Find Academic Supervisors

Longzhi YangView profile

Professor

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +11 more

Longzhi Yang is a Professor at Northumbria University’s Department of Computer and Information Sciences, currently serving as Director of Education. He holds a PhD in Computing Science from Aberystwyth University (2011) and has extensive research experience in Artificial Intelligence, Robotics, Cybersecurity, and Manufacturing. His work emphasizes real-world applications under uncertain environments, supported by grants from EPSRC, Innovate UK, and industry partners. Research interests include Computational Intelligence, Machine Learning, Cybersecurity, Robotics, Planning/Scheduling, and Additive Manufacturing. Recent projects focus on energy storage systems, fuzzy logic control, and data-driven robotic systems. He has organized major conferences like the UK Workshop on Computational Intelligence and the IEEE ICMLA. Publications span topics such as reinforcement learning for EVs, fuzzy neural networks, and cybersecurity frameworks. Awards include the Best Paper Award at UKCI 2018 and 2016. He actively supervises PhD students and leads interdisciplinary initiatives like the COVER project for emergency response drones and 3D-printed furniture manufacturing. Education: PhD in Computing Science (Aberystwyth University, 2011) Funding: EPSRC, ESRC, Innovate UK, RAEng Grants: 4 active/funded projects including smart manufacturing and emergency response systems Labs/Teams: Involved in robotics, cybersecurity, and intelligent manufacturing research groups

• Cosmology
• Gravitational Waves
• Dark Energy
• +5 more

Dr. Miguel Zumalacarregui is the Group Leader of the Cosmology group within the Astrophysical and Cosmological Relativity Division at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Potsdam, Germany. He is a senior co-chair of the LISA Cosmology Working Group and deputy lead for the Euclid Gravitational Waves Science Working Group, highlighting his leadership in major international astrophysics collaborations. PhD in Physics, Autonomous University of Madrid (2012) Graduate Student, University of Barcelona (2008–2012) MA in Theoretical Physics, Autonomous University of Madrid (2009) His research focuses on cosmology, gravitational waves, and fundamental physics, particularly the nature of dark energy and dark matter, testing gravity beyond Einstein, and the cosmological implications of gravitational wave observations. He is the main developer of the hi_class code, a public tool for cosmological predictions in generalized dark energy models. His theoretical work is tightly coupled with observational data from missions like Euclid and LISA. His recent publications span topics such as dark energy reconstruction, gravitational wave lensing, multi-messenger astronomy, modified gravity, and machine learning applications in strong lensing detection. These works, appearing in top journals like Physical Review D and Astronomy & Astrophysics , reflect a strong trend toward combining theoretical modeling with cutting-edge observational and computational techniques to address cosmological tensions and probe new physics. Editor's suggestion, Physical Review D Marie Curie Global Fellow Dr. Zumalacarregui leads a research group and is deeply involved in large collaborative projects like Euclid and LISA, contributing to science working groups and data analysis efforts. His work is supported by significant institutional and collaborative funding, and he plays a key role in shaping the cosmological science case for future gravitational wave observatories. He has no listed formal advisees, but his leadership position implies mentorship of junior researchers. He is a key member of the Cosmology group at the Albert Einstein Institute, contributing to the institute’s focus on astrophysical and cosmological relativity. His team collaborates extensively with the Euclid and LISA consortia, integrating theoretical cosmology with observational data analysis.

Greg HamerlyView profile

Professor

• Machine Learning
• Unsupervised Learning
• Data Clustering
• +10 more

Greg Hamerly is a Professor and former Interim Chair (2022–2025) in the Department of Computer Science at Baylor University’s School of Engineering and Computer Science. He holds a PhD from the University of California, San Diego (2003) and specializes in machine learning, particularly unsupervised learning and data clustering, with real-world applications in medical imaging and ecological monitoring. PhD, University of California San Diego (2003) MS, University of California San Diego (2001) BS, California Polytechnic State University (1999) His research centers on the development and application of efficient machine learning algorithms. Key areas include improving k-means clustering techniques, federated learning, and applying AI to early detection of retinoblastoma through leukocoria in photographs and monitoring invasive aquatic species using video data. He has also contributed to computational thinking curriculum development. His recent publications reflect strong trends in unsupervised and self-supervised learning, with a focus on algorithmic efficiency, robustness, and real-world deployment in healthcare and environmental science. His work often combines clustering, deep learning, and computer vision for practical applications. Dubious awards (as humorously noted on personal website) Chief Judge for ICPC South Central USA regional competition (2018, 2019) Site Director for ICPC South Central USA regional competition (2010–2013, 2015–2019) Member of ICPC Live Analytics team at ICPC World Finals (2011–2015) Participant in ICPC World Finals as contestant (2000, 2001) Greg Hamerly has advised numerous graduate students, including PhD candidates now at Texas A&M Kingsville and Meta. He has secured research funding from the NSF, NIH, Department of the Interior, and Texas Parks and Wildlife Department. He is also deeply involved in competitive programming, having coached students and developed the Competitive Learning course at Baylor with over 300 original problems. He leads research projects on eye disease detection and invasive species monitoring, often in collaboration with interdisciplinary teams. His lab integrates algorithmic innovation with practical deployment, emphasizing accessibility and real-world impact.

Michael MuehlebachView profile

Researcher

• Machine Learning
• Dynamical Systems
• Control Theory
• +3 more

Michael Muehlebach is a Research Group Leader at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany, leading the Learning and Dynamical Systems group. He holds a B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich (2010, 2013) and a Ph.D. from the Institute for Dynamic Systems and Control (2018), advised by Prof. R. D'Andrea. He later pursued postdoctoral research under Prof. Michael I. Jordan at UC Berkeley. Research Interests: Machine Learning, Dynamical Systems, Control Theory, Optimization, Physics-Informed Learning for Cyber-Physical Systems. Scientific Awards: Outstanding D-MAVT Bachelor Award, Willi-Studer Prize, ETH Medal, HILTI Prize, Branco Weiss Fellowship (2018), Emmy Noether Fellowship (2020), Amazon Fellowship (2024). Students: Supervised Julien Kohler (Master's thesis on Quadrotor Control). Projects: Developed Floaty (wind-energy robot), Flying Platform (ducted fan actuation testbed), One-Wheel Cubli (3D inverted pendulum), and electromagnetic navigation systems.

• Natural Language Processing
• Computer Vision
• Artificial Intelligence
• +3 more

Aditya Sharma is a researcher at the University of California Santa Barbara , focusing on Natural Language Processing , Computer Vision , and Artificial Intelligence . His work bridges vision-language models, medical AI evaluation, and knowledge graph reasoning. PhD : Advancing AI Understanding in Language & Vision (UCSB, 2024) Research Interests: • Vision-Language Models for geometric reasoning and mixed reality • Medical LLM evaluation and hallucination reduction • Temporal knowledge graph reasoning and multimodal systems Article Trends: Recent publications emphasize vision-language integration (e.g., GeoCoder, OCTO+), LLM reliability in medical contexts (MedHELM), and temporal reasoning (TwiRGCN). He explores applications in dark web security , healthcare diagnostics , and energy-efficient computing .

Sam OttoView profile

Assistant Professor

• Scientific Machine Learning
• Dynamical Systems
• Model Reduction
• +7 more

Sam Otto is an Assistant Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University, joining the faculty in July 2024. His research lies at the intersection of machine learning and continuum mechanics, with a focus on developing rigorous, data-driven methods for modeling, forecasting, and controlling high-dimensional nonlinear systems such as fluid flows. He leads the Otto Lab, which advances scientific machine learning through principled algorithms and theoretical understanding. Research Interests: Dr. Otto’s work centers on scientific machine learning, particularly in developing algorithms that integrate physical knowledge—such as symmetry, scale hierarchy, and smoothness—into data-driven models. His research addresses fundamental questions about what can be learned from limited data and how to ensure reliability in engineering applications. Key areas include model reduction, operator learning, sensor placement, and symmetry enforcement in neural networks. He applies these methods to fluid dynamics and other continuum mechanics problems. Publication Trends: His recent publications (2019–2024) show a strong trajectory in developing theory and algorithms for learning in nonlinear dynamical systems. There is a clear emphasis on incorporating physical structure into machine learning models, with recurring themes of symmetry, dimensionality reduction, and operator-based modeling. The work spans theoretical advances and practical computational methods, often leveraging Koopman theory, autoencoders, and covariance-based reductions. Education: Ph.D. in Mechanical and Aerospace Engineering, Princeton University, 2022 B.S. in Aeronautics and Astronautics, Purdue University, 2016 Scientific Awards: Advising and Grants: While no current students or grants are listed, Dr. Otto is expected to advise graduate students through the Mechanical and Aerospace Engineering graduate program at Cornell. As a new faculty member, he is likely pursuing funding for his research in scientific machine learning and dynamical systems. His lab is actively publishing in top journals and preprint servers, indicating ongoing research support, possibly through postdoctoral fellowships or early-career grants. Labs and Teams: Dr. Otto leads the Otto Lab at Cornell, which focuses on machine learning for high-dimensional dynamics. The lab develops computational tools that combine data and physical principles to model complex systems. It is part of the broader research ecosystem in the Sibley School, collaborating with experts in fluid mechanics, control theory, and applied mathematics.

• Stochastic Analysis
• Measure Theory
• Fractal Geometry
• +3 more

Alexander Steinicke is a Professor in the Department of Applied Mathematics, specializing in stochastic analysis and measure theory. His research bridges pure mathematics with applications in finance and fractal geometry, evidenced by 17 publications in high-impact journals including Advances in Mathematics and Stochastic Analysis and Applications . His primary research interests include: Stochastic Analysis Measure Theory Fractal Geometry Mathematical Finance Probability Theory Stochastic Processes Steinicke's recent work reveals a cohesive trajectory in geometric measure theory, particularly through his development of permeable set theory and its applications to fractal structures. His 2025 article on dimension theory represents a significant advancement, while his finance-related publications demonstrate practical extensions of stochastic calculus to market incompleteness problems. The integration of Lévy processes and semimartingale SDEs forms a consistent methodological thread across his publications. As an active academic, Steinicke regularly participates in conferences including the Austrian Stochastics Days and delivers invited talks on stochastic analysis. His consultancy work for research evaluations demonstrates engagement with broader academic governance.

Hongpeng ZhouView profile

Research Fellow

• Machine Learning
• Deep Learning
• Interpretable Learning
• +3 more

Hongpeng Zhou is a Dame Kathleen Ollerenshaw Fellow at the Department of Computer Science, University of Manchester. His research focuses on interpretable machine learning for cancer research, Bayesian deep learning, and bioinformatics. Previously, he was a postdoctoral researcher at the same university, developing ML algorithms for single-cell transcriptomics data. Education: PhD in Bayesian deep learning, Delft University of Technology (2018–2022) MEng in Control Science, Harbin Institute of Technology (2015–2017) BEng in Control Science, Harbin Institute of Technology (2011–2015) Research Interests: Dr. Zhou specializes in creating interpretable ML models for biological and engineering systems. His work bridges deep learning, Bayesian methods, and domain-specific applications like materials science, geotechnical engineering, and cancer genomics. Core themes include neural network compression, physics-informed learning, and unsupervised exploration of complex datasets. Publication Trends: Recent articles demonstrate interdisciplinary collaboration across computer science, bioinformatics, and mechanical engineering. Dominant themes include interpretable ML architectures, physics-informed neural networks, and applications in biomedicine/materials science, with emphasis on uncertainty quantification and real-world system optimization. Awards: Dame Kathleen Ollerenshaw Fellowship Projects & Advising: Leads the TICM R&D project on efficient 3D Gaussian splatting and co-investigates breast cancer prevention initiatives. Accepts PhD students and collaborates with groups like Prof. Magnus Rattray's bioinformatics team.

Nicolas BoulleView profile

Assistant Professor

• Numerical Analysis
• Numerical and Computational Mathematics
• Knowledge Representation
• +4 more

Nicolas Boulle is an Assistant Professor in Applied Mathematics at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. His research focuses on the intersection of numerical analysis and deep learning, particularly in discovering mathematical models (e.g., partial differential equations) from data and developing theoretically grounded numerical techniques. Education: DPhil (PhD) in Numerical Analysis from the University of Oxford (2018–2022) Research interests include numerical analysis, operator learning, machine learning, and their applications in solving complex mathematical problems. His work emphasizes data-driven methods for Green’s functions and the development of rational neural networks for enhanced accuracy in deep learning. Recent publications explore topics like dynamic mode decomposition, Koopman operators, and large language models' behavior. He advises PhD students and visiting researchers on projects involving operator learning and neural networks. Boulle contributes to open-source projects such as GreenLearning (for PDE Green’s functions) and RationalNets (rational activation functions in neural networks), hosted on GitHub.

Ting-Kam Leonard WongView profile

Associate Professor

• Mathematical Finance
• Probability
• Optimal Transport
• +2 more

Ting-Kam Leonard Wong is an Associate Professor jointly appointed at the Department of Statistical Sciences and the Department of Computer and Mathematical Sciences at the University of Toronto and University of Toronto Scarborough , respectively. He also holds a non-budgetary cross-appointment in the Department of Mathematics since May 2025. Education: PhD in Mathematics, University of Washington (2016) MPhil in Mathematics, The Chinese University of Hong Kong (2011) Research Interests: His research lies at the intersection of mathematical finance, probability theory, optimal transport, and information geometry . He explores deep theoretical questions in stochastic processes and their applications in financial modeling, market analysis, and data science. His work often involves developing geometric frameworks to understand complex probabilistic systems, including the study of Wasserstein distances, Bregman divergences, and their roles in optimization and inference. Research Trends: Across his recent publications, a clear trend emerges toward integrating geometric methods with stochastic modeling and financial applications . He has contributed to the development of new divergence measures, optimization algorithms, and portfolio frameworks. His work often bridges theoretical probability with practical tools in finance and machine learning, such as portfolio optimization, PCA under transport metrics, and scalable inference methods. Awards & Funding: NSERC Discovery Grants (2019, 2025) Connaught New Researcher Award (2020) Data Science Institute Seed Funding for Methodologists (2022) Best Paper Award, Geometric Science of Information Conference (2019) Advising & Grants: He currently supervises several PhD students including Elijah French , Madhu Gunasingam , and Amanjit Kainth . He previously co-supervised Steven Campbell , who successfully defended in 2023. His research is supported by multiple federal and institutional grants, reflecting his active role in advancing methodological research in statistics and finance. Editorial & Service Roles: He serves as an Associate Editor for the journal Information Geometry (2022–present), actively contributing to the dissemination of research in geometric methods for data science and inference.

Scott KaschnerView profile

Assistant Professor

• Complex Dynamics
• Julia Sets
• Rational Maps
• +11 more

Scott Kaschner is an Assistant Professor in the Department of Mathematics and Actuarial Science at Butler University, College of Liberal Arts and Sciences. His work bridges pure mathematics, applied mathematical biology, and mathematics education. He is actively engaged in research and undergraduate teaching, with a diverse portfolio of scholarly output. Education: Ph.D. in Mathematics, Department of Mathematical Sciences, IUPUI, 2013 M.S. in Theoretical Mathematics, University of Akron, 2008 B.S. in Theoretical Mathematics, University of Akron, 2003 His research interests span Complex Dynamics , particularly Julia sets, rational maps, and bicomplex analysis; Mathematical Virology , including modeling of coronavirus and respiratory syncytial virus (RSV) replication; and Mathematics Education , with focus on student success in introductory courses, assessment design, and interdisciplinary co-teaching. His work reflects a strong interdisciplinary approach, combining deep mathematical theory with real-world applications in biology and pedagogy. The 15 most recent publications reveal a consistent trajectory across three domains: foundational work in complex dynamics and operator theory, applied modeling in virology, and scholarship of teaching and learning in mathematics. The keywords and sub-fields reflect a sophisticated blend of pure and applied mathematics, with increasing engagement in biological modeling and educational research. Scientific Awards: No awards explicitly mentioned in the text. Scott Kaschner has advised or collaborated with numerous students and researchers, particularly evident in his co-authored papers in virology and mathematics education. His involvement in the NSF GK-12 program as a fellow indicates a long-standing commitment to STEM outreach and pedagogical development. He has secured research support through collaborative grants, especially in virology and education. He co-authored an open textbook on linear transformations, contributing to accessible educational resources. He is involved in interdisciplinary research teams, particularly in virology (with Christopher Stobart and others) and mathematics education (with Aubrey Neihaus and others). His work on virus modeling suggests collaboration with biologists, and his educational research involves partnerships across disciplines. These teams reflect a commitment to collaborative, cross-cutting scholarship.

Alexander HermansView profile

Researcher

• Computer Vision
• Deep Learning
• Robotics
• +7 more

Dr. Alexander Hermans is a researcher at the Institute for Vision and Graphics, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University. He is actively engaged in cutting-edge research at the intersection of computer vision, deep learning, and robotics, with a focus on 3D perception, segmentation, and anomaly detection. Research Interests: His primary research areas include Computer Vision , Deep Learning , Robotics , 3D Scene Understanding , Semantic and Instance Segmentation , and LiDAR-based Perception . His work often addresses the practical challenges of deploying vision systems in real-world robotic applications. Publication Trends: Dr. Hermans' recent publications demonstrate a strong trend towards leveraging transformer architectures for 3D and video understanding, developing robust methods for anomaly detection, and creating unified frameworks for diverse vision tasks. His research on diffusion models and self-supervised learning also highlights his engagement with the latest advancements in AI. Scientific Awards: Best Vision Paper Award at ICRA 2014 Advising and Grants: While no specific students or grants are listed, his role as a senior author on numerous publications and his leadership in creating benchmarks (like OoDIS) and software indicate a significant mentoring and project leadership role. His work is often supported by large-scale datasets and collaborations, suggesting involvement in substantial research grants and projects (e.g., the STRANDS project). Labs and Teams: He is a core member of the research group led by Prof. Bastian Leibe at RWTH Aachen, a group renowned for its work in computer vision and robotics. His research is closely tied to this lab, which focuses on developing robust, real-world applicable vision systems for autonomous agents.

Andrea TorselloView profile

Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +6 more

Andrea Torsello is a Full Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. His research focuses on Computer Vision, Machine Learning, and Artificial Intelligence with applications in quantum computation, graph theory, and 3D modeling. He is actively involved in teaching courses on quantum computation, artificial intelligence foundations, and computer science fundamentals at both undergraduate and graduate levels. Prof. Torsello's research interests include graph neural networks, quantum machine learning, and structural pattern recognition. His work often bridges theoretical computer science with practical applications in cultural heritage digitization and environmental monitoring. His recent articles highlight advancements in graph generation via spectral diffusion, quantum lens-based 3D shape analysis, and interpretable graph neural networks. He serves on the editorial boards of journals like Pattern Recognition and has contributed to projects funded by the EU’s H2020 program, including the VEiL initiative for visualizing engineered landscapes. His teaching spans topics such as quantum computation, advanced algorithms, and machine learning across multiple engineering programs.

Tan Chee KeongView profile

Senior Lecturer

• Software-defined networks
• Beyond 5G networks
• Indoor positioning systems
• +8 more

Dr. Tan Chee Keong is a Senior Lecturer at the School of Information Technology, Monash University, Malaysia. He holds a PhD in Information and Communication Technology from Multimedia University (2014). His expertise lies in network system design, with a focus on software-defined networks, beyond 5G networks, indoor positioning systems, game theory, machine learning, and data science. He has secured over RM 800K in research grants as a principal or co-investigator and contributed to over 10 international journal papers. Notably, he received the Best Teaching Award from Multimedia University in 2016. Research activities include projects funded by government and industry, such as AI-Driven Energy Management for Smart Cities and IoT-based energy monitoring systems. His work addresses challenges in network efficiency, indoor positioning, and smart city technologies. Dr. Tan has collaborated on projects involving UAV swarming intelligence, reinforcement learning, and autonomous systems. Education: B.Eng (Hons) in Electronics (Telecommunication), Multimedia University, 2006 M.Eng.Sc (Information and Communication Technology), Multimedia University, 2009 Ph.D (Information and Communication Technology), Multimedia University, 2014 Research Interests: Optimizing wireless networks and protocols Indoor positioning and trajectory tracking Game theory applications in network resource management Machine learning for signal processing and localization Recent publications (2023–2025) emphasize AI-driven solutions for energy efficiency, path planning in disaster environments, and blockchain-based logistics optimization. These reflect a trend towards integrating AI/ML with traditional networking challenges. Awards: Best Teaching Award, Multimedia University (2016) Grants & Projects: AI-Enabled Energy Management (RM 800K+) IoT-Based Energy Monitoring (2022–2025) Smart City IoT Applications He plays a key role in industry-academia collaborations, particularly in Malaysia’s tech sector. His labs focus on developing practical solutions for smart city infrastructure and next-generation communication systems.

Orhan AkyılmazView profile

Professor

• Geodesy
• Satellite Gravity Missions
• Water Resource Monitoring
• +5 more

Orhan Akyılmaz serves as a Professor in the Department of Geomatics Engineering at Istanbul Technical University, Turkey, with continuous research activity since 2004. His work bridges geodetic science with environmental monitoring through satellite gravity missions and advanced computational methods. His research focuses on satellite gravity field modeling (GRACE/GRACE-FO missions), machine learning applications for hydrological monitoring, and geodetic algorithm development . Key contributions include deep learning-aided temporal downscaling of water storage data, high-resolution gravity field products, and optimized topographic correction algorithms using digital elevation models. His fingerprint reveals strong expertise in Fuzzy Inference Systems (100%), Earth Rotation Parameters (72%), and Quaternion-based transformations (66%). Recent publications (2022-2024) demonstrate a pronounced trend toward integrating deep learning with satellite geodesy to address water resource challenges, particularly in groundwater monitoring and gap-filling between satellite missions. His work consistently targets practical environmental applications while advancing computational geodesy. Scientific recognition includes: TUJK Bilim Ödülü (Science Award) from the Turkish National Union of Chambers of Survey Engineers (2011) He secured TÜBİTAK funding (2020-2023) for the project "Grace ve Çoklu-Görev Uydu Uzaktan Algılama Verileri ile Yüksek Çözünürlüklü Yeraltı Su Bütçesi İzleme Sistemi" (High-Resolution Underground Water Budget Monitoring System). His supervised work encompasses 8 students, though specific names aren't provided in source materials.