Find Academic Supervisors

Judy HartView profile

Associate Professor

• Renewable Energy Materials
• Photocatalysis
• Solar Cells
• +5 more

Associate Professor Judy Hart is a materials scientist at the School of Materials Science & Engineering, UNSW Sydney , specializing in the development of semiconducting materials for renewable energy applications. Her work integrates computational (DFT) and experimental approaches to understand composition-property relationships in systems like solid solutions , heterostructures , and doped materials for photocatalysis and solar cells . She leads projects funded by ARC Discovery and Linkage grants , including work on photo-electro-catalysis systems and stabilizing ceramic materials . Education: PhD in Materials Engineering (Monash University, 2007), BEng (Materials) (Monash, 2002) Professional Experience: Senior Lecturer (UNSW, 2017–), Lecturer (UNSW, 2013–2017), University of Bristol (2007–2012) Research Interests Her research focuses on designing materials for renewable energy , particularly photoelectrochemical water splitting and organic oxidation reactions . Key areas include Density Functional Theory (DFT) , defect engineering , band gap tuning , and nanostructured materials . She investigates ferroelectric polarization effects , metal oxide heterostructures , and stability of battery components , with applications in hydrogen production , CO2 conversion , and advanced battery materials . Scientific Awards Ramsay Memorial Fellowship (University of Bristol, 2007–2009) Teaching Contributions She is co-author of the 1st Australian & New Zealand edition of "Materials Science and Engineering: An Introduction" , and teaches courses on computational materials science , corrosion-resistant surfaces , mechanical behavior of metals , and materials design .

Kate FarrahiView profile

Associate Professor

• Machine Learning
• Digital Health
• Human Sensing
• +3 more

Dr. Kate Farrahi is an Associate Professor in the ECS department at the University of Southampton, where she leads research in the Vision, Learning and Control (VLC) Group. Previously, she was a Research Assistant at the Idiap Research Institute and earned her PhD in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL). Her work focuses on the intersection of machine learning and digital health, particularly in developing human sensing methods using vision and wearable technologies. She currently supervises four PhD students in Computer Science and actively accepts new PhD applications. Her research interests span machine learning applications in healthcare, including wearable device analytics, epidemiological modeling via AI, and drug discovery through generative methods. She has been recognized with a Best Paper Award (2022) and contributes to interdisciplinary research groups such as the Institute for Life Sciences and Centre for Machine Intelligence. Her work bridges computational methods with real-world health challenges, emphasizing practical deployment of AI solutions in clinical and public health contexts. Research Groups: Vision, Learning and Control; Institute for Life Sciences; Centre for Health Technologies; Centre for Machine Intelligence Key Collaborations: Cross-disciplinary projects combining computer science with biomedical engineering and public health

• Compact and reconfigurable antennas
• Antenna arrays
• Microwave components
• +7 more

Dimitrios E. Anagnostou is Associate Professor in the Institute of Sensors, Signals & Systems within Heriot-Watt University’s School of Engineering & Physical Sciences, Edinburgh. He directs an anechoic-chamber & microwave characterisation facility, leads a thriving research group, and is currently recruiting PhD candidates. Education & Career: BSEE, Democritus University of Thrace, Greece (2000) MSEE, University of New Mexico, USA (2002) PhD, University of New Mexico, USA (2005) Post-doc, Georgia Tech (2005-2006) Assistant → tenured Associate Professor, South Dakota School of Mines & Technology (2007-2016) Associate Professor, Heriot-Watt University (2016-present) Research Focus: His work spans compact & reconfigurable antennas, 5G Massive-MIMO arrays, metasurfaces, metamaterials, functional materials (VO 2 ), RF-MEMS, microwave packaging, radar sensing for assisted-living, and AI/deep-learning applications in electromagnetics. He pursues “green” RF electronics printed on paper/organic substrates and hybrid integration of antennas on solar cells. Recent Publication Trends (2022-2025): Output is dominated by metasurface-enabled beam-steering antennas, VO 2 -based reconfigurable devices, radar absorbers/rasorbers, biomedical radar for vital-sign monitoring, and AI-assisted signal processing, with strong emphasis on experimental validation and open-access dissemination. Scientific Awards & Fellowships: IEEE John D. Kraus Antenna Award DARPA Young Faculty Award Marie Skłodowska-Curie Individual Fellowship (H2020) ASEE Campus Star Award Young Alumni Award, University of New Mexico Honored Faculty Award, SDSMT (×4) Distinguished Scientist Living Abroad, Hellenic Ministry of Defense Advising & Grants: He has mentored numerous PhD and MSc researchers; his students have won Best PhD Thesis and faculty-wide Engineering Prizes. He is supported by EU and UK research grants and continuously seeks motivated doctoral applicants. Facilities & Teams: He manages the Microwave Facility (anechoic chamber, mm-wave measurement systems) and collaborates with international academic/industry partners across Europe, North America and beyond.

Alex ArenasView profile

Professor

• Complex Systems
• Network Science
• Multilayer Networks
• +12 more

Alex Arenas is a Full Professor in the Department of Computer Engineering and Mathematics at Universitat Rovira i Virgili (URV), Tarragona, Spain. He is also an External Faculty member at the Complexity Science Hub in Vienna and Chief of Complex Systems Science at the Pacific Northwest National Laboratory, USA. His research spans complex systems, network science, computational epidemiology, and multilayer dynamics, with applications in public health, neuroscience, and social systems. Research Interests: His work focuses on the physics of multilayer networked systems, particularly the interplay between structure and function in complex networks. Key areas include synchronization, epidemic modeling, network medicine, the physics of the microbiome, and higher-order interactions in spreading processes. He investigates dynamic transitions using functional multilayer frameworks and develops models for real-world systems like urban mobility and misinformation diffusion. The recent articles highlight a strong trend in computational epidemiology, especially post-COVID modeling of vaccination strategies, rebound dynamics, and wastewater surveillance. There is also significant work on synchronization in oscillator networks, chimera states, and higher-order network effects, reflecting a deep engagement with nonlinear dynamics and theoretical network science. Applications span medicine, urban planning, and social systems. Scientific Awards: Fellow, American Physical Society (2018) Fellow, Network Science Society (2020) ICREA Academia (2011, 2017, 2022) Narcís Monturiol Medal (2022) Web Science Trust Test of Time Award (2024) Complex Systems Society Senior Award (2024) Advising and Grants: Arenas has supervised numerous PhD students and postdoctoral researchers, though specific names are not listed. He has been Principal Investigator on 47 research projects, including EU FP7 projects, a James S. McDonnell Foundation grant, and Horizon Europe's CREXDATA project. He has served as an editor for Physical Review E , Journal of Complex Networks , and Network Neuroscience , and has reviewed for major funding agencies including ERC, MINECO, and international bodies. Labs and Teams: He leads the Alephsys Lab at URV, which develops tools like Radatools for network analysis and community detection. His team focuses on interdisciplinary modeling of real-world complex systems using data-driven and theoretical approaches.

• Multilayer Networks
• Data Analysis
• Graph Mining
• +3 more

Abhishek Santra is a Senior Lecturer in the Department of Computer Science and Engineering at The University of Texas at Arlington. He holds a PhD in Computer Science from UT Arlington (2020), and earlier degrees from the University of Delhi (BS 2011, MS 2013). His research focuses on multilayer networks, graph mining, and data analysis, with contributions to complex data modeling and visualization tools like MLN-geeWhiz and ModViz. He is also a Post-Doctoral Research Scholar in the Information Technology Lab (ITLab), led by Dr. Sharma Chakravarthy. Teaching interests include Discrete Structures, Database Systems, and DBMS Models. He has advised numerous students on research projects and thesis work, such as substructure discovery in multilayer networks and video content analysis. Recent grants include REU-funded projects for dashboard development (2022–2025). His service roles include committee memberships in the CSE department and organizing technical workshops like QVC and MLN-DIVE. Key research areas involve analyzing multi-source data through multilayer network frameworks, with applications in healthcare monitoring, big data analytics, and visualization systems. Publications span conferences like BDA, IC3K, and IEEE BigDataService, emphasizing algorithmic innovation for network-centric data problems.

• Physics
• Computer Science
• Mathematics
• +10 more

Cristopher Moore is a Professor at the Santa Fe Institute, where he conducts interdisciplinary research at the intersection of physics, computer science, and mathematics. His work focuses on understanding phase transitions in computational problems, statistical inference, and network analysis. Moore has made significant contributions to the fields of complex systems, quantum computing, and algorithmic justice. Moore's primary research areas include phase transitions in computational problems and statistical inference, where he investigates how problems suddenly become hard or impossible to solve when certain thresholds are crossed. His work spans social networks, big data analysis, quantum computing, algorithmic transparency, and decarbonization efforts. He is particularly known for applying physics-inspired approaches to computational problems, using techniques from spin glass theory, network theory, and computational complexity. His recent publications reveal a strong focus on community detection in networks, phase transitions in data science problems, algorithmic fairness in criminal justice systems, and quantum computing applications. Moore's work demonstrates consistent patterns across multiple disciplines, with recurring themes of phase transitions, computational limits, and the application of physics concepts to computational problems. Moore actively mentors students and has advised numerous PhD and Master's students who have gone on to successful careers in academia and industry. His work on algorithmic justice has influenced policy discussions in New Mexico and beyond, particularly regarding risk assessment in the criminal justice system.

Tsun-Ming TsengView profile

Professor

• Microfluidic Large-Scale Integration
• Optical Network-on-Chip
• Microfabrication
• +3 more

Tsun-Ming Tseng is a Professor and principal investigator at the Chair of Electronic Design Automation at the Technical University of Munich (TUM). He leads the Emerging Technology Group and oversees multiple DFG/BMBF-funded research projects in the areas of microfluidic large-scale integration, optical network-on-chip design, and novel microfabrication techniques. Dr. Tseng's research focuses on design automation for emerging technologies, with particular expertise in three main areas: microfluidic large-scale integration, optical network-on-chip systems, and novel microfabrication processes. His work bridges the gap between electronic design automation and cutting-edge applications in bioengineering, photonics, and advanced manufacturing. His research group develops sophisticated algorithms and tools for optimizing design, reliability, and performance in these emerging domains. Analysis of Dr. Tseng's recent publications reveals a strong focus on practical implementation challenges in emerging technologies. His work spans both theoretical algorithm development and practical system implementation, with particular emphasis on reliability, performance optimization, and manufacturing considerations. The research shows increasing integration between different technology domains, particularly the convergence of microfluidics, optical networking, and electronic design automation. Dr. Tseng has been awarded multiple significant research grants including: "DE-TW-CloudWRONoC" (BMBF-NSTC project, PI, 2025-2028, EUR 797.7K) "DE-TW-PI3D" (BMBF-NSTC project, PI, 2024-2027, EUR 391.6K) "Physical Design for Microfluidic Large-Scale Integration" (DFG research grant, PI, 2024-2026, EUR 331.9K) Multiple other DFG and industrial projects totaling over EUR 3 million in funding He has successfully supervised numerous doctoral researchers and postdoctoral fellows, with current group members including Jiahui Peng, Debraj Kundu, Liaoyuan Cheng, and several others. Dr. Tseng leads the Emerging Technology Group at TUM, which focuses on developing design automation methodologies for next-generation technologies. The group maintains strong collaborations with international institutions, including partnerships with researchers in Taiwan and Hong Kong. The team operates state-of-the-art facilities for research in microfluidics, optical networking, and advanced microfabrication techniques.

Jon WarrenView profile

Academic

• Probability Theory
• Stochastic Flows
• Random Matrices
• +2 more

Dr Jon Warren is a Reader in Statistics at the University of Warwick, specializing in probability theory. His research spans stochastic flows, random matrices, and properties of Brownian motion, with significant contributions to understanding complex stochastic systems. Research Interests: Dr Warren's work is centered on probability theory, particularly in the areas of stochastic flows, random matrices, and Brownian motion. His research delves into the intricate behaviors of these systems, exploring their properties and applications in various mathematical contexts. Publications: His recent publications cover a wide range of topics within probability theory, including stochastic heat equations, Dyson Brownian motion, and random matrix theory. These works highlight his expertise in both theoretical developments and practical applications of stochastic processes. Teaching: He teaches ST910 Introduction to graduate probability, demonstrating his commitment to educating the next generation of statisticians and probabilists. Contact: Dr Warren can be reached at J.Warren@warwick.ac.uk for academic inquiries or collaboration opportunities.

• Social Network Analysis
• Network Science
• Data Science
• +7 more

Giacomo Fiumara is an Associate Professor at the University of Messina, Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences. He holds academic rank since October 2021. Previously, he served as a Permanent Researcher (2008–2021) and secondary school teacher (1997–2008). He earned a Doctorate in Physics (1993) and a Degree in Physics (1989), both from the University of Messina. He is an associate member of the Accademia Peloritana dei Pericolanti and qualified as an associate professor in INF/01 and ING-INF/05 sectors. His research focuses on social network analysis, network science, data science, criminal networks, knowledge representation, bioinformatics, and computational modeling. He has supervised over 170 theses and advised PhD students in Mathematics and Computational Sciences. Key collaborations include work with Prof. Pasquale De Meo on criminal networks and complex systems, and international projects with institutions in the US, UK, China, and Australia. Teaching includes courses on Algorithms, Data Structures, Bioinformatics, and Machine Learning across Computer Science, Engineering, and Medical programs since 2000. He also contributed to international programs at Lviv Polytechnic, Birzeit University, Cluj-Napoca, and Murcia. His editorial roles include Associate Editor of IEEE Access and Academic Editor of Complexity. He holds a patent for predictive analysis of criminal organizations' social structures and has received FFABR research funding. Key awards include FFABR funding (2017) and recognition in the FFABR Unime 2020 II edition. He organized conferences like Crimenet 2014 and participated in high-profile events such as the 2022 Complex Networks conference in Palermo, presenting on quantum walks for criminal network analysis.

Dani S. BassettView profile

Professor

• Network Science
• Complex Systems
• Brain Connectivity
• +5 more

Dani S. Bassett is the J. Peter Skirkanich Professor at the University of Pennsylvania with primary appointment in the Department of Bioengineering (School of Engineering and Applied Science) and secondary appointments in Physics & Astronomy, Electrical & Systems Engineering, Neurology, and Psychiatry. They serve as an external professor at the Santa Fe Institute and lead a research group focused on complex systems and network science. B.S. in Physics, Penn State University (2004) Ph.D. in Physics, University of Cambridge as Churchill Scholar and NIH Health Sciences Scholar (2009) Postdoctoral position at UC Santa Barbara and Junior Research Fellow at Sage Center for the Study of the Mind Their research integrates complex systems science, statistical mechanics, and applied mathematics to study network dynamics in physical and biological systems. Key areas include brain connectivity mechanisms, cognitive processes, neurological disease modeling, granular matter physics, and collective human curiosity. Bassett employs advanced methodologies including algebraic topology, network control theory, and multilayer network analysis to investigate how network architecture influences system function across diverse domains. Recent publications reveal a strong trend toward interdisciplinary network science applications, particularly in modeling human curiosity through Wikipedia navigation patterns and analyzing brain network reconfiguration during cognitive development. Their work bridges physics, neuroscience, and behavioral science with emphasis on topological network properties and dynamical processes. American Psychological Association's Rising Star (2012) MacArthur Fellow Genius Grant (2014) Lagrange Prize in Complex Systems Science (2017) Erdos-Renyi Prize in Network Science (2018) American Physical Society Fellow (2021) Web of Science Highly Cited Researcher (3 consecutive years) Bassett's research is supported by major agencies including NSF, NIH, DoD, ONR, and private foundations (MacArthur, Sloan, Paul Allen). Their lab actively recruits students from physics, engineering, neuroscience, and computer science backgrounds, emphasizing diversity in academic perspectives. Current projects include the 'Curious Minds' initiative exploring collective knowledge building and network-based models of neurological disorders. Bassett co-authored the MIT Press book 'Curious Minds: The Power of Connection' with philosopher Perry Zurn.

Vito LatoraView profile

Professor

• Complex Systems
• Network Science
• Applied Mathematics
• +13 more

Vito Latora is a Professor of Applied Mathematics and Chair of Complex Systems at the School of Mathematical Sciences, Queen Mary University of London, and also holds the position of Professor of Theoretical Physics at the University of Catania. He leads the Complex Systems and Networks Group, driving cutting-edge research at the intersection of physics, mathematics, and interdisciplinary sciences. His research focuses on complex systems, particularly the structure and dynamics of networks, including multiplex, temporal, and higher-order networks such as simplicial complexes and hypergraphs. He explores applications in social, biological, financial, and cognitive systems, with recent work on creativity, innovation, and success through network analysis. The 15 most recent publications reveal a strong trend in advancing network theory beyond pairwise interactions, with a focus on higher-order structures, memory effects, synchronization, and epidemic spreading. His work combines rigorous mathematical modeling with real-world applications, often published in high-impact journals like Nature Communications , Physical Review Letters , and Science Advances . Dual communities in spatial and biological networks Modeling epidemics with limited detection resources Synchronization via higher-order and directed interactions AI-driven financial risk management Evolutionary games on hypergraphs Interdisciplinary success and funding dynamics Vito Latora has mentored several researchers who appear as co-authors, including Iacopini, Williams, Di Bona, and Lacasa. While specific grants are not listed, his collaborative projects with neuroscientists and anthropologists, along with frequent publications, suggest active funding. He is involved in major scientific events such as NetSci 2023, indicating leadership in the network science community. He leads the Complex Systems and Networks Group at Queen Mary, fostering a collaborative environment for studying complex systems through theoretical, computational, and data-driven approaches.

Federico BattistonView profile

Associate Professor

• Network Science
• Complex Systems
• Computational Social Science
• +8 more

Federico Battiston is an Associate Professor of Network Science and Director of the PhD Program in Network Science at Central European University (CEU), the first such program in Europe. He holds a PhD in Applied Mathematics from Queen Mary University of London and degrees in statistical physics from Sapienza University of Rome. His research focuses on network science, complex systems, and computational social science, with contributions in leading journals like Nature Physics , Physical Review Letters , and Science Advances . He coordinates the software project Hypergraphx and was Chair of NetSci2023, the largest Network Science conference. He has received awards including the Complex Systems Society's Junior Award (2022) and the European Physical Society's Early Career Prize (2021). Education: PhD in Applied Mathematics, Queen Mary University of London MSc in Theoretical Physics, Sapienza University of Rome BSc in Physics, Sapienza University of Rome Research Interests: Battiston explores generalized network structures (e.g., multilayer and higher-order networks), dynamics on networks (epidemics, social/cultural dynamics, synchronization), and their applications in social systems, neuroscience, and ecology. He emphasizes how network topology influences collective behavior and emergent phenomena. Key Contributions: His work includes hypergraph modeling, collaboration in escape rooms, and the role of higher-order interactions in brain networks. He co-authored the book Higher-order systems and guest-edited a Focus Collection on higher-order networks in Communications Physics . Awards & Roles: Junior Award of the Complex Systems Society (2022) Early Career Prize, European Physical Society (2021) Elected Member, Complex Systems Society Council Editor, Communications Physics Advising & Grants: Advised PhD students such as Milan Janosov, Luis Natera, and Rebeka Szabo. Two students received CEU Advanced Awards. His projects include Mapping the Higher-Order Dynamics of Neurodegeneration and DYNASNET . Labs/Teams: Leads the Hypergraphx team and collaborates on interdisciplinary projects in network science, including ecological networks and urban mobility analysis.

• Complexity Science
• Urban Systems
• Urban Scaling Laws
• +7 more

Elsa Arcaute is a Professor of Complexity Science at the Centre for Advanced Spatial Analysis (CASA) at University College London. She is a physicist with expertise spanning mathematical physics, urban systems, and complexity science. Her work bridges theoretical physics with practical applications in urban planning and policy. Her educational background includes a PhD in Theoretical Physics from the University of Cambridge (2006) and a Masters in Mathematics (part III of the Mathematical Tripos). She began her career in theoretical physics with research on Clifford algebras applied to Penrose's twistors before transitioning to complex systems during a visit to Prof. Henrik Jensen at Imperial College London. Arcaute's research focuses on modeling urban systems through the lens of complexity science, with particular emphasis on urban scaling laws, hierarchical structures in cities, and the definition of city boundaries. She applies network theory, percolation theory, and multifractal methodologies to analyze urban infrastructure, socio-economic patterns, and spatial inequality. Her work often involves interdisciplinary collaboration with economists, geographers, and data scientists. Analysis of her recent publications reveals a strong focus on urban complexity, with significant contributions to understanding city hierarchies, commuting networks, knowledge spillovers, and the impact of digital platforms on urban systems. Her research demonstrates how complex systems theory can provide novel insights into urban dynamics, scaling phenomena, and spatial organization. Arcaute has been involved in several major research projects, including an EPSRC grant on Digital Economies, an ERC-funded project MECHANICITY (led by Prof. Michael Batty) focusing on morphology, energy, and climate change in cities, and a MacArthur-funded project on smart cities and policy. She serves as a Review Editor for Frontiers in Energy Systems and Policy and has contributed to numerous publications that have garnered significant attention across academic and policy circles. Her work has been referenced in policy sources, news outlets, and social media, demonstrating the real-world impact of her research.

Carolyn Conner SeepersadView profile

Professor

• Design for Additive Manufacturing
• Engineering Design
• Additive Manufacturing
• +4 more

Carolyn Conner Seepersad serves as the J. Mike Walker Professor of Mechanical Engineering at the University of Texas at Austin and directs the Center for Additive Manufacturing and Design Innovation. She holds membership in the U.T. System Academy of Distinguished Teachers and maintains active leadership in the additive manufacturing community through roles such as co-organizer of the Solid Freeform Fabrication Symposium and ASME Design Engineering Division Executive Committee membership. Her academic credentials include: PhD in Mechanical Engineering from Georgia Tech (2004) MA/BA in Philosophy, Politics and Economics from Oxford University (1998, Rhodes Scholar) BS in Mechanical Engineering from West Virginia University (1996) Dr. Seepersad's research centers on computational design methodologies and additive manufacturing innovation , with particular expertise in simulation-based design of complex systems, environmentally conscious product development, and materials engineering. Her work bridges theoretical design frameworks with practical manufacturing applications, emphasizing sustainability and performance optimization across aerospace, automotive, and energy systems. Current projects explore reactive extrusion additive manufacturing, negative stiffness materials, and machine learning integration for process-aware design. Analysis of her 15 most recent publications reveals a dominant focus on process innovation in additive manufacturing (70%), particularly stereolithography and selective laser sintering, with growing emphasis on data-driven design approaches (20%) and sustainable engineering applications (10%). Her work demonstrates consistent progression from fundamental material design toward integrated system optimization and industrial scalability. Her scientific recognition includes: International Outstanding Young Researcher Award in Freeform and Additive Manufacturing (2009) UT System Regents’ Teaching Award (2010) ASME Design Automation Committee Outstanding Young Investigator Award (2010) ASEE Outstanding New Mechanical Engineering Educator Award (2013) Multiple ASME and ASEE best paper awards U.T. System Academy of Distinguished Teachers membership Dr. Seepersad maintains an extensive advising portfolio with 48 graduate students (16 PhD, 24 MS, and 8 current) plus 2 postdoctoral researchers, reflecting sustained research productivity and educational impact. Her Product, Process, and Materials Design Lab fosters interdisciplinary collaboration between mechanical engineering, materials science, and computational design teams.

Robert ReadyView profile

Associate Professor

• Asset Pricing
• Commodity Markets
• International Finance
• +1 more

Robert Ready is an Associate Professor of Finance and the Doug McKay Research Scholar at the Lundquist College of Business , University of Oregon . He is affiliated with the Department of Finance , Cameron Center for Finance and Securities Analysis , and programs including the Oregon MBA , Master of Science in Finance , and Undergraduate Programs . Research Interests include Asset pricing Commodity markets International finance Macroeconomics Scientific Awards and Recognitions : Most Outstanding Master of Science in Finance Instructor Award (2024–2025) Goulet Outstanding Research Award (2018) Best Paper at 2014 Utah Winter Finance Conference 2015 AQR Insight Prize winner 2016 Best Paper Prize from Jacobs Levy Center Publications focus on commodity markets, currency risk, and energy economics, with recent work analyzing negative oil prices, oil supply uncertainty, and global trade dynamics. His articles appear in journals like the Review of Financial Studies , Journal of Finance , and Journal of Monetary Economics .