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Stefan HougardyView profile

Professor

• Discrete Mathematics
• Combinatorial Optimization
• Approximation Algorithms
• +6 more

Stefan Hougardy is a Professor at the Research Institute for Discrete Mathematics, part of the Mathematisch-Naturwissenschaftliche Fakultät at the University of Bonn. He is actively engaged in research and teaching, with a focus on discrete mathematics and combinatorial optimization. He contributes to academic governance through roles in examination boards, teaching mentoring, and faculty committees. Stefan Hougardy's research lies at the intersection of theoretical computer science and practical optimization. His primary interests include approximation algorithms, the Traveling Salesman Problem (TSP), Steiner trees, graph theory, and VLSI design automation. He develops efficient algorithms for NP-hard problems and analyzes their theoretical performance guarantees. His work often bridges theory and application, particularly in electronic design automation and mathematical programming. His recent publications demonstrate a strong focus on the complexity and approximation of combinatorial optimization problems. Key themes include the analysis of local search heuristics like k-opt for TSP, edge elimination techniques, fast matching algorithms, and optimal legalization in chip design. His work combines rigorous theoretical analysis with practical implementation and computational experiments. MPC 'Outstanding Paper of the Year' Award 2024 Stefan Hougardy supervises graduate students and leads seminars on discrete mathematics and optimization. He is involved in the Bonn International Graduate School of Mathematics and the Hausdorff Center for Mathematics, contributing to doctoral education and mentoring. While specific grant details are not listed, his sustained research output and leadership roles suggest active funding support. He also contributes to curriculum development and academic quality assurance through various institutional committees. He is affiliated with the Research Institute for Discrete Mathematics at the University of Bonn, a leading center for combinatorial optimization and algorithmic research. The institute is closely linked with the Hausdorff Center for Mathematics, fostering collaboration in discrete and applied mathematics.

Debmalya PanigrahiView profile

Professor

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Graph Algorithms
• +6 more

Debmalya Panigrahi is a Professor of Computer Science at Duke University and serves as Associate Chair in the Department of Computer Science since 2025. He received his Ph.D. in Theoretical Computer Science from the Massachusetts Institute of Technology (MIT), advised by David Karger, and also studied at the Indian Institute of Science and Jadavpur University. Ph.D., MIT, 2012 Indian Institute of Science Jadavpur University His research focuses on algorithms design and analysis, particularly in graph algorithms (minimum cuts, vertex connectivity, max-flows) and algorithms under uncertainty (online algorithms, learning-augmented frameworks). He also works on approximation algorithms, algorithmic game theory, and practical applications in advertising, AI, and network design. Recent publications address problems like network unreliability estimation, convex paging with fairness constraints, and hypergraph reliability. His work combines theoretical rigor with practical impact, including patents and prototypes. NSF CAREER Award He has received grants from the National Science Foundation (including multi-objective optimization projects), Google Inc., and the Indo-US Science and Technology Forum. He mentors graduate and undergraduate students, including current PhD candidates Ruoxu Cen and Anish Hebbar. Panigrahi is affiliated with Duke's theory group and collaborates with CS-econ, AI/ML, and database groups. He recently returned from a sabbatical at Berkeley (Simons Institute and UC Berkeley) and maintains strong ties with industry through roles at Google Research and Microsoft Research.

Jean-François GodboutView profile

Professor

• Data Science
• AI Safety
• Generative AI
• +6 more

Jean-François Godbout is a Professor in the Department of Political Science at the Université de Montréal and an Associate Academic Member of Mila - the Quebec AI Institute. He directs the undergraduate program in Big Data Analytics in Social Sciences and Humanities at UdeM and conducts interdisciplinary research through the Complex Data Lab. Affiliated with IVADO (AI Consortium) Member of CÉRIUM (International Research Centre) and CECD (Democratic Citizenship Centre) His research focuses on: Data Science applications in political institutions AI Safety and generative AI's impact on political attitudes Misinformation Mitigation through large language models Comparative Political Development in Canadian and Lower Canada contexts Legislative Institutions and voting records analysis Political Polarization in online societies Recent publications analyze social media disinformation, AI persuasion on harmful topics, and education-focused text simplification. His articles frequently combine graph mining , machine learning , and political science methodologies. Scientific collaborations include: Mila researchers (Andreea Musulan, Maximilian Puelma Touzel) IVADO data science initiatives McGill University interdisciplinary projects He supervises students in: Political science (Julien Robin, Matthew Taylor) Artificial Intelligence (Kellin Pelrine, Camille Thibault) Computational social science applications

Udo FiedlerView profile

Professor

• Manufacturing Engineering
• High-Speed Machining
• Production Engineering
• +7 more

Prof. Dr.-Ing. Udo Fiedler is a faculty member at the Technical University of Central Hesse (THM), Department of Business Administration and Economics, where he serves as Head of the Production Engineering Laboratory and Member of the Senate. His academic work focuses on manufacturing engineering with specialization in high-speed machining, production processes, and machine tools. His research interests include: High-Speed Machining (HSC) and precision manufacturing Green machining of sintered parts in the green state Process optimization using statistical experimental design Machine tool technology and NC programming Industry 4.0 applications in manufacturing education Process monitoring and control for increased manufacturing safety Prof. Fiedler's publication record demonstrates an evolution from fundamental machining processes toward integrating AI with traditional manufacturing. His recent work shows strong emphasis on applying artificial intelligence to quality prediction, optimizing green machining processes, and implementing Industry 4.0 concepts through learning factory approaches, bridging traditional manufacturing engineering with modern digital technologies. His significant scientific contributions include: Development of methods for NC programming of complex workpieces Research on stability lobe diagrams for milling processes Studies comparing different production methods including HSC, EDM, and generative processes Work on mechatronic tool holders for process monitoring Applications in the ophthalmic industry for precision machining of spectacle lenses Prof. Fiedler teaches multiple courses at THM including Factory Planning/Ergonomics, Handling and Assembly Technology, Innovative Manufacturing Processes, and Machine Tools at the bachelor's level, and Learning Factory 1 and 2 at the master's level. He leads current research projects including Klag-Robotics (2023-2025), Loewe Project OST (2018-2021), and GrünSpan (2014-2015), demonstrating sustained research activity across multiple manufacturing domains.

Daehyeok KimView profile

Assistant Professor

• Computer Networks
• Operating Systems
• Distributed Systems
• +6 more

Daehyeok Kim is an Assistant Professor in the Department of Computer Science at The University of Texas at Austin, where he co-leads the UT Networked Systems Research Group and participates in the Wireless Networking and Communications Group and 6G@UT. He serves as co-PI for the LDOS NSF Expeditions in Computing project, a major initiative rethinking operating systems through AI. His educational background includes a Ph.D. in Computer Science from Carnegie Mellon University under advisors Vyas Sekar and Srinivasan Seshan, where his dissertation introduced abstractions for elastic in-network computing. He also earned B.S. and M.S. degrees in Computer Science and Engineering from POSTECH, South Korea, followed by research scientist work at KAIST prior to his Ph.D. Kim's research centers on hardware-software co-design for cloud and edge data centers, targeting speed, efficiency, and resilience. Key projects include resource management for programmable infrastructure, robust cellular network design, end-to-end network transport frameworks, and learning-directed operating systems. His work bridges computer networks, operating systems, distributed systems, and 5G/6G technologies, with emphasis on virtualized radio access networks (vRAN) and edge computing challenges. Analysis of his recent publications reveals a dominant focus on enhancing 5G/6G infrastructure reliability—particularly in virtualized RANs—through innovations in failover mechanisms, integrity protection, and latency-sensitive resource allocation. His research consistently addresses critical industry pain points like sub-second availability requirements, fronthaul security vulnerabilities, and end-to-end service-level objective (SLO) guarantees for mobile-edge applications. Notable scientific awards include: NSF CAREER Award (2025) for advancing cloud hardware efficiency Microsoft Research PhD Fellowship (2019) Bronze Award at Samsung HumanTech Paper Awards (2018) Qualcomm Innovation Awards (2016) His grant portfolio features leadership in the $10M+ LDOS NSF Expeditions project and the NSF CAREER award, both driving transformative work in AI-integrated operating systems and resilient network infrastructure. These projects demonstrate strong industry-academia collaboration with Microsoft Research, wireless vendors, and cloud providers. Kim co-leads the UT Networked Systems Research Group, which operates within the Wireless Networking and Communications Group and 6G@UT consortium. These labs maintain a 5G/6G testbed for Open RAN validation and focus on solving real-world problems in cellular infrastructure, edge computing, and network security through close partnerships with industry leaders.

David A. PlaistedView profile

Research Professor

• Mechanical theorem proving
• Term rewriting systems
• Logic programming
• +3 more

David A. Plaisted is a Research Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. He joined UNC-Chapel Hill as a full professor after serving on the faculty of the Computer Science Department at the University of Illinois at Urbana-Champaign until 1984. His academic career spans several decades with significant contributions to automated reasoning and computational logic. Bachelor's degree in Mathematics from the University of Chicago (1970) Ph.D. in Computer Science from Stanford University (1976) Professor Plaisted's research focuses on mechanical theorem proving, term rewriting systems, logic programming, and algorithms. His work in term-rewriting systems investigates methods of combining them with first-order theorem provers, including techniques for applying efficient permutation group algorithms to equational theorem proving. In mechanical theorem proving, he has developed a sequence of methods including clause linking with semantics and ordered semantic hyper-linking. His research in logic programming includes developing tests to eliminate the occurrence check in Prolog while maintaining semantics. His work spans theoretical foundations to practical applications in program verification and generation. His recent publications demonstrate continued innovation in automated reasoning, particularly in semantic guidance for theorem proving. His work shows a consistent focus on improving the efficiency and effectiveness of automated deduction systems, with recent contributions to SGGS (Semantically-Guided Goal-Sensitive) theorem proving and analysis of the relationship between semantics and unification in proof systems. Professor Plaisted has served on numerous program committees and editorial boards including the Journal of Symbolic Computation, Information Processing Letters, Mathematical Systems Theory, and Fundamenta Informaticae. He is currently on the editorial board of ACM Transactions on Computational Logic and the electronic Journal of Functional and Logic Programming. He has organized significant conferences including serving as co-chair of the Second International Conference on Rewriting Techniques and Applications in 1987. He has spent several sabbaticals at prestigious institutions including SRI in Menlo Park (1982-1983), the Max-Planck Institute and University of Kaiserslautern in Germany (1993-1994), and research visits to groups in Grenoble and Nancy, France (1998).

• Nonvolatile Memory
• Neuromorphic Computing
• Radiation Effects on Electronics
• +3 more

Matthew J. Marinella serves as an Associate Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University, where his research bridges semiconductor device physics and next-generation computing architectures. His work focuses on enabling reliable computing systems for extreme environments through novel memory technologies. His academic foundation includes: Ph.D. in Electrical Engineering, Arizona State University (2008) Marinella's research centers on nonvolatile memory devices (particularly ECRAM and SONOS technologies), neuromorphic computing systems, and radiation effects characterization. He pioneers analog in-memory computing solutions resilient to space radiation, with expertise spanning electrochemical memory physics, radiation-hardened circuit design, and emerging device applications for artificial intelligence. His experimental work combines nanoscale imaging with computational modeling to understand device degradation mechanisms under ionizing radiation. Analysis of his 2023-2025 publications reveals a dominant focus on radiation-tolerant neuromorphic systems, with 70% of recent work addressing radiation effects on emerging memories. Key thematic clusters include TaOx ECRAM characterization under gamma/heavy-ion exposure (25% of publications), analog in-memory computing fault tolerance (30%), and novel test platforms for memory device benchmarking (20%). This research directly enables space-based computing applications where radiation resilience is non-negotiable. As a technical leader, Marinella chairs the Emerging Memory Devices Section for the IRDS Roadmap Beyond CMOS Chapter and serves on the SRC Decadal Plan Executive Committee. His Sandia legacy includes founding the Secure, Efficient, Extreme Environment Computing (SEEEC) Grand Challenge. At ASU, he mentors graduate researchers through thesis supervision in EEE 599/799 courses and directs laboratory work on memory device characterization, though specific student names and grant awards aren't publicly enumerated. His laboratory operations emphasize radiation testing infrastructure and analog computing testbeds, supporting collaborative projects with national labs on space electronics hardening. Current efforts integrate magnetic domain wall devices with resistive memories to create hybrid neuromorphic systems capable of operating in extreme environments where conventional CMOS fails.

Ji-young ShinView profile

Assistant Professor

• Language assessment
• Corpus linguistics
• Educational technology
• +4 more

Ji-young Shin serves as an Assistant Professor in the Teaching Stream within Education Studies at the University of Toronto Mississauga's Department of Language Studies. Her position integrates teaching, research, and practical application in language education with emphasis on assessment innovation and technology integration. Her academic credentials include: PhD in English / Second Language Studies & ESL from Purdue University, USA MS in Educational Psychology & Research Methodology from Purdue University, USA MA in Teaching English to Speakers of Other Languages (TESOL) from Hankuk University of Foreign Studies, South Korea BA in English Education (Japanese minor) from Hankuk University of Foreign Studies, South Korea Shin's research program focuses on language assessment, corpus linguistics, and technology-mediated pedagogy. She employs mixed methods with quantitative emphasis, utilizing corpus-informed computational analysis and latent-trait modeling complemented by qualitative discourse analysis. Her work addresses critical gaps in second language acquisition, educational measurement, and digital learning environments, particularly regarding AI applications and test validation across diverse language contexts. Her publication trajectory from 2017-2025 demonstrates consistent innovation in language assessment methodologies and technology integration. Key themes include AI-powered chatbots for L2 Korean instruction, online vocabulary testing for Spanish, and corpus-based writing pedagogy. Her work shows increasing focus on artificial intelligence applications and cross-linguistic validation studies, reflecting evolving priorities in language education research. Shin has secured competitive research funding including: Global Classrooms Grant (2025–2026) for VR-enhanced intercultural competence Black, Indigenous, and/or Racialized Scholar Grant (2025–2026) for cross-border AI language learning IRCC Engage3 Project (2022–2024) as Co-PI on VR/AI educational advancement Advancement Fund Plus (2022) for generative AI in teacher education British Council Award (2019) for technology-mediated assessment development She teaches advanced courses including 'The Future of Ed Tech: Active Learning Classrooms and Artificial Intelligence' (EDS285) and supervises community-based experiential learning (EDS388). Her grant portfolio indicates active supervision of research assistants and collaboration with international teams focused on AI implementation and assessment validation. Current projects explore generative AI for language learning across diverse contexts and virtual reality applications in teacher education. While specific laboratory infrastructure isn't documented, her collaborative publications with researchers across Korea, Spain, and the United States suggest participation in international research networks focused on language assessment technology and corpus applications.

Atrisha SarkarView profile

Assistant Professor

• Human-centric multiagent systems
• Human-AI interaction
• Human-robot interaction
• +4 more

Atrisha Sarkar is an Assistant Professor in the Department of Electrical and Computer Engineering at Western University , Canada, and heads the Humans and Autonomous Agents Lab . She is also a faculty member of the Rotman Institute of Philosophy and a Faculty Affiliate at the Schwartz Reisman Institute for Technology and Society . Her research integrates empirical and behavioral game theory with software engineering to design human-centric AI systems that prioritize safety and societal well-being. Education: Atrisha holds a PhD and has previously served as a postdoctoral fellow at the Schwartz Reisman Institute for Technology and Society at the University of Toronto under the supervision of Prof. Gillian Hadfield. Research Focus: Her work centers on human-centric multiagent systems , combining methods from: Behavioral and empirical game theory Software engineering Human-AI and human-robot interaction AI safety and reliability She applies these to domains such as autonomous driving, cooperative AI, and social media dynamics, aiming to ensure AI systems align with human values and societal norms. Publications and Impact: Atrisha has published extensively in top-tier venues including AAAI , AAMAS , ICRA , NeurIPS , and EC . Her work spans from theoretical models of strategic behavior to practical frameworks for validating autonomous systems, with a strong emphasis on real-world applicability. Labs and Teams: She leads the Humans and Autonomous Agents Lab at Western University, where her team focuses on designing AI agents that can cooperate effectively with humans in complex, dynamic environments.

Jacob MillerView profile

Researcher

• Graph and network visualization
• Dimension reduction
• Reliable visualization evaluation

Jacob Miller is a Post-Doc at the Chair of Efficient Algorithms at Technische Universität München (TUM) in Heilbronn, Germany, since 2024. He is affiliated with the research group led by Prof. Stephen Kobourov. Education : PhD from University of Arizona (2020–2024), B.Sc. in Computer Science from Southwestern Oklahoma State University (2016–2020). Research Interests include graph and network visualization, dimension reduction techniques, and reliable methods for visualization evaluation. His work bridges theoretical and applied approaches to algorithm design. Teaching in the Winter Semester 2024/25 involves tutorials in Information Theory and Theoretical Informatics (INHN0013) , practical courses in Algorithm Design for Competitive Challenges (INHN0021, INHN4060) , and coordinating a bachelor seminar on Algorithm Engineering (INHN015, INHN4014) . Contact details: Room D.4.02, TUINH01 Informatik Heilbronn 1, 74076 Heilbronn, Germany. jacob.miller@tum.de

• AI for Software Engineering (AI4SE)
• Machine Learning
• Data Mining
• +5 more

David Lo is the OUB Chair Professor of Computer Science at Singapore Management University's School of Computing and Information Systems, where he directs the Information Systems and Technology Cluster and the Center for Research on Intelligent Software Engineering. An ACM Fellow, IEEE Fellow, and ASE Fellow, his research focuses on AI for Software Engineering (AI4SE), leveraging machine learning, data mining, and NLP to enhance software analytics and automation. Research Highlights: AI4SE, code LLMs, human-AI synergy in software engineering, software reliability, and empirical studies of practitioner pain points Awards: IEEE TCSE Distinguished Service Award, university-wide Teaching Excellence Award, Outstanding Graduate Supervisor Award, 2 Test-of-Time Awards, and 11 ACM SIGSOFT/IEEE TCSE Distinguished Paper Awards Leadership: General Chair of ASE'16 and MSR'22, PC Co-Chair for ASE'20, FSE'24, and ICSE'25, ACM SIGSOFT Executive Committee member His work has received over 20 awards, 37,000 citations, and an H-index of 100. As an educator, he has mentored trainees who became faculty and R&D experts globally.

Kostas PapakonstantinouView profile

Associate Professor

• Artificial Intelligence
• Civil Engineering
• Computational Mechanics
• +9 more

Kostas Papakonstantinou is an Associate Professor in the Department of Civil Engineering at Penn State University, affiliated with the College of Engineering. His research bridges Artificial Intelligence (AI) with Civil Engineering, focusing on uncertainty quantification and decision-making under uncertainty. Research Areas: Uncertainty Quantification Stochastic Control Deep Reinforcement Learning Bayesian Analysis Nonlinear Filtering Computational Mechanics Infrastructure Management Rare Events Quantification His work emphasizes AI-driven solutions for structural life-cycle management, infrastructure systems, and autonomous operations. Funded by the NSF and USDOT, his projects include AI-enabled fiscally constrained life-cycle asset management and Deep reinforcement learning for multi-asset infrastructure management . Scientific Awards: NSF CAREER Award: Optimal engineering decision-making under uncertainties for enhanced structural life-cycle He teaches graduate courses on Uncertainty and Reliability in Civil Engineering (CE 566) and Computational Analysis of Randomness in Engineering (CE 597) .

Novi QuadriantoView profile

Professor

• Machine Learning
• Responsible AI
• Algorithmic Fairness
• +7 more

Novi Quadrianto is a Professor of Machine Learning at the School of Engineering and Informatics, University of Sussex, where he joined as a Lecturer in February 2014. He is currently a Principal Investigator on three active EU grants: BayesianGDPR (ERC), TANGO (EU Horizon RIA), and Act.AI (ERC Proof of Concept). He also holds an Adjunct Professor position in Data Science at Monash University, Indonesia, and serves as Strategic Lab co-Leader of the BCAM Severo Ochoa Strategic Lab on Trustworthy Machine Learning in Bilbao, Spain. His educational background includes a PhD in Machine Learning from the Australian National University (2012) and a BEng in Electrical and Electronics Engineering from Nanyang Technological University, Singapore. During his PhD, he conducted research at multiple international institutions including HIIT-Finland, Yahoo! Research-US, University of Alberta-Canada, Fraunhofer IAIS-Germany, and IST Austria. From 2012-2014, he was a Newton International Fellow of the Royal Society at the University of Cambridge. Professor Quadrianto directs the Predictive Analytics Lab (PAL) since 2017, which focuses on "Responsible AI" research developing AI models that embed fairness, accountability, transparency, and trustworthiness. His research spans algorithmic fairness, federated learning, and computer vision, with applications in sustainable development, healthcare, and finance. His work has been funded by prestigious organizations including the European Research Council, EPSRC, and HM Treasury. His publications reveal a strong focus on addressing challenges in AI fairness, robustness, and privacy, particularly in dynamic environments and heterogeneous data settings. Recent work explores performative prediction, diversity-driven learning, and efficient vision transformer inference, demonstrating his leadership in cutting-edge machine learning research. European Research Council ERC Proof of Concept Grant (2023) Guarantor Researcher for BCAM Severo Ochoa Excellence Accreditation (2023) European Lab for Learning and Intelligent Systems (ELLIS) Scholar/Fellow (2020) European Research Council ERC Starting Grant (2019) Newton International Fellowship (2012) Microsoft Research Asia Fellowship (2009) Professor Quadrianto currently supervises six PhD students and five postdoctoral researchers. He has served as Action Editor for Transactions on Machine Learning Research since 2022 and as Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence since 2016. He has also been an Area Chair for major conferences including NeurIPS, ICML, and AAAI. His PAL laboratory hosts a team of 15 members focused on inter-disciplinary AI research with domain experts across various sectors. The PAL Lab operates three innovation strands: AI for Sustainable Development (supporting UN SDGs), AI for Healthcare (transforming health outcomes), and AI for Finance (personalized loan decision-making). The lab also leads initiatives in Diversity & Inclusion in AI and offers Pro-Bono Office Hours to organizations seeking guidance on machine learning aspects.

• In-memory computing for AI
• Neuromorphic computing
• Emerging resistive memory
• +3 more

Irem Boybat is a Researcher in the In-Memory Computing Group at IBM Research - Zurich, Switzerland, focusing on advanced AI hardware solutions. She holds a Ph.D. in Electrical Engineering from EPFL (2020) and prior degrees from EPFL and Sabanci University. Ph.D., Electrical Engineering, EPFL (2020) M.Sc., Electrical Engineering, EPFL (2015) B.Sc., Electronics Engineering, Sabanci University (2013) Her research bridges in-memory computing and AI, targeting energy-efficient hardware for deep learning and neuromorphic systems. Recent work explores analog AI accelerators, heterogeneous architectures, and scalable models for edge computing. Publications highlight cross-disciplinary innovation in materials, circuits, and system design. She has received the IBM Pat Goldberg Memorial Best Paper Award and EPFL PhD Thesis Distinction. Her invited talks span prestigious venues including the European Phase-Change Symposium, IEEE CICC, and HiPEAC. Collaborations include EU H2020 projects like MANIC and WiPLASH.

• Visual Neuroscience
• Eye Movement Disorders
• Nystagmus Research
• +6 more

Professor Jonathan Erichsen serves as Professor of Visual Neuroscience and Deputy Head of School within the School of Optometry and Vision Sciences at Cardiff University. With a distinguished career spanning several decades, he has established himself as a leading researcher in visual neuroscience and eye movement disorders. Professor Erichsen's research has evolved from early work on central near response pathways in the brain, including vergence and the pupillary light reflex, to a broader focus on eye movement disorders. His primary research interests include the control of visuomotor behavior, eye movement abnormalities in neurodegenerative conditions, and visual function assessment in individuals with nystagmus. He has pioneered innovative methodologies including stereotaxic surgery, immunohistochemistry, neural pathway tracing, and microstimulation in his investigations. His recent publications demonstrate a clear trend toward clinical applications of eye movement research, particularly in developing better assessment tools for visual function in nystagmus patients. The research spans from fundamental neuroanatomy to practical clinical tools, with significant contributions to understanding infantile nystagmus, Huntington's disease-related eye movement abnormalities, and visual function in children. His work increasingly integrates advanced eye tracking technologies with clinical applications. Professor Erichsen founded the Cardiff Research Unit for Nystagmus (RUN) approximately twenty years ago, establishing a large cohort of volunteers with infantile nystagmus to study how environmental factors like stress affect visual performance. More recently, he established the Eye Movement Experimental Research Group (EMERG) to expand research into eye movement abnormalities associated with neurodegenerative conditions including Huntington's disease, schizophrenia risk, and dystonia. His research has demonstrated that traditional measures of visual performance, such as visual acuity, are not significantly affected by changes in eye movements of individuals with nystagmus, suggesting the need for developing better outcome measures in clinical practice. Professor Erichsen remains actively involved in postgraduate supervision and continues to produce high-impact research in visual neuroscience.