Combinatorial Optimisation Group (ECCO) and the Evolutionary Computation and Machine Learning Research Group (ECRG) at Victoria University of Wellington. He also chairs the IEEE Taskforce on Evolutionary Scheduling and Combinatorial Optimisation (TESCO), fostering international collaboration in evolutionary computation applications.

Ankush DasView profile

Assistant Professor

• Programming Languages
• Cryptographic Protocols
• Distributed Systems
• +6 more

Ankush Das is a tenure-track assistant professor in the Computer Science Department at Boston University. His research focuses on programming languages with applications in cryptographic protocols, distributed systems, and probabilistic and machine learning models. Prior to joining Boston University, he worked as an applied scientist at Amazon in the Automated Reasoning Group until December 2023. Dr. Das received his PhD from Carnegie Mellon University in 2021, where he was advised by Prof. Jan Hoffmann and worked closely with Prof. Frank Pfenning. He completed his undergraduate studies at IIT Bombay, India in 2015. His research interests span a wide range of topics within programming languages, with specific focus on resource analysis, session types, distributed protocols, and language design for smart contracts on the blockchain. He is particularly interested in developing type systems that can ensure safety and efficiency properties in concurrent and distributed systems. His work has led to the development of domain-specific languages like Nomos for implementing smart contracts and Rast for resource-aware session types with arithmetic refinements. Dr. Das's publication record demonstrates a consistent focus on advancing type theory and its applications to practical systems. His work frequently appears in top-tier programming languages conferences such as POPL, PLDI, and ICFP. A notable trend in his recent work is the extension of session types to handle probabilistic computations and resource-aware programming, reflecting the growing importance of these areas in modern distributed systems. His scientific achievements have been recognized with several prestigious awards: Distinguished paper award at POPL 2024 for "Parametric Subtyping for Structural Parametric Polymorphism" Best system description paper award by a junior researcher at FSCD 2020 for "Rast: Resource-Aware Session Types with Arithmetic Refinements" Dr. Das is actively mentoring PhD and undergraduate students at Boston University. His current advisees include Anthony DeRossi, Toby Ueno, Brendan Coyne, Qiancheng Fu, June Wunder, Sam Buxbaum, and Sakshi Sharma. He is looking for motivated PhD students to join his research group, with opportunities to work on cutting-edge problems in programming languages and their applications to distributed systems and security. At Boston University, Dr. Das leads research efforts in programming language theory and its applications. His work bridges theoretical foundations with practical implementations, particularly in the areas of smart contracts and distributed protocols. He has collaborated extensively with researchers from Carnegie Mellon University, including his former advisors Jan Hoffmann and Frank Pfenning.

Marcus DeiningerView profile

Professor

• Software Engineering
• Programming Languages
• Business Process Modeling
• +2 more

Prof. Dr. Marcus Deininger serves as Professor of Computer Science at Stuttgart University of Applied Sciences within the Faculty of Surveying, Computer Science and Mathematics since 2004, concurrently holding the position of Dean of Studies for Software Technology since 2007. His academic leadership spans multiple degree programs including Bachelor of Applied Mathematics and AI, Bachelor of Computer Science, and Master's in Software Technology. His educational foundation includes a Doctorate in Computer Science from the University of Stuttgart (1995), preceded by significant industry experience: development and project management roles at debis Systemhaus (1996-2001) and T-Systems (2001-2005) provided practical grounding in enterprise IT solutions before his academic transition. Deininger's research manifests primarily through pedagogical contributions in software engineering education, with concentrated expertise in Java programming ecosystems, BPMN-based business process modeling, and UML-driven system analysis. His scholarly identity centers on translating complex technical concepts into accessible educational frameworks, evidenced by his extensive publication record focused on student skill development. Analysis of his 12 publications (2009-2024) reveals consistent emphasis on practical computer science education, evolving from foundational programming guides toward specialized exam preparation materials across software engineering domains. This trajectory demonstrates sustained commitment to curriculum development rather than theoretical research. His professional recognition includes membership in the German Informatics Society (GI), reflecting peer acknowledgment within Germany's primary computing professional organization. As Dean of Studies and thesis supervision guide author, Deininger shapes academic journeys through structural program oversight and pedagogical frameworks. His collaborative works with Thomas Kessel across 10+ publications indicate sustained research partnerships, while leadership in three distinct degree programs underscores institutional impact beyond narrow technical specialization. Though specific research laboratories aren't documented, his collaborative publication pattern suggests integration within computer science education communities, with recent works (2024) confirming active engagement in evolving pedagogical approaches for contemporary programming challenges.

Fang LiuView profile

Assistant Professor

• Software Engineering
• Artificial Intelligence for Software Engineering
• Program Understanding
• +3 more

Fang Liu is an Assistant Professor at the School of Computer Science & Engineering, Beihang University, China. She has made significant contributions to the field of software engineering, particularly in the intersection of artificial intelligence and software development practices. Dr. Liu received her Ph.D. in Computer Science from Peking University (Sep. 2017 - Jul. 2022), supervised by Prof. Zhi Jin and Prof. Ge Li. Prior to that, she earned her B.S. in Computer Science from Chongqing University (Sep. 2013 - Jul. 2017). Her research interests focus on AI for Software Engineering, including program understanding and generation, program repair, and applications of Large Language Models to software development tasks. Dr. Liu teaches Compiler Technology as a compulsory course at Beihang University (Fall 2024) and Programming in Cangjie Language as an elective course (Spring 2025). Her recent publications demonstrate a clear trend toward leveraging large language models for various software engineering tasks, with a particular emphasis on code editing, program repair, and code translation. Her research spans both theoretical advancements in model architectures and practical applications to real-world software development challenges, with numerous publications in top-tier venues like ASE, ICSE, and FSE. Distinguished Paper Award at ICPC'20 for "A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning" Dr. Liu actively participates in the software engineering research community as a program committee member for major conferences including ASE, FSE, and ICSE. Her work has significant implications for improving developer productivity, software quality, and the integration of AI technologies into the software development lifecycle.

Diego GarbervetskyView profile

Associate Professor

• Static analysis
• Program verification
• Smart Contract analysis
• +5 more

Diego Garbervetsky is an Associate Professor at the Computer Science Department, School of Sciences, University of Buenos Aires, and a Researcher at ICC/CONICET. He also serves as Director of the Institute of Research in Computer Sciences (ICC). His academic career spans multiple roles in software engineering research and education. His educational background is not explicitly stated in the provided texts, but his current position reflects extensive academic achievement. Garbervetsky's research focuses on static analysis techniques for Java-like programs and Smart Contracts, automated program verification , program understanding , and validation . His specific interests include program understanding, testing and verification of programs featuring rich protocols, static analysis for program verification, and automatic symbolic resource analysis (gas consumption, dynamic memory, energy, etc.). His research has significant implications for blockchain technology, particularly in smart contract verification. Analysis of his recent publications reveals a strong trend toward smart contract security and verification , with multiple papers on modal abstractions, predicate abstractions, and tools like VeriSol for Solidity smart contracts. His work bridges formal methods with practical software engineering challenges, particularly in the blockchain domain. His scientific contributions include the development of multiple research tools: Contractor: Automated tool for behavior validation Contractor.NET: Visual Studio extension for .NET validation JConsume2: Compositional analysis for Java heap memory Consume.Net: Compositional analysis for .NET heap memory BudaPest: Automated software verifier VInTime: Verification suite for Real Time systems Garbervetsky has supervised numerous PhD students including Daniel Wappner, Javier Godoy, and Alexis Soifer, with former students now working at companies like Microsoft, Veritran, and Dialpad. He has served on program committees for major conferences including ICSE, FSE, ASE, and ISSTA, demonstrating his standing in the software engineering research community. His service includes chairing workshops and serving on artifact evaluation committees, showing his commitment to research quality and reproducibility. He currently teaches Software Engineering 2 at the University of Buenos Aires, having previously taught courses in algorithms, automatic software validation, program analysis, programming paradigms, and computer organization.

• Educational Technology
• Digital Transformation
• Higher Education
• +3 more

Maritha Dahlin serves as a Lecturer in the Department of Informatics and Media at Uppsala University, located at Campus Gotland in Visby, Sweden. Her role involves teaching and research within the domain of informatics and media, with a specific focus on educational applications. Her primary research interests lie at the intersection of educational technology and digital innovation in higher education. She investigates how artificial intelligence, particularly chatbot systems, can enhance student learning experiences and support services. Key areas include the design and implementation of conversational agents for tutoring, the impact of digital tools on pedagogical practices, and the broader implications of digital transformation in academic institutions. Dahlin's scholarly output centers on the application of chatbots in educational contexts. Her 2020 publication on "Tutorbot" exemplifies her work in leveraging conversational AI to address challenges in higher education. This research contributes to the fields of Educational Technology and Human-Computer Interaction, with specific relevance to E-Learning systems and student support technologies. No scientific awards or honors were documented in the available sources. Information regarding student advising, research grants, or supervisory roles was not provided in the source material. There are no details available about specific laboratories or research teams associated with Maritha Dahlin in the provided information.

Mirela DjordjevicView profile

Professor

• programming languages design
• domain specific languages
• electronic document systems
• +3 more

Mirela Djordjevic is a Professor at Manhattanville University, where she has served since 2002 after prior appointments at Trinity University (2000-2002) and the University of Belgrade in Serbia (1984-1999). She holds a PhD and master's in computer science from the University of Maryland, complemented by a master's in computer science and bachelor's in mathematics from Belgrade University. Education: Numerical Mathematics, BS, Belgrade University Computer Science, MS, Belgrade University Computer Science, MS, University of Maryland Computer Science, PhD, University of Maryland Research Focus: Dr. Djordjevic's work centers on programming languages design , domain specific languages , and constructive type theory , with significant contributions to program verification and electronic document systems. Her research bridges theoretical computer science and practical software development methodologies. Publication Trends: Her scholarly output reveals dual expertise in computer science education and applied systems development. She has authored textbooks on Java, Visual Basic, and database design while publishing research on pedagogical models like the Spiral Method and theoretical foundations of type systems. This corpus demonstrates consistent focus on improving programming education and language design since the 1990s. Scientific Recognition: No major awards or fellowships are documented in available sources. Academic Service: Current teaching responsibilities include Computer Programming I/II, Systems Architecture, Database Design, and Web Applications Development. Information regarding graduate advisees, research grants, or laboratory leadership is not provided in the source material.

• Software Engineering
• Protocol Specification and Testing
• Quality Assurance
• +3 more

Andreas Grontoudis serves as Assistant Professor in the Department of Computer Science and Engineering at the School of Sciences, European University of Cyprus since October 2007, following prior appointments as Assistant Professor at Cyprus College (2003-2007) and senior software development manager at Quad Computer Services (2001-2003). His academic credentials include: PhD in Computer Science, University of Sheffield (2000) - Thesis: X-machine based specification and design for testing of the CATV protocol MSc (Eng) in Computer Science, University of Sheffield (1994) - Thesis: An initial approach to X-machine specification of Distributed systems BSc in Computer Science, University of East Anglia (1993) - Thesis: A computer automated measurement system Dr. Grontoudis specializes in bridging theoretical computer science with practical applications, particularly in protocol specification using X-machines, quality assurance systems for medical imaging, and disaster monitoring technologies. His research evolved from foundational work in formal methods for protocol testing to applied solutions in diagnostic imaging toolboxes and integrated surveillance systems for forest fires/disasters using aerial and space-based platforms. Recent work focuses on educational software for IT vocational training. His publication history demonstrates consistent interdisciplinary contributions spanning biomedical engineering, environmental monitoring, and software engineering, with emphasis on translating theoretical models into real-world safety-critical applications. Professional recognition includes: Third Party Developer Pegasus Opera II award (Pegasus Software, 2001) First Certificate in English (University of Cambridge, 1985) Research leadership spans multiple EU-funded initiatives: Interfaces project (2016-2020): Website and learning platform management for EU interdisciplinary music program JOBIT project (2015-2017): Researcher for Erasmus+ vocational training software development YPE NEPRO0204_02 (2004-2006): Research assistant and software programmer for Cyprus Research Foundation He maintains active scholarly engagement through his Google Scholar profile and continues developing applied solutions at the intersection of computer science and critical domain applications.

Tirado FranciscoView profile

Professor

• Power Aware Computing
• Heterogeneous Computing
• Applications optimization
• +4 more

Francisco Tirado is a Full Professor of Computer Architecture at Complutense University of Madrid since 1986. He has held leadership roles including Vice-Chancellor for Research (2012-2015), Dean of the Faculty of Physics (1994-2002), and Director of the Complutense Supercomputing Center (2002-2007). He is a founding member of the Spanish Society for Computer Science (SCIE) and Spanish Society for Computer Architecture (SARTECO). His research focuses on power-aware computing, heterogeneous systems, bioinformatics, and hardware design automation. He has authored over 242 publications and supervised 15 PhD theses. His professional service includes chairing 138 international conferences and serving on numerous committees for EUROMICRO, IEEE, and national research agencies. Education: No explicit details provided, but has been active at Complutense University since 1978. Affiliations: Member of COSCE Executive Board (2012), IEEE Senior Member (2005), and EUROMICRO Board (1991-2004). Research Interests: - Optimizing energy efficiency in high-performance computing systems - Design of heterogeneous architectures and accelerators - Bioinformatics applications through hardware/software co-design - Parallel programming models for emerging architectures - Automation tools for FPGA and embedded systems development Conference Contributions: Played multiple roles (General Chair, Program Chair) for major events like ParCo, HPCA, and EUROMICRO PDP conferences. Delivered 64 keynote/plenary lectures globally. Scientific Awards: Doctor 'Honoris Causa' from universities in Spain, Paraguay, and Peru 2013 National Computer Science Award IEEE Senior Membership Advising: Supervised 15 PhD students. Active in technology transfer contracts with industry. Labs/Teams: Founder and leader of the ArTeCS research group at Complutense University, specializing in computer architecture and high-performance computing innovations.

Wei XuView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +7 more

Wei Xu is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology. She holds affiliations with the Machine Learning Center (ML@GT) and the NSF AI CARING Institute. Her work emphasizes ethical AI applications, cultural bias mitigation, and assistive technologies. Education: Xu earned a Ph.D. in Computer Science from New York University, and both B.S. and M.S. degrees from Tsinghua University. Research Interests Natural Language Processing (NLP) with a focus on text generation, stylistics, and controllability of ML models Machine Learning robustness and ethical implications Social media analysis, including misinformation detection and user privacy Assistive technologies for reading/writing accessibility Cultural biases in large language models (LLMs) Human-centered evaluation of AI systems Key Contributions Her recent work explores privacy risks in online self-disclosures, LLM responses to implicit misinformation, and regional cultural awareness in AI. She also develops benchmarks like CROSSNEWS for authorship verification and Thresh for text evaluation. Awards NSF CAREER Award (2021) CrowdFlower AI for Everyone Award (2021) Criteo Faculty Research Award (2021) Best Paper Award at COLING'18 (2018) Grants & Funding Xu has secured funding from DARPA, IARPA, and the NSF AI CARING Institute. Her collaborations include the Machine Learning Center at Georgia Tech.

• Neuroscience
• Molecular Biology
• Neurodevelopmental Disorders
• +3 more

Hong Li is an Associate Research Scholar in the Department of Neuroscience at Yale University's Yale School of Medicine. Their research focuses on molecular mechanisms of cortical developmental malformations in neurodevelopmental and psychiatric disorders, employing animal models and integrative methodologies including molecular biology, genetics, morphology, and transcriptomics. Dr. Li holds a DPhil from the University of Helsinki (2007) and an MD from Anhui University (1997). Their work aims to advance understanding of neuropsychiatric diseases and develop therapeutic strategies. Educational Background: DPhil in Neuroscience, University of Helsinki (2007) MD, Anhui University (1997) Research Interests: Investigating molecular pathways underlying cortical development abnormalities, with emphasis on translational research to bridge basic science and clinical applications. Techniques include advanced genetic engineering, transcriptomic profiling, and cross-disciplinary approaches combining neuroscience and molecular biology. Advising & Grants: No specific grants or trainees listed in available materials, though their publications suggest collaborative research in quantum computing, embedded systems, and telecommunication networks. Recent work includes cross-domain contributions to quantum error correction systems and LLM-based robotic applications. Professional Contributions: Active in systems engineering, publishing on operating systems design, FPGA-based quantum decoders, and secure memory management. Their work bridges biomedical research with computational and hardware innovations.