Find Academic Supervisors

• Computer Vision
• Sensor Fusion for Mobile Robotics
• Low Power Computing
• +4 more

Jorge Lobo is an Assistant Professor at the Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Coimbra. His research focuses on computer vision, sensor fusion for mobile robotics, and low power computing, with recent emphasis on quantum computing and bio-inspired computational approaches. B.Sc/M.Sc in Electrical Engineering (University of Coimbra) Ph.D. in Electrical and Computer Engineering (2007, thesis: 'Integration of Vision and Inertial Sensing') Research Interests: Specializes in artificial perception systems combining computer vision and inertial sensing for robotics. Develops probabilistic models for sensor fusion and explores unconventional computing architectures like stochastic circuits and quantum computing for efficient Bayesian inference. Key applications include autonomous robotic grasping and disaster response systems. Scientific Leadership: Principal Investigator for Q-Bet: Bridging classical/quantum computing via HPC and bio-inspired methods Founding member of Quantum@UC interest group Coordinator of quantum computing specialization courses (collaboration with Physics & Computer Science departments) Key Projects: Contributed to European initiatives BACS (Bayesian Cognitive Systems), HANDLE (robotic dexterity), BAMBI (Bayesian inference hardware), and ECOBOTICS.SEA (marine ecosystem robotics). Academic Recognition: IEEE Senior Member and former president of the Portuguese IEEE RAS chapter. Developed innovative remote labs for stochastic computing education.

• Cybersecurity
• Code Obfuscation
• Malware Analysis
• +3 more

Sebastian Schrittwieser is a Researcher in the Research Group Security and Privacy, part of the Faculty of Computer Science. His work focuses on cybersecurity, code obfuscation, malware analysis, and machine learning applications in security. He leads and contributes to projects like INODES (Cyber Defense Strategies) and EMRESS (Resilience Evaluation Models). His research bridges theoretical foundations and practical applications, addressing challenges in software protection and threat detection. Key research interests include: Code Obfuscation Techniques and Resistance Adversarial Machine Learning and Risk Assessment Malware Analysis and Program Simulation User Behavior in Cybersecurity Contexts Recent publications emphasize empirical studies on IT/OT infrastructure security, graph neural network vulnerabilities, and quantum-inspired machine learning. He actively collaborates with institutions like SBA Research and presents at international conferences. Grants include Research Funding for projects on optimal cyber defense strategies (INODES) and software resilience evaluation (EMRESS). His work aligns with interdisciplinary efforts in security engineering and privacy-preserving technologies.

• Computational Complexity Theory
• Machine Learning
• Parallel Computation
• +5 more

Leslie Valiant serves as the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University's School of Engineering and Applied Sciences, where he has been faculty since 1982. Previously, he held positions at Carnegie Mellon University, Leeds University, and the University of Edinburgh. His academic journey began with education at King's College Cambridge, Imperial College London, and Warwick University, where he earned his PhD in computer science in 1974. Valiant's research spans theoretical computer science with primary focus areas including computational complexity theory, machine learning foundations, parallel computation systems, computational neuroscience, and evolutionary computation. His work bridges artificial and natural computational phenomena, addressing fundamental limitations in both engineered systems and biological processes. Key contributions include the development of the PAC (Probably Approximately Correct) learning model, holographic algorithms, robust logics for reconciling reasoning and learning, and theoretical frameworks for understanding cortical computation and evolvability. His publication record demonstrates sustained impact across decades, with recent work focusing on cortical computation primitives, multi-core algorithm design, and evolutionary dynamics with drifting targets. These publications reveal a consistent trajectory toward understanding computational principles in both artificial systems and biological cognition. Nevanlinna Prize (1986) Knuth Award (1997) EATCS Award (2008) A.M. Turing Award (2010) Fellow of the Royal Society Member of the National Academy of Sciences Valiant's research program integrates theoretical rigor with profound questions about natural computation, maintaining active engagement with both computer systems design and fundamental neuroscience questions. His work continues to influence multiple disciplines through formal frameworks that address computational limitations in learning, evolution, and neural processing.

Siddhartha BhattacharyyaView profile

Professor

• hybrid intelligence
• pattern recognition
• multimedia data processing
• +10 more

Dr. Siddhartha Bhattacharyya is a Professor in the Department of Computer Science and Engineering at Christ University, Bangalore, with expertise spanning hybrid intelligence, quantum computing, and multimedia data processing. He has authored/edited 65 books and published over 300 research articles, focusing on interdisciplinary applications of machine learning and computational methods. Editorial Board Member, PeerJ Computer Science Holder of two PCT patents Active in academic leadership (organizing conference committees) His research integrates Artificial Intelligence , Computer Vision , and Quantum Computing to solve complex problems in education, healthcare, and environmental monitoring. Recent work includes multimodal student learning assessment, gas plume detection, and Metaverse applications. He leads an active academic lab focused on hybrid intelligence systems and their practical implementations. Key trends in his publications include: deep learning architectures for computer vision (YOLOv7, CNN-Transformer), quantum-inspired algorithms for graph coloring and bioinformatics, and educational technology innovations for remote learning environments. His work bridges theoretical advancements with real-world applications across diverse domains.

• Machine Learning
• Neural Networks
• Optimization Algorithms
• +7 more

Mehrdad Mahdavi is an Associate Professor in the field of Computer Science and Engineering. He has secured multiple National Science Foundation (NSF) grants, including EFRI BRAID: Neuroscience Inspired Visual Analytics, CAREER: Foundations of Collaborative Machine Learning, and CNS Core: Medium: When Next Generation Wireless Networks Meet Machine Learning. His research spans diverse areas of machine learning and optimization. His work focuses on Machine Learning , Optimization Algorithms , Quantum Computing , and Graph Neural Networks . He investigates memory-efficient training methods, generalization in unsupervised learning, quantum sampling for complex distributions, and distributed algorithms for collaborative learning. His research also intersects with healthcare applications, such as AI-driven lung ultrasound analysis for diseases like COVID-19. Recent publications highlight trends in Continual Learning , Temporal Graph Learning , Quantum Algorithms , and Federated Learning . His studies address theoretical frameworks for generalization, optimization challenges in non-convex and non-logconcave problems, and scalable solutions for graph-based machine learning tasks. He has contributed to energy consumption modeling, quantum sampling, and distributed risk minimization. As a Principal Investigator (PI) and Co-PI, he has led NSF-funded projects on collaborative machine learning , neuroscience-inspired visual analytics , and AI-enabled materials discovery . These grants underscore his focus on foundational research with applications in wireless networks, quantum computing, and interdisciplinary domains.

• Machine Learning
• Artificial Intelligence
• Data Analytics
• +9 more

Dr. Mukesh Prasad is an Associate Professor at the School of Computer Science , University of Technology Sydney (UTS). With expertise in Machine Learning , Artificial Intelligence , and Computer Vision , his research addresses applications in healthcare, biomedical science, and smart infrastructure. He holds a Ph.D. in Computer Science from National Chiao Tung University, Taiwan, and an M.S. in Computer and Systems Sciences from Jawaharlal Nehru University, India. Key research areas: Machine Learning, AI, Brain-Computer Interfaces, IoT, and Evolutionary Computation Industry experience: Principal Engineer at TSMC (2016-2017), Postdoctoral Researcher at National Chiao Tung University Dr. Prasad has secured competitive grants for AI applications in disaster response, conversational agents, and medical diagnostics. His work has been published in high-impact venues like IEEE , ACM Transactions , and Springer Nature , with over 200 peer-reviewed papers. He serves on editorial boards for journals including Frontiers in Neurorobotics and ACM Computing Surveys . Scientific Awards: Vice Chancellor Teaching and Learning Citation Award (2019) Alumni Fellowship for Ph.D. (2014) Golden Bamboo NCTU Fellowship (2010) Professional Members: IEEE (2011), ACM (2019)

• Magnetism
• Superconductivity
• Strongly Correlated Systems
• +4 more

Yusuke Miyajima is an Assistant Professor at the School of Advanced Science and Engineering, focusing on magnetism, superconductivity, and strongly correlated systems. His research integrates machine learning with combinatorial optimization problems and phase transitions in spin models. Research Interests: Dr. Miyajima's work bridges computational physics and neuromorphic AI hardware, particularly targeting Berezinskii-Kosterlitz-Thouless transitions and optimization algorithms inspired by biological systems like amoebas. He investigates mathematical models for physical implementation in devices such as analog circuits and spintronics. Recent Publications: His studies analyze scaling laws in optimization machines, detect multiple phase transitions using machine learning, and propose simplified models for physical realization. These contributions highlight interdisciplinary approaches in condensed matter physics and artificial intelligence. Projects: In 2024, he advanced amoeba-inspired optimization machines, demonstrating universal performance improvements for large-scale problems. Earlier work (2023) focused on theoretical implementations using domain walls, enhancing device compatibility and solution efficiency.

• Energy Systems Optimization
• Systems Engineering
• Robotics
• +3 more

Professor Yoshiharu Amano is affiliated with the Faculty of Science and Engineering at Waseda University in the Department of Applied Mechanics and Aerospace Engineering . With a Doctor of Engineering degree from Waseda University, his research spans energy systems, robotics, and environmental engineering. Education: Ph.D. in Mechanical Engineering (Waseda University) Current Position: Full Professor at Waseda University His research interests include: Optimization of energy systems with renewable integration Development of autonomous mobile systems and UAVs GNSS signal processing and multipath mitigation 3D mapping for industrial and environmental applications Control Moment Gyro (CMG) technology for flight stability Standardization of industrial energy management systems (IEC 63376) Recent publications focus on: Macroeconomic modeling of distributed energy resources Geothermal system optimization using deep learning CO2 emission reduction through advanced district cooling systems High-precision 3D piping measurement methods Hydrogen production economics with hybrid energy sources GNSS reliability improvements in urban environments Scientific awards highlight his contributions to: Best energy systems research (ECOS 2015, IJPGC ASME) Thermal engineering excellence (JSME 2005, 2012) Robotics innovations (ROBOMECH Journal 2011) International recognition (SICE 2021)

Clemens DlaskaView profile

Professor

• Cardiology
• Digital Medicine
• Artificial Intelligence
• +3 more

Clemens Dlaska serves as Professor of Digital Medicine in Cardiology at the University Clinic for Internal Medicine III (Cardiology and Angiology, Digital Cardiology Lab) of the Medical University of Innsbruck since February 2024. His work integrates advanced computational methods with clinical cardiology to develop next-generation diagnostic and therapeutic solutions. He holds dual academic foundations in human medicine (Medical University of Innsbruck) and physics (Leopold Franzens University of Innsbruck), completing master's and doctoral studies in theoretical quantum physics at the University of Innsbruck. His research trajectory was shaped during tenure at ETH Zurich's Institute of Neuroinformatics and Institute of Biomedical Engineering, where he cultivated expertise in artificial intelligence and machine learning applications for biomedical challenges. Professor Dlaska's research program centers on AI-driven cardiac diagnostics , digital pathology integration , and quantum computing applications for cardiovascular medicine. Leading the Digital Cardiology Lab, his team pioneers algorithms for cardiac imaging analysis, predictive modeling of heart failure, and quantum-inspired optimization of treatment protocols. His interdisciplinary approach bridges theoretical physics, clinical cardiology, and machine learning to address complex cardiovascular pathologies through computational innovation. Current initiatives focus on translating quantum technologies into clinical cardiology frameworks, with emphasis on real-time hemodynamic modeling and personalized intervention planning. The lab maintains active collaborations with neuroinformatics specialists and quantum computing researchers to accelerate the development of clinically viable AI/quantum hybrid systems for cardiac care.