Find Academic Supervisors

• Quantum Physics
• Computational Physics
• Mesoscopic Physics
• +5 more

Mugurel TOLEA is a Scientific Researcher II at the National Institute of Materials Physics (NIMP) in Bucharest, Romania, working within the Laboratory of Theoretical Physics and Computational Modeling. He has been employed at NIMP since 2001 and was promoted to his current position in 2017. His research focuses on theoretical and computational approaches to quantum physics phenomena, particularly in condensed matter systems. Dr. TOLEA earned his PhD in Theoretical Physics from the Faculty of Physics at the University of Bucharest in November 2010 with "summa cum laude" distinction. His doctoral thesis was titled "Spin dependent transport in mesoscopic systems." Prior to his current position, he held a research position at the Institute of Molecular Physics, Polish Academy of Science in Poznan, Poland during a 6-month Marie-Curie Fellowship from May to October 2005. His research interests span Quantum Physics, Computational Physics, Mesoscopic Physics, Strongly Correlated Electrons, Keldysh Formalism, Configuration Interaction, Condensed matter theory, and Modeling of Solid state Phase Transitions. His work demonstrates a consistent focus on quantum transport phenomena, phase transitions in materials, and computational modeling of physical systems. His recent publications show particular emphasis on shape memory alloys, graphene-based nanostructures, and quantum interference effects. Dr. TOLEA has served as a reviewer for prestigious journals including Physical Review Letters, Physical Review A/B, Physica E, and Physics Status Solidi. His research has been supported through participation in over 10 national projects, and he successfully led a Young Teams project (TE 90/05.10.2011) coordinating a team of 5 scientists for three years, resulting in 7 ISI publications in highly ranked journals. His current work involves the Laboratory of Theoretical Physics and Computational Modeling at NIMP, where he continues to investigate quantum transport phenomena and phase transitions in various material systems. His research bridges theoretical physics with practical applications in materials science and nanotechnology.

• Robotics
• Computer Vision
• Simultaneous Localization and Mapping (SLAM)
• +6 more

Niko Sünderhauf is a Professor in the School of Electrical Engineering and Robotics at Queensland University of Technology (QUT), specializing in robotics, computer vision, and autonomous systems. His research spans visual place recognition, simultaneous localization and mapping (SLAM), neural radiance fields, and out-of-distribution detection for robotics applications. His research interests focus on enabling robots to understand and navigate complex environments through advanced computer vision techniques. He has made significant contributions to visual place recognition, particularly for handling viewpoint and environmental changes. His work on switchable constraints has improved robustness in SLAM systems, while his more recent research explores neural radiance fields, Gaussian splatting, and integrating large language models with robotic perception. Prof. Sünderhauf's publication record demonstrates consistent research productivity with 115 publications spanning from 2005 to 2025. His recent work shows a clear progression from traditional SLAM techniques toward more advanced neural scene representations and integration with large language models. His research has been published in top venues including IEEE ICRA, CVPR, IROS, and the International Journal of Robotics Research. Among his scientific contributions are novel approaches to visual place recognition that work across seasons and viewpoints, robust SLAM techniques that handle outliers and non-linearities, and recent innovations in neural scene representations for robotics. His work on switchable constraints has been particularly influential in the robotics community. Prof. Sünderhauf actively supervises PhD students including Dimity Miller, Krishan Rana, and Jad Abou-Chakra, who frequently appear as first authors on publications. His collaborative network includes researchers from QUT and international institutions, demonstrating strong research leadership in the robotics community.

• Artificial Intelligence
• Computer Vision
• Data Science
• +4 more

Dr. Hongda Tian is a Senior Lecturer at the University of Technology Sydney's Data Science Institute within the Faculty of Engineering and Information Technology. With a strong background in AI and data science, he focuses on translating research into practical solutions for real-world problems across multiple sectors including water, transport, energy, and retail. BSc and MEn from Beijing University of Posts and Telecommunications (2006, 2009) PhD from University of Wollongong, Australia (2015) Postdoctoral Fellow with DATA61 | CSIRO Computer Vision Scientist with Kandao Australia Pty Ltd Associate Research Fellow with University of Wollongong Dr. Tian's research spans artificial intelligence, computer vision, data science, and machine learning with a focus on practical applications. His work combines theoretical advancements with industry implementation, particularly in environmental sustainability, water management, and infrastructure monitoring. He excels at transforming real-world issues into data science problems and developing appropriate solutions that demonstrate industrial applicability to stakeholders. His publication record shows a clear trend toward applying AI to critical infrastructure and environmental challenges, with recent work focusing on water quality prediction, electricity price forecasting, carbon intensity modeling, and bushfire smoke detection. These publications appear in top journals including International Journal of Computer Vision, IEEE Transactions on Image Processing, and IEEE Transactions on Multimedia. 2025 The Australian Financial Review AI Awards (Sustainability category) 2024 NSW iAwards Winner for Sustainability & Environmental Solution 2024 NSW Merit iAwards for Government & Public Sector Solution 2024 Distilling Research Impact 2023 NSW Merit iAwards for Sustainability & Environmental Solution Chinese Government Award for Outstanding Self-financed Students Abroad Dr. Tian has secured approximately $1.07 million in external research funding as Chief Investigator since 2020 and has led or delivered over 15 research innovation projects with government and industry partners. His projects span multiple sectors including water (Dynamic Prediction of Raw Water Quality, Water Quality Prediction for Drinking Water Delivery Systems), transport (Structural Health Monitoring for Sydney Harbor Bridge, Computer Vision-Based Track Defect Detection), energy (Electrical Network-Related Incidents Prediction), and retail (Woolworths Endcap Compliance). He serves on thesis examination committees and as an editorial board member and reviewer for over 20 peer-reviewed journals and conferences.

Patrick CoiraultView profile

Professor

• Automatic Control
• Nonlinear Systems
• Multi-agent Systems
• +5 more

Patrick Coirault is a Full Professor in Automatic Control and Systems at the University of Poitiers' Institute of Technology. He is affiliated with the LIAS research laboratory, with operations at both ENSIP in Poitiers and ISAE-ENSMA. His research spans theoretical and applied control systems with significant contributions to multiple engineering domains. Professor Coirault's research interests encompass nonlinear control systems, multi-agent formation control, chaotic system synchronization, and practical applications in automotive systems. His work in hybrid vehicle control addresses critical challenges including torque ripple reduction, combustion control, and energy management. He has also made significant contributions to antenna array synchronization, power systems control, and wind engineering applications. His methodological approach combines theoretical advances in control theory with practical implementation in real-world systems, often bridging traditional model-based control with emerging data-driven techniques. Analysis of his recent publication record reveals a clear evolution toward distributed control systems, particularly for power networks and multi-agent coordination. His 2023-2025 publications demonstrate increasing integration of machine learning techniques with traditional control approaches, exemplified by work on data-enabled predictive control for LPV systems. The applications of his research span automotive systems, power grids, wireless communications, and aerodynamics, showcasing the versatility and impact of his control methodologies across multiple engineering disciplines. Professor Coirault maintains active collaborations within the LIAS research laboratory and with international partners. His publication record in top-tier journals including Automatica , IEEE Transactions on Automatic Control , and Control Engineering Practice demonstrates his sustained contribution to the field of control systems engineering over several decades.

Wolfram BurgardView profile

Professor

• Artificial Intelligence
• Robotics
• Mobile Robotics
• +3 more

Prof. Wolfram Burgard is a Professor for Robotics and Artificial Intelligence and Founding Chair of the Engineering Department at the University of Technology Nuremberg. Previously, he was a professor of computer science at the University of Freiburg (1999–2021), where he established the renowned Autonomous Intelligent Systems research lab. His research focuses on probabilistic techniques for robot navigation, state estimation, and control, with notable contributions to SLAM, path-planning, and mobile robot applications such as museum tour-guides (Rhino/Minerva) and autonomous vehicles. His research interests span artificial intelligence, mobile robotics, and autonomous systems. Key projects include deploying interactive museum robots, developing autonomous parking systems (2008), and the pedestrian-like Obelix robot (2012). He has authored over 350 papers, co-authored seminal books like Probabilistic Robotics , and holds the prestigious Gottfried Wilhelm Leibniz Prize (2009) and ERC Advanced Grant (2010). He is an ECCAI and AAAI Fellow, and member of the German Academy of Sciences Leopoldina and Heidelberg Academy of Sciences. Prof. Burgard coordinated the BrainLinks-BrainTools Cluster of Excellence (2012–2019), funded by the German Research Foundation. His work emphasizes real-world applications, including industrial plant supervision and autonomous driving systems. His Erdős number is ≤4, and his publications are indexed on Google Scholar.

• Neuroscience
• Molecular Biology
• Neurodevelopmental Disorders
• +3 more

Hong Li is an Associate Research Scholar in the Department of Neuroscience at Yale University's Yale School of Medicine. Their research focuses on molecular mechanisms of cortical developmental malformations in neurodevelopmental and psychiatric disorders, employing animal models and integrative methodologies including molecular biology, genetics, morphology, and transcriptomics. Dr. Li holds a DPhil from the University of Helsinki (2007) and an MD from Anhui University (1997). Their work aims to advance understanding of neuropsychiatric diseases and develop therapeutic strategies. Educational Background: DPhil in Neuroscience, University of Helsinki (2007) MD, Anhui University (1997) Research Interests: Investigating molecular pathways underlying cortical development abnormalities, with emphasis on translational research to bridge basic science and clinical applications. Techniques include advanced genetic engineering, transcriptomic profiling, and cross-disciplinary approaches combining neuroscience and molecular biology. Advising & Grants: No specific grants or trainees listed in available materials, though their publications suggest collaborative research in quantum computing, embedded systems, and telecommunication networks. Recent work includes cross-domain contributions to quantum error correction systems and LLM-based robotic applications. Professional Contributions: Active in systems engineering, publishing on operating systems design, FPGA-based quantum decoders, and secure memory management. Their work bridges biomedical research with computational and hardware innovations.

Biplab BasakView profile

Research Fellow

• Topology
• Combinatorics
• Geometry
• +3 more

Biplab Basak is a Research Fellow (SERB Indo-US Postdoctoral Fellow) and Visiting Scholar affiliated with the College of Arts and Sciences, Department of Mathematics. His work explores combinatorial and topological structures in geometric contexts. Research Focus: Dr. Basak investigates the interplay between topology, combinatorics, and geometry, specializing in manifolds and crystallization theory. His research includes: Topological invariants (gem-complexity, regular genus) Combinatorial representations of manifolds Algorithmic approaches to manifold construction Minimal triangulations of 3- and 4-dimensional spaces Awards: SERB Indo-US Postdoctoral Fellowship Publications: His recent articles (2014–2017) cluster in combinatorial topology, emphasizing crystallizations, manifold classification, and graph-theoretic methods. Recurring themes include low-dimensional topology, algorithmic solutions, and complexity bounds.

Zhaoyang ShiView profile

Research Fellow

• Network Analysis
• Geometric Statistics
• Topological Data Analysis
• +2 more

Zhaoyang Shi is a Postdoctoral Research Fellow in the Department of Statistics at Harvard University. His research develops statistical methods for geometric and topological data analysis. Research interests include network analysis, manifold learning, nonparametric methods, and kernel-based approaches. Current work focuses on normal approximation techniques for machine learning algorithms and nonparametric regression using spectral graph methods. Publications demonstrate strong theoretical focus, with recent work establishing minimax rates for Laplacian-based regression and Gaussian approximation methods for random forests.

• Software Engineering
• Computer Security
• Machine Learning
• +5 more

Yun Lin is a researcher affiliated with Shanghai Jiao Tong University, with a former position at the National University of Singapore's School of Computing. Their work focuses on software engineering, computer security, and machine learning. Key research interests include formal methods for program verification, adversarial machine learning, phishing detection, and deep learning applications in security. Lin has collaborated extensively with institutions like NUS and the Institute of High Performance Computing, producing over 70 publications since 2014 across venues such as ICSE, USENIX Security, and NeurIPS. Research interests span security (e.g., phishing detection, smart contract vulnerabilities), program analysis (bug localization, delta debugging), and AI-driven software development (code editing recommendations, LLM integration). Their contributions address challenges in software reliability, adversarial robustness, and scalable security solutions. Prominent publications include works on automated program refinement with LLMs, phishing webpage identification via deep vision models, and conflict resolution in version control systems. Lin’s collaborations emphasize interdisciplinary approaches blending formal methods with machine learning. No awards or grants are explicitly listed in the provided text.

Mubashara AkhtarView profile

Research Fellow

• Natural Language Processing
• Multimodal Machine Learning
• AI Safety
• +3 more

Mubashara Akhtar is a Research Fellow at ETH Zurich (ETH AI Center) and a visiting researcher at the University of Cambridge. She completed her PhD in Natural Language Processing at King's College London under Prof. Elena Simperl and Dr. Oana Cocarascu. Her research focuses on multimodal approaches for fact-checking, AI safety, and benchmarking for large language models. She has held internships at Google DeepMind and contributed to projects like the Croissant dataset documentation format. Education: PhD in Informatics (King's College London), MSc and BSc in Computer Science (TU Wien) Research Interests include multimodal machine learning, knowledge graphs, and ethical AI. Her work bridges NLP, computer vision, and data science to improve automated fact-checking systems and responsible AI practices. Key awards include the Best Short Paper Award at DEEM 2024 and King’s Research Impact Award for multimodal fact-checking. She actively contributes to conferences like NeurIPS, ACL, and EMNLP, co-leading initiatives such as the FEVER workshop and the Croissant project.

• Artificial Intelligence
• Cybersecurity
• Recommender Systems
• +7 more

Eugenio DiSciascio is a Full Professor and Scientific Coordinator at the Department of Information Engineering (DEI) of the University of Bari. His research spans Artificial Intelligence, Cybersecurity, Recommender Systems, and Semantic Technologies with a focus on Trustworthy AI principles. He leads projects in healthcare informatics, user modeling through Brain-Computer Interfaces (BCI), and multimodal learning frameworks like Ducho 2.0. Key research areas include explainable AI for medical diagnosis (e.g., Alzheimer’s detection), ethical AI frameworks such as POLARIS, and adversarial defenses in recommender systems. He organizes international events like ICWE 2022 and workshops focused on cybersecurity and BCI applications. His work integrates interdisciplinary approaches, combining machine learning with neuroimaging, wearable devices, and regulatory compliance. Recent publications emphasize multimodal recommendation systems, cybersecurity in AI models, and human-centric AI development. Ongoing projects include NeuroSense (EEG-based emotion detection) and ARIEL (BCI-driven emotional support systems). He contributes to standards for trustworthy AI through frameworks like POLARIS, addressing ethical, legal, and social implications of AI technologies. DiSciascio’s lab, SisInf Lab, explores intelligent systems in software engineering, big data, and IoT. His work bridges theoretical advancements with real-world applications in healthcare, cybersecurity, and user-centric technologies.

• Catalysis
• Reaction Engineering
• Chemical Engineering
• +6 more

Honorary Professor Dr. Liisa Rihko-Struckmann holds a dual role as Senior Scientist and Laboratory Manager at the Max Planck Institute for Dynamics of Complex Technical Systems, and serves as an Adjunct Lecturer at Otto-von-Guericke-University Magdeburg’s Process Systems Engineering department. She earned her Master’s (1989) and PhD (1997) in Chemical Engineering from Helsinki University of Technology, Finland. Her career spans industrial catalysis research at Neste Oil, postdoctoral positions at Helsinki University and the Max Planck Institute, and leadership in process systems engineering. Her research focuses on catalysis, CO₂ utilization (e.g., CO2RRECT project), biomass-to-fuel conversion (Probio project), and sustainable polymer recycling. Her work integrates computational methods, solvent design, and lifecycle assessment to advance renewable energy systems and biorefineries. Recent publications emphasize polyamide recycling, algal biomass fractionation, and machine learning-driven process optimization. Research interests include: catalysis and reaction engineering, CO₂ conversion technologies, biomass valorization, and sustainable chemical processes. She has pioneered solvent selection frameworks (e.g., PSEvolve) and applied computational tools to design zero-waste biorefineries. Key projects involve depolymerization of plastics, lignin upgrading, and exergy efficiency analysis in energy storage systems. Publications highlight advancements in polymer recycling strategies, algal lipid extraction, and process intensification via microwave/ultrasound techniques. Her articles underscore interdisciplinary approaches combining chemical engineering with AI and systems biology to address global sustainability challenges. Advising and grants: No formal student advising list is provided, but her research groups likely involve graduate students in Max Planck and university collaborations. Projects are funded through German research initiatives focusing on circular economy and renewable energy technologies. Labs/Teams: Leads the Process Systems Engineering group at Max Planck, collaborating with international teams on biorefinery design and CO₂ utilization.

Balder ten CateView profile

Associate Professor

• Logic in computer science
• Data management
• Knowledge representation
• +5 more

Balder ten Cate is an Associate Professor at the University of Amsterdam, where he leads the Theoretical Computer Science research unit within the Institute for Logic, Language and Computation (ILLC). His academic journey includes previous positions at INRIA, UC Santa Cruz, Stanford, LogicBlox, and Google. He maintains active collaborations across institutions including the University of Bergen (co-advising PhD students) and Tsinghua University. Primary Affiliation: Theoretical Computer Science (TCS) Secondary Affiliation: Mathematical & Computational Logic (MCL) Office: Room L6.38, LAB42, Science Park 900, Amsterdam Dr. ten Cate's research spans diverse applications of logic in computer science and AI, with particular emphasis on data management, knowledge representation, and machine learning. His work bridges theoretical foundations with practical applications, focusing on how logical frameworks can enhance data systems and AI capabilities. He has developed significant contributions in finite model theory, database theory, and computational learning theory, with recent work exploring connections between universal algebra and learning theory. His publication record shows a strong trajectory in theoretical computer science, with recent papers focusing on query algorithms, interpolation in logical fragments, and the learnability of database queries. The research demonstrates consistent contributions at top venues including PODS, ICDT, IJCAI, and ACM Transactions journals. His work on 'Extremal Fitting Problems for Conjunctive Queries' received the ACM PODS 2023 Best Paper Award, while 'SAT-Based PAC Learning of Description Logic Concepts' earned an IJCAI 2023 Distinguished Paper Award. 2024 ETAPS Best Paper Nomination 2023 ACM SIGMOD Research Highlights Award 2023 Alberto Mendelzon Test-of-Time Award ICDT 2012 Best Paper Award 2006 EACSL Ackermann Prize for best dissertation Dr. ten Cate actively supervises PhD and MSc students, with recent theses covering topics like modal formula characterization, conjunctive queries, and temporal logic. He serves on numerous program committees including PODS 2025 and ICALP 2024, and has organized workshops on learning and logic. His current MSCA European Re-Integration Fellowship 'LLAMA: Logic and Learning: an Algebra and Finite Model Theory Approach' (2021-2025) demonstrates ongoing research leadership. He also contributes to academic service through committee memberships including the ASL Committee on Education and the editorial board of the Journal of Logic, Language and Information.

• Phylogenetics
• Systematic Biology
• Evolutionary Biology
• +3 more

David Morrison is a Visiting Researcher at the Department of Organismal Biology, Systematic Biology at Uppsala University, Sweden. His contact information lists the Evolutionary Biology Centre at Norbyvägen 18D, 752 36 Uppsala, with email David.Morrison@ebc.uu.se. Morrison's research focuses on advanced phylogenetic methods, particularly the development and application of phylogenetic networks as alternatives to traditional tree-based representations of evolutionary relationships. His work explores reticulate evolution , multiple sequence alignment , and the theoretical foundations of systematics. Morrison has published extensively on the limitations of the "tree of life" metaphor and advocates for network-based approaches that better represent complex evolutionary processes like hybridization and horizontal gene transfer. His research bridges computational biology, evolutionary theory, and systematic practice, with applications ranging from pathogen evolution to botanical systematics. Analysis of Morrison's recent publications reveals a clear trajectory toward more sophisticated representations of evolutionary history. His work increasingly emphasizes phylogenetic networks over traditional trees, recognizing that evolutionary processes are often reticulate rather than strictly divergent. This shift reflects broader trends in evolutionary biology toward more complex models that account for horizontal gene transfer, hybridization, and other non-treelike phenomena. Morrison's publications show strong engagement with both theoretical foundations and practical applications, particularly in the areas of sequence alignment methodology and data visualization for evolutionary relationships. Morrison has served as a reviewer and contributor to numerous publications in systematic biology, including book reviews for significant works in the field. His review of Multiple Sequence Alignment Methods highlighted the critical importance of alignment techniques in modern biology, noting that alignment methods are actually more cited than tree-building programs in scientific literature. This work underscores his expertise in foundational bioinformatics methods that underpin evolutionary analysis. As a Visiting Researcher at Uppsala University's Evolutionary Biology Centre, Morrison contributes to one of Scandinavia's leading institutions for evolutionary research. His work appears to intersect with multiple research groups focused on phylogenetics, systematics, and evolutionary genomics, though specific laboratory affiliations are not detailed in the available information.

• Remote Sensing
• Earth Observation
• Radar Interferometry
• +7 more

Professor Linlin Ge is a distinguished academic specializing in remote sensing and earth observation at the University of New South Wales (UNSW), Sydney, Australia. He holds a position in the School of Civil and Environmental Engineering and has been studying earth observation techniques since 1985. His work has significant applications in natural disaster monitoring, land subsidence measurement, and integrating radar and optical remote sensing with GPS and GIS systems. Professor Ge's educational background includes a PhD in GPS and remote sensing from UNSW, an MSc in Crustal Deformation from the Institute of Seismology, and a BEng (1st Hons and University Medal) in Optical Engineering from the Wuhan Technical University of Surveying and Mapping. He was also a postdoctoral research fellow in the Meteorological Research Institute of the Japan Meteorological Agency sponsored by the Science and Technology Agency of Japan during 1997-1998. His research interests focus on using digital images to map the Earth's surface, combining remote sensing with GPS and GIS to produce cost-effective and reliable maps, and integrating radar and optical remote sensing with GPS and GIS to measure subtle surface changes with minimal latency. Professor Ge's work aims to make remote sensing more timely, accurate, affordable, and widely applicable across various disciplines. His recent publications demonstrate a strong focus on advanced remote sensing techniques, particularly in the areas of hyperspectral image classification, landslide monitoring, earthquake damage assessment, and land subsidence analysis. His research increasingly incorporates deep learning and AI approaches while maintaining strong applications in natural disaster monitoring and management. Major Awards: JK Barrie Award for Overall Excellence (2008 Asia-Pacific Spatial Excellence Awards) NSW Scientist of the Year 2009 (Physics, Earth Sciences, Chemistry and Astronomy) UNSW Inventor of the Year finalist (2010) Australian Research Council Postdoctoral Fellowship (2002-2004) Professor Ge has supervised 25 graduated students and currently has 5 students in his lab. His research is supported by multiple grants including ARC Discovery grants, Australian Space Research Program funding, and various government and industry partnerships focused on SAR formation flying, subsidence monitoring, flood mapping, and carbon capture safeguarding. His work with satellite missions such as Envisat, ALOS, COSMO-SkyMed, Radarsat-2 and ALOS-2 demonstrates his leadership in practical applications of remote sensing technology.