Find Academic Supervisors

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +3 more

Ioanna Ntinou is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. She teaches the Artificial Intelligence undergraduate module, focusing on core AI techniques like search algorithms, logic, and decision theory. Her research emphasizes computer vision, deep learning, and action recognition in videos, with contributions to spatio-temporal modeling, generative adversarial networks, and human pose estimation. Her work bridges theory and application, addressing challenges in video analysis, emotion understanding, and biomedical signal processing. Research interests include advanced computer vision techniques, machine learning frameworks for temporal data, and AI-driven solutions for real-world problems. Recent work explores multiscale vision transformers for action localization and transfer learning in facial expression analysis. Teaching focuses on foundational AI concepts, preparing students to design intelligent agents and apply AI tools. No grants or awards are explicitly listed in the provided materials.

Constantine DovrolisView profile

Professor

• Network Science
• Data Mining
• Computational Science
• +3 more

Constantine Dovrolis is a Professor at the School of Computer Science, Georgia Institute of Technology. His affiliations include Machine Learning @ GT and the Online Master of Science in Computer Science (OMSCS) program. He holds a Computer Engineering degree from Technical University of Crete (1995), an M.S. from the University of Rochester (1996), and a Ph.D. from the University of Wisconsin-Madison (2001). His research focuses on cross-disciplinary applications of network analysis and data mining in neuroscience and biology, alongside prior work on Internet economics, network measurement, and protocol adoption. Recent research trends emphasize neuro-inspired machine learning algorithms (e.g., continual learning, sparse networks) and computational methods for biological networks (e.g., brain connectomics, C. elegans analysis). Notable projects include Pythia (network performance diagnosis), GENESIS (agent-based interdomain network modeling), and contributions to climate science via network analysis of CMIP5 models. He has also explored Internet peering dynamics and optimization of network topologies (e.g., water distribution networks). His work bridges theory and practice, with applications spanning computational neuroscience, environmental modeling, and telecommunications infrastructure. Active collaborations include the PuNDIT project for network monitoring and PerfSONAR for performance analysis.

• Medical Imaging
• Biomedical Engineering
• Computational Biology
• +5 more

Sang Hoon Ahn is a Clinical Associate Professor of Medicine at the University of Southern California (USC) and serves as Director of Keck Medicine of USC in Buena Park and Koreatown. His academic affiliation lies within the Department of Medicine at the School of Medicine. Research Interests: His work focuses on advanced medical imaging techniques, including: Fluorescence Molecular Tomography Time-of-Flight PET Imaging Iterative Image Reconstruction Algorithms Fourier Rebinning for Medical Imaging Optimization of Illumination Patterns in Biomedical Imaging Computational Modeling for Radiology Applications Publications: His research spans biomedical imaging, computational modeling, and medical physics, with a focus on optimizing imaging techniques and algorithm development. Contact: sanghoon.ahn@med.usc.edu

Elahe AraniView profile

Assistant Professor

• Continual Learning
• Self-Supervised Learning
• Scene Understanding
• +6 more

Elahe Arani is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. She also holds external positions as Head of AI Research at Wayve (since October 2023) and previously served as Senior AI Manager and Senior Research Scientist at Wayve from September 2020 to September 2023. Her research interests are centered around Continual Learning, Self-Supervised Learning, and Learning under Noisy Labels. She focuses on developing algorithms for efficient, reliable, and adaptable AI models, particularly in the context of Scene Understanding and Multi-Task Learning. Her work also explores the application of AI in Autonomous Vehicles and integrates insights from Neuroscience to design more biologically plausible AI systems. Key areas include improving generalization in neural networks, mitigating catastrophic forgetting, and leveraging shape-awareness for robust model training. Elahe Arani's recent publications highlight advancements in Continual Learning and Self-Supervised Learning techniques, with a focus on improving model efficiency and adaptability. Her work also delves into applications for Autonomous Vehicles, such as vision-language alignment in SimLingo and generative world models in Gaia-2. She has contributed to frameworks addressing catastrophic forgetting and neural network optimization, often combining biological plausibility with computational methods. No scientific awards are explicitly mentioned in the provided text. No supervised students or specific grant information is listed. Her research emphasizes practical applications and theoretical contributions to AI, with a notable focus on reducing data dependency and environmental impact through efficient model designs. While no specific lab or team names are mentioned, her research involves collaborations in AI-driven systems, including work on road maintenance inspection and autonomous driving technologies. These collaborations aim to bridge academic and industry applications of AI.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +5 more

Jian Chen is a prolific academic researcher with affiliations across multiple institutions worldwide, primarily in China but also including international universities. The DBLP disambiguation page lists 47 distinct individuals with this name, working in various departments including Computer Science, Electrical Engineering, Telecommunications Engineering, and other technical fields at prestigious institutions such as Zhejiang University, Xidian University, Northeastern University, and Ohio State University. Research interests span a wide range of technical domains including artificial intelligence, machine learning, computer vision, network security, medical imaging, and electrical engineering. Recent publications indicate active research in deep learning applications, image processing, trajectory analysis, and biomedical informatics. The publication output is substantial, with numerous papers appearing in IEEE Access, Pattern Recognition, and other high-impact journals. The research trends show a strong focus on practical AI applications across multiple domains, with particular emphasis on medical imaging, network security, and intelligent control systems. Many publications involve collaborative work with researchers across different institutions, indicating an active research network. While specific awards are not listed in the provided DBLP information, the volume and quality of publications suggest recognition within the academic community. The research output demonstrates expertise in both theoretical development and practical implementation of computational methods. Jian Chen appears to be actively mentoring students, as evidenced by the numerous collaborative publications, though specific student names aren't detailed in the DBLP records. The research spans both fundamental algorithm development and applied problem-solving across various engineering and computer science domains.

• Text Mining
• Information Retrieval
• Machine Learning
• +2 more

Jean-Charles Lamirel is a Researcher affiliated with the University of Strasbourg, University of Tartu, and the SYNALP Team at LORIA in Nancy. His research focuses on developing advanced statistical methods for text analysis and knowledge discovery in evolving data environments. Developed novel feature maximization techniques for clustering and categorization of textual data, addressing challenges in high-dimensional, heterogeneous information spaces. These methods enable efficient analysis of dynamic datasets through incremental learning approaches. Research contributions include innovative algorithms for knowledge extraction from scientific texts, semantic analysis, and adaptive information retrieval systems.

Jiangpeng HeView profile

Research Fellow

• Continual Learning
• Long-Tail Learning
• AI for Healthcare
• +2 more

Jiangpeng He is a Research Fellow in Mechanical Engineering at MIT, working on rehabilitation robotics with Dr. Hermano Igo Krebs. Previously an Adjunct Professor at Purdue University, he holds a PhD in Electrical and Computer Engineering from Purdue and a bachelor's from the University of Electronic Science and Technology of China. He will join Indiana University Bloomington as Assistant Professor in Fall 2025. His research focuses on developing AI systems for healthcare applications, particularly continual learning approaches for visual recognition tasks. Current projects include food recognition for dietary monitoring and rehabilitation technology development. His publications demonstrate consistent focus on machine learning applications in healthcare, with recent advances in continual learning architectures and their implementation in medical domains. Work spans theoretical algorithms to clinical applications in motor assessment and rehabilitation. Awards include: Top-5 Postdoctoral Mentoring Award (Purdue University) Outstanding Reviewer for CVPR 2024

Richard PengView profile

Associate Professor

• Algorithms
• Theoretical Computer Science
• Graph Theory
• +4 more

Richard Peng is an Associate Professor in the Computer Science Department at Carnegie Mellon University, part of the School of Computer Science. He specializes in designing efficient algorithms for fundamental computational problems, particularly in graph algorithms, dynamic algorithms, and linear algebraic computations. Prior to joining CMU in 2023, he earned his BMath from the University of Waterloo, a PhD from CMU under Gary L. Miller, and completed a postdoc at MIT's Applied Math department. His research focuses on advancing algorithmic efficiency, including work on sparse linear systems, graph convolutions, and flow optimization. He advises PhD students Hoai-An Nguyen, Alicia Stepin, and Junzhao Yang. His teaching includes courses such as 15495 and 15151, reflecting his engagement in both research and education. Peng’s articles span topics like approximate spanning tree counting, dynamic graph algorithms, and Laplacian solvers, emphasizing practical scalability and theoretical guarantees. His contributions bridge theoretical computer science with applied challenges in network analysis and numerical computation.

Jelena DiakonikolasView profile

Assistant Professor

• Large-Scale Optimization
• Machine Learning
• Networked Systems
• +3 more

Jelena Diakonikolas is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin–Madison, within the School of Computer, Data & Information Sciences. Her research focuses on large-scale optimization with applications to machine learning and networked systems. She explores algorithm design, convergence analysis, and robust learning techniques, with contributions to optimization theory and practical implementations in wireless networks and distributed systems. Her work spans theoretical advancements, such as analyzing convergence properties of incremental methods and variance-reduced algorithms, alongside applied research in robust learning, adversarial noise mitigation, and resource allocation in wireless networks. Notable areas include fixed-point equations, primal-dual methods, and block-coordinate optimization techniques. She has authored numerous papers on topics like stochastic gradient descent, variational inequalities, and distributionally robust optimization. Dr. Diakonikolas' research also intersects with wireless communication systems, including full-duplex networking and integrated circuit design. Her contributions address challenges in full-duplex systems, such as interference cancellation and resource allocation. She has explored fairness and delay in heterogeneous networks, as well as energy harvesting in wireless networks.

• Robotic Imaging
• Computational Imaging
• Light Field Processing
• +1 more

Donald Dansereau is a Senior Lecturer in the School of Aerospace, Mechanical and Mechatronic Engineering at the University of Sydney and Perception Theme Lead for the Sydney Institute for Robotics and Intelligent Systems. His research develops novel imaging systems for robotic perception. His work pioneers light field and computational imaging techniques to enable robust robotic vision in challenging environments. Current projects address underwater imaging, low-light conditions, space applications, and privacy-preserving vision systems. Recent advances include event-based satellite docking simulations, adaptive neural radiance fields for 3D reconstruction, and task-specific camera optimization frameworks. Key research directions include: Real-time light field processing for robotic navigation Neural rendering techniques for scene understanding Privacy-preserving computer vision architectures Hardware-software co-design for specialized imaging He received the Best Paper Award at ACRA (2014) and Distinguished Poster Awards from Stanford SCIEN (2016, 2017). Teaching includes Mechatronic Design and System Design courses.

• Compiler Optimization
• Static Program Analysis
• Datalog Systems
• +5 more

Professor Bernhard Scholz is a faculty member in the School of Computer Science at the University of Sydney . He specializes in compiler optimization, static program analysis, Datalog systems, blockchain technology, and parallel processing. His research focuses on improving compiler efficiency, analyzing complex programs, and developing tools for smart contract security. He holds a professorial position and has been involved in significant projects like the Fantom blockchain partnership, which funds specialized blockchain research. His work includes developing the Soufflé Datalog compiler and tools like MadMax for smart contract analysis. Research interests span compiler design, formal verification, distributed systems, and energy-efficient computing. His recent work emphasizes blockchain security, Datalog scalability, and parallel execution optimization. Notable contributions include EVMTracer for Ethereum analysis and Julia Cloud Matrix Machine for cloud-based matrix computations. Key grants include ARC Discovery Projects on heterogeneous systems and smart contracts. Awards include industry partnerships and sustained research funding. He advises on large-scale program analysis and has collaborated globally with institutions like ETH Zurich and Fantom Foundation. Labs and collaborations include the University of Sydney's Blockchain Lab and Fantom-funded projects. His work bridges theoretical compiler research with practical applications in security and distributed systems.

Peyman MoghadamView profile

Adjunct Associate Professor

• Self-Supervised Learning for Robotics
• Embodied AI
• 3D Multi-Modal Perception (3D++)
• +4 more

Peyman Moghadam is an Adjunct Associate Professor at the University of Queensland (UQ) and a Principal Research Scientist at CSIRO Data61. He also holds an adjunct professorship at Queensland University of Technology (QUT). His roles include leading the Embodied AI Research Cluster at CSIRO Data61 and overseeing the Spatiotemporal AI portfolio within CSIRO's Machine Learning and Artificial Intelligence (MLAI) Future Science Platform. He has held visiting appointments at ETH Zürich (2022) and the University of Bonn (2019). His research focuses on self-supervised learning for robotics, embodied AI, 3D multi-modal perception, and computer vision applications in robotics and environmental science. Education details are not explicitly provided in the text, but his professional experience and research output suggest advanced academic training in robotics, computer science, and machine learning. Awards include the CSIRO Julius Career Award, National and Queensland iAwards, and the Lord Mayor's Budding Entrepreneurs Award. He has led large-scale interdisciplinary projects and published extensively in top-tier journals and conferences. His research themes span robotics perception, AI-driven environmental modeling, and sensor fusion. Notable contributions include benchmark datasets (e.g., WildScenes), novel algorithms for LiDAR place recognition, and geo-encoded transformers for plant species prediction. His work bridges robotics, machine learning, and real-world applications in agriculture, environmental monitoring, and autonomous systems. Awards: CSIRO Julius Career Award, Collaboration Medal, National/Queensland iAwards, Lord Mayor's Budding Entrepreneurs Award Grants & Projects: Led multidisciplinary projects in robotics, AI, and environmental science funded by CSIRO and industry collaborators Labs/Teams: Embodied AI Research Cluster (CSIRO Data61), Spatiotemporal AI portfolio (CSIRO MLAI)

Casey KenningtonView profile

Associate Professor

• Natural Language Processing
• Artificial Intelligence
• Robotics
• +3 more

Casey Kennington, Ph.D., is an Associate Professor in the Department of Computer Science at Boise State University. He specializes in Natural Language Processing (NLP), Robotics, and Human-Robot Interaction. His research focuses on dialogue systems, language model learning, and embodied AI, with a particular emphasis on creating practical applications for educational and healthcare contexts. He leads the Speech, Language, & Interactive Machines (SLIM) research group. Education: B.S. in Computer Science from Brigham Young University, M.Sc. in Computational Linguistics (Saarland University, Germany) and Cognitive Science (University of Lorraine, France), and Ph.D. in Computer Science from Bielefeld University, Germany. Research Interests: Developing language models for efficiency and interpretability, multimodal learning, and improving accessibility of NLP tools for children. His work bridges theory and application, with projects like HADREB (emotional robot behaviors) and KidSpell (child-focused spellchecking). Key Contributions: NSF CAREER Award recipient, creator of the AI Science degree program at Boise State, and developer of the SLIM group's dialogue systems. His publications span computational linguistics, robotics, and educational technology. Grants & Awards: National Science Foundation CAREER Award (research on language models and dialogue systems). Teaching: Courses in NLP, Deep Learning, and Data Science. Labs/Teams: Leads the SLIM Group, advancing research in interactive dialogue systems and robot communication.

• Multi-objective optimization
• Artificial intelligence
• Renewable energies
• +4 more

Ouahib GUENOUNOU is a Researcher-Lecturer at CESI (Conservatoire d'Enseignement des Industries) in Le Mans, France. His primary research focuses on multi-objective optimization, artificial intelligence, renewable energy systems, and modeling/control of engineering systems. He holds a PhD in Automatic Systems from Toulouse University (2009), a Master's in Automatic and Signal Processing from Bejaia University (2003), and an Engineering degree in Electronics (1999). His work integrates fuzzy logic, genetic algorithms, and particle swarm optimization (PSO) to address challenges in photovoltaic energy conversion, MPPT (Maximum Power Point Tracking), and control systems. Key contributions include self-adaptive MPPT algorithms, hybrid energy systems optimization, and TSK fuzzy model simplification. He has published extensively in IEEE journals and conferences, with notable work on real-time implementation of fuzzy controllers and multi-objective optimization frameworks. GUENOUNOU teaches Electronic Control and Photovoltaic Energy at CESI, reflecting his expertise in applying theoretical models to practical engineering problems. His research team, Engineering and Numerical Tools, emphasizes computational methods for energy systems and control engineering.

• Automatic Control
• Systems Engineering
• Biomedical Signal Processing
• +4 more

William PASILLAS-LEPINE is a Senior Researcher at CentraleSupélec’s Laboratoire des signaux et systèmes (L2S), focusing on automatic control, systems engineering, and their applications in automotive, biomedical, and energy systems. His work bridges theoretical advancements in control systems with practical implementations in domains such as electric vehicles, biomedical monitoring, and power electronics. He is affiliated with L2S’s MODESTY and SYCOMORE teams, specializing in modeling, observer design, and robust control strategies. Research Interests : His research emphasizes automatic control theory, nonlinear systems, and hybrid control architectures. Key areas include observer-based stabilization, robust control under sensor imperfections, and applications in automotive systems (ABS, electric power steering) and biomedical engineering (respiratory effort estimation, neural population modeling). Publications : Over 15 peer-reviewed articles in journals like IEEE Transactions on Automatic Control and conferences such as CDC, showcasing contributions to hybrid control systems, biomedical signal processing, and dynamic models for electric vehicles. Recent work includes non-invasive respiratory monitoring and robust control under feedback delays. Collaborations & Patents : He co-authored a patent (FR3134928) on polyphase electrical systems for vehicles, reflecting industry-relevant innovations. His interdisciplinary projects span automotive safety, energy-efficient power converters, and neural systems modeling. Labs/Teams : Active member of L2S’s teams focusing on Modélisation pour la commande des Systèmes Dynamiques (COMEDY) and Commande robuste sous contraintes (SYCOMORE), advancing both theoretical and applied control methodologies.