Find Academic Supervisors

• Cortex Functional Organisation and Development
• Neural Coding and Dynamics
• Neural Data Analysis
• +7 more

Matthias Kaschube is a Professor in the Faculty of Computer Science and Mathematics at Goethe University Frankfurt and a Senior Fellow at the Frankfurt Institute for Advanced Studies (FIAS). His research group focuses on understanding how the brain forms efficient representations of sensory environments and internal states through dynamic neural processes. He maintains active collaborations with leading neuroscience institutions including the University of Minnesota, Max Planck Florida Institute for Neuroscience, and Technion. Dr. Kaschube completed his physics studies at Goethe University Frankfurt and Georg-August-University Göttingen, graduating in 2000 and earning his doctoral degree in physics in 2005. His doctoral work was conducted at the Max Planck Institute for Dynamics and Self-Organization under Fred Wolf and Theo Geisel. He then held a Bernstein Fellowship before becoming a Theory Fellow at Princeton University's Lewis Sigler Institute from 2006-2011. In 2011, he joined Goethe University as Professor for Computational Neuroscience. His research spans four primary areas: the developmental emergence of cortical representations, flexible representations underlying learning and creativity, cognitive maps and representational spaces, and analysis methods for neural data. His group combines dynamic models of neural circuit function with neural data modeling techniques in close collaboration with experimental groups, creating an interdisciplinary interface between computer science, physics, biology, and AI. Notably, his work has revealed highly structured cortical networks prior to sensory experience that share similar architectural principles across sensory and association cortices. Analysis of his recent publications shows a consistent focus on understanding how endogenous neural activity patterns develop into reliable cortical representations through experience. His work spans multiple scales from molecular and cellular mechanisms to whole-brain functional organization, with particular emphasis on developmental processes in visual and auditory cortices. His methodological contributions include advanced techniques for analyzing chronic imaging data, tracking chromatophores in cuttlefish, and characterizing latent spaces in deep neural networks. Lewis Sigler Theory Fellowship (2006-2011) Bernstein Fellowship (2005) Professor Kaschube actively mentors a large group of PhD students including Lorenzo Butti, Jonas Elpelt, Santiago Galella, Deyue Kong, Maurycy Miekus, Ana Pamela Osuna Vargas, and Sigrid Trägenap. His research has been supported by multiple grants including NIH grants EY011488 and EY026273, Bernstein Focus Neurotechnology grant 01GQ0840, and BMBF project D-USA-Verbund: SpontVision. His group currently pursues three major research directions: the origin of distributed modular activity in neocortex, quantitative growth models for cuttlefish based on physical models, and the role of self-organization in linking endogenous cortical networks to sensory input.

Nuttida RungratsameetaweemanaView profile

Assistant Professor

• Neural computations
• Cognitive neuroscience
• Computational neuroscience
• +6 more

Nuttida Rungratsameetaweemana is an Assistant Professor in the Department of Biomedical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science. She also serves as a Provost Research Fellow at Columbia University, demonstrating her significant research contributions early in her academic career. NuttidaLab, her research group, focuses on integrating computational and experimental approaches to investigate the neural computations underlying complex cognitive functions in both health and disease. The lab's research spans several key areas including learning mechanisms, decision-making processes, and social behavior, with particular emphasis on understanding how these cognitive functions become disrupted in neurological and neuropsychiatric disorders. The lab has developed several important computational neuroscience tools including NeuralDecoder for representational similarity analysis, state_space_analysis for modeling neural dynamics, MARLAX for multi-agent reinforcement learning, and dynamax for state space modeling. These tools reflect the lab's commitment to developing robust analytical frameworks that can be widely adopted by the neuroscience community. NuttidaLab maintains an active GitHub presence with multiple repositories that demonstrate the lab's focus on open science and reproducible research. The lab's work combines cutting-edge computational approaches with experimental neuroscience to develop a deeper understanding of brain function. The lab actively seeks talented students and researchers interested in interdisciplinary work at the intersection of engineering, neuroscience, and computational science. Professor Rungratsameetaweemana's research represents an important bridge between theoretical computational models and experimental neuroscience, with significant potential for advancing our understanding of both healthy brain function and neurological disorders.

Yang LiuView profile

Professor

• Formal Methods
• Software Engineering
• Security
• +7 more

Yang Liu is a Full Professor and University Leadership Forum Chair at the School of Computer Science and Engineering, Nanyang Technological University (NTU) in Singapore. He serves as Programme Director for HP-NTU Digital Manufacturing Corp Lab, Deputy Director of the National Satellite of Excellence of Singapore, and Cluster Director in Cybersecurity at Energy Research Institute @NTU. His research spans Cybersecurity , Software Engineering , and Artificial Intelligence . He leads research in malware modeling and detection, vulnerability analysis using machine learning and program analysis, formal verification of security systems, program specification learning, performance analysis, Android system security, and AI security, robustness, fairness, and explainability. His notable work includes the Process Analysis Toolkit (PAT) for model checking and the Deep-Series tools for deep learning testing. Professor Liu has published extensively in top-tier conferences including ASE, ICSE, FSE, ISSTA, and S&P. His research demonstrates strong trends toward integrating AI/ML techniques with traditional software engineering and security approaches, particularly focusing on large language models for code analysis, vulnerability detection, and program repair. Recent publications show a growing emphasis on blockchain security, smart contract analysis, and addressing security challenges in AI systems. NRF Investigatorship (Class 2020) ACM's Distinguished Speaker Nanyang Research Award (Young Investigator) Microsoft Asia Research Fellowship 20 Year ICFEM Most Influential System Award for PAT Multiple ACM SIGSOFT Distinguished Paper Awards Professor Liu actively advises students and has seen notable student achievements, including Singapore Data Science Consortium research award winners and AISG PhD Fellowship recipients. His research is supported by numerous grants including a $900,000 NTU-NAP grant for Formal Verification on Cloud and a $471,000 grant for Vulnerability Detection in Binary Code. He leads the HP-NTU Digital Manufacturing Corp Lab and contributes to RollsRoyce@NTU Corporate Lab research on complex business systems simulation.

Ali MesbahView profile

Professor

• Software Engineering
• AI-driven Software Analysis
• Software Testing
• +5 more

Ali Mesbah is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), where he leads the SALT lab. His research focuses on software engineering with emphasis on AI-driven software analysis, software testing, and software evolution. Previously, he was a Visiting Research Scientist at Google during 2017-2018. Dr. Mesbah received his BSc/MSc (2003) and PhD (2009) degree cum laude in Computer Science from the Delft University of Technology (TUDelft). After completing a postdoctoral fellowship with the Software Engineering Research Group at TUDelft and a Visiting Researcher position at Fujitsu Laboratories of America, he joined UBC in 2011. His research interests span software engineering with particular focus on AI-driven software analysis, software testing, software evolution, program comprehension, fault localization and repair. His work has significant applications in web application testing, JavaScript analysis, and automated program repair. He has pioneered techniques for testing modern web applications, analyzing JavaScript code, and leveraging AI for software maintenance tasks. His recent publications demonstrate a clear evolution toward integrating large language models with traditional program analysis techniques, focusing on test generation, bug repair, and understanding multi-hunk patches. His work bridges theoretical software engineering concepts with practical applications, particularly in web technologies and AI-assisted development. Amazon Research Award (2023) Killam Accelerator Research Fellowship (KARF) (2020) Killam Faculty Research Prize (2019) NSERC Discovery Accelerator (DAS) award (2016) ACM Distinguished Paper Awards at ICSE (2009, 2014) IEEE Distinguished Paper Award at ICST (2018) Best Paper Award at ESEM (2015) Best Paper Award at ICWE (2013) Dr. Mesbah has advised numerous PhD and MASc students, many of whom have gone on to positions at leading technology companies including Google, Amazon, Apple, Microsoft, and SAP. His research has been supported by various grants including the Amazon Research Award and NSERC funding. He leads the SALT lab at UBC, which focuses on software analysis, testing, and learning, with current research directions including AI-driven software engineering, web application testing, and program repair. The lab maintains active collaborations with industry partners and academic institutions worldwide.

Xin XiaView profile

Professor

• Software Engineering
• Mining Software Repositories
• Empirical Software Engineering
• +5 more

Xin Xia is a Qiushi Distinguished Professor at the College of Computer Science and Technology, Zhejiang University. Previously, he served as the Chief Expert and Director of the Software Engineering Application Technology Lab at Huawei Technologies, China from 2021 to 2025. His academic career spans software engineering research with a focus on AI applications in the field. Ph.D. from Zhejiang University (2014) Supervised by Prof. Xiaohu Yang and Prof. Jianling Sun Visiting student at Singapore Management University (2012-2014) under Prof. David Lo Xin Xia's research primarily focuses on applying data science techniques to software engineering problems. His work spans AI for Software Engineering, Mining Software Repositories, Empirical Software Engineering, and Large Language Models for code understanding and generation. He employs data mining, information retrieval, natural language processing, search-based algorithms, and program analysis to transform software engineering data into automated tools and insights. His recent publications show a strong trend toward leveraging Large Language Models for various software engineering tasks, including code generation, vulnerability detection, and test generation. He has been exploring how to make these models more effective, reliable, and practical for real-world software development scenarios, with a particular focus on Java and Python ecosystems. ACM SIGSOFT Early Career Researcher Award (2022) ACM Distinguished Member 16 best or distinguished paper awards, including nine ACM SIGSOFT Distinguished Paper Awards Recipient of the IEEE Transactions on Software Engineering 2021 Best Paper Award Runner-Up Xin Xia has advised numerous students who have gone on to publish in top software engineering venues. His research has been supported by grants from both academic institutions and industry partners, particularly during his time at Huawei. He actively collaborates with researchers worldwide, especially with David Lo at Singapore Management University. At Zhejiang University, Professor Xia leads research in the intersection of AI and Software Engineering. His work has practical applications in improving developer productivity through automated tools that analyze software repositories and provide actionable insights.

Hui LiuView profile

Professor

• Software Engineering
• AI-based Software Development
• LLM-based Program Generation
• +5 more

Hui Liu is a Professor in the School of Computer Science and Technology at Beijing Institute of Technology, where he leads research in AI-based software development with a focus on LLM applications. His work spans software refactoring, quality improvement, and maintenance, funded by the National Natural Science Foundation of China and the National Key Research and Development Program of China. PhD from Peking University (2008) Former graduate student at Software Engineering Institute, Peking University Distinguished member of China Computer Federation Secretary-General of CCF Technical Committee on Software Engineering Professor Liu's research centers on LLM-based program generation, evaluation and testing of large language models, software refactoring techniques, and automatic construction of software engineering datasets. His work bridges artificial intelligence and software engineering, with particular emphasis on improving code quality through empirical studies and machine learning techniques. Current projects include code contamination detection, context-aware naming recommendations, and refactoring validation using LLMs. His research has evolved from traditional code smell detection to cutting-edge applications of large language models in software development. Liu's publication record shows a strong trend toward LLM applications in software engineering, with recent work focusing on code review generation, commit message generation, and refactoring validation using large language models. His research combines empirical methods with machine learning approaches, often analyzing large code corpora from open-source projects. The work spans both theoretical foundations and practical tool development, with several contributions merged into Eclipse as part of the open-source community. ACM Distinguished Paper Award (ESEC/FSE 2023) ACM Distinguished Paper Award (ICSE 2022) RE'2021 Best Research Paper Award IET Software Premium Award (2018) New Century Excellent Talents in University (2013) Beijing Higher Education Young Elite Teacher (2013) Professor Liu actively mentors PhD and Master's students, with recent graduates including Waseem Akram (awarded Outstanding Graduate) and several students publishing at top venues. His research is supported by major Chinese funding agencies, and he serves on program committees for leading software engineering conferences including ASE, ICSE, and FSE. He maintains strong industry connections through contributions to Eclipse and studies of open-source ecosystems like Rust. Liu leads a research group focused on AI for software engineering, with active projects on code generation, refactoring, and quality improvement. The group collaborates extensively with international researchers and contributes directly to open-source tools, particularly in the Eclipse ecosystem where multiple refactoring improvements have been merged.

Zhiyuan WanView profile

Associate Professor

• Software Engineering
• Software Security
• Programming Languages

Zhiyuan Wan is an Associate Professor in the College of Computer Science and Technology at Zhejiang University, China. His academic career spans multiple prestigious institutions across North America and Asia, with a focus on advancing software engineering practices through empirical research and tool development. Dr. Wan's educational background includes: Ph.D. in Computer Science from Zhejiang University (2014) His postdoctoral journey featured positions at: University of British Columbia, Canada (2019-2020) Singapore Management University (2018) Zhejiang University (2016-2020) Lehigh University, United States (2014-2015) Dr. Wan's research program centers on empirical software engineering with particular expertise in blockchain technologies and software security. His work bridges theoretical insights with practical tool development, focusing on: Smart contract security and vulnerabilities in cryptocurrency ecosystems Code search and recommendation systems for developer productivity Empirical studies of developer practices and challenges Impact of machine learning on software development workflows His approach combines rigorous empirical methods with practical tool building to address real-world challenges faced by software practitioners. Analysis of Dr. Wan's recent publications reveals a strategic evolution toward blockchain security research, beginning around 2020 with studies on smart contract security and expanding to cover NFT ecosystems, Solana blockchain transactions, and cross-chain vulnerabilities. His work consistently applies empirical methods to uncover practical insights while developing tools that directly address identified challenges in software development. Dr. Wan actively contributes to the software engineering community through service on program committees for major conferences including ASE, ICSE, ESEC/FSE, and ISSTA. His academic leadership extends to mentoring relationships with students and collaborators across international institutions, though specific advisees are not documented in the provided materials.

Tatyana SharpeeView profile

Professor

• Computational Neuroscience
• Information Theory in Neural Systems
• Sensory Processing
• +7 more

Tatyana Sharpee is a Professor at the Salk Institute for Biological Studies, where she leads the Computational Neurobiology Laboratory. She holds the Edwin K. Hunter Chair and is affiliated with the Crick-Jacobs Center for Theoretical and Computational Biology and the Center for Theoretical Biological Physics at UCSD. Her research focuses on understanding how the brain processes information despite constant changes in its components and environment. Dr. Sharpee's research interests center on computational principles of sensory processing, particularly how the brain creates robust representations of the external world. Her work integrates methods from statistical physics, mathematics, and information theory to study neural coding in vision, audition, and olfaction. She has made significant contributions to understanding how neural feature selectivity is shaped by natural stimuli statistics and how invariant representations are achieved despite neural variability. Her recent publications reveal groundbreaking discoveries in odor perception (structured by a hyperbolic metric similar to Pringles potato chips), visual processing (how neurons combine selectivity for shape and texture), and theoretical frameworks for understanding neural coding. Her lab's work on Maximally Informative Dimensions has revolutionized how scientists analyze neural responses to complex stimuli. 2024 Prebys Research Hero 2022 Delano Prize in Computational Biology from ASBMB American Physical Society Fellow, 2018 National Science Foundation Career Award, 2013 WM Keck Foundation Research Excellence Award, 2009 2009 McKnight Scholar Award Ray Thomas Edwards Foundation Career Award, 2008 Alfred P. Sloan Research Fellow, 2008 Dr. Sharpee mentors numerous graduate students and postdoctoral fellows, and her lab develops open-source computational tools widely used in neuroscience. Her research has significant implications for understanding neurological disorders including autism, Alzheimer's disease, schizophrenia, depression, and anxiety, with potential applications in brain-machine interfaces and novel treatments for sensory disorders.

Mohammad Adnan HamdaqaView profile

Associate Professor

• Model-Based Software Engineering
• Cloud Computing
• Blockchain
• +4 more

Mohammad Adnan Hamdaqa is an Associate Professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal (Canada), where he leads the Software and Emerging Technologies Lab. He holds a Ph.D. in Software Engineering from the University of Waterloo (2016), along with a Master's in Electrical and Computer Engineering from Concordia University, an MBA from New York Institute of Technology, and a Bachelor's in Computer Engineering from Jordan University of Science and Technology. His research focuses on the intersection of emerging technologies and software engineering, particularly how software engineering approaches can be adapted for new platforms like Cloud Computing and Blockchain. His work spans model-driven software engineering, cloud applications, and blockchain technologies. Analysis of his recent publications reveals a strong emphasis on blockchain technology, especially smart contracts and Ethereum, with significant work on security, evolution, and visualization of these systems. His research also demonstrates growing integration of large language models in software engineering tasks, particularly for model specification and code generation. There's also a notable thread of work on sustainability in infrastructure as code and security practices across cloud platforms. Hamdaqa actively contributes to the academic community as a member of IEEE Computer Society and ACM, and has served on program committees for numerous conferences in software engineering and services communities. He has successfully supervised multiple Master's students, with recent theses focusing on smart contract auditing, epidemiological modeling using model-driven approaches, and security practices in infrastructure as code. His lab maintains active research in both theoretical and applied aspects of software engineering for emerging technologies.

Yutian TangView profile

Assistant Professor

• AI+SE
• Large-language Model+SE
• Program Analysis
• +5 more

Dr. Yutian Tang serves as an Assistant Professor (UK Lecturer) and Principal Investigator at the School of Computing Science, University of Glasgow, where he supervises PhD students and leads research in AI-driven software engineering. His academic journey includes a PhD from The Hong Kong Polytechnic University's Department of Computing. His research spans AI+SE integration , particularly focusing on Large Language Models for program analysis, software testing, and Android security. Key areas include: LLM-assisted vulnerability detection and repair Empirical studies of real-world software systems Privacy protection mechanisms Configuration compatibility in mobile applications Smart contract security optimization His publication portfolio shows a clear trajectory toward AI-augmented software engineering , with recent work demonstrating how LLMs can enhance taint analysis, binary code similarity detection, and test generation. This evolution reflects the field's broader shift toward AI integration while maintaining rigorous empirical validation. Award highlights include: Best Industry Paper Award at ISSRE'18 Elevation to IEEE Senior Member (2024) Three Android OS defects confirmed by Google Security Team As an active researcher and community contributor, Tang serves on 40+ program committees including PLDI, ICSE, and FSE. His work receives funding from National Natural Science Foundation of China, Shanghai Science Commission, OpenAI, and Google. Current projects focus on automated bug localization and LLM-based testing frameworks, with recent grants from OpenAI Cybersecurity and Google Cloud programs. He leads research groups investigating Android security and AI-assisted program analysis, collaborating with institutions like Lund University.

Hongyu ZhangView profile

Professor

• Intelligent software engineering
• Software analytics
• Data-driven software engineering
• +4 more

Hongyu Zhang is a Professor and Dean of the School of Big Data and Software Engineering at Chongqing University, China, and an Honorary Professor at The University of Newcastle, Australia. Previously, he served as a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, China. He received his PhD from the National University of Singapore in 2003. His academic journey spans prestigious institutions, combining industry research experience with academic leadership. Dr. Zhang's research interests focus on intelligent software engineering, software analytics, data-driven software engineering, software fault management, testing and debugging, and software maintenance and reuse. His work centers on improving software quality and productivity by mining and analyzing vast amounts of software data. Over the years, he has developed innovative methods that apply data mining, machine learning (including deep learning), and information retrieval techniques to extract knowledge from software data and solve complex software engineering problems. His research spans three major areas: intelligent programming (code search, code summarization, code generation), intelligent quality prediction (defect prediction, cloud failure prediction, performance prediction), and intelligent fault detection and diagnosis (log-based fault detection, crash-based fault localization, bug report analytics). His recent publications demonstrate a clear trend toward integrating large language models and deep learning techniques with traditional software engineering practices. The research spans intelligent programming assistance, code security, UI automation, distributed systems optimization, and performance analysis. His work increasingly focuses on practical applications of AI in software engineering, with emphasis on real-world impact in industrial settings, particularly in microservices, cloud systems, and large-scale software development environments. 8 ACM Distinguished Paper Awards Best Paper Award: How Long Will it Take to Mitigate this Incident for Online Service Systems? David Lorge Parnis Fellowship Senior Member of IEEE Distinguished Member of ACM Distinguished Member of CCF Fellow of Engineers Australia (FIEAust) Recognized in The Australian's Top Researchers special edition as leading researcher in Software Systems World's Top 2% Scientists (career-long) Dr. Zhang has successfully advised numerous PhD and Master's students who have gone on to prominent positions at leading technology companies and academic institutions worldwide. His research has been supported by significant grants including Australian Research Council Discovery Projects (as Lead CI) and multiple National Science Foundation of China projects. His work has made tangible impacts in industry, most notably through the Microsoft Developer Assistant project which received over 450K downloads in 2016. He leads research groups focused on intelligent software engineering and software analytics, with strong collaborations between Chongqing University, The University of Newcastle, and Microsoft Research. His teams develop practical tools for code intelligence, log analysis, and fault diagnosis that are deployed in real-world online service systems.

Qing HuangView profile

Associate Professor

• Software Engineering
• Knowledge Graphs
• Human-Computer Interaction
• +5 more

Qing Huang is an Associate Professor in the School of Computer and Information Engineering at Jiangxi Normal University in Nanchang, China. His academic career focuses on bridging software engineering with artificial intelligence, particularly through the application of large language models to enhance software development processes. His research interests span multiple interconnected domains: Software Engineering Knowledge Graphs Human-Computer Interaction Programming Languages Artificial Intelligence applications in software development Dr. Huang's recent work demonstrates a strong focus on leveraging large language models (LLMs) to address fundamental challenges in software engineering. His research explores how AI can enhance code generation, API understanding, software testing, and knowledge representation in programming contexts. A significant portion of his work investigates the intersection of knowledge graphs and LLMs to create more intelligent software development tools. His publications reveal a consistent pattern of innovation in applying cutting-edge AI techniques to practical software engineering problems, with particular emphasis on improving developer productivity through better tooling and knowledge management. Dr. Huang has made notable contributions to the field of prompt engineering for software development tasks, exploring how natural language interfaces can serve as "APIs" for human-AI interaction. His work on AI Chains represents a novel approach to connecting human developers with LLM capabilities through structured knowledge representations. Additionally, he has conducted significant research on smart contract analysis, code reuse, and type inference in partial code contexts. His scientific contributions have been recognized through publications at major software engineering conferences including ASE and ICSE, with multiple papers accepted across different tracks (Research Papers, Journal-First, Tool Demonstrations). Dr. Huang serves as a Program Committee member for ASE 2025 in the Research Papers track, demonstrating his standing in the software engineering research community. Dr. Huang collaborates extensively with researchers from various institutions, including Data61 (Australia), Nanyang Technological University, and other Chinese universities. His work demonstrates strong interdisciplinary connections between traditional software engineering and emerging AI technologies.

Anju SaxenaView profile

Professor

• Linguistics
• Himalayan Languages
• South Asian Languages
• +7 more

Anju Saxena is a Professor in General Linguistics at the Department of Linguistics and Philology, Uppsala University. Her research focuses on the linguistic landscape of the Himalayan region, particularly on Kanashi and other lesser-known languages of South Asia. Professor Saxena's research interests span multiple areas of linguistics with particular emphasis on: Himalayan and South Asian languages documentation and analysis Language contact and historical linguistics Computational linguistics approaches to language documentation FrameNet development for linguistic domains Language variation and typological studies Numeral systems and grammaticalization processes Her recent publications demonstrate a consistent focus on Kanashi language studies, with multiple comprehensive works published in 2022 including a linguistic sketch, sound system analysis, vocabulary documentation, and investigations into Kanashi's historical development and relationship to other West Himalayish languages. She has also made significant contributions to computational linguistics through her work on LingFN (Linguistic FrameNet) and applying digital resources to language documentation. Professor Saxena maintains an active research profile with collaborations across international institutions, as evidenced by her extensive publication record extending to 2023.

Tadashi WadayamaView profile

Professor

• Coding Theory
• Signal Processing
• Wireless Communications
• +7 more

Professor Tadashi Wadayama serves in the Department of Computer Science within the Faculty of Engineering at Nagoya Institute of Technology. He holds a full professorship position and leads research initiatives in coding theory, signal processing, and deep learning applications for next-generation communication systems. Professor Wadayama received his B.E., M.E., and D.E. degrees from Kyoto Institute of Technology in 1991, 1993, and 1997 respectively. He began his academic career at Okayama Prefectural University in 1995 as a research associate and spent 1999-2000 as a visiting researcher at Essen University in Germany. He joined Nagoya Institute of Technology as an associate professor in 2004 and was promoted to full professor in 2010. He maintains active memberships in IEEE and the Institute of Electronics, Information and Communication Engineers (IEICE). His research spans multiple interconnected domains with primary focus on Coding Theory , Signal Processing for Wireless Communications , and Deep Learning applications . Professor Wadayama has made significant contributions to LDPC codes, MIMO signal detection, and the emerging field of deep unfolding techniques that bridge neural networks with traditional signal processing algorithms. His work increasingly incorporates physics-aware modeling of communication channels governed by partial differential equations. Recent research demonstrates strong interdisciplinary integration between information theory, machine learning, and communication engineering principles. Analysis of his recent publications reveals a clear trajectory toward developing foundational technologies for post-Shannon communication architectures. His work emphasizes ultra-large-scale coding, goal-oriented communication, digital homeostasis mechanisms, physics-embedded signal processing, and dual-process learning systems that combine fast reactive processing with deliberative meta-learning using LLM orchestrators. Fundamentals Review Best Author Award, IEICE, 2022 SRC 2010 Paper Award, Storage Research Promotion Organization, 2011 Professor Wadayama has successfully led multiple competitive research grants including JSPS Grant-in-Aid projects. He currently serves as Principal Investigator for the JST CRONOS project "Digital Cybernetics: Towards Next-Generation Communication Architecture" (2025-2031), which aims to develop foundational technologies supporting autonomous, adaptive, and robust large-scale AI systems. As IEEE Information Theory Workshop General Co-chair (2020-2021) and former chair of IEICE's Information Theory Research Committee (2020-2022), he maintains active leadership roles in the academic community. He leads the Wadayama Group at Nagoya Institute of Technology, which focuses on digital cybernetics and communication physics. The group develops physics-aware signal processing implementations, dual-process learning systems, digital homeostasis mechanisms, and system integration for next-generation communication architectures. His team collaborates with researchers from Kyoto University, Hiroshima University, Institute of Science Tokyo, and Tokyo University of Science, creating a robust research ecosystem focused on post-Shannon communication frameworks.