Find Academic Supervisors

• Computer Vision
• Deep Learning
• Semantic Segmentation
• +3 more

Dr. Guangliang Cheng is an Associate Professor in the Department of Computer Science at the University of Liverpool. His research focuses on deep learning, computer vision, and perception algorithms with applications in remote sensing, medical imaging, and autonomous systems. Prior to his current role, he served as a vice research director in the Autonomous Driving Group at SenseTime and completed postdoctoral research at the Aerospace Information Research Institute, Chinese Academy of Sciences. Ph.D. in Pattern Recognition from the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) Postdoctoral Researcher at Aerospace Information Research Institute, Chinese Academy of Sciences (2017–2019) Dr. Cheng’s research integrates computer vision and deep learning to address challenges in semantic segmentation, domain adaptation, and robust detection. Recent work explores wavelet-based multimodal fusion for remote sensing and attention-guided architectures for medical imaging. His 2025 publications span journals like GIScience & Remote Sensing and Knowledge-Based Systems , emphasizing scalable solutions for geospatial and biomedical applications. In 2025, Dr. Cheng’s article trends highlight remote sensing semantic segmentation, cross-domain medical imaging, and drone-based fire detection. His collaborations span institutions such as SenseTime, Chinese Academy of Sciences, and University of Liverpool teams, focusing on frequency-domain fusion, attention mechanisms, and GPU optimization. As a supervisor, Dr. Cheng seeks highly motivated PhD students to join projects supported by scholarships including the Centres for Doctoral Training (CDT) and Duncan Norman Scholarship. He serves as Module Co-ordinator for COMP338: Computer Vision (2024–2025) and actively reviews for top-tier journals and conferences.

Timothy M. JonesView profile

Professor

• Compilers
• Computer Architecture
• Binary Modification
• +3 more

Timothy M. Jones is a Professor of Computer Architecture and Compilation at the University of Cambridge Computer Laboratory, where he leads research in systems-level computing. He is also a Fellow at Gonville and Caius College, contributing to academic leadership and student mentorship within the collegiate system. His primary affiliation with the Computer Laboratory positions him at the forefront of systems research within the university. Dr. Jones's research focuses on extracting various forms of parallelism (thread-level, data-level, memory-level) to enhance computational performance while addressing energy efficiency and reliability challenges. His work spans compiler design, binary translation, and microarchitecture optimization, with specific interest areas including: Compiler technologies for functional and parallel programming Hardware reliability and fault tolerance mechanisms Binary analysis and instrumentation frameworks Memory system optimization and virtual memory management Security enhancements through binary modification Runtime systems for heterogeneous architectures Analysis of his recent publications reveals strong emphasis on systems-level innovation, particularly in fault tolerance techniques, binary analysis tools, memory optimization, and parallel execution frameworks. His work consistently bridges theoretical computer science with practical hardware implementation challenges. Dr. Jones maintains active participation in the academic community through conference leadership roles, including serving as Program Co-Chair for CGO 2026 and committee positions at premier venues including ISMM, CGO, and ECOOP. He contributes to open-source academic resources through GitHub and maintains professional engagement via Twitter.

Pierre MarionView profile

Researcher

• Deep Learning Theory
• Optimization of Neural Networks
• Generative Models
• +2 more

Pierre Marion is a Researcher at INRIA Paris, working within the Sierra research team since September 2025. His work focuses on the theoretical foundations of deep learning and he is beginning to explore applications of AI in mathematics. Marion has established collaborations across multiple institutions including EPFL, Sorbonne Université, and Google DeepMind. His educational background includes: Engineering degree from École polytechnique (2015-2018) with specialization in Applied Mathematics Master's degree from Sorbonne Université (2019-2020) PhD from Sorbonne Université (2020-2023) under the supervision of Gérard Biau and Jean-Philippe Vert Postdoctoral research at EPFL (2024) supervised by Lénaïc Chizat Marion's research interests primarily focus on the theory of deep learning, where he investigates the optimization and statistical properties of various neural network architectures. His work spans from shallow networks to complex generative models, with a particular emphasis on understanding the mathematical foundations that govern deep learning performance. Recently, he has begun exploring applications of AI in mathematical research, aiming to bridge the gap between theoretical machine learning and mathematical discovery. His research often combines rigorous theoretical analysis with practical implications for training deep neural networks. Analysis of Marion's recent publications reveals several key trends in his research. He has made significant contributions to understanding the role of large learning rates in optimization dynamics, demonstrating how they can accelerate convergence in logistic regression and prevent memorization in score-based generative models. His work on attention mechanisms has provided theoretical guarantees for their effectiveness in specific tasks like single-location regression and clustering. Additionally, Marion has extensively studied the connections between residual networks and neural ordinary differential equations , establishing generalization bounds and exploring scaling properties in the large-depth regime. His earlier work included contributions to natural language processing and quasi-Monte Carlo methods, reflecting a broad mathematical foundation that informs his current deep learning research. Marion has received several notable scientific awards: Runner-up PhD Award of AFIA (French Association for Artificial Intelligence) in 2024 Google PhD Fellowship in 2022 Ecole polytechnique Grand Prize of Research Internships in 2018 As an advisor, Marion currently supervises PhD student Yu-Han Wu (since 2024), with whom he has co-authored multiple publications on large learning rates and denoising score matching. Previously, he co-supervised several Master's students including Seorim Park, Yerkin Yesbay, and Nathan Doumèche. Marion has been actively involved in the machine learning community through conference organization (NeurIPS@Paris meetups), session chairing (ICSDS 2022), and extensive reviewing activities. He has served as a reviewer for top journals including JASA and Annals of Statistics, and conferences including NeurIPS and ICLR, where he was recognized as a top reviewer at NeurIPS 2023. Marion is a member of the Sierra research team at INRIA Paris, which focuses on machine learning theory and applications. He has also collaborated with researchers at CREST (Center for Research in Economics and Statistics), as evidenced by his participation in seminars organized by Anna Korba and Karim Lounici. His work often bridges theoretical computer science, statistics, and applied mathematics, reflecting the interdisciplinary nature of modern machine learning research.

Ioachim PupezaView profile

Professor

• Optical Spectroscopy
• Field-Resolved Spectroscopy
• Precision Optical Measurements
• +6 more

Prof. Dr. Ioachim Pupeza serves as Group Leader in the Department of Spectroscopy/Imaging at the Leibniz Institute of Photonic Technology (Leibniz-IPHT) in Jena, Germany. His research focuses on advanced optical measurement techniques, particularly in the field of field-resolved spectroscopy and precision optical measurements. Dr. Pupeza's research interests center around optical spectroscopy with a particular emphasis on field-resolved techniques that capture the complete electric field waveform of light-matter interactions. His work spans infrared spectroscopy , molecular fingerprinting , ultrafast laser technology , and precision optical measurements . He has made significant contributions to electro-optic sampling techniques, which enable characterization of electric-field waveforms across the terahertz to visible spectral range. His research also extends to mid-infrared light generation , terahertz spintronic emitters , and cavity-enhanced spectroscopy , with applications ranging from fundamental physics to medical diagnostics. Analysis of Dr. Pupeza's recent publications reveals a strong trend toward increasingly sophisticated field-resolved spectroscopy techniques with applications in both fundamental science and practical diagnostics. His work has evolved from basic measurement techniques to applications in cancer detection through molecular fingerprinting of biofluids. A consistent theme across his publications is the pursuit of higher precision, broader bandwidth, and improved sensitivity in optical measurements, often achieving attosecond-level precision. His research bridges physics, engineering, and medical applications, demonstrating how fundamental optical advances can translate to real-world diagnostic tools. Dr. Pupeza leads the research group "Field-Resolved Optical Precision Measurement Methods" at Leibniz-IPHT, which appears to collaborate extensively with other research institutions and groups. His work involves sophisticated laser systems including high-power Yb:YAG thin-disk oscillators, femtosecond enhancement cavities, and dual-oscillator systems for precision measurements. The group's research has implications for molecular spectroscopy, medical diagnostics, and fundamental studies of light-matter interactions at the most fundamental time scales.

• Economics of Artificial Intelligence
• Machine Learning
• Algorithmic Fairness
• +5 more

Yan Huang is an Associate Professor of Business Technologies at the Tepper School of Business, Carnegie Mellon University. She holds a Ph.D. in Information Systems and Management from Carnegie Mellon University (2013) and a B.Sc. (with honors) in Information Systems and Management from Tsinghua University, Beijing, China (2009). Prior to joining Carnegie Mellon University, she served as an Assistant Professor of Technology and Operations at the University of Michigan–Ann Arbor, Ross School of Business (2013-2018). Her educational background includes: B.Sc. (with honors) in Information Systems and Management, Tsinghua University, Beijing, China (2009) Ph.D. in Information Systems and Management, Carnegie Mellon University, Pittsburgh, United States (2013) Dr. Huang's research examines the economic and social impacts of technologies and identifies effective designs and policies for technology-enabled markets and platforms. She employs economic theories, structural modeling, statistical modeling, machine learning methods, and an understanding of the underlying technologies in her research. Her recent work focuses on the economics of artificial intelligence (AI) and machine learning (ML), with particular attention to algorithmic fairness, transparency, and collusion. She is among the first to bring economic and social perspectives to research on fair ML. Additionally, she studies digital platforms and online markets, examining how firms can leverage data-driven strategies to optimize pricing, personalization, and user engagement. Her recent publications demonstrate a strong focus on the intersection of AI/ML with economic principles, particularly in areas like algorithmic bias, pricing strategies, and platform regulation. A significant portion of her work examines how machine learning algorithms impact financial lending decisions, housing markets, and content creation platforms. Her research methodology frequently combines structural econometric modeling with empirical analysis of real-world data, providing both theoretical insights and practical implications for platform design and policy. Dr. Huang has received several prestigious awards for her scholarly contributions: AIS Senior Scholar Best Publication of 2023 Award for "Algorithmic Transparency with Strategic Users" Runner Up, Best Paper Published in Information Systems Research for 2021 for "Crowds, Lending, Machine, and Bias" INFORMS Information Systems Society Sandy Slaughter Early Career Award Finalist, Best Student Paper Award, CIST 2021 for "Human-Algorithmic Bias: Source, Evolution, and Impact" Pounds Fellowship As an active member of the academic community, Dr. Huang serves on various committees at CMU including the MSBA Curriculum Review Committee and the Tepper School Strategic Plan Task Force. She has also held editorial positions for Management Science, Information Systems Research, and the International Conference on Information Systems. Her teaching portfolio includes courses on Human and Algorithmic Bias, Modern Data Management, and PhD-level instruction at the Tepper School.

Miklós KorenView profile

Professor

• International Trade
• Economic Development
• Productivity Analysis
• +5 more

Miklós Koren is a Professor of Economics at Central European University and Senior Research Fellow at the HUN-REN Centre for Economic and Regional Studies. His work bridges international trade , economic development , and managerial economics , focusing on trade policy, productivity spillovers, and the role of managers in development. Ph.D., Harvard University (2005) M.A., Central European University (2000) M.Sc., Budapest University of Economics (1999) His research explores trade facilitation , managerial impact on firm performance , and technological diversification . Recent work includes studies on expatriate managers, pandemic-related business disruptions, and the legacy of communist-era management practices. Key trends in his publications (2020–2024) emphasize managerial mobility and firm productivity (2024) machine learning vs. gravity models (2024) trade volatility and development (2023) data transparency standards (2022) Scientific awards include ERC Starting Grant (2012) Nicholas Káldor Prize (2014) Young Economist Award (2002, 2004) As Data Editor for Review of Economic Studies and Associate Editor for Journal of International Economics , he shapes methodological rigor in empirical research. His 2013 paper on technological diversification remains foundational for understanding volatility in developing economies.

Jonathan CaleView profile

Assistant Professor

• Forest Ecology
• Fungal Ecology
• Forest Pathology
• +4 more

Dr. Jonathan Cale is an Assistant Professor in the Department of Forest Ecology and Management at the University of Northern British Columbia, affiliated with the NRES Graduate Program. His research program merges basic and applied approaches to investigate fungal ecology and forest pathology, focusing on how fungus-tree interactions influence forest health challenges. Specializes in forest fungi roles across disturbance stages: development, impact, and recovery Key interests: emerging forest diseases, climate change impacts on fungi, bark beetle-fungus interactions, chemical defense mechanisms Supervises graduate students in Biology and Forestry (MSc NRES) and serves as a media-contactable expert in Forest Pathology His recent publications address topics such as fungal community dynamics in disturbed forests, chemical defense responses in pines, and bark beetle-fungus coevolutionary mechanisms. No scientific awards are mentioned in the provided text.

Vincent John Mooney IIIView profile

Associate Professor

• Hardware-Software Co-Design
• Cyber Physical Systems Security
• System Level Design
• +7 more

Vincent John Mooney III is an Associate Professor at the School of Electrical and Computer Engineering and an Adjunct Associate Professor at the School of Computer Science, Georgia Institute of Technology. His research focuses on Hardware-Software Co-Design , Cyber Physical Systems Security , and Low-Power Architectures . He has authored numerous publications on topics such as probabilistic computing, hardware security, and embedded systems design. Dr. Mooney has received prestigious awards including the NSF Career Award , National Semiconductor Fellowship , and ARCS Best Paper Award . Education: Ph.D. in Electrical Engineering (1998), Stanford University MA in Philosophy (1997), Stanford University MS in Electrical Engineering (1994), Stanford University Certificate of Graduate Study (1992), University of Navarra BS in Electrical Engineering and Computer Science (1991), Yale University Research interests span hardware/software codesign, cybersecurity in embedded systems, and synthesis of reconfigurable architectures. His recent work includes Gridtrust for decentralized supply chain cybersecurity and COPPER for computation obfuscation. Dr. Mooney has supervised numerous Ph.D. students and held leadership roles in conferences such as HOST and CASES . Scientific awards include NSF Career Award (2000) National Semiconductor Fellowship (1997-1998) AT&T Engineering Scholarship Program (1987-1991) NCAA Postgraduate Scholar (1991) Senior Member, IEEE (2003) ARCS 2012 Best Paper Award Advising and grants highlight his mentorship of students like Jun Cheol Park and Yudong Tan , along with grants such as the U.S. Air Force Summer Faculty Fellowship (2007). He leads the Hardware/Software Codesign for Security Group at Georgia Tech and has contributed to advancements in real-time operating systems and deadlock detection algorithms.

Noura LimamView profile

Assistant Professor

• Network Management
• SDN/NFV
• Network Security
• +3 more

Noura Limam is a Research Assistant Professor at the University of Waterloo's Cheriton School of Computer Science. Her research spans network operations, with emphases on software-defined networking (SDN), 5G/6G architectures, network security, and autonomous network management. Recent work focuses on AI-driven solutions for encrypted traffic analysis, network slicing security, and satellite communication systems. She develops frameworks like Monarch for network slice monitoring and 5Guard for secure slicing. Contributions include blockchain-assisted authentication protocols, meta-reinforcement learning for threat mitigation, and novel handover mechanisms for non-terrestrial networks. Her publications demonstrate consistent innovation in making networks more adaptive, secure, and efficient.

• Gender Studies
• Ageing and Care Policy
• Work-Life Balance
• +5 more

Associate Professor Myra Hamilton is a Principal Research Fellow at the University of Sydney Business School, specializing in gender, ageing, and care. She holds a BA (Hons 1) and PhD from the University of Sydney. Her research focuses on work/care reconciliation, policy needs of carers, and retirement income inequalities. She is a member of the Sydney Centre for Healthy Societies and Sydney Policy Lab, and serves on the Board of COTANSW. Education: BA (Hons 1), University of Sydney PhD, University of Sydney Research Interests: Myra’s work examines how policies and services can improve wellbeing in work and care across lifecourses. Key areas include unpaid care responsibilities, retirement income disparities, and intergenerational relationships. She combines academic research with applied policy projects for governments and NGOs. Articles Trends: Recent publications emphasize multigenerational workforce policies, grandparent childcare dynamics, and barriers to formal aged care access. Her 2024 book The Multigenerational Workforce explores age and gender in workplaces. Grants & Advising: Current grants include 'New models of replacement care for working carers' (ARC) and 'Women in Construction Industry Innovation Program'. She has supervised students on topics like aged care services and mature-age labor markets. Labs/Teams: Active in the ARC Centre of Excellence in Population Ageing Research and collaborates with organizations like the Social Policy Research Centre (UNSW).

Reuben BinnsView profile

Associate Professor

• Data Protection
• Machine Learning
• AI Ethics
• +5 more

Dr. Reuben Binns is an Associate Professor of Human Centred Computing at the University of Oxford , where he investigates intersections between computer science, law, and philosophy. His research focuses on data protection , machine learning ethics , and regulation of technology .

Mendel RosenblumView profile

Professor

• System Software
• Distributed Systems
• Computer Architecture
• +4 more

Mendel Rosenblum is the Cheriton Family Professor and holds dual appointments as Professor in the Departments of Computer Science and Electrical Engineering at Stanford University. He is a co-founder of VMware Inc. and served as its Chief Scientist for its first decade, playing a pivotal role in designing foundational virtualization technologies. Rosenblum's research focuses on system software, distributed systems, and computer architecture, with notable contributions to virtualization, data center networks, and operating systems. He leads the Platform Lab at Stanford, exploring next-generation data center technologies and high-performance computing systems. Administrative Role: Faculty Director of Stanford Computer Forum (2012–present) Education: PhD (UC Berkeley, 1992), MS (UC Berkeley, 1989), BA (University of Virginia, 1984) His research interests span disk storage management, computer simulation, scalable operating systems, and security. Recent work emphasizes deployable consensus algorithms, programmable smartNICs, and self-programming networks. Rosenblum has authored over 80 publications and holds multiple patents in virtualization and system software. Awards & Recognition: ACM System Software Award (2009) IEEE Computer Entrepreneur Award (2011) ACM Thacker Breakthrough in Computing Award (2018) Member, National Academy of Engineering (2013) He advises PhD and Master's students, including current advisees Sina Jandaghi Semnani and Zixi Liu. Rosenblum teaches advanced courses on web applications, distributed systems, and independent research projects.

Vivek NallurView profile

Assistant Professor

• Machine Ethics
• Multi-Agent Systems
• Complex Systems
• +5 more

Dr. Vivek Nallur is an Assistant Professor in the School of Computer Science at University College Dublin. His research focuses on Machine Ethics, Multi-Agent Systems, emergence in socio-technical systems, and decentralized adaptation mechanisms. He holds a PhD from the University of Birmingham and advanced certifications in university teaching from UCD. Education: BBS, Delhi University Post-Graduate Diploma in Advanced Software Technology, National Centre for Software Technology MS, Carnegie Mellon University PhD, University of Birmingham Prof Cert University Teaching & Learning, University College Dublin Research Interests: Machine Ethics explores ethical decision-making in autonomous systems, while Multi-Agent Systems (MAS) are his primary tool for studying self-adaptation and emergence. He serves on key committees like IEEE P7008 (Ethically Driven Nudging) and the AAAI 2021 Spring Symposium on AI Ethics. His work bridges computer science with philosophy, law, and social sciences. Advising & Grants: Supervises three PhD students focusing on ethical AI, elder-care robotics, and AI governance. Leads the Machine Ethics Research Group and participates in the ML-Labs SFI grant (2019–2027). Professional Roles: Senior Member of IEEE, organizer for AAAI ethics symposia, and contributor to OpenAAL's ELSI panel. Teaches modules on Machine Learning, Operating Systems, and Web Development.

Wolfgang LorenzonView profile

Professor

• Particle Physics
• Nuclear Physics
• Astrophysics
• +4 more

Wolfgang Lorenzon is a Professor of Physics at the University of Michigan, specializing in experimental particle physics, nuclear physics, and astrophysics. His research spans three major experimental programs: the LUX-ZEPLIN (LZ) dark matter experiment at SURF, the MUSE experiment at PSI for proton radius measurements, and the SpinQuest collaboration at Fermilab studying hadronic physics. He has held significant roles in major collaborations including SeaQuest and HERMES, where he served as Deputy Spokesman from 1997-1998. His educational background includes a Ph.D. (1988) and Diploma (1984), both from the University of Basel. Lorenzon has built a distinguished research career focusing on precision measurements in particle and nuclear physics, with particular expertise in detector development and experimental techniques. Lorenzon's research interests center on fundamental questions in particle physics. His work on the LZ experiment involves developing the in-line radon removal system for the central time-projection chamber, crucial for enhancing the detector's sensitivity to WIMPs. At PSI, he leads the development of liquid hydrogen targets for the MUSE experiment, which aims to resolve discrepancies in proton charge radius measurements. His hadronic physics work with SeaQuest and SpinQuest focuses on understanding nucleon structure through antiquark distributions and polarized Drell-Yan processes. His research bridges theoretical questions with cutting-edge experimental techniques, often requiring innovative detector solutions. Analysis of his recent publications (2023-2025) reveals a strong focus on dark matter detection using liquid xenon technology, precision measurements of nucleon structure, and development of next-generation detectors. His work spans theoretical interpretation of experimental results, detector development, and analysis of fundamental particle interactions. The research shows increasing collaboration across international boundaries, with significant contributions to multiple major experiments simultaneously. Scientific Awards: Fellow of the American Physical Society Lorenzon has mentored numerous graduate students through completion of their Ph.D. degrees, with recent graduates including Haley Reid (2024), Noah Wuerfel and Chami Amarasinghe (2023), Maris Arthurs (2022), Marshall Scott (2020), and Daniel Morton (2019). His current research group includes postdocs, graduate students, and undergraduate researchers. His research is supported by multiple grants from the National Science Foundation (Grant 2110229) and the Department of Energy (Grant SC0019193 and Subcontract 734299), as well as University of Michigan funding. Lorenzon leads a research group with active laboratories at both the Homer A. Neal Laboratory (3265 HANL) and West Hall (357 WH) at the University of Michigan. His team collaborates with international groups at Fermilab, SURF in South Dakota, and the Paul Scherrer Institute in Switzerland. The group maintains strong connections with the LZ collaboration, MUSE experiment, and SpinQuest collaboration, contributing both technical expertise and physics analysis capabilities to these major international efforts.

David JacobsView profile

Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

David W. Jacobs is a Professor in the Department of Computer Science at the University of Maryland, with a joint appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS). He holds a Ph.D. from MIT (1992) and has expertise in computer vision, machine learning, and deep learning. His research focuses on object recognition, lighting analysis, and applications like electronic field guides (e.g., Leafsnap and Birdsnap). He has been recognized with awards including the Honda Initiation Grant (2007) and the Edward O. Wilson Biodiversity Technology Pioneer Award (2011). Jacobs has taught courses such as CMSC 422 (Machine Learning) and CMSC 828L (Deep Learning), and advised multiple Ph.D. students. His work spans theoretical advancements and practical applications, including collaborations with institutions like the Smithsonian and Columbia University. Education: B.A. Yale, M.S./Ph.D. MIT Positions: Interim Director of UMIACS (2018), Program Co-Chair CVPR Key Projects: Leafsnap (1M+ downloads), Birdsnap Awards: CVPR Best Paper Honorable Mention (2000), Eurographics Best Paper (2016)