جستجوی استاد راهنمای دانشگاهی

Márton Karsaiمشاهده پروفایل

دانشیار

• Network Science
• Human Dynamics
• Temporal Networks
• +۱۱ مورد دیگر

Márton Karsai is an Associate Professor and Head of the Department of Network and Data Science at the Central European University in Vienna, and a Research Professor at the HUN-REN Alfréd Rényi Institute of Mathematics in Budapest. He leads the Computational Human Dynamics Lab, focusing on data-driven modeling of social and biological systems. He is also the Editor-in-Chief of the journal Advances in Complex Systems . His research interests lie at the intersection of network science, human dynamics, and socioeconomic systems. He specializes in temporal and spatial networks, modeling contagion processes (both social and biological), and analyzing large-scale human behavioral datasets. His work integrates computational methods with real-world data to understand complex social phenomena such as mobility patterns, migration, segregation, and epidemic spread. He is particularly known for using remote sensing and digital trace data to infer poverty and socioeconomic conditions in urban areas. The recent publications highlight a strong trend in applying network science and machine learning to societal challenges. His work spans high-impact journals in complex systems, data science, and computational social science, with recurring themes in epidemic modeling, urban analytics, socioeconomic inference, and the structure of temporal and spatial networks. The research is highly interdisciplinary, combining physics, computer science, and social science methodologies. He has been invited to speak at major events such as the Conference on Complex Systems, the Lake Como School on Complex Networks, and workshops on data for vulnerability assessment. He served as general co-chair of CCS 2021 in Lyon, demonstrating leadership in the complexity science community. General Co-Chair, Conference on Complex Systems (CCS) 2021, Lyon Invited speaker, 4th Workshop on Data for the Wellbeing of the Most Vulnerable @ ICWSM'23 Invited speaker, Complexity72h Workshop Invited lecturer, Lake Como School on Complex Networks Invited talk, Hungarian Academy of Sciences on COVID-19 modeling While specific grant details are not listed, his coordination of projects on segregation, migration, and poverty inference—often in collaboration with the Complexity Science Hub—suggests active involvement in externally funded interdisciplinary research. He advises students through the Department of Network and Data Science at CEU, though specific advisees are not named. His lab, the Computational Human Dynamics Lab, serves as a hub for data-driven research on social systems.

• Obesity prevention
• Physical activity
• Nutrition
• +۸ مورد دیگر

Dr. Joel Williams is an Associate Professor in the Department of Public Health Sciences at Clemson University's College of Behavioral, Social and Health Sciences. His academic foundation includes a Ph.D. in Public Health (University of South Carolina, 2004), MPH (2001), Graduate Certificate in Applied Statistics (2008), and B.S. in Exercise Studies (Lander University, 1996). With clinical experience in sports medicine and hospital settings, Dr. Williams specializes in implementing and evaluating health promotion programs. His research examines obesity prevention, chronic disease management, and mobile health technology applications. Primary interests include: Community-based interventions for physical activity and nutrition Pediatric and population health behavior change Telemedicine solutions for chronic conditions Methodological approaches to program evaluation Dr. Williams' recent publications (2017-2022) demonstrate consistent focus on diabetes management, mobile health innovations, pediatric health, and epidemiological studies. His work frequently employs multidisciplinary approaches across public health, clinical medicine, and digital technology domains. Honors include: Clemson University School of Health Research Scholar (2017) Institute for Advancement of Health Care Faculty Fellow (2015) Clemson University Service Alliance Faculty Fellow (2008) He has advised 21 graduate students as primary or co-advisor, with mentees contributing to research on pediatric asthma, diabetes, nutrition, and telemedicine. His externally funded research portfolio includes projects supported by CDC, NIH (NCI, NHLBI, NICHD), DoD, USDA, and HRSA. Dr. Williams leads interdisciplinary teams for community and clinical trials, frequently collaborating with medical institutions and public health agencies. His research emphasizes practical applications of mobile technology for chronic disease self-management in diverse populations.

Nan Yangمشاهده پروفایل

استاد

• Terahertz Communications
• Ultra-Reliable and Low-Latency Communications
• Cyber-Physical Security
• +۴ مورد دیگر

Prof. Nan Yang is a Professor at the Australian National University's ANU College of Engineering, Computing and Cybernetics, leading the Information and Signal Processing Cluster and the Emerging Communications Laboratory. He holds a PhD in Electronic Engineering from Beijing Institute of Technology (2011) and has held postdoctoral roles at CSIRO and UNSW before joining ANU in 2014. His research focuses on terahertz communications, ultra-reliable low-latency systems, and cyber-physical security, with notable contributions to molecular communications and massive MIMO systems. Education: B.S. in Electronics, China Agricultural University (2005) M.S. in Electronic Engineering, Beijing Institute of Technology (2007) Ph.D. in Electronic Engineering, Beijing Institute of Technology (2011) Key Roles: Associate Dean for Higher Degree Research (2019–2021) Editorial Board Member of IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, IEEE Communications Letters, and others Organizer of workshops at IEEE ICC, GlobeCOM, and ACM MobiCOM His research interests span terahertz communication systems, cyber-physical security, and intelligent communications. Recent work emphasizes secure beamforming, UAV-assisted networks, and molecular communication protocols. He has authored over 180 publications and secured grants totaling millions in funding for projects like the Ultra-Fast and Secure Terahertz Communications for 6G Wireless Systems (2023–2026). Awards & Recognition: IEEE ComSoc Distinguished Lecturer (2023–2024) Best Paper Awards at IEEE ICC 2024, GlobeCOM 2022, and VTC Spring 2013 Exemplary Editor/Reviewer Awards from IEEE Transactions Grants & Projects: iLAuNCH: SWIFT-iLAuNCH Project A (SC-9) (2024–2026) Ultra-Fast and Secure Terahertz Communications for 6G (2023–2026) Facility for Energy Security and Resilience Research (2022) His lab, the Emerging Communications Laboratory, develops cutting-edge solutions for 6G networks, including hybrid beamforming for terahertz systems and secure short-packet protocols. Collaborations span global institutions, emphasizing interdisciplinary research in communications and signal processing.

Brian Appelbeمشاهده پروفایل

پژوهشگر ارشد

• Inertial Confinement Fusion (ICF)
• Nuclear Physics
• Plasma Physics
• +۴ مورد دیگر

Brian Appelbe is a Research Fellow in the Department of Physics at Imperial College London, affiliated with the Centre for Inertial Fusion Studies (CIFS), Plasma Physics Group, and the Faculty of Natural Sciences. His research focuses on inertial confinement fusion (ICF) and high-energy-density physics, particularly nuclear processes in ICF plasmas, neutron production, and plasma-plasma interactions. He explores applications of ICF experiments to stellar nucleosynthesis and quantum computing in plasma physics. Research topics include measuring neutron capture cross sections in excited states, optimizing neutron sources, and leveraging quantum computing for plasma modeling. He collaborates on projects at facilities like the National Ignition Facility (NIF) and Omega laser, investigating magnetic field effects in plasma dynamics and burn propagation mechanisms. His work addresses challenges like plasma temperature/density measurement, magnetic field transport in burning plasmas, and mitigating hydrodynamic instabilities. Recent articles highlight advancements in neutron spectroscopy, magnetized implosion performance, and diagnostic innovations bridging magnetic and inertial confinement fusion. Despite no listed awards, his contributions to CIFS and plasma physics research are significant. He advises on experimental platforms for magnetized implosions and explores multi-disciplinary applications of ICF diagnostics. Labs/teams include the Plasma Physics Group and CIFS, advancing ICF theory and experimental techniques.

John Wrightمشاهده پروفایل

استادیار

• Quantum Computing
• Theoretical Computer Science
• Quantum Complexity Theory
• +۳ مورد دیگر

John Wright is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research centers on theoretical computer science with a focus on quantum computing, specifically quantum state learning, quantum complexity theory, property testing, and approximation algorithms. He is affiliated with the Simons Institute for the Theory of Computing. Education includes a Ph.D. in Computer Science from Carnegie Mellon University (2016), advised by Ryan O'Donnell, and a B.Sc. in Computer Science from the University of Texas at Austin. Research interests span quantum complexity, interactive proofs (e.g., MIP* = RE), quantum algorithms, and foundational aspects of quantum computation. His work bridges computer science, physics, and mathematics, with emphasis on understanding computational limits through quantum paradigms. Publications primarily explore quantum complexity, algorithms, and verification, with recent trends including quantum cryptography, tomography, and hardness proofs. Articles frequently involve collaborations with researchers like Thomas Vidick, Henry Yuen, and Ryan O'Donnell. Awards include the IEEE CS TCPAMI Young Researcher Award (2015). Teaching covers graduate and undergraduate courses such as CS 170 (Efficient Algorithms and Intractable Problems) and CS 294 (Quantum Complexity Theory). He advises students in quantum computing research and collaborates extensively across institutions. Labs/teams include the Quantum Computing group at UC Berkeley, with ties to the Simons Institute. Research support is managed by Amy Frithsen.

Marek Petrikمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Robust Decision Making
• Risk-Averse Optimization
• +۱۳ مورد دیگر

Marek Petrik is an Associate Professor in the Department of Computer Science at the University of New Hampshire, where he is also a member of the artificial intelligence research group. Prior to joining UNH, he was a Research Staff Member at IBM’s T. J. Watson Research Center. He received his Ph.D. in Computer Science from the University of Massachusetts Amherst in 2010 under the supervision of Shlomo Zilberstein. In 2022–2023, he was a visiting faculty researcher at Google Research. Education: Ph.D. in Computer Science, University of Massachusetts Amherst, 2010 His research focuses on robust, data-driven decision making , particularly in reinforcement learning with limited data, risk aversion, and Bayesian models of uncertainty. His work spans theoretical advances in robust Markov decision processes (MDPs), policy optimization, and imitation learning, with applications in natural resource management, agriculture, renewable energy, and space systems. Recent publications emphasize provable robustness, percentile optimization, and safe offline reinforcement learning. His recent work (2023–2025) demonstrates strong trends in risk-averse reinforcement learning , robust policy gradients , offline learning under uncertainty , and Bayesian approaches to decision-making . He frequently publishes in top venues such as NeurIPS, ICML, UAI, AAAI, and journals like JMLR and Mathematics of Operations Research. Scientific Awards: Best Student Paper Award, Uncertainty in Artificial Intelligence (UAI), 2015 Marek Petrik has advised numerous students and collaborators, particularly in the areas of robust RL and safe decision-making. His research has been supported by academic and industry collaborations, including IBM and Google. He is not currently accepting new PhD students. He is actively involved in developing methods for controlling invasive species , pest monitoring , and optimizing environmental systems . Labs and Research Groups: Artificial Intelligence Research Group, University of New Hampshire Former member: Mathematical Sciences Department, IBM T. J. Watson Research Center Visiting researcher: Google Research, 2022–2023

Charles Marcusمشاهده پروفایل

استاد

• Condensed Matter Physics
• Quantum Computing
• Quantum Coherent Electronics
• +۴ مورد دیگر

Charles Marcus is a Professor at the University of Copenhagen's Niels Bohr Institute, holding the Villum Kann Rasmussen Chair in Quantum Sciences. He directs the Center for Quantum Devices and Microsoft Station Q – Copenhagen, while affiliating with the Niels Bohr International Academy. Education : Stanford University (B.S. 1984), Harvard University (Ph.D. 1990), IBM Postdoctoral Fellow (1990-92) Employment : Faculty at Stanford (1992-2000), Harvard (2000-2011), and UCPH (2012-present) His research focuses on experimental condensed matter physics, particularly quantum coherent electronics in semiconductors/superconductors. Key areas include spin qubits for quantum computing, Majorana modes in nanowires, quantum Hall systems, and superconductor-semiconductor hybrids. Recent work explores topological quantum information schemes and novel magnetic resonance imaging approaches. Scientific publications span quantum devices, Josephson junctions, and topological materials. Awards include the H.C. Ørsted Gold Medal, AAAS Newcomb-Cleveland Prize, and fellowships from AAAS and APS. He serves on advisory boards for quantum technology centers globally. Significant Awards : H.C. Ørsted Gold Medal (2020) Industry Prize, Danish Academy of Natural Sciences (2019) Member, National Academy of Sciences (2018) Award for Research Excellence in Nanotechnology (2014) Professional Roles : Director, Center for Quantum Devices (2012-2019) Lab Director, Microsoft Quantum (2016-2021) Scientific Director, Harvard Center for Nanoscale Systems (2004-2009)

• Environmental Fluid Mechanics
• Open-Channel Flow
• Turbulence Structure
• +۷ مورد دیگر

Dr Stuart Cameron is a Senior Research Fellow in the School of Engineering at the University of Aberdeen, where he has been contributing to research since 2007. His work is centered on environmental fluid mechanics, with a focus on open-channel flows, turbulence, sediment transport, and bio-inspired hydrodynamics. He is affiliated with a series of high-impact interdisciplinary research projects involving advanced experimental and theoretical fluid dynamics. His research interests include: Open-channel flow turbulence structure Sediment entrainment and transport mechanisms Flow-biota interactions in aquatic ecosystems Development and error analysis of Particle Image Velocimetry (PIV) techniques Hydrodynamics of bioinspired surfaces and fish-shaped bodies The most recent publications highlight a strong trend in analyzing complex turbulent flows over rough beds, secondary currents, and bio-inspired surface morphologies. His work frequently involves high-resolution experimental techniques and theoretical modeling, often in collaboration with leading researchers such as V. Nikora. Articles span topics from drag forces on sediment particles to hydrokinetic turbine performance and field-based PIV studies in natural water bodies. Notable scientific contributions have been supported by major grants from EPSRC, NERC, and the European Commission. These include projects such as: "Secondary currents in turbulent flows over rough walls" (EPSRC, 2021–2024) "River flow regulation, fish behaviour and status – RIBES" (European Commission, 2020–2023) "Field stereoscopic PIV system for freshwater and marine ecosystems" (NERC, 2019–2020) "Bed friction in rough-bed free-surface flows" (EPSRC, 2014–2017) "HYTECH" (Marie Curie ITN, 2013–2016) "High-resolution numerical and experimental studies of turbulence-induced sediment erosion" (EPSRC & DFG, 2010–2014) Dr Cameron has not advised any named students in the available data, but he collaborates extensively within research teams focused on environmental hydraulics and experimental fluid mechanics. He is involved in the development of advanced measurement systems and theoretical frameworks for understanding complex flows in natural and engineered environments. He is actively engaged in laboratory and field-based research, contributing to both fundamental fluid mechanics and applied environmental engineering challenges. His work bridges engineering, ecology, and physics, particularly in the context of river systems and aquatic habitats.

Kate Kemsleyمشاهده پروفایل

استاد

• Chemometrics
• Machine Learning
• Deep Learning
• +۱۷ مورد دیگر

Kate Kemsley is a Professor in the School of Chemistry, Pharmacy and Pharmacology at the University of East Anglia (UEA), where she leads the Centre of Expertise in Food Authenticity, part of the UK's Food Authenticity Network. She also holds an external role as Scientific Director at Mestrelab Research S.L., a scientific software division of Bruker Corporation. Education: Doctor of Philosophy Master of Physics, University of Oxford Her research focuses on the application of computational statistics, machine learning, and deep learning to diverse datasets including NMR, LC-MS, infrared, and Raman spectroscopy, with a strong emphasis on food authenticity and natural product integrity. She applies AI and cheminformatics to detect adulteration in edible oils, spices, coffee, fruits, and meat. A significant portion of her recent work involves high-resolution and benchtop NMR data analysis using convolutional and message-passing neural networks. The most recent articles highlight a strong trend in applying deep learning models—particularly message passing neural networks (MPNNs)—to predict NMR chemical shifts and verify molecular structures. These works bridge cheminformatics and artificial intelligence, demonstrating applications in small molecule analysis, metabolomics, and food authenticity. The research spans analytical chemistry, machine learning, and industrial applications in food, pharma, and instrumentation. Scientific Awards and Recognition: h-index of 37 (Scopus) Leader of UEA's Centre of Expertise in Food Authenticity Member of DEFRA Expert Committee, Authenticity Methodology Working Group (2024–2027) She is actively involved in advising and research grants, currently leading or participating in multiple funded projects such as AI-driven small molecule structure elucidation, metabolomics studies on arthritis, and integration of compact NMR with mass spectrometry. Her collaborations span forensic, food, pharmaceutical, and instrumentation sectors, including partnerships with Mestrelab Research and Versus Arthritis. She is accepting PhD students and regularly contributes to training and dissemination through webinars and workshops. Kate Kemsley is affiliated with key research groups including the UEA Centre of Expertise in Food Authenticity and collaborates with the Community for Analytical Measurement Science and the UK Food Authenticity Network. Her work integrates academic research with industrial software development through her role at Mestrelab, advancing automation and high-throughput analysis in chemical and food sciences.

Wendy Meiringمشاهده پروفایل

استاد

• Statistical Methods for Brain Imaging
• Spatial and Temporal Processes
• Computational Statistics
• +۴ مورد دیگر

Wendy Meiring is a Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara. Her research focuses on statistical methods for analyzing complex data in neuroscience, environmental science, and biomedical applications. She specializes in spatial and temporal processes, computational statistics, machine learning, and uncertainty quantification. Her work integrates advanced statistical techniques with real-world challenges, such as analyzing brain imaging data, pharmacokinetic models, and environmental monitoring. She has contributed to methodologies for functional data analysis, clustering-based correlation estimation, and spatial-temporal modeling. Dr. Meiring has published extensively in top journals, including The Journal of Computational and Graphical Statistics , focusing on topics likeFréchet regression, pharmacokinetic modeling, and environmental phenology. Her research bridges theoretical statistics with practical applications in health, neuroscience, and ecology. She collaborates across disciplines, contributing to programs like the Interdepartmental Graduate Program in Dynamical Neuroscience at UCSB. Her lab develops innovative statistical tools for analyzing high-resolution datasets, emphasizing reproducibility and methodological rigor.

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Maxime CORDY is a Research Scientist at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) , University of Luxembourg, within the Security, Design and Validation group (SerVal) . He holds a PhD from the University of Namur (Belgium, 2014) and specializes in software engineering, applied artificial intelligence, and cybersecurity. His work focuses on adversarial machine learning, deep learning robustness, and model checking for critical systems. Research interests include adversarial attacks on tabular data , energy system optimization , code understanding models , and software quality assurance . Recent projects address challenges in secure AI deployment, automated test generation, and fault detection in large language models. Publications emphasize empirical studies on adversarial defenses, data augmentation for code models, and energy consumption forecasting. He contributes to tools like Daedalux (variability-aware model checking) and benchmarks like Tabularbench for adversarial robustness evaluation. Current affiliations include leadership within the SerVal group and collaborations on interdisciplinary projects such as MALETSQUE (Machine Learning Techniques for Software Quality Evaluation). His work bridges theoretical computer science with practical applications in energy systems, medical imaging, and space program design.

Ian Abelمشاهده پروفایل

پژوهشگر

• Fusion Energy
• Plasma Physics
• Magnetic Confinement Systems
• +۳ مورد دیگر

Dr. Ian Abel is an Associate Research Scientist at the Institute for Research in Electronics & Applied Physics (IREAP) at the University of Maryland, where he has been since 2018. His expertise spans fusion energy, plasma physics, and computational modeling. Abel holds a B.A. in Mathematics (2006) and M.S. in Applied Mathematics (2007) from the University of Cambridge, followed by a Ph.D. in Theoretical Physics from the University of Oxford (2012). His research focuses on magnetically confined fusion systems, particularly edge dynamics in tokamaks and innovative centrifugal mirror concepts. He has contributed to the development of gyrokinetic simulation tools like the GX code and the MaNTA transport model. Abel’s work also explores machine learning applications in plasma turbulence analysis and centrifugal mirror fusion reactor design for space propulsion. His research leverages advanced numerical methods, including GPU-native algorithms and adjoint-based optimization techniques for plasma equilibria. Key projects include the Centrifugal Mirror Fusion Experiment (CMFX), where he investigates plasma confinement and transport phenomena. His publications emphasize interdisciplinary approaches, integrating computational fluid dynamics, statistical physics, and high-performance computing to address challenges in fusion energy and plasma dynamics. While no specific awards are listed, his contributions to gyrokinetic turbulence modeling and centrifugal confinement systems are central to current fusion research.

Lai-Sang Youngمشاهده پروفایل

استاد

• Dynamical Systems
• Ergodic Theory
• Non-uniform Hyperbolicity
• +۷ مورد دیگر

Lai-Sang Young is the Henry & Lucy Moses Professor of Science at the Courant Institute of Mathematical Sciences, New York University. She has held previous positions at Northwestern University, Michigan State University (1980–1986), the University of Arizona (1987–1990), and UCLA (1991–1999). She has also held visiting positions at MSRI, Universitat Bielefeld, the University of Warwick, and the Institute for Advanced Study at Princeton. Her primary research interests lie in dynamical systems and ergodic theory , particularly focusing on strange attractors , non-uniform hyperbolicity , SRB measures , entropy , and statistical properties of chaotic systems . Her work bridges pure mathematics with applications in mathematical physics , probability , and neuroscience . She has made groundbreaking contributions to the understanding of chaotic behavior in deterministic and stochastic systems. The 15 most recent publications highlight her sustained focus on non-uniformly hyperbolic systems , random perturbations , Lyapunov exponents , SRB measures , and nonequilibrium statistical mechanics . Her research spans abstract mathematical theory and its applications to physical and biological models, including neuronal networks and heat conduction. Her scientific awards include: Ruth Lyttle Satter Prize (1993) Guggenheim Fellowship (1997) Election to the American Academy of Arts and Sciences (2004) Noether Lecturer, AWM (2005) Sonia Kovalevsky Lecture, AWM-SIAM (2007) Rolf Schock Prize in Mathematics (2024) She has advised several students and collaborated with prominent mathematicians such as Qiudong Wang, François Ledrappier, and Mark Demers. Her work has been supported by prestigious fellowships and grants, though specific funding sources are not detailed in the provided text. She has not held administrative or part-time roles, and there is no indication of retirement. She leads a research group focused on chaotic and stochastic dynamical systems, contributing to both theoretical advances and interdisciplinary applications.

• Finance
• Financial Economics
• Cryptocurrencies
• +۵ مورد دیگر

Rui Manuel Teixeira Santos Dias is a Postdoctoral Fellow in Finance at the State University of Feira de Santana (Brazil) , affiliated with the Department of Exact Sciences. His work bridges finance, econophysics, and sustainability, focusing on market efficiency, cryptocurrency dynamics, clean energy investments, and commodity market interdependencies. Education : PhD in Management (University of Évora, Portugal), DEA in Financial Economics (University of Extremadura, Spain) Research Themes : Cryptocurrency markets, sustainable finance, portfolio optimization, oil-currency nexus, and behavioral finance His recent articles analyze multifractal efficiency in green investing, cross-market contagion, and the role of precious metals as safe havens in clean energy portfolios. Publications span journals like Fractals , PLOS ONE , and Energy Procedia , with a strong emphasis on quantitative and econophysics approaches. No explicit scientific awards are mentioned in the provided data.

• Probabilistic combinatorics
• Extremal combinatorics
• Graph theory
• +۴ مورد دیگر

Ross J. Kang is a Canadian mathematician currently serving as an Associate Professor at the Korteweg–de Vries Institute for Mathematics within the Faculty of Science at the University of Amsterdam since 2022. He is an active member of the Discrete Mathematics and Quantum Information group and the NETWORKS consortium. Previously, he held positions as Assistant/Associate Professor at Radboud University Nijmegen (2014-2022), Assistant Professor at Utrecht University (2013), and Researcher at Centrum Wiskunde & Informatica (2012-2013). His academic journey includes postdoctoral positions at Durham University (2010-2012) and McGill University (2008-2010), where he was advised by Bruce Reed and Louigi Addario-Berry. DPhil in Mathematics, University of Oxford (2008) - Thesis: 'Improper colourings of graphs', advised by Colin McDiarmid BSc (Hons) in Mathematics and Computer Science, University of Victoria (2003) - Governor General's Silver Academic Medal recipient Ross J. Kang's research focuses on probabilistic and extremal combinatorics, random discrete structures, graph coloring, geometric graphs, and algorithms. His work bridges theoretical mathematics with practical applications, exploring fundamental questions in discrete mathematics. He has made significant contributions to understanding graph coloring problems, particularly in the contexts of list coloring, distance coloring, and strong coloring. His research often employs probabilistic methods to establish bounds and structural properties in graph theory. Kang's work on the hard-core model, local occupancy method, and triangle-free graphs has advanced our understanding of the interplay between local constraints and global structure in discrete systems. Analysis of his recent publications reveals a strong emphasis on graph coloring problems, particularly list coloring variants and their extensions. His work frequently explores the relationship between graph structure (such as degree constraints, girth, or forbidden subgraphs) and coloring properties. A notable trend is his development and application of the local occupancy method to establish improved bounds for chromatic numbers in various graph classes. His research also demonstrates a consistent interest in extremal problems, seeking optimal configurations under specific constraints, particularly in the context of triangle-free graphs and geometric representations. NWO Open Competition M-1 grant entitled 'Asymptotic triangle-free structure (3Free)', 2022-2026 NWO Vidi grant entitled 'On the edge: theory and techniques at the frontiers of edge-colouring', 2017-2023 NWO Veni grant entitled 'Generalised colouring for random graph models', 2012-2015 Van Gogh travel grants (2020-2021 with Marthe Bonamy; 2016-2017 with Louis Esperet) Governor General's Silver Academic Medal (2003) Ross J. Kang has successfully supervised multiple PhD students including Eoin Hurley (defending May 2025), Stijn Cambie (defended April 2022), and François Pirot (winner of 2020 prix Charles Delorme). His research is supported by significant grants from the Netherlands Organisation for Scientific Research (NWO), including the prestigious Open Competition M-1 grant. Kang is actively involved in the academic community through his editorial role at Combinatorial Theory, co-organization of conferences like the Dutch Days of Combinatorics, and leadership in initiatives such as Innovations in Graph Theory, a diamond open access journal he helped launch in August 2023. As a member of the Discrete Mathematics and Quantum Information group at the University of Amsterdam and the NETWORKS consortium, Kang collaborates with researchers across various institutions. He has established strong international connections through his Van Gogh travel grants and participation in collaborative projects like the Sparse (Graphs) Coalition sessions. His research group focuses on theoretical aspects of discrete mathematics with connections to quantum information science, and he maintains active collaborations with researchers across Europe and North America.