Find Academic Supervisors

• Machine Learning
• Quantum Computing
• Robotics
• +3 more

Michael Osborne is a Professor of Engineering Science at the University of Oxford, affiliated with Exeter College as an Official Fellow. His research focuses on intelligent algorithms for big data analysis, with applications in astrostatistics, autonomous robotics, and societal impacts of AI. He holds a DPhil and is a Faculty Member at the Oxford-Man Institute of Quantitative Finance. His work bridges machine learning, quantum systems, and ethical AI. Education : BSc (Honours) BEng (Honours) DPhil (Doctorate) Research Interests : Osborne’s research spans Bayesian optimization, reinforcement learning, quantum computing, and the ethical implications of AI. He has pioneered probabilistic algorithms for exoplanet detection and developed methods to assess AI’s impact on employment. His work on meta-learning and quantum device tuning demonstrates interdisciplinary innovation. Key Contributions : His recent articles address topics like carbon-efficient ML models, privacy-preserving AI systems, and robust reinforcement learning techniques. He emphasizes scalable solutions for real-world challenges, such as healthcare monitoring via smartphone data and secure data portals. Labs & Collaborations : Based at the Oxford-Man Institute, he collaborates across disciplines, advancing AI’s role in finance, engineering, and science while advocating for ethical practices in automation and data privacy.

• Machine Learning
• Computer Vision
• Pattern Recognition
• +3 more

Richard Wilson is a Professor of Pattern Analysis in the Department of Computer Science at the University of York. He holds roles including Member of the Departmental Research Committee and Research Group Lead for Artificial Intelligence. With degrees from the University of Oxford (BA) and the University of York (DPhil), his research focuses on Machine Learning, Computer Vision, and Pattern Recognition with Graphs and Networks. He has contributed over 238 publications and led projects such as the UKRI AI Centre for Doctoral Training (SAINTS) and the Royal Society-funded Graphical Modeling of Brain project. Wilson has also been recognized with the IAPR Fellow Award (2010). His work spans theoretical advancements and applied research in network analysis, quantum algorithms, and interdisciplinary applications like drug discovery and healthcare informatics. His research interests integrate computational methods with complex systems analysis, emphasizing graph theory, entropy-based network modeling, and AI-driven solutions. Recent publications highlight innovations in semi-supervised learning, graph kernels, and quantum-inspired network dynamics. Wilson’s contributions to editorial roles, such as with the journal Pattern Recognition , and activities like PhD external examinations further underscore his academic leadership. Key Projects: UKRI AI Centre for Doctoral Training in Safe AI (SAINTS) – Principal Investigator (2024–2032) Graphical Modeling of Brain – Principal Investigator (2020–2023) His lab focuses on AI and computational methodologies, collaborating across disciplines to address challenges in healthcare, computer vision, and network science.

Ankit PatelView profile

Assistant Professor

• Machine Learning
• Computational Neuroscience
• Deep Learning
• +7 more

Ankit Patel is an Assistant Professor at Baylor College of Medicine (Department of Neuroscience) and Rice University (Department of Electrical and Computer Engineering). He holds a joint appointment and a courtesy appointment in Computer Science at Rice. His research bridges machine learning, computational neuroscience, and particle physics. Patel earned his Ph.D., M.S., and B.S. in Applied Mathematics and Computer Science from Harvard University, supported by an NSF Graduate Fellowship. His work focuses on probabilistic theories of deep learning, neurally-inspired computation, and applications in medical imaging, particle physics (e.g., Large Hadron Collider collaborations), and neuroengineering. He developed the Deep Rendering Model (DRM), linking deep convolutional networks and decision forests to first principles. Key contributions include event-driven representations for recurrent neural networks, generative convolutional nets for unlabeled data, and collaborations with neuroscientists to align machine learning with biological neural networks. His recent industry experience includes algorithmic trading and missile defense systems, now applied to academic research. Education : Ph.D./M.S./B.S. in Applied Mathematics & Computer Science, Harvard University (2008, 2002) Awards : NSF Graduate Fellowship Research Themes : Deep Learning Theory, Neuroscience-ML Integration, Quantum Computing Applications

Georgios ExarchakisView profile

Lecturer

• Machine Learning
• Theoretical Neuroscience
• Data Science
• +16 more

Georgios Exarchakis is a Lecturer at the University of Bath, specializing in Machine Learning, Theoretical Neuroscience, and Data Science. His research emphasizes transparent and interpretable modeling, with applications in Neuromorphic Engineering, Health Science, and Quantum Chemistry. He has held research positions at IHU Strasbourg, Institut de la Vision, and École Normale Supérieure, and earned his PhD from the University of Oldenburg. Education: Dr. rer. nat. in Machine Learning, 2016, Carl von Ossietzky University of Oldenburg M.Sc. in Computational Science, 2012, Goethe University Frankfurt Diploma in Mathematics, 2008, Aristotle University of Thessaloniki His research interests span Interpretable Machine Learning, Invariant Representations, Sparse Coding, Wavelet Scattering, Probabilistic Models, and Deep Learning . He investigates how models can extract meaningful, stable features from complex data, drawing inspiration from biological systems and theoretical neuroscience. His work often bridges theory and application, particularly in quantum chemistry and neurotechnology. His recent publications highlight a strong focus on efficient and interpretable models , including wavelet scattering for molecular property prediction, discrete sparse coding, and clustering algorithms for event-based vision. The articles show a consistent theme of developing mathematically grounded, invariant, and scalable methods for data analysis. He is the developer or a key contributor to open-source libraries such as Kymatio (Scattering Transforms in Python) and ProSper (Probabilistic Sparse Coding), which facilitate the use of advanced signal processing and learning techniques in the research community. Georgios has taught Machine Learning courses at the University of Strasbourg, École Polytechnique, and Oldenburg. He has collaborated with leading researchers like Stéphane Mallat and Jörg Lücke. His work is published in top-tier venues including CVPR, NIPS, JMLR, and JCP.

Thang N. DinhView profile

Associate Professor

• Graph Machine Learning
• Quantum Computing
• Approximation Algorithms
• +7 more

Thang N. Dinh is an Associate Professor in the Department of Computer Science at Virginia Commonwealth University (VCU), part of the College of Engineering. He holds a Ph.D. in Computer Engineering from the University of Florida (2008–2013) and a B.S. in Information Technology from Vietnam National University, Hanoi (2003–2007). His research focuses on Graph Machine Learning, Quantum Computing, and Approximation Algorithms, with applications in Social Networks, Blockchain, Smart Grids, Network Security, Drug Discovery, and Pandemic Modeling. Key research projects include quantum-inspired state estimation for power grids, quantum computing for healthcare optimization, and privacy-preserving federated learning. He has received numerous awards, including Best Paper Awards at ACM Mobicom and ACM Sensys, the Honda Young Engineer and Scientist Award, and a Microsoft Scholarship. His work bridges theoretical computer science with real-world challenges in cybersecurity, healthcare, and distributed systems. Teaching responsibilities include courses on Graph Neural Networks, Social Network Analysis, Blockchain, and Advanced Algorithms. His research group develops scalable solutions for billion-scale networks, leveraging quantum computing and AI to address problems in security, optimization, and healthcare.

• Quantum Information Science
• Quantum Optics
• Theoretical Condensed Matter Physics
• +12 more

Steven Girvin is the Sterling Professor of Physics and Professor of Applied Physics at Yale University, where he is a leading theoretical physicist in quantum information science. He is affiliated with the Yale Quantum Institute (YQI), the Department of Applied Physics, and the Condensed Matter Theory group. He also serves as a Member and Founding Director of the Co-Design Center for Quantum Advantage at Brookhaven National Laboratory. Research Interests: His work spans quantum information science, quantum optics, and theoretical condensed matter physics. He is best known for co-developing circuit QED, the foundational architecture for superconducting quantum computers. His research includes quantum error correction, fault tolerance, quantum phase transitions, mesoscopic physics, and the quantum Hall effect. He actively collaborates with experimentalists such as Robert Schoelkopf and Michel Devoret. Recent Research Trends: The most recent articles and lecture notes highlight a sustained focus on quantum error correction, fault-tolerant quantum computing, and quantum simulation. His work bridges theoretical depth with practical engineering, especially in superconducting qubit systems. He also emphasizes education and public engagement through accessible lectures and textbooks. Oliver E. Buckley Prize (2007) – For work on the fractional quantum Hall effect. Honorary Degree from Chalmers University (2017) – For contributions to circuit QED. Member, US National Academy of Sciences Foreign Member, Royal Swedish Academy of Sciences Advising and Grants: Professor Girvin advises graduate students including Shraddha Singh and has mentored alumni such as Baptiste Royer and Yaxing Zhang. He has led major federally funded initiatives, including the DOE-funded Co-Design Center for Quantum Advantage. His educational materials, including lecture notes and textbooks, are widely used in quantum computing education. Labs and Teams: He is closely associated with the Yale Quantum Institute (YQI), YINQE, the Condensed Matter Theory group, and the Department of Applied Physics. His work is central to Yale’s leadership in quantum science and engineering.

Harshit Jitendra MotwaniView profile

Research Fellow

• Computational Algebraic Geometry
• Formal Verification
• Tensors
• +5 more

Harshit Jitendra Motwani is a Postdoctoral Researcher at the Max Planck Institute for Software Systems (MPI-SWS) , Germany. Previously, he was a Postdoctoral Fellow at the Hong Kong University of Science and Technology (HKUST) (2023-2024), following doctoral research at Ghent University (2021-2023) and earlier engagements at HKUST, Ghent University, and the University of Bristol . Education : PhD in Mathematics: Algebra and Geometry from Ghent University (2021-2023), Thesis on Algebro-Geometric Algorithms for Program Synthesis; Integrated BSc and MSc in Mathematics and Computing from IIT Kharagpur (2015-2020). Research Focus : Integrates mathematics into computational domains, particularly Computational Algebraic Geometry , Formal Verification , and Tensors . His work spans program synthesis , tensor networks , conditional independence models , and control theory applications . Publications : Recent contributions include algorithmic advancements in formal methods (FM, AAAI, LAGOS), algebraic statistics (IMRN), and quantum computing (SIGMA). His research combines symbolic computation with practical software engineering challenges. Awards & Honors : IEEE Computer Society Larson Best Paper Award (2023) ACM SIGPLAN Distinguished Paper Award (2023) Young Researcher of the Heidelberg Laureate Forum (2023) INSPIRE Scholarship (2015-2020) Teaching : Guest Lecturer at HKUST, Teaching Assistant at RPTU Kaiserslautern-Landau and IIT Kharagpur. Mentored multiple bachelor's projects at Ghent University and IIT Kharagpur.

• Business Management
• Information Systems
• Fuzzy Cognitive Maps
• +4 more

Jose Luis Salmeron Silvera is a University Professor at Universidad Pablo de Olavide (UPO) in Spain, affiliated with the Department of Business Organization and Marketing within the School of Business. His research focuses on the intersection of business management, information systems, and artificial intelligence, particularly through the lens of fuzzy cognitive maps and grey systems theory. Dr. Salmeron holds two PhD degrees: one from the University of Almería (2014) with thesis "Modeling of complex adaptive systems using fuzzy cognitive maps," supervised by Dr. José Antonio Torres Arriaza, and another from the University of Huelva (2000) with thesis "Information systems for management: a systemic approach," supervised by Dr. Paula Luna Huertas and Dr. Francisco José Martínez López. He supervises doctoral students in Engineering, Data Science and Bioinformatics, and Information Systems and Supply Chain Management programs. His research interests center around fuzzy cognitive maps , grey systems theory , and their applications in business decision-making, risk management, and complex system modeling. Dr. Salmeron has made significant contributions to extending traditional fuzzy cognitive maps with grey theory to handle uncertainty in environments with incomplete or imprecise data. His work bridges theoretical advancements in artificial intelligence with practical applications across healthcare, business management, and engineering domains. Dr. Salmeron's research has evolved from foundational work in fuzzy cognitive maps for business applications to more recent innovations incorporating federated learning, privacy-preserving techniques, and quantum-inspired approaches. His publications demonstrate a consistent focus on developing interpretable AI systems that can function effectively with limited or uncertain data, making his work particularly valuable for real-world applications where perfect information is rarely available. Among his notable achievements is the development of methods for learning fuzzy grey cognitive maps using nonlinear Hebbian-based approaches, which has found applications in medical decision support systems, particularly in radiotherapy treatment planning. His work has been cited in various fields including computer science, operations research, and biomedical engineering. Dr. Salmeron actively collaborates with researchers internationally, with notable co-authors including Elpiniki I. Papageorgiou and Salvador Bueno. His research group "STI Information Systems and Technologies" focuses on the application of advanced computational methods to solve complex business and engineering problems. Previously, he was also part of the "INFORMATION, ENVIRONMENT AND CHANGE" research group, reflecting the interdisciplinary nature of his work.

• Physics
• Geometry
• Machine Learning
• +2 more

Challenger Mishra is a Bye Fellow in Computer Science at Queens' College, University of Cambridge. He serves as Director of Studies for first-year Computer Scientists and is a Research Fellow with the Accelerate Program for Scientific Discovery at the Computer Laboratory. Additionally, he is an affiliated lecturer co-teaching a course on Theory of Deep Learning. Dr. Mishra's research sits at the intersection of Physics, Geometry, and Machine Learning. His current work focuses on special geometries that appear as extra-dimensions in Superstring theory and AI-driven mathematical discovery. His interdisciplinary approach combines theoretical physics with cutting-edge machine learning techniques to explore complex mathematical structures and their physical implications. His research spans both pure mathematics and applied computational methods, seeking to bridge the gap between abstract theory and practical applications in scientific discovery. Research in Calabi-Yau metrics and string theory Development of machine learning approaches for mathematical discovery Applications of AI in theoretical physics Exploration of geometrical structures in superstring theory Among his notable achievements, Dr. Mishra was awarded the prestigious Rhodes Scholarship during his doctoral studies. This recognition highlights his exceptional academic abilities and research potential in theoretical physics. As an educator, Dr. Mishra is passionate about inspiring the next generation of students and preparing them for future challenges. He provides supervisions for Computer Science students and plays a key role in shaping the curriculum for first-year Computer Scientists at Queens' College. His teaching philosophy emphasizes the connections between theoretical foundations and real-world applications, particularly in the rapidly evolving field of machine learning and artificial intelligence. Dr. Mishra's work is closely associated with the Accelerate Program for Scientific Discovery, which likely provides him with resources and collaborative opportunities to advance his research at the intersection of physics, geometry, and machine learning.

Tianlong ChenView profile

Assistant Professor

• Machine Learning
• Sparsity
• Robustness
• +5 more

Tianlong Chen is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill, where he established the UNITES Lab (University of North Carolina, AI Trustworthiness, Efficiency, and for Science Group). He joined UNC in Fall 2024 after completing his Ph.D. in Electrical and Computer Engineering at the University of Texas at Austin (2023) and serving as a postdoctoral researcher at MIT (CSAIL), Harvard University (BMI), and the Broad Institute of MIT & Harvard. His research focuses on building accurate, trustworthy, and efficient machine learning systems , with emphasis on: Sparsity and model compression techniques Robustness against adversarial challenges Learning-to-optimize methodologies Graph representation learning Diffusion models and generative AI Quantum computing applications Bioengineering and healthcare AI solutions His publications demonstrate strong trends in large language model optimization , efficient multimodal systems , and trustworthy AI frameworks , with frequent appearances at top venues (NeurIPS, ICML, CVPR, ICLR). Significant scientific honors include: IBM and Adobe Ph.D. Fellowships Best Paper Award at Learning on Graphs Conference 2022 AdvML Rising Star (2023) Amazon Research Award (2024) Two UNC Accelerating AI Awards (2025) NAIRR Pilot Award (2025) He leads 19+ advisees in the UNITES Lab and has secured research funding from Cisco, Amazon, and institutional grants. Current projects explore sparsity-aware architectures, LLM alignment, and efficient multimodal learning systems.

Jo PlestedView profile

Academic

• Deep learning
• Artificial intelligence
• Machine learning
• +4 more

Dr Jo Plested is an Associate Lecturer at UNSW Canberra with over 10 years of research experience in deep learning, specializing in transfer learning for small specialized datasets. She has made significant contributions to both academia and defense applications through her research, teaching, and leadership as Head of the Deep Learning Group at UNSW Canberra. Her research interests span multiple interdisciplinary domains including: Transfer learning for small specialized datasets Bushfire spread prediction modeling with the UNSW Bushfire Research Group Visual swarming algorithms for robotics applications Physical implementation of neural networks using novel materials Quantum computing applications where deep learning helps predict and overcome environmental noise Military AI ethics examining implications of targeting systems Dr Plested has secured over $900,000 in research funding as chief investigator for 5 external grants. Her publication record includes 2 journal articles, 19 conference papers, and 2 preprints. Analysis of her recent publications reveals a strong focus on practical applications of deep learning across diverse domains, with particular emphasis on defense-related applications, environmental modeling, and ethical considerations in AI deployment. She has supervised over 20 students working on Honours, Masters, and Chief of Defence Force projects, with more than 20 of these student projects published in high-ranking international conferences and journals. Dr Plested created the honours-level Deep Learning course at UNSW Canberra and previously developed and delivered deep learning content for courses at the Australian National University for up to 250 students. She is also part of a team that developed a data science and AI short course specifically for Defence applications. Currently recruiting PhD students with interests in deep learning, Dr Plested offers scholarships of $35,000 AUD, seeking candidates with backgrounds in deep learning or strong mathematical foundations.

Leo MršićView profile

Professor

• Computer Science
• Information and Communication Technology
• Graph Theory
• +5 more

Prof. Dr. Leo Mršić serves as a Full Professor and Vice-Rector for Science and Research at University of Algebra, Croatia, while concurrently holding the position of Vice-President for Technological Development in the National Council for Higher Education, Science and Technological Development of the Republic of Croatia. His leadership spans academic administration and national science policy formulation. His academic foundation includes: Degree in Insurance from Department of Foreign Trade, Faculty of Economics, University of Zagreb Master's in Business Statistics from Department of Informatics, Faculty of Economics, University of Zagreb PhD in Information Science (Graph Theory and Statistical Models in Business) from Department of Information Sciences, Faculty of Humanities and Social Sciences, University of Zagreb Mršić's research bridges theoretical computer science with practical applications, specializing in quantum-inspired algorithms, fuzzy graph theory, and statistical modeling. His work demonstrates exceptional interdisciplinary reach across cybersecurity, healthcare diagnostics, transportation systems, and sustainable supply chains, consistently translating complex mathematical frameworks into real-world solutions for industry and government sectors. Analysis of his recent publications reveals dominant trends in quantum-fuzzy hybrid systems, with 78% of 2024-2026 works integrating quantum computing principles with fuzzy logic for optimization problems. Key application domains include secure communications (22% of publications), healthcare analytics (18%), and traffic/logistics systems (15%), demonstrating strategic alignment with Croatia's national technological development priorities.

Bert KappenView profile

Professor

• Quantum Machine Learning
• Stochastic Control Theory
• Neural Networks
• +4 more

Bert Kappen is a Professor of Physics at Radboud University Nijmegen, affiliated with the Department of Biophysics and the Donders Center for Neuroscience. His research focuses on the intersection of physics, machine learning, and neuroscience, with emphasis on quantum machine learning, stochastic control theory, and Bayesian inference methods applied to neural systems. His core research integrates statistical physics and quantum mechanics to develop computational methods for AI, exploring how intelligence emerges in biological systems. Key areas include: Quantum Machine Learning : Developing quantum algorithms for Boltzmann machines and exploring quantum advantage in optimization Path Integral Control : Creating efficient solutions for stochastic optimal control problems in robotics and neuroscience Bayesian Inference : Building probabilistic models for medical diagnosis and DNA identification (e.g., Bonaparte system used by Interpol) Atomic-scale Neural Networks : Implementing neuromorphic computing using nanoscale atomic switches Kappen leads an active research group with multiple PhD and Master's students, working on projects ranging from quantum perceptrons to multi-agent UAV control. His publication output shows strong recent focus on quantum computing applications (2020-2025), atomic-scale machine learning implementations, and advanced control theory. He teaches courses including Introduction to Machine Learning , Advanced Computational Neuroscience , and Statistical Machine Learning at Radboud University. Externally funded projects include EU FP7 initiatives (NETT, CompLACS), NWA Quantum Learning, and collaborations with Thales Nederland.

Bakhrom OripovView profile

Research Fellow

• Superconducting Single-Photon Detector Arrays
• Superconducting Electronics
• Neuromorphic Hardware
• +3 more

Bakhrom Oripov is a Research Fellow at the University of Colorado Boulder , affiliated with the Quantum Nanophotonics Group , and a Researcher at the Applied Physics Division of the National Institute of Standards and Technology (NIST) in Boulder, Colorado. B.S. in Physics, Bogazici University, 2012 Ph.D. in Condensed Matter Physics, University of Maryland, 2020 His research focuses on Superconducting Single-Photon Detector Arrays , Superconducting Electronics , and Neuromorphic Hardware , with applications in UV astronomy , bio-optical devices , and energy-efficient AI . He develops scalable readout architectures for mega-pixel superconducting nanowire arrays and explores general training techniques for hardware neural networks . Recent publications highlight advancements in thermally coupled imager designs and gradient descent methods without backpropagation , emphasizing low-energy data routing and bio-inspired hardware scalability . His work has been featured in Nature and Applied Physics Letters as a Cover of the Issue and Featured Article .

Sun-Yuan KungView profile

Professor

• Machine Learning
• Deep Learning
• Neural Networks
• +7 more

Sun-Yuan Kung is a Professor of Electrical and Computer Engineering at Princeton University specializing in high-performance learning networks and explainable AI. His research develops beyond back-propagation paradigms to create optimal neural architectures through structural learning and discriminant information metrics. His academic credentials include: Ph.D. in Electrical Engineering, Stanford University (1977) M.S. in Electrical Engineering, University of Rochester (1974) B.S. in Electrical Engineering, National Taiwan University (1971) Professor Kung pioneered Explainable Neural Networks (XNN) incorporating Internal Neuron's Learnability (INL) using internal teacher labels and discriminant information (DI) for node/layer ranking. This enables deep compression while enhancing model robustness and adaptability for real-time applications in image processing, privacy protection, and security-sensitive environments. His work directly addresses DARPA's Explainable AI (XAI) initiatives through end-user-adaptive labeling mechanisms. Recent publications (2023-2025) demonstrate sustained innovation in efficient deep learning architectures, with dominant themes in remote sensing applications (object detection, image captioning, change detection), quantum-inspired algorithms, and multimodal representation learning. Key trends include neural architecture search, model compression techniques, and reinforcement learning integration for specialized domains. His distinguished honors include: Life Fellow of IEEE (2016) IEEE Third Millennium Medal (2000) IEEE Signal Processing Society Best Paper Award (1996) IEEE Signal Processing Society Technical Achievement Award (1992) IEEE Fellow (1988) Professor Kung mentors graduate researchers including Katherine Shu-Min Li, Xiaxin Shen, and Zhuoqing Song, with funding supporting projects in deep compression, privacy-preserving ML, and XAI frameworks. His group develops foundational techniques for discriminant information-based network pruning and structural gradient methods. He leads a research team advancing the XNN framework for DARPA's Explainable AI initiatives, focusing on real-time decision support through internal neuron explainability and channel exploration for model optimization.