Find Academic Supervisors

Barbara ReckView profile

Researcher

• Sustainable Materials Management
• Circular Economy
• Industrial Ecology
• +3 more

Barbara Reck is a Senior Research Scientist at the Yale School of the Environment (YSE) and Node Lead for Systems Analysis and Integration at the REMADE Institute, a U.S. Manufacturing Institute focused on reducing embodied energy in manufacturing. Her research emphasizes sustainable material use, circular economy strategies, and recycling technologies. She holds a Ph.D. (Dr.-Ing.) in Environmental Engineering from TU Berlin and an M.Sc. in Environmental Engineering from the University of Applied Sciences, Bingen. Her work spans metals (nickel, stainless steel), plastics, and biogenic materials like mass timber. Notable contributions include metal recycling indicators, criticality assessments, and future supply-demand scenarios for key metals. Prior to Yale, she worked at Lufthansa German Airlines as Manager of Environmental Affairs. Recent publications highlight topics such as global material stocks, recycling automation, and circular plastics supply chains. She collaborates on projects like the Future Manufacturing Research Grant for recyclable soft electronics and MSW-to-SAFT fuel analysis in Connecticut. Her research also addresses workforce impacts of AI-driven recycling systems and decarbonization pathways for steel. Affiliated with the Center for Industrial Ecology at YSE, her work intersects environmental science, policy, and engineering to advance sustainable resource management strategies globally.

James DemmelView profile

Professor

• Numerical Analysis
• Linear Algebra
• Scientific Computing
• +4 more

James Demmel is a Professor of Computer Science and Mathematics at the University of California, Berkeley, with joint appointments since 1990. His research focuses on numerical linear algebra, high-performance computing, and parallel algorithms. He co-developed widely used libraries like LAPACK and ScaLAPACK. Demmel holds ACM, SIAM, and IEEE Fellowships, and is a member of both the National Academy of Engineering and Sciences. Education: Ph.D. in Computer Science, UC Berkeley, 1983 B.S. in Mathematics, Caltech, 1975 Research Interests: Demmel’s work emphasizes numerical methods for linear algebra, including algorithms for eigenvalue problems, matrix factorizations, and high-performance computing architectures. His contributions bridge theory and practice, addressing challenges in floating-point arithmetic and algorithm scalability. Publications & Awards: Over 150+ publications, including foundational papers on LAPACK and iterative methods. Recipient of the SIAM Activity Group Linear Algebra Best Paper Prize (1988, 1991), Wilkinson Prize (1993), and ACM Supercomputing Test of Time Award (2019). Labs & Teams: Demmel collaborates with groups like Berkeley Benchmarking and Optimization Group (BeBOP) and CLIMB, focusing on algorithmic efficiency and exascale computing.

Nadia Saad NooriView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Computer & Machine Vision
• +10 more

Nadia Saad Noori is an Associate Professor at the Department of Information and Communication Technology at the University of Agder (UiA). Since 2016, she has conducted research and teaching at CIEM - Centre for Integrated Emergency Management , and joined NORCE Norwegian Research Center as a Senior Researcher in 2018. Her work bridges industry experience (Cisco, hi-tech startups) with academic rigor , focusing on technology integration in crisis management , cybersecurity , and industrial monitoring systems . Her educational background includes: B.Sc. & M.Sc. in Computer Systems Engineering M.A.Sc. in Technology Innovation Management Ph.D. in Electronic and Information Systems Engineering Research interests span machine learning , autonomous systems , and security frameworks through: Disaster response coordination systems Industrial condition monitoring Humanitarian technology solutions Cyber-physical systems Recent publications demonstrate technical breadth : 2024: Thermal gesture recognition and UAV navigation in industrial spaces 2023: Cybersecurity frameworks and ecological pattern recognition 2022: Industrial seal diagnostics and autonomous systems She leads research groups in: Autonomous and Cyber-Physical Systems (ACPS) CIEM - Integrated Emergency Management Communication and System Security

• Quantum Software Engineering
• Cyber-Physical Systems
• Digital Twin Technologies
• +3 more

Dr. Shaukat Ali is a leading researcher at Simula Research Laboratory (Certus Software V&V Center, Norway), with a PhD from the University of Oslo . His work bridges quantum software engineering and cyber-physical systems (CPS) validation, focusing on digital twins, autonomous vehicles, and healthcare IoT applications. Collaborates with institutions like South Dakota State University, COMSATS University, and Mohammad Ali Jinnah University Co-developed tools: QuCAT (Quantum combinatorial testing), DeepScenario (autonomous driving datasets), and EvoCLINICAL (medical digital twin evolution) Research spans quantum software testing (IBM quantum computers), uncertainty-aware validation of medical devices, and search-based optimization in CPS. His 2025 work introduces quantum circuit mutants, uncertainty-wise test oracles, and foundational models for digital twin creation. Publications in journals like ACM Transactions on Software Engineering and conferences including ICSE , GECCO , and IEEE Quantum Software reflect his interdisciplinary approach. Current projects address quantum software robustness, LLM-driven scenario realism, and safe adaptation in robotics.

• Computer Vision
• Biometrics
• Authentication Systems
• +3 more

Nuno Gonçalves is a Tenured Assistant Professor at the Department of Electrical and Computer Engineering and Senior Researcher at the Institute for Systems and Robotics (ISR) - Coimbra, University of Coimbra. He also serves as Innovation Manager at the Portuguese Mint and Official Printing Office (INCM) since 2018, focusing on technology transfer and product development. His roles include coordinating the AI group of APDSI and leading research in biometrics, facial recognition, and steganography. PhD in Computer Vision (University of Coimbra, 2008) His research spans computer vision, biometrics, and visual information security, with specific focus on facial recognition, morphing attack detection, presentation attack detection, graphical security, security coding, printer-proof steganography, and robotics. Recent work emphasizes deepfake detection, neural implicit representations, and robust biometric solutions. Article trends reveal expertise in 3D face reconstruction, multimodal deep learning for medical applications, and security-enhancing steganography. He leads projects integrating geometric modeling with neural networks for signed distance functions and develops benchmarks for morphing attack robustness in facial recognition systems. Member, IEEE He has coordinated grants from Fundação para a Ciência e a Tecnologia and private industry partners, including projects like BLOCKDFAKE (2025-2026) for deepfake detection in public administration and VISUAL-ID (2022-2024) for unique visual identities. His lab at ISR-Coimbra collaborates with INCM on security document validation and printer-proof steganography, with patents granted by EUIPO and USPTO.

Madhur BehlView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Cyber-Physical Systems
• +2 more

Dr. Madhur Behl is an Associate Professor in the departments of Computer Science and Systems and Information Engineering at the University of Virginia. He is a member of the Cyber-Physical Systems Link Lab and leads the Cavalier Autonomous Racing team, which develops autonomous electric vehicles for high-speed racing. Behl co-founded the F1/10 International Autonomous Racing Competitions, fostering student engagement in autonomous systems research. Behl earned his Ph.D. (2015) and M.S. (2012) in Electrical and Systems Engineering from the University of Pennsylvania and a B.S. (2009) in ECE from PEC University of Technology, India. His research focuses on the intersection of Machine Learning, Predictive Control, and AI for applications in Cyber-Physical Systems, Autonomous Vehicles, Robotics, and Smart Cities. Notable projects include data-driven stormwater management (dMIST) and safety-hardening frameworks (CRASH) for autonomous systems. Behl’s awards include the NSF CAREER Award (2021) and multiple best-paper awards for contributions to autonomous systems, energy optimization, and infrastructure resilience. He serves on editorial boards and advisory committees promoting public understanding of autonomous vehicles. Research Highlights: Agile Autonomy, Safe Autonomy, Smart City Infrastructure, and Energy Optimization. Labs/Teams: Autonomous Vehicles Lab (Link Lab), F1tenth Lab, and Cavalier Autonomous Racing Club.

Shanay RabView profile

Lecturer

• Metrology
• Mechanical System Design
• Robotics
• +12 more

Shanay Rab is a Lecturer in the School of Architecture, Technology and Engineering at the University of Brighton. Their research lies at the intersection of metrology, mechanical system design, robotics, and additive manufacturing, with a strong emphasis on quality infrastructure and industrial innovation. Research interests include: Metrology and high-pressure equipment design Automation and robotics in manufacturing Additive manufacturing and material behavior under external stimuli Integration of AI tools in engineering systems Quality infrastructure encompassing standardization, accreditation, and conformity assessment Environmental noise modeling using neural networks and land use regression The recent publications indicate a growing interdisciplinary focus, combining engineering precision with digital transformation trends such as Industry 4.0, IoT, and AI-driven analysis. Key themes include metrology policy (e.g., Metre Convention), science communication in the digital era, and urban environmental modeling. The work bridges theoretical foundations with practical applications, particularly in the context of India and the Global South. Scientific contributions include over 70 peer-reviewed publications in journals such as Nature Reviews Physics , Measurement , and Environmental Science and Pollution Research . Notable topics span face recognition technologies, strategic noise mapping, and international cooperation in measurement science. Shanay Rab actively supervises research in areas related to metrology, advanced manufacturing, and automation. Though no specific grants or awards are listed, the volume and impact of publications suggest sustained research activity and external collaboration across countries. Their work contributes significantly to both academic discourse and practical advancements in engineering and quality systems. Collaborative work is evident through co-authored papers in environmental modeling, digital science communication, and international metrology policy, reflecting a broad academic network and engagement with global challenges.

Jayabadhrinath KrushnanView profile

Researcher

• Robotics
• Industrial Engineering
• Mechanical Engineering
• +3 more

Jayabadhrinath Krushnan is a researcher at the Faculty of Mechanical Engineering , Schmalkalden University of Applied Sciences. His work focuses on robotics , Industry 4.0 , and educational module development . Key research areas: Robotics , Industrial Engineering , Digitalization in Education Labs: Robotics Applications , Industry 4.0 Benchmarking Publications highlight MicroROS-based robot controllers , soft robotic systems , and self-optimized industrial plants , with a focus on educational applications. His 2016 work on metal-bonded fullerenes demonstrates interdisciplinary interest in materials science. 2023: Soft robotics and Industry 4.0 benchmarking 2022: Low-cost Industry 4.0 educational plant Collaboration with Prof. F. Schrödel and others indicates team-based research in applied mechanics and educational technology.

• Artificial Intelligence
• Reinforcement Learning
• Generative AI
• +10 more

Álvaro Jesús López López is a researcher at the Instituto de Investigación Tecnológica (IIT) and a professor at the Higher Technical School of Engineering (ICAI), both part of Comillas Pontifical University. He holds the position of Associate Research Fellow and serves as a principal investigator at the Chair of Smart Industry, where he leads applied research on digital technologies such as generative AI, computer vision, and synthetic data generation. He also directs executive programs in generative artificial intelligence for professionals at Comillas Onexed. His research interests include Artificial Intelligence, Reinforcement Learning, Generative AI, Smart Industry, and Railway Systems . His work focuses on applying advanced AI techniques to industrial and transportation challenges, particularly in energy efficiency and automation. He has published extensively in high-impact journals such as Engineering Applications of Artificial Intelligence , Applied Intelligence , and Transportation Research Part C . The recent articles highlight a strong trend toward applied AI in industrial and robotic systems , with a focus on sim-to-real transfer, keyphrase extraction, and reinforcement learning enhanced with semantic knowledge. His work bridges the gap between theoretical AI and real-world industrial applications. Scientific Awards: Honorable Mention from the Industry 4.0 Observatory (2021) Honorable Mention at the 6th Connected Industry Promotion Award Erasmus Teaching/Research Staff Training Grant (2014) He has advised multiple PhD students and leads several industry-funded research projects with organizations such as Ferrovial, Endesa, Metro de Madrid, and the European Commission. He has also organized scientific events and delivered numerous invited talks on AI and digitalization. His research has led to practical applications, including a patent on utilizing regenerative braking energy in railways. He completed a research stay at the Royal Institute of Technology (KTH) in Stockholm and has contributed to books and policy reports on digitalization in industry.

• Cultural Algorithms
• Evolutionary Computation
• Artificial Intelligence
• +5 more

Robert G. Reynolds is a Professor of Computer Science at Wayne State University's College of Engineering, where he leads pioneering research in Cultural Algorithms and evolutionary computation. With over 197 publications spanning from 1982 to 2024, he has established himself as a leading authority in computational intelligence and socially-inspired optimization techniques. His work bridges computer science, social modeling, and practical applications across diverse domains including education, healthcare, and archaeology. Reynolds' research interests center around Cultural Algorithms, which he developed as a framework for modeling how cultures solve problems through social learning and knowledge transmission. His work extends to evolutionary computation, swarm intelligence, and agent-based modeling, with applications ranging from virtual reality educational systems to medical imaging analysis. He has published the influential book 'Culture on the Edge of Chaos - Cultural Algorithms and the Foundations of Social Intelligence' (2018) that formalized his theoretical contributions. His recent publications (2022-2024) demonstrate continued innovation in applying Cultural Algorithms to diverse challenges including STEM education through virtual reality environments, medical image analysis, economic modeling, and climate change impact assessment. These works consistently show his approach of integrating social dynamics into computational problem-solving frameworks. Among his notable scientific contributions is the development of the Cultural Algorithms framework as a comprehensive approach to optimization and problem-solving that incorporates social dimensions often missing in traditional evolutionary algorithms. His work has influenced numerous researchers in the field of computational intelligence. Professor Reynolds has mentored numerous students who have become frequent collaborators, including Mostafa Z. Ali, Thomas Palazzolo, Sarah Saad, and Chencheng Zhang. His research has been supported by various grants that have enabled the development of projects like the Deep Dive Virtual Reality system for exploring ancient submerged civilizations and educational applications of AI. His laboratory work focuses on developing the Cultural Algorithms Toolkit (CAT) and applying it to real-world problems through virtual environments that simulate social and cultural dynamics. Current projects include extending Cultural Algorithms for trustable AI systems and developing more sophisticated models of cultural knowledge transmission.

• Artificial Intelligence
• Semantic Web
• Natural Language Processing
• +7 more

Philipp Cimiano is a Professor at Bielefeld University, Germany , with a prolific research record in Artificial Intelligence, Semantic Web, Natural Language Processing, Knowledge Graphs, Explainable AI, Clinical Decision Support Systems, Ontology Engineering, and Federated Learning . His work bridges theoretical AI concepts with practical applications in healthcare and robotics. Key research themes include dialogue-based XAI for user understanding, federated learning for healthcare data privacy, and LLM-driven robotics for embodied commonsense reasoning. Recent publications analyze dynamic explanatory interactions , perspectivized argumentation frameworks , and benchmarks for robot manipulation using large language models. His collaborations span institutions in Germany and Europe, with frequent co-authorship on topics like counterfactual generation , stakeholder group analysis , and lexicalization in QALD systems .

• Vehicle Dynamics
• Control Systems
• Tribology
• +4 more

Francesco Bottiglione serves as Full Professor in the Department of Mechanics, Mathematics and Management at Politecnico di Bari, where he directs the Aerospace Systems Engineering degree program. His academic specialization in Applied Mechanics (ING-IND/13) focuses on mechanical systems dynamics with significant contributions to automotive engineering and surface science. Professor Bottiglione's research integrates theoretical and experimental approaches across five core domains: vehicle dynamics (developing nonlinear model predictive control for multi-actuated vehicles and trailer sway mitigation), surface mechanics (characterizing topography and friction through multi-laboratory benchmarking), biomechanics (applying machine learning to fencing performance analysis), additive manufacturing (fused filament fabrication for sensor integration), and seismic engineering (nonlinear viscoelastic damping systems). His work consistently bridges fundamental mechanics with practical applications in transportation, sports science, and structural safety. Analysis of his 2021-2025 publications reveals three dominant trends: (1) Advancement of model predictive control architectures for complex vehicle systems, particularly in articulated configurations; (2) Standardization efforts in surface topography characterization through multi-laboratory collaboration; and (3) Emerging integration of explainable machine learning in biomechanical analysis for aging populations and athletic performance. His research methodology emphasizes experimental validation of theoretical models, with increasing interdisciplinary collaboration evident in recent works.

Michael RudermanView profile

Professor

• motion control
• robotics and mechatronics
• nonlinear systems with memory
• +3 more

Michael Ruderman is a Professor in the Department of Engineering Sciences at the University of Agder, Norway, where he has been working since June 2020 (previously as Associate Professor from October 2015 to June 2020). He is currently on sabbatical from January 2025. Prior to his position at UiA, he held academic appointments at Nagaoka University of Technology, Nagoya Institute of Technology, and Technical University Dortmund. His research focuses on: Motion control and robotics Mechatronics systems Nonlinear systems with memory Nonlinear, hybrid, and robust control methodologies Ruderman's work centers on the analysis and compensation of kinetic friction in robotic and mechatronic control systems, culminating in his 2023 book 'Analysis and Compensation of Kinetic Friction in Robotic and Mechatronic Control Systems'. His research bridges theoretical control concepts with practical engineering applications, particularly in hydraulic systems, robotic actuators, and systems with hysteresis and friction effects. His recent publications (2024-2025) demonstrate a strong emphasis on advanced control techniques for complex mechanical systems, with particular focus on sliding mode control, nonlinear damping, hysteresis compensation, and oscillation control. Many of his papers address real-world challenges in hydraulic actuators, robotic systems, and mechanical interfaces with friction. Professionally, Ruderman serves on the editorial boards of IEEE Transactions on Control Systems Technology, IFAC Control Engineering Practice, IFAC Mechatronics, and IEEE/ASME Transactions on Mechatronics. He previously chaired the IEEE-IES Technical Committee on Motion Control (2018-2021) and served on the Management Committee of IEEE/ASME Transactions on Mechatronics (2020-2023). Ruderman has been actively involved in numerous research projects including DAAD mobility grants, H2020-MSCA-RISE projects on robust control, and several Research Council of Norway (RCN) projects focusing on fractional-order systems, nonlinear control methods, and compensators for non-minimum phase systems. He teaches courses including Control Theory (MSc), Advanced Control and Robotics (PhD), Electromagnetic Modeling (PhD), and Feedback Control Systems 1 (BSc), demonstrating his commitment to educating the next generation of control engineers and robotics specialists.

Soumick ChatterjeeView profile

Researcher

• Deep Learning
• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• +5 more

Soumick Chatterjee is a Postdoctoral Researcher at the Glastonbury Group within the Genomics Research Centre of Human Technopole , Milan, Italy. He completed his PhD in Computer Science (summa cum laude) at Otto von Guericke University Magdeburg , Germany, focusing on reducing artifacts in MRI using deep learning. Education : PhD (2022), MSc (2017), and BCA (2013) in Computer Science. Current Role : Postdoc in Genomics Research Centre, Milan. His research spans deep learning applications in medical imaging, including MRI reconstruction , motion correction , brain tumor classification , and vascular segmentation . He also emphasizes interpretability of AI models through tools like TorchEsegeta. Publications (23+ in 2023) include work on undersampled MRI reconstruction, uncertainty quantification, and vessel segmentation via maximum intensity projection loss. Notable scientific achievements include winning the CHAOS Challenge (2019) and runner-up in the MOOD Challenge (2021). As a supervisor , he guided multiple Master’s students in deep learning projects. He co-organized events like ISACT 2021 and SMILE-UHURA 2023. Open science advocate with code publicly available on GitHub.

Marc G. BellemareView profile

Adjunct Professor

• Reinforcement Learning
• Deep Reinforcement Learning
• Generative Modeling
• +5 more

Marc G. Bellemare is a Canada CIFAR AI Chair at Mila, an Adjunct Professor at the School of Computer Science at McGill University and Université de Montréal, and the Chief Scientific Officer at Reliant AI, a Montréal-Berlin startup. His research focuses on reinforcement learning and generative modeling, with significant contributions to distributional reinforcement learning, exploration in high-dimensional spaces, and the development of the Arcade Learning Environment (ALE). He has held previous roles at the Google Brain team and DeepMind. Marc’s work includes both theoretical and applied advancements, such as distributional reinforcement learning theory, exploration strategies, and the commercial application of RL for Loon’s stratospheric balloons. His publications span journals like Nature and top conferences including NeurIPS, ICML, and AAAI. He has received multiple best paper awards, including at NeurIPS 2021 and ICML 2019 Workshop. Current Students: Adrien Ali Taiga, Jacob Buckman, Harley Wiltzer, Pierluca D'Oro, Nathan U. Rahn, Jesse Farebrother Graduated Students: Rishabh Agarwal, Charline Le Lan, Max Schwarzer, Johan Obando Céron, Philip Amortila, Vishal Jain His recent articles emphasize distributional RL, exploration strategies, and large-scale applications. Scientific awards include best papers at NeurIPS, ICML, and RLDM. He is also a co-founder of Reliant AI and developed the ALE benchmark, which underpins deep RL research.