جستجوی استاد راهنمای دانشگاهی

Jian Caoمشاهده پروفایل

استاد

• Manufacturing Processes
• Deformation-based Manufacturing
• Laser Additive Manufacturing
• +۵ مورد دیگر

Jian Cao is the Cardiss Collins Professor of Mechanical Engineering at Northwestern University's McCormick School of Engineering, with courtesy appointments in Civil Engineering and Materials Science. She serves as Director of the Northwestern Initiative for Manufacturing Science and Innovation (NIMSI) and Associate Vice President for Research. Her education includes a PhD in Mechanical Engineering from MIT and a BS from Shanghai Jiao Tong University. Professor Cao leads pioneering research in manufacturing systems, focusing on deformation-based processes, laser additive/subtractive technologies, and AI-driven manufacturing innovations. Her work integrates material behavior characterization, computational modeling, and machine learning to advance energy-efficient manufacturing and surface engineering. Recent publications demonstrate strong emphasis on AI-accelerated simulations, real-time process control, and multi-material systems for additive manufacturing. Recognized with elite honors including membership in both the National Academy of Engineering and American Academy of Arts & Sciences, her scientific awards span: ASME DeVor-Kapoor Manufacturing Medal (2023) Vannevar Bush Faculty Fellowship (2019) SME Gold Medal (2020) Frederick W. Taylor Research Medal (2016) She directs the Cao Research Group developing novel manufacturing equipment and leads NIMSI's industry-academic partnerships. Her professional service includes editorial roles for leading journals and leadership positions in ASME/SME manufacturing divisions.

Husnu Yenigunمشاهده پروفایل

استاد

• Automata Theory
• Concurrency Theory
• Formal Methods
• +۵ مورد دیگر

Husnu Yenigun is a Professor at Sabanci University's Computer Science and Engineering Program , Faculty of Engineering and Natural Sciences (Istanbul, Turkey). He earned his BSc, MSc, and PhD in Electrical and Electronics Engineering from Middle East Technical University (Ankara) in 1992, 1995, and 2000 respectively. His professional career includes roles at TUBITAK (1992-1997), Bell Laboratories (1997-1998), and as a consultant at Bell Labs (1999-2000). Research interests: Dr. Yenigun specializes in automata and concurrency theory, formal methods, software quality assurance, model checking, and complexity relief techniques for software verification. His work bridges theoretical automata analysis with practical testing frameworks. Key publication trends: His recent work spans automata synchronization (2018-2021), matrix optimization (2017-2018), and adaptive testing sequences (2016-2018), with applications in Wireless positioning systems Finite state machine verification Parallel computing Formal method implementations Professional activities: He serves on technical program committees for major conferences like MODELSWARD, QRS, and ICTSS (2016-2025). He chaired the programming committee for ICTSS 2013 and 2017, and co-chaired UYMS 2018. He also acted as guest editor for the International Journal on Software Tools for Technology Transfer (2016). Contact: yenigun@sabanciuniv.edu | Office: FENS 2054, Sabanci University

Jiannong Caoمشاهده پروفایل

استاد

• Distributed Systems
• Blockchain
• Wireless Sensing and Networking
• +۴ مورد دیگر

Jiannong Cao is the Otto Poon Charitable Foundation Professor in Data Science and Chair Professor of Distributed and Mobile Computing at the Department of Computing, The Hong Kong Polytechnic University (PolyU). He currently serves as Dean of the Graduate School and directs multiple research entities including the Research Institute for Artificial Intelligence of Things (RIAIoT), Internet and Mobile Computing Lab (IMCL), and PolyU's University Research Facility in Big Data Analytics (UBDA). Previously, he was the Head of the Department of Computing from 2011 to 2017. Education : BSc from Nanjing University, MSc and PhD from Washington State University Prof. Cao's research focuses on distributed systems , blockchain , wireless sensing , big data analytics , machine learning , mobile cloud , and edge computing . His work includes over 800 publications, 34 patents, and leadership in 130+ research projects with HK$187 million in grants. Recent research trends highlight his contributions to blockchain interoperability , edge AI optimization , secure GPU execution , federated learning frameworks , and IoT-based control systems . These align with his expertise in distributed computing, blockchain, and data-driven intelligent systems. Scientific Awards : PolyU President Award for Excellent Performance/Achievement in Research Ministry of Education Higher Education Outstanding Scientific Research Output Awards – Natural Science (Second Class) China Computer Federation (CCF) Overseas Outstanding Contribution Award IEEE TCCLD Research Innovation Award Silver Medal at Geneva International Exhibition of Inventions Best Paper Awards across multiple IEEE conferences (2011-2021) Prof. Cao has secured over HK$187 million in grants from agencies like NSFC, MOST, RGC, ITC, and industry partners (Alibaba, IBM, Nokia). He has delivered 50+ keynote/invited talks and serves on editorial boards of top journals including IEEE TC, IEEE TPDS, and ACM ToSN. He leads research labs such as RIAIoT and IMCL, focusing on artificial intelligence of things, blockchain integration, and edge computing systems. Current projects include collaborative edge computing, Web3 policies, and secure autonomous cooperation in edge nodes.

Xipeng Shenمشاهده پروفایل

استاد

• Programming Systems
• Machine Learning
• High-Performance Computing
• +۱۲ مورد دیگر

Xipeng Shen is a Professor in the Department of Computer Science at North Carolina State University and Director of the High-Performance Intelligent Computing (HiPIC) lab. He joined NC State in 2014 as part of the Chancellor's Faculty Excellence Program after serving as the Adina Allen Term Distinguished Associate Professor at the College of William and Mary. Shen's research spans programming systems and machine learning, with a focus on enabling extreme-scale, data-intensive, and intelligent computing through innovations in compilers, runtime systems, and ML algorithms. His work has significantly influenced the development of heterogeneous computing and modern AI systems. His research areas include High-Performance Machine Learning & Real-Time AI, Heterogeneous Massively Parallel Computing, and Foundations of Programming Systems & Languages. His recent publications demonstrate strong trends in optimizing AI deployment across various platforms including mobile devices, serverless environments, and edge computing systems, with particular emphasis on efficiency, power consumption, and real-time performance. His research increasingly integrates compiler techniques with machine learning approaches to solve complex optimization problems. ACM Distinguished Member (2018) University Faculty Scholar (2017-18) ACM Distinguished Speaker (2016-2019) IEEE Senior Member (2016) IBM CAS Faculty Research Fellow (2010-2016) Google Faculty Research Award (2015) DOE Early CAREER Award (2011) NSF CAREER Award (2010) Shen has successfully advised 15 PhD students who have gone on to positions at top universities and companies including UC Santa Cruz, UMass Amherst, Google, Meta, and Huawei. His research has been funded by numerous grants from NSF, DOE, and NIH totaling over $4 million. He co-founded CoCoPIE Inc. to commercialize his research on efficient AI deployment and serves as a consultant to major technology companies including Intel, Microsoft, Huawei, Cisco, and Meta. He leads the PICTure research group and teaches courses including Compiler Construction (CSC412/512), Code Optimization for Scalar and Parallel Programs (CSC766), and Real-time AI and Machine Learning Systems (CSC591/791).

Benjamin Watsonمشاهده پروفایل

دانشیار

• Computer Graphics
• Human-Computer Interaction
• Visual Perception
• +۶ مورد دیگر

Benjamin Watson serves as an Associate Professor in the Department of Computer Science at North Carolina State University, leading the Visual Experience Lab which investigates how humans derive meaning from visual information through computer graphics, perception, design, and interaction. His work spans applications in entertainment, security, finance, education, and medicine, with leadership roles in major conferences including Graphics Interface, IEEE Virtual Reality, and ACM I3D. He holds senior membership in IEEE and active membership in ACM. Watson's educational background includes: Ph.D. from Georgia Tech (1997) M.S. from Georgia Tech (1997) B.S. from UC Irvine (1987) His research centers on visual perception and interaction, with core expertise in computer graphics, human-computer interaction, and user experience design. Key investigations include perceptual asymmetries in rendering, display technologies for immersive environments, and human performance optimization in high-stakes contexts like esports. The Visual Experience Lab bridges theoretical computer science with practical applications in healthcare, finance, and entertainment through interdisciplinary collaboration. Analysis of Watson's 2019-2025 publications reveals consistent focus on display technology limitations and human performance metrics. His work demonstrates increasing specialization in esports HCI (7 of 10 recent papers), particularly examining display size, rendering techniques, and frame rate impacts on aiming accuracy. Concurrently, he maintains strong contributions to light field displays and multi-view rendering, developing novel point-based methods that exploit perceptual redundancies for real-time performance. Scientific recognition includes: NVIDIA Professor Partnership Award (2007) National Science Foundation Faculty Early Career Award (2006) Watson actively mentors graduate researchers including Ph.D. graduate Adam Marrs (now at NVIDIA) and Holle Christensen, whose work on experience analytics received workshop acceptance at ACM DIS. His $772k+ in active and recent funding spans NSF grants for adaptive rendering systems ($497k), human-machine collaboration projects with the Laboratory for Analytic Sciences ($180k), and Caterpillar-sponsored AR applications ($20k), demonstrating strong industry-academia translation. The Visual Experience Lab operates as a cross-disciplinary hub connecting computer science, design, and cognitive psychology researchers. Current projects focus on real-time GPU acceleration for multi-view rendering, esports performance optimization, and AR applications for industrial equipment, with active partnerships including Caterpillar, NVIDIA, and the Laboratory for Analytic Sciences.

Vikram Adveمشاهده پروفایل

استاد

• Compiler Infrastructure
• Computer Systems and Architecture
• Edge Computing
• +۶ مورد دیگر

Vikram Adve is the Donald B. Gillies Professor of Computer Science at the University of Illinois at Urbana-Champaign, with appointments in both the Computer Science Department and the Center for Digital Agriculture. He co-founded and co-leads the Center for Digital Agriculture and serves as the director of AIFARMS, a $20M National Artificial Intelligence Research Institute funded by USDA NIFA and NSF. Adve has been a professor at UIUC since August 2011 and previously served as Interim Head of the Computer Science Department from 2017 to 2019. Adve received his Ph.D. in Computer Science from the University of Wisconsin-Madison in 1993. His academic journey has been marked by significant contributions to compiler infrastructure and computer systems research, culminating in his current distinguished professorship at one of the world's leading computer science departments. Adve's research spans multiple cutting-edge domains in computer systems. His work on the LLVM Compiler Infrastructure has revolutionized how software is compiled and optimized across diverse hardware platforms. Currently, his research focuses on three primary thrusts: Digital Agriculture and AI : Through the Center for Digital Agriculture and AIFARMS Institute, he's developing AI solutions for agricultural challenges, including the CropWizard system for generative AI in farming Edge Computing : His HPVM, ApproxHPVM, and ApproxTuner projects address the programming challenges of heterogeneous computing at the network edge Compiler Innovation : His Hydride and MISAAL projects use program synthesis to automatically build compilers for complex hardware architectures His work bridges theoretical compiler research with practical applications in agriculture, autonomous systems, and distributed computing. Adve's publication record demonstrates a consistent trajectory from foundational compiler research to applied AI systems. Early work focused on memory safety (SAFECode), deterministic parallel programming (DPJ), and the LLVM infrastructure. More recently, his publications reflect a strategic pivot toward agricultural AI and edge computing, with significant contributions to generative AI applications, compiler techniques for heterogeneous systems, and multimodal data processing for precision agriculture. His work maintains strong theoretical foundations while addressing real-world challenges in resource-constrained environments. Adve's scientific recognition includes numerous prestigious awards: ACM Software System Award (2012) for LLVM ACM Fellowship (2014) NSF CAREER Award (2001) Multiple best paper awards at top conferences including PLDI 2005, SOSP 2007, and CGO 2004 (retrospective) University Scholar designation at UIUC (2015) Donald B. Gillies Professorship (2018) Distinguished Alumnus Award from IIT Bombay (2023) As an advisor, Adve has mentored numerous successful students, including Chris Lattner (co-creator of LLVM), Robert Bocchino (ACM SIGPLAN Outstanding Dissertation Award winner), and John Criswell (ACM Doctoral Dissertation Award Honorable Mention). His research group has secured significant funding from diverse sources including USDA NIFA, NSF, Intel Corporation, Amazon-Illinois AICE Center, and the state of Illinois through the Discovery Partners Institute. Current projects include the $20M AIFARMS institute and multiple edge computing initiatives focused on agricultural robotics and distributed AR/VR systems. Adve leads the Programming Languages, Systems, and Networking research group at UIUC, which maintains strong connections with industry partners. His group's work on LLVM has had widespread industry impact, with applications in Apple's iOS ecosystem, Android, NVIDIA GPUs, and numerous other commercial products. The group's current focus on agricultural AI through the Center for Digital Agriculture represents a strategic expansion into domain-specific applications of systems research.

Richard Alligierمشاهده پروفایل

مدرس

• Machine Learning
• Air Traffic Management
• Optimization Algorithms
• +۵ مورد دیگر

Richard Alligier is a lecturer and researcher at Ecole Nationale de l'Aviation Civile (ENAC) specializing in artificial intelligence applications for air traffic management. His work bridges machine learning, optimization algorithms, and trajectory prediction to address critical challenges in conflict detection and resolution for both manned and unmanned aerial systems. Research Focus : Trajectory prediction, conflict resolution, UAV collision avoidance, mass/thrust estimation, and uncertainty modeling Key Collaborations : Nicolas Durand, David Gianazza, Xavier Olive, Kim Gaume, Sarah Degaugue His publications (2011–2024) analyze trajectory uncertainty quantification, 3D maneuver visualization, and human-aligned deconfliction strategies using ADS-B data. Notable contributions include: Dual-horizon collision avoidance algorithms integrating human factors High-confidence interval prediction frameworks Wind parameter extraction from flight paths Machine learning models for climb/descent phase optimization Awarded the 2019 best paper in trajectory prediction , his work emphasizes operational alignment between automated systems and air traffic controller decision-making. He employs GPU acceleration, metaheuristics, and deep learning architectures while maintaining a focus on practical implementation through partnerships with ONERA and ISAE-SUPAERO.

Kari Tammiمشاهده پروفایل

استاد

• Mechatronics
• Autonomous Vehicles
• Electric Vehicles
• +۷ مورد دیگر

Kari Tammi serves as Professor and Dean of Aalto University's School of Engineering since 2015, concurrently holding the position of Chief Engineer Counselor at Finland's Administrative Supreme Court. His career spans industrial research leadership at VTT Technical Research Centre (2000-2015), postdoctoral work at North Carolina State University (2007-2008), and foundational research at CERN (1997-2000). His academic credentials include: MSc, Helsinki University of Technology, 1999 LicSc, Helsinki University of Technology, 2003 DSc, Helsinki University of Technology, 2007 Teacher’s Pedagogical Qualification, Häme University of Applied Sciences, 2017 Research Focus: Tammi pioneers in Mechatronics , Autonomous/Electric Vehicle Systems , and Energy Efficiency Optimization , with specialized expertise in Dynamics , Control Systems , and Digital Twin Applications . His work bridges theoretical innovation with industrial deployment across maritime, automotive, and manufacturing sectors. Publication Trends: Recent output (2024-2025) demonstrates concentrated advancement in industrial digital twins for crane operations, winter-condition autonomous perception, and marine energy systems. Key patterns include GPU-free real-time processing, semantic-enhanced metaverse architectures, and snow-robust sensor fusion techniques. Professional Leadership: As former VTT Team Leader and current Engineering Dean, Tammi directs cross-disciplinary research initiatives connecting academic theory with industrial practice, particularly in sustainable transportation and smart manufacturing ecosystems.

• Computational Fluid Dynamics
• Aerospace Engineering
• Quantum Computing Applications
• +۷ مورد دیگر

Rene Steijl serves as a Senior Lecturer in Aerospace Engineering within the James Watt School of Engineering at the University of Glasgow, specializing in Autonomous Systems & Connectivity. His academic profile demonstrates a sustained research career focused on computational fluid dynamics with increasing emphasis on quantum computing applications in recent years. Dr. Steijl's research interests center on modeling and simulation of complex flows for aerospace applications, particularly focusing on multi-physics/multi-scale methods for challenging flow regimes including strong shock waves and expansions in supersonic and hypersonic flows. His work spans computational aerodynamics for high-speed transport aircraft, unmanned aerial vehicles, space launch systems, re-entry vehicles, and helicopters. Since approximately 2020, his research has significantly expanded into quantum computing applications for fluid dynamics, with numerous publications exploring quantum algorithms for computational fluid dynamics, quantum circuit implementations, and quantum simulation of fluid phenomena. This represents a strategic evolution of his expertise in advanced computational methods toward emerging quantum computing paradigms. Analysis of his recent publications reveals a clear trajectory from traditional computational fluid dynamics toward quantum computing applications, with nearly half of his 2023-2024 publications focusing on quantum algorithms and implementations. His work bridges fundamental fluid dynamics with cutting-edge computational approaches, maintaining relevance to practical aerospace applications while exploring next-generation computing technologies. The publications demonstrate consistent collaboration with colleagues at Glasgow and internationally, particularly with George Barakos and Craig White. Dr. Steijl maintains active research in both conventional computational fluid dynamics and emerging quantum computing applications, with recent work addressing challenges in shock wave interactions, gas kinetic modeling, rotorcraft aerodynamics, and quantum circuit optimization. His ORCID profile (0000-0002-1872-8687) documents over 100 publications spanning two decades of research contributions to aerospace engineering and computational science.

• High-Performance Computing (HPC)
• Supercomputers
• GPU Architecture
• +۵ مورد دیگر

Celso Luiz Mendes is a Senior Research Programmer at the National Center for Supercomputing Applications (NCSA) at the University of Illinois. His work focuses on High-Performance Computing (HPC), supercomputer reliability, and performance optimization of computational models. Institution: University of Illinois Department: National Center for Supercomputing Applications (NCSA) Research Interests: Celso's research spans GPU architecture, hydrological modeling, and parallel systems. He contributes to optimizing computational performance and scalability in HPC environments. Publications: His recent work on the Blue Waters supercomputer analyzes system reliability and user-support challenges. He also explores performance tuning for environmental models like MGB, targeting multi-core and GPU architectures. Collaborations: Celso works with teams at NCSA and institutions like IEEE, focusing on HPC tools, grid applications, and load balancing solutions.

Amanda Randlesمشاهده پروفایل

دانشیار

• Biomedical Simulation
• High-Performance Computing (HPC)
• Computational Fluid Dynamics (CFD)
• +۵ مورد دیگر

Amanda Randles is the Alfred Winborne and Victoria Stover Mordecai Associate Professor of Biomedical Sciences at Duke University's Pratt School of Engineering. She holds joint appointments in the Department of Mechanical Engineering and Materials Science, Computer Science, and Mathematics. Her research combines high-performance computing and biomedical simulation to model complex biological systems such as blood flow and cancer progression. Duke Cancer Institute Member Randles Lab Leader Research Focus: Development of computational tools like HARVEY and HarVI frameworks for 3D hemodynamic simulations, digital twins of vasculature, and machine learning applications in cardiovascular disease prediction. Her work bridges biomedical engineering, computational fluid dynamics, and GPU-accelerated modeling. Scientific Awards: 2025 Sony Women in Technology Award (associated $250,000 prize) 2022 NIH Director’s Pioneer Award ($3.5M) 2020 NSF CAREER Award 2017 MIT Technology Review 35 Innovators Under 35 IEEE-CS TCHPC Early Career Award Recent Publications: Focus on adaptive physics refinement, fluid-structure interaction optimization, digital twins for coronary analysis, GPU acceleration of biomedical simulations, and predictive modeling of cancer cell transport.

Christos Papadopoulos Filelisمشاهده پروفایل

استادیار

• Computational Mathematics
• Mathematical and Computational Physics
• Numerical Methods
• +۶ مورد دیگر

Christos Papadopoulos Filelis is an Assistant Professor at the Department of Electrical and Computer Engineering, Democritus University of Thrace, where he conducts research and teaches courses in computational mathematics and physics. His academic journey began with a Diploma in Electrical and Computer Engineering from Democritus University of Thrace in 2010, followed by a PhD in Numerical Analysis and High Performance Computing from the same institution in 2014. His research interests span Computational Mathematics, Mathematical and Computational Physics, Numerical Methods, Scientific Calculations, High Performance Computing, and Machine Learning applications in Mathematics and Physics. He has made significant contributions to pre-coordinated iterative methods, multi-grid and multi-level methods, parallel computing, cloud computing, and big data analytics. His work bridges theoretical mathematics with practical computational applications across multiple domains. Analysis of his recent publications reveals a strong focus on developing advanced computational techniques for solving complex mathematical problems, with particular emphasis on matrix computations, preconditioning methods, and time series analysis. His research demonstrates a consistent trajectory toward more efficient algorithms for high-performance computing environments, with increasing applications in machine learning and environmental sustainability. Dr. Papadopoulos Filelis has taught undergraduate courses including Physics, Numerical Analysis, Mathematical Software, Scientific Calculations, and Calculus of Changes. He has participated in European (H2020) and National funded projects such as the SFI FinTechNext project (UCC, Ireland), demonstrating his ability to secure competitive research funding. His work with the Physics Laboratory at Democritus University of Thrace involves developing computational frameworks that integrate theoretical physics with advanced numerical methods. He has collaborated extensively with researchers across Europe, particularly in cloud computing and high-performance computing projects that address real-world computational challenges.

Mosharaf Chowdhuryمشاهده پروفایل

دانشیار

• AI/ML Systems
• Energy Efficiency in AI
• Memory Disaggregation
• +۴ مورد دیگر

Mosharaf Chowdhury is an Associate Professor at the University of Michigan's College of Engineering, Department of Electrical Engineering and Computer Science. He leads the SymbioticLab (https://symbioticlab.org/), focusing on AI/ML systems optimization and energy-efficient computing. Current roles: Associate Professor, Director of SymbioticLab Key projects: Infiniswap (memory disaggregation), Salus (GPU sharing), FedScale (federated learning), Zeus (GPU energy optimization) Original co-creator of Apache Spark Research Interests center on large-scale systems for AI/ML and big data, with emphasis on: Energy consumption optimization Memory disaggregation via CXL GPU resource management Data privacy in distributed systems Federated learning scalability Scientific Awards include multiple paper awards from top systems venues (NSDI, OSDI, ATC, MICRO) and fellowships. Teaching includes courses like EECS 489 (Computer Networks) and EECS 598 (Systems for AI/ML).

Li Xiaoliمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۷ مورد دیگر

Professor Li Xiaoli is the Head of the Information Systems Technology and Design (ISTD) Pillar at Singapore University of Technology and Design (SUTD), effective 15 August 2025. An internationally recognised AI researcher with over 30 years in academia and industry, he was previously Department Head of Machine Intellection at A*STAR I²R and Technical Director of the S$35.8 million national AIMfg centre. He holds adjunct professorial appointments at NUS and NTU and has advised Singapore government agencies including MTI, MOE, MOH and SNDGO. Education: PhD, Institute of Computing Technology, Chinese Academy of Sciences, 2001 Research Interests: Prof Li’s work straddles machine learning, data mining, graph learning and time-series analytics, with high-impact applications in smart manufacturing , semiconductors and the digital economy . He pioneered sensor feature learning with deep neural networks and co-coined the term positive-unlabelled (PU) learning ; his 2015 IJCAI paper alone exceeds 1,600 citations. Additional contributions span social & biological network mining, NLP & text analytics, and AI sustainability. Recent Publication Landscape (2022-2025): His latest 15 papers reveal a dual focus on time-series intelligence (domain adaptation, self-supervised representation, sensor alignment, remaining useful-life prediction) and efficient AI deployment (green AI, hardware-aware deep learning, game-theoretic NAS). Interdisciplinary threads link biomedical informatics (sleep stage, gene networks) and advanced NLP (preference optimisation, video grounding), underscoring a strategy of algorithmic innovation coupled with real-world validation . Honours & Awards: IEEE Fellow (2024) Fellow, Asia-Pacific Artificial Intelligence Association (2023) Clarivate Highly Cited Researcher Listed among World’s Top 2% Scientists (Stanford University) Three IEEE/Conference Best Paper Awards for sensor-data and network-mining research Grants, Labs & Industry Collaboration: Prof Li has secured and led more than 10 major collaborative grants with industry partners such as DBS, Singtel and KPMG, including the S$35.8 million AIMfg programme. He directs joint research labs, translating AI advances into aerospace, telecom, insurance and aviation solutions. His group maintains cutting-edge GPU clusters and sensor test-beds for manufacturing analytics, and he currently supervises a large multi-disciplinary team of research staff and graduate students at SUTD.

Dávid Szabóمشاهده پروفایل

دانشیار

• Sociolinguistics
• French Language and Literature
• Lexicography
• +۵ مورد دیگر

Dávid Szabó is a habilitated Associate Professor at the Institute of Romance Studies , Department of French Language and Literature , Eötvös Loránd University. His academic profile bridges sociolinguistics , focusing on argot and slang studies in French-Hungarian contexts, with computer graphics and C# software development . He has contributed to diverse fields, including AI in education , sustainable finance , and real-time graphics APIs . Research Highlights: His work explores the intersection of language evolution and technology, with recent publications on Green Finance , AI-driven educational tools , and parallel processing in graphics programming . He investigates linguistic taboos in Hungarian politics and translation challenges of urban French slang into Hungarian, while developing educational software like StudyHelper. Technical Contributions: Szabó has pioneered the integration of modern C# with graphics APIs (Vulkan, OpenGL) for real-time rendering , creating frameworks for multi-platform applications and shader program development . His technical papers emphasize code efficiency, API abstraction, and GPU optimization.