Find Academic Supervisors

• Data Science
• Artificial Intelligence
• Algorithm Design
• +7 more

Bojie Shen is a Research Fellow in the Department of Data Science & AI at Monash University. His research lies at the intersection of artificial intelligence, data science, and algorithmic transportation systems, with a focus on efficient and eco-friendly route planning for electric vehicles, public transport, and multi-agent systems. His primary research interests include: Artificial Intelligence and Multi-Agent Path Finding Graph Algorithms and Route Planning Energy-Efficient Navigation for Electric Vehicles Public and Multi-Modal Transport Routing Combinatorial Search and Optimization Sustainable Mobility and Green Logistics The analysis of his recent publications reveals a consistent trajectory in developing scalable, exact, and energy-aware routing algorithms. His work frequently appears in high-impact venues such as IEEE Transactions on Intelligent Transportation Systems and AAAI conferences, demonstrating strong technical rigor and relevance to real-world transportation challenges. A significant portion of his research contributes to UN Sustainable Development Goals related to sustainable cities and climate action. Notable scientific contributions include: Taxonomy and survey of eco-friendly route planning algorithms Efficient public transport routing using transfer connection databases Advancements in multi-agent path finding beyond pairwise constraints Exact and dynamic shortest path computation for electric vehicles Dr. Shen actively collaborates with leading researchers including Muhammad Aamir Cheema, Daniel Harabor, and Peter Stuckey. While no formal advising or grant information is available in the text, his publication volume and co-authorship network suggest active involvement in funded research projects and potential mentorship roles within the department. He is affiliated with a vibrant research group in data science and AI at Monash University, contributing to advancements in intelligent transportation systems and combinatorial search algorithms.

• Dynamical Systems
• Differential Equations
• Bifurcation Theory
• +3 more

Joan Torregrosa i Arús is an Associate Professor in the Department of Mathematics at the Universitat Autònoma de Barcelona (UAB). His work focuses on dynamical systems, differential equations, and bifurcation theory, with a particular emphasis on limit cycles and nonlinear dynamics. He has held academic positions at UAB since 1991, including roles as an Assistant Lecturer and Full-Time Associate Professor. His research explores topics like periodic orbits, integrability, and applications to ecological models. He has led and participated in multiple research projects funded by institutions such as the European Social Fund and the Spanish Ministry of Economy and Competitiveness. Ph.D. in Mathematical Sciences from UAB (1998) Masters in Mathematical Sciences from UAB (1993) Bachelor's in Mathematical Sciences from UAB (1991) His research interests include the qualitative analysis of differential systems, bifurcation phenomena, and mathematical modeling of ecological systems. Recent work examines limit cycles in piecewise systems, slow-fast dynamics, and invariant manifolds in 3D vector fields. He has contributed to over 95 publications and actively collaborates on projects studying continuous and discrete dynamical systems. Key projects include the study of bifurcations in dynamical systems theory (DYNAMICS) and the analysis of continuous/discrete systems' periodic orbits. No scientific awards are explicitly listed, but his extensive contributions highlight sustained academic impact. He has advised two theses, including work on singular points and periodic orbits in vector fields. His research group, 'Sistemes Dinàmics,' focuses on theoretical and applied aspects of dynamical systems, emphasizing interdisciplinary applications.

Disi ChenView profile

Lecturer

• Machine Vision
• Machine Learning
• Robotics
• +5 more

Dr. Disi Chen is a Lecturer in Robotics and Machine Vision at the Faculty of Environment and Technology (FET), University of the West of England, Bristol. He is affiliated with the Centre for Machine Vision (CMV) and Bristol Robotics Laboratory (BRL). BEng in Mechanical Engineering MEng in Engineering PhD in Probabilistic Framework for Human-Robot Skill Transfer (University of Portsmouth) His research focuses on Machine Vision , Machine Learning , and Robotics , with specific expertise in human-robot skill transfer, gesture recognition, and surface electromyography (sEMG) signal processing. He has developed novel frameworks like the Curved Gaussian Mixture Model (CGMM) and Virtual Reality systems for intuitive skill demonstration perception. Disi Chen's publications emphasize gesture recognition , sEMG signal analysis , and robotic control systems , often integrating deep learning , image segmentation , and human-computer interaction . His work spans applications in prosthetic limbs , rehabilitation robotics , and urban computing . He is part of the Centre for Machine Vision and Bristol Robotics Laboratory , collaborating with institutions like the University of Portsmouth and Wuhan University of Science and Technology. His teaching includes modules on Machine Vision and Robotics .

Nikolaos LaskarisView profile

Assistant Professor

• Archaeometry
• Cultural Heritage Science
• Non-Destructive Testing
• +5 more

Nikolaos Laskaris serves as Assistant Professor at the University of West Attica's Department of Industrial Design and Production Engineering, specializing in Electronics with Applications in Art and Environment through Non-Destructive Testing and Systems Diagnosis Methodologies. His academic foundation includes: Bachelor's in Electronics (Industrial Electronics specialization) from Technological Educational Institute of Lamia Doctoral/postdoctoral research in non-destructive analysis techniques for cultural heritage and environmental applications Dr. Laskaris pioneers Archaeometry and Cultural Heritage Science , revolutionizing obsidian hydration dating to redefine Aegean navigation timelines. His research integrates Electronics and Materials Science for pigment identification in Byzantine icons, 3D heritage digitization, and micro/nano-electronics fabrication. Recent expansion into Robotics for environmental monitoring and Health Technology for aphasia rehabilitation demonstrates exceptional interdisciplinary range. Publication trends reveal strategic diversification: while maintaining core expertise in cultural heritage diagnostics (evident in 2024 sarcophagus and sigillia studies), he now leads cutting-edge work in unmanned vehicle systems (2025 defense/environmental applications), human-robot interfaces (2025 speech recognition), and neurorehabilitation technologies (2024 aphasia studies). His research portfolio includes: Characterization of CdTe/CdZnTe defect structures Non-destructive analysis of Thessalian ecclesiastical metalwork Development of modified XRF/Raman techniques Sediment pollution studies in Gulf of Elefsina He serves as NSF USA reviewer (2019-2020) and journals reviewer for Elsevier/Springer/MDPI. Dr. Laskaris directs the Non-Destructive Testing and Systems Diagnosis Methodologies laboratory, driving innovation in heritage preservation through advanced electronics and robotics integration.

• Electronics
• Metrology
• Integrated Circuits
• +3 more

Piotr Otfinowski is a full-time Professor at AGH University of Science and Technology, affiliated with the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Metrology and Electronics. His research focuses on advanced electronics, detector systems, and integrated circuit design, with expertise in photon-counting technologies, ASIC development, and signal processing for high-energy physics and synchrotron applications. Research Interests: His work spans: Design of ultra-fast imaging detectors for synchrotrons CMOS-based readout circuits for particle/photon detection Algorithms for pile-up compensation and spatial resolution enhancement In-pixel neural networks for real-time pulse analysis High-rate X-ray detection systems Publication Trends: Recent articles (2021-2025) emphasize photon-counting ASICs, interpixel communication, energy-resolved imaging, and detector optimization for experiments at facilities like ALICE (CERN) and synchrotrons. Dominant themes include small-pixel designs, neural network hardware implementations, and solutions for high-flux environments. Collaborations: Involved in international projects including ALICE (CERN) and the SPHIRD initiative for next-generation synchrotron detectors.

Wangda ZuoView profile

Professor

• Building Energy Modeling
• Computational Fluid Dynamics
• Smart and Sustainable Cities
• +4 more

Wangda Zuo is a Professor in the Department of Architectural Engineering at Pennsylvania State University. His research focuses on modeling and simulation of smart, sustainable, and resilient cities, with significant contributions to open-source tools such as the Fast Fluid Dynamic model for indoor airflow simulation, the Modelica Buildings library for energy systems, and URBANopt for geospatial energy analysis. He has secured funding from NSF, DOE, DOD, ASHRAE, and DTRA for projects on grid-interactive district systems, indoor environmental modeling, and data center cooling optimization. Professor, Architectural Engineering, Pennsylvania State University Research Interests: His work spans Building Energy Modeling , Computational Fluid Dynamics , and HVAC Optimization , emphasizing smart cities, district energy, and climate-adaptive building systems. He integrates Data-Driven Machine Learning with Physiological Network Models to enhance urban sustainability. Recent Publications: Highlighted trends include advancements in district heating optimization , chiller plant controls , climate-resilient building design , and grid-interactive energy systems , reflecting his focus on computational methods and sustainability. Scientific Awards: Elected Fellow of International Engineering Society (2025) Two Recognitions from Building-Simulation Association (2024) Labs & Collaborations: Leads the Sustainable Building Systems Lab (SBS Lab), collaborating with Lawrence Berkeley National Laboratory and National Renewable Energy Laboratory. His team develops open-source tools for energy simulation and climate resilience analysis.

• Cosmology
• Transient Astrophysics
• Machine Learning
• +4 more

Dr. Anais Moller is a Research Fellow at Swinburne University of Technology's School of Science, Computing and Emerging Technologies. She leads the Fink broker (https://fink-broker.org) with real-time access to Vera C. Rubin Observatory transients, focusing on cosmology and transient astrophysics through machine learning-driven analysis of large astronomical datasets. Doctorat de physique de l'Univers (PhD), Université Paris Diderot 7, France Master 2 NPAC, Université Paris Diderot 7, France Licenciatura en Física, Universidad Simón Bolívar, Venezuela Her research intersects three major domains: Cosmological constraints using Type Ia supernovae as standardizable candles for dark energy studies Multi-messenger astrophysics through searches for gravitational wave counterparts and fast transients Machine learning algorithm development for transient classification and data-intensive astronomy Recent publications demonstrate her leadership in: Photometric redshift estimation for supernovae Host galaxy dust modeling Weak lensing analysis Time dilation measurements through supernova light curves Kilonova detection algorithms Scientific recognition includes: 2025 School of Science, Computing and Engineering Technologies Research Excellence Award 2024 Asia-Pacific Women in AI Award (space category) She actively supervises HDR (PhD) students in topics spanning: Primordial black hole density determination Fast transient detection Gamma-ray burst analysis Multi-wavelength transient studies Machine learning applications Gravitational wave host galaxy identification As Associate Investigator at ARC Centre of Excellence for Gravitational Wave Discovery (OzGrav), she contributes to the detection of electromagnetic counterparts to gravitational waves, while leading two Australian Research Council grants focused on explosive astrophysics and dark universe illumination.

Emma CarliView profile

Research Fellow

• Radio Astronomy
• Pulsar Timing
• Astrophysics
• +4 more

Dr. Emma Carli is a Postdoctoral Research Fellow at Swinburne University of Technology's School of Science, Computing and Emerging Technologies , affiliated with the OzGrav and Centre for Astrophysics and Supercomputing . She specializes in Radio Pulsar research using the MeerKAT telescope , working closely with Professors Matthew Bailes and Ryan Shannon. Her work focuses on extragalactic pulsar discovery, pulsar timing analysis, and fast radio burst (FRB) detection. Education : PhD in Astrophysics (2020-2024) from the University of Manchester's Jodrell Bank Centre for Astrophysics; MSci in Physics (2015-2020) from the University of Glasgow. Grants : Principal investigator for the Science and Technology Facilities Council grant (2020-2024) on Searching for extragalactic pulsars with MeerKAT . Supervision : Currently supervising HDR (PhD) candidates in Advanced Signal Processing for Fast Radio Burst and Pulsar Discovery . Dr. Carli's research combines observational radio astronomy with advanced data processing techniques. She leads efforts in the TRAPUM collaboration to discover new pulsars in nearby galaxies like the Small Magellanic Cloud (SMC), Large Magellanic Cloud (LMC), and Sculptor Galaxy. Her work has established new flux density limits, identified pulsar wind nebulae (PWN) in extragalactic environments, and developed the PulsarX software package for efficient candidate folding analysis using pruned Fast Dispersion Measure Transform (pFDMT) algorithms. Key publications include: 2024 studies on SMC pulsar populations, revealing young glitching pulsars and their nebula associations 2024 LMC pulsar survey with 3x improved sensitivity 2024 FRB detection in Sextans A/B galaxy observations 2023 development of PulsarX folding pipeline Scientific awards: Science and Technology Facilities Council Grant Dr. Carli actively contributes to the TRAPUM Large Survey Project and collaborates with international teams using facilities like Chandra X-ray Observatory and HARPS spectroscopy. Her work bridges observational astrophysics with computational method development for next-generation radio surveys.

• Astronomical Sciences
• Space Sciences
• Neutron Star Physics
• +8 more

Dr. Yuanming Wang is a Postdoctoral Fellow in Fast Radio Bursts at the School of Science, Computing and Emerging Technologies , Swinburne University . Holding a PhD in Astronomy from the University of Sydney , he specializes in detecting rapidly changing radio transients using the ASKAP telescope in Western Australia, with notable contributions including the discovery of the most luminous pulsar in the Large Magellanic Cloud. His research spans Fast Radio Bursts , neutron star physics , and long-period radio transients , aiming to bridge gaps in pulsar spin periods and tidal disruption event models. Key projects include FRB localization , radio star cataloging with eROSITA cross-matching, and transient detection pipeline optimization for SKA-scale data processing. Recent publications (2023–2025) focus on multiwavelength observations of periodic nuclear transients, X-ray-radio correlations in long-period sources, and astrometric refinements for cosmological tools. His work also highlights highly scattered pulsars and redback binary candidates , expanding understanding of extreme astrophysical phenomena. Dr. Wang is available for supervising Doctorate (PhD) projects in Fast Radio Burst discovery and advanced signal processing . He contributes to the ADS publication library and collaborates with international teams on radio surveys for the Deeper, Wider, Faster program .

• Computer Arithmetic
• Approximate Computing
• Neuromorphic Computing
• +5 more

Somayyeh Timarchi is a Lecturer in Electronic Circuits and Systems at the School of Electronic Engineering and Computer Science, Queen Mary University of London . She holds a B.Sc. in Computer Engineering from Shahid Beheshti University (SBU), M.Sc. and Ph.D. in Computer System Architecture from Sharif University of Technology (SUT) and SBU, and completed postdoctoral research on computer arithmetic at Delft University of Technology (TUDelft). Previously an assistant professor and associate professor at SBU's Electrical Engineering Department, she now focuses on high-speed, low-power digital architecture. Research Interests include Computer arithmetic Approximate computing for neural networks Neuromorphic computing ASIC/FPGA design for signal processing and cryptography Residue and Redundant Number Systems Recent Publications emphasize approximate computing, error compensation, and energy-efficient hardware for IoT and biomedical applications. Key trends include optimizing CORDIC-based architectures for spiking neural networks, improving FPGA memory allocation, and developing low-power cryptographic solutions. Scientific Awards : Fellow of the Higher Education Academy (FHEA) Teaching includes ECS502U - Microprocessor Systems Design , covering microcontroller architecture, programming, and design principles for electronic circuits.

Ashvin GoelView profile

Professor

• Systems Software
• Cloud Computing
• Storage Systems
• +5 more

Ashvin Goel is a Professor in the Department of Electrical and Computer Engineering and Department of Computer Science at the University of Toronto , where he leads research at the intersection of Operating Systems , Reliability Engineering , and Computer Security . His work focuses on ensuring software systems can withstand bugs and vulnerabilities, with publications in top venues like SOSP , OSDI , and FAST . He received his PhD in Computer Science and Engineering from Oregon Graduate Institute in 2003, with prior degrees from UCLA (MS) and IIT Kanpur (BS). Current research projects include runtime verification systems like Recon and intrusion recovery frameworks such as Taser , often in collaboration with Professor Angela Demke Brown. His team explores kernel instrumentation, file system consistency, and security recovery methods. NSERC Discovery Accelerator Award 2012 FAST Best Paper Award 2012 Google Faculty Research Award 2011 Netapp Faculty Award 2008 Ontario Early Researcher Award Professor Goel's recent publications address deterministic concurrency control, distributed graph mining, and storage system reliability. He teaches graduate courses in Dependable Software Systems and Distributed Systems , with prior course development in operating systems security and time-sensitive applications.

Mikael MortensenView profile

Professor

• Computational Fluid Dynamics (CFD)
• Turbulent Flows
• Spectral Methods
• +6 more

Mikael Mortensen is a Professor in Fluid Mechanics at the University of Oslo 's Department of Mathematics. His research spans Computational Fluid Dynamics (CFD), turbulent flow modeling, spectral numerical methods, and applications in biomedical flows, turbulent combustion, and space physics. He contributes to the 4DSpace strategic research initiative studying ionospheric instabilities. Primary Affiliation: Department of Mathematics, University of Oslo Collaborations: Center for Biomedical Computing (Simula), KAUST, Chinese Academy of Sciences Research Themes High-performance spectral Galerkin methods ( Shenfun ) Direct Numerical Simulation of turbulence ( spectralDNS ) Space plasma physics and ionospheric turbulence Multiphase flow modeling in industrial contexts Machine learning applications in fluid dynamics Publication Trends Mortensen's recent work focuses on spectral methods for turbulence modeling, plasma instability simulations, and machine learning applications in fluid dynamics. His articles demonstrate expertise in Python-based high-performance computing frameworks for CFD and interdisciplinary applications in space physics and biomedical engineering. Supervision PhD Students: Anna Piterskaya (2019-2025), Tormod Landet (2016-2019), Christopher Friedemann (2018-2020) Master Students: Jacob H. Hudtwalcker, Jacob T.S. Langmoen, Kei Yamamoto, Sverre Vinje Collaborative Initiatives Co-founder of the Kobe-Oslo Partnership (10 MNOK funding from Norwegian Research Council) Organized 15+ international workshops on plasma simulations Active in Python-based scientific computing education

Ivan Porres PaltorView profile

Professor

• Software Engineering
• Cyber-Physical Systems
• Trustworthy AI
• +3 more

Ivan Porres Paltor is a Professor in Software Engineering at Åbo Akademi University's Faculty of Natural Sciences and Engineering, Information Technology department. His research focuses on trustworthy software & AI , particularly in cyber-physical system validation and adaptive testing . Developed STGEM - a leading tool for formal requirement falsification in cyber-physical systems using generative machine learning Active in multiple research projects: MADNESS Centre of Excellence (antimicrobial resistance solutions), FAST Doctoral Network , and Virtual Sea Trial His work contributes to: UN Sustainable Development Goals (SDGs) Advancements in mission-critical software verification Integration of AI for software engineering and software engineering for AI Key research areas: Formal requirement validation through generative adversarial networks Scenario-based testing for adaptive systems Metamorphic relations in test case generation Technical report frameworks for system verification

• Gravitational Waves
• Machine Learning
• Astrophysics
• +3 more

Dr. Melissa Lopez is a Researcher at Utrecht University's Faculty of Science, affiliated with the Gravitational and Subatomic Physics (GRASP) department. Her work centers on gravitational wave detection and data analysis, with specialized focus on machine learning applications for signal processing in astrophysical contexts. She actively contributes to international collaborations including LIGO, Virgo, and KAGRA gravitational wave observatories. Research Focus: Dr. Lopez's interdisciplinary research spans gravitational wave physics, machine learning implementation for astrophysical data, and cosmology. Primary domains include: Advanced detection algorithms for transient gravitational-wave events Machine learning-driven noise reduction and glitch classification Multi-messenger astronomy coordination and electromagnetic counterpart identification Cosmic microwave background analysis and early universe signatures Next-generation detector development for Einstein Telescope Publication Trends: Her recent articles demonstrate a strong emphasis on deep learning solutions for gravitational wave challenges. Key themes include autoencoder-based anomaly detection, CNN/FPGA-accelerated signal identification, and generative modeling for data augmentation. Significant work focuses on LIGO/Virgo/KAGRA data analysis, cosmic string detection, and core-collapse supernova modeling for future observatories. Collaborations: Dr. Lopez works within the GRASP research group and maintains active membership in the LIGO Scientific Collaboration, contributing to data analysis pipelines and algorithm development for O3/O4 observing runs. She frequently co-authors multi-institutional papers involving gravitational wave observatories globally.

• Particle Physics
• Collider Physics
• Lattice QCD
• +3 more

Rafael Delgado Lopez is a Professor in the Department of Applied Mathematics for Information and Communication Technologies at the School of Telecommunications Engineering, Technical University of Madrid (UPM). He joined UPM as a Profesor Ayudante Doctor in September 2020 and was promoted to Profesor Contratado Doctor in September 2023. His research is primarily focused on theoretical particle physics, with expertise in collider phenomenology, lattice QCD, and effective field theories. He is a member of the Geometría y sus aplicaciones research group and has been part of the ADAI project (PID2021-124473NB-I00) since 2021. Delgado Lopez's research spans several key areas in theoretical physics. His primary interests include Higgs physics, where he investigates multi-Higgs production processes and the differences between Standard Model Effective Field Theory (SMEFT) and Higgs Effective Field Theory (HEFT). He has made significant contributions to lattice QCD, particularly in studying the static energy in multi-flavor QCD and charm mass effects. His work also extends to graviton scattering, unitarization techniques, and computational physics, where he developed the MaMuPaXS tool for numerical integration of cross sections and contributed to the QuantumFDTD computational framework. His recent publications demonstrate a focus on cutting-edge topics in particle physics, with particular attention to multi-Higgs production, vector boson scattering, and the search for physics beyond the Standard Model. His work often involves complex computational techniques and collaborations with international research groups including those at INFN-Firenze and TUM. As an academic supervisor, Delgado Lopez has directed multiple student projects, including bachelor theses on elliptic curves cryptography and image classification via machine learning for medical purposes, as well as a master's thesis on the Quantum Walk Algorithm. He has also been involved in the acquisition, administration, and maintenance of high-performance computing resources for the ADAI project.