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Paulo RauberView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +4 more

Dr. Paulo Rauber is a Lecturer in Artificial Intelligence at the School of Electronic Engineering and Computer Science, Queen Mary University of London. He holds a PhD from the joint program between University of Campinas (Brazil) and University of Groningen (Netherlands), and previously conducted postdoctoral research at the Swiss AI Lab (IDSIA) under Jürgen Schmidhuber. Education: BSc in Computer Science, Federal University of Santa Catarina (2011) PhD in Computer Science, University of Campinas & University of Groningen (2017) Research: Focuses on Bayesian reinforcement learning methods addressing exploration, planning, and generalization. His work spans scalable algorithms for non-stationary environments and deep reinforcement learning applications. Teaching: Teaches postgraduate courses such as Neural Networks and Deep Learning, Artificial Intelligence in Games, and Data Mining. Grants & Projects: Secured a Swiss National Science Foundation grant (NEUSYM, ~$700,000) in 2019, and leads projects like Colosseum and Hindsight Policy Gradients. Affiliations: Member of the Centre for Multimodal AI and actively supervises PhD students in reinforcement learning and Bayesian methods.

Tim PearceView profile

Professor

• Machine Olfaction
• Neuromorphic Engineering
• Neuroscience
• +4 more

Tim Pearce is a Reader in Bioengineering at the School of Engineering, University of Leicester. He holds a PhD from Warwick University and has served as a Research Assistant Professor at Tufts University Medical School. His work focuses on machine olfaction, neuromorphic engineering, and interdisciplinary applications of chemical sensing technologies. Key contributions include the development of electronic noses, biomimetic infochemical communication systems, and neuromorphic implementations inspired by insect olfactory pathways. Research Interests: Machine olfaction and sensor arrays Neuromorphic engineering and bio-inspired computation Integration of neuroscience principles into engineering systems Applications in environmental monitoring, healthcare, and security Publications highlight advancements in odor classification, spatio-temporal signal processing, and AI-driven world modeling. Notable collaborations include EU projects like Neuro-IT and AMOTH, which translated biological olfactory principles into real-world technologies. He has authored over 100 articles, including the seminal Handbook of Machine Olfaction . Awards include Fellowships from the Institute of Physics and the Higher Education Academy. Editorial roles span Frontiers in Neuromorphic Engineering and Connection Science . He actively contributes to global initiatives in neuroengineering and computational neuroscience.

• Artificial Intelligence
• Machine Learning
• Case-Based Reasoning
• +7 more

Professor Kerstin Bach is affiliated with the Department of Computer Technology and Informatics at NTNU's Faculty of Information Technology and Electrical Engineering. Her research focuses on artificial intelligence, machine learning, and their applications in health informatics and robotics. Notable projects include the selfBACK app for musculoskeletal pain management and reinforcement learning for robotic systems. She has supervised numerous doctoral students in areas like activity recognition and explainable AI. Education: Formal academic degrees not explicitly listed in text. Research interests span: - Case-Based Reasoning for clinical decision support - EHealth/mHealth applications (e.g., wearable sensor analytics) - Explainable AI methodologies - Human activity recognition using accelerometer data Recent work emphasizes: - Machine learning models for health monitoring (sleep/wake detection, gait analysis) - Reinforcement learning for robotics and autonomous systems - AI-driven clinical tools for pain management Key Projects: selfBACK app: Digital self-management tool for musculoskeletal pain (RCT validated) UtiliGEM: Energy management framework for IoT devices Labs/Teams: Active in NTNU's AI research groups focusing on healthcare informatics and robotics. Collaborates with medical institutions on clinical AI applications.

Nan ChenView profile

Professor

• Financial Engineering
• FinTech
• Quantitative Finance
• +7 more

Prof. Nan Chen is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong (CUHK). He holds a PhD from Columbia University (2006) and directs the Bachelor of Engineering in Financial Technology (FinTech) and the Master of Science in Financial Engineering at CUHK-Shenzhen. His research focuses on quantitative finance, systemic risk, Monte Carlo simulation, and reinforcement learning. He has authored over 40 papers in top journals like Operations Research and Review of Financial Studies . Education : BSc and MSc in Probability & Statistics, Peking University (1998–2001) MPhil/PhD in Operations Research, Columbia University (2005–2006) Research Interests : FinTech and quantitative finance Systemic risk modeling and network analysis Reinforcement learning and stochastic control Monte Carlo methods and computational finance Awards & Grants : Best Student Paper Award, INFORMS (2006) Exemplary Teaching Awards (2009, 2017) Over HK$7M in GRF grants (2008–2023) Second Prize, Jiangsu Province Social Science Award (2023) Public Service : Member of Hong Kong's Payment Systems Tribunal and Insurance Appeals Tribunal, and reviewer for major journals like Mathematical Finance and Operations Research . Key Contributions : Pioneered Ito-Taylor expansions for diffusion models, dual methods for stochastic control, and liquidity risk frameworks. Led development of Hong Kong's first FinTech undergraduate program.

Sivakumar RathinamView profile

Professor

• Motion Planning
• Autonomous Vehicles
• Collaborative Decision Making
• +3 more

Dr. Sivakumar Rathinam is a Professor in the Department of Mechanical Engineering at Texas A&M University, affiliated with the College of Engineering and the Computer Science & Engineering department. He holds certifications as a Fellow of ASME (2021) and Senior Member of IEEE (2019). His research focuses on motion planning for autonomous vehicles, collaborative decision-making, combinatorial optimization, and vision-based control systems. He leads the Autonomy Lab, addressing challenges in multi-agent systems, path planning, and rural autonomous vehicle accessibility. Education: Ph.D., Civil Systems Engineering, University of California, Berkeley (2007) M.S., Electrical Engineering & Computer Science, UC Berkeley (2006) M.S., Mechanical Engineering, Texas A&M University (2001) B.Tech., Mechanical Engineering, Indian Institute of Technology Madras (1999) Research Highlights: Dr. Rathinam's work spans autonomous vehicle navigation, UAV coordination, and sensor fusion for adverse conditions. His lab develops algorithms for multi-agent pathfinding and persistent monitoring missions. Recent efforts include rural road detection datasets (R2D2) and thermal/LIDAR sensor fusion for safety in challenging environments. Awards: Outstanding Faculty Contribution Award (2021) Best Paper Runner-Up, ICAPS (2021) Teaching Excellence Award (2012) Labs & Teams: Directs the Autonomy Lab, collaborating with industry partners through the Mechanical Engineering Industry Advisory Council. Active in NSF-funded projects on equitable rural autonomy and multi-UAV recharging frameworks.

Lingzhou XueView profile

Professor

• High-Dimensional Statistics
• Nonparametric Statistics
• Statistical Learning
• +9 more

Lingzhou Xue is a Professor of Statistics at The Pennsylvania State University, affiliated with the Eberly College of Science. He holds dual roles as a faculty member and the Associate Director of the National Institute of Statistical Sciences (NISS). His research focuses on high-dimensional statistics, nonparametric methods, statistical learning, and optimization, with applications in biomedical, environmental, and social sciences. He leads the SLDM (Statistical Learning and Data Mining) Lab and MDS (Microbiome Data Science) Lab. Education: B.Sc. in Statistics from Peking University (2008), Ph.D. in Statistics from the University of Minnesota (2012), postdoctoral training at Princeton University (2012–2013). Professional roles include Associate Editorships at the Journal of the American Statistical Association, Annals of Applied Statistics, and ACM Transactions on Probabilistic Machine Learning. Research Interests: Federated learning, causal inference, graphical models, reinforcement learning, optimal transport, and large-scale optimization. Recent work emphasizes theoretical guarantees for sparse PCA and federated Q-learning algorithms. Awards: IMS Fellow (2024), ASA Fellow (2023), COPSS Emerging Leader Award (2021), and Bernoulli Society New Researcher Award (2019). He has mentored 17 Ph.D. students and 3 postdocs, with four students securing tenure-track faculty positions. Service: Organized multiple NISS writing workshops, co-chaired the Ingram Olkin Statistics Serving Society Forum on Gun Violence, and contributed to the ASA whitepaper 'Discovery with Data' (2014).

• Digitalization
• Control Systems
• Optimization
• +4 more

Guoyuan Li is a Professor at the Department of Marine Operations and Engineering Technology at NTNU, affiliated with the Faculty of Engineering. His research focuses on digitalization, control systems, robotics, maritime operations, and human-machine interaction. He holds IEEE Senior Membership (2019) and editorial roles in IEEE Journal of Oceanic Engineering and IEEE Transactions on Intelligent Transportation Systems . Education includes a Ph.D. in Computer Science from Hamburg University (2013), M.S. and B.S. from Chongqing University (2009/2006). Key projects include EU-funded RoboSAPIENS (robotic adaptation), Digital Twin for Green Ship Operations, and TwinShip (vessel lifecycle services). Recent awards include 2024 IEEE Robotics & Automation Magazine Best Paper and multiple IEEE conference recognitions. His work spans AI-driven maritime safety, digital twin applications, and autonomous systems. Visit Intelligent Systems Lab for more.

Francesca PariseView profile

Assistant Professor

• Multi-Agent Systems
• Network Theory
• Control Systems
• +6 more

Francesca Parise is an Assistant Professor in the School of Electrical and Computer Engineering at Cornell University. Her research focuses on multi-agent systems, particularly analyzing large networks of autonomous decision-makers in transportation, finance, and social systems, employing tools from control theory, game theory, and optimization. She holds a PhD from ETH Zurich and was a Postdoctoral Research Fellow at MIT's Laboratory for Information and Decision Systems under Prof. Asuman Ozdaglar. Her work bridges theoretical foundations with practical applications in networked systems and interventions. Education: Bachelor's and Master's from University of Padua, Italy PhD in Control Engineering from ETH Zurich (2016) Her research interests span game-theoretic models for network analysis, learning dynamics in time-varying systems, and optimal intervention strategies. Key themes include synchronization in power grids, epidemic control via testing, and dynamic resource allocation. Recent work emphasizes graphon-based frameworks for large-scale network games and computational methods for equilibrium analysis. She explores both theoretical guarantees and real-world applications in transportation and energy systems. Publications highlight contributions to networked game theory, stochastic systems, and control applications, with a focus on scalable solutions for complex systems. Her work often intersects with interdisciplinary challenges in systems biology and socio-technical networks. She currently holds an office at Frank H. T. Rhodes Hall, Cornell University.

Vlachos KostasView profile

Assistant Professor

• Robotics
• Control of Robotic Mechanisms
• Microrobotics
• +7 more

Kostas Vlachos is an Assistant Professor in the Department of Computer Science and Engineering at the University of Ioannina, Greece. He has been in this position since 2014, following prior teaching roles at the University of Thessaly (2007–2013). He is a member of the Information Processing and Analysis (I.P.AN.) research group and actively supervises PhD, MSc, and diploma students in robotics and control systems. PhD, School of Mechanical Engineering, National Technical University of Athens, 2004 MSc, Interdepartmental Postgraduate Program in Automation Systems, National Technical University of Athens, 2000 Diploma in Electrical Engineering, Technical University of Dresden, Germany, 1993 His research focuses on robotics and control, with emphasis on microrobotics , haptic mechanisms , medical simulators , and autonomous navigation . He has made significant contributions to over-actuated marine platforms, reinforcement learning for navigation, and tactile robotic systems. His work bridges mechanical engineering and computer science, particularly in intelligent robotic control. The 15 most recent publications highlight a strong trend in autonomous marine robotics , multi-agent reinforcement learning , and intelligent control systems . Key themes include energy-efficient control, obstacle avoidance, sensor fusion, and learning-based navigation. The research spans from theoretical control design to real-world implementation in unmanned surface vehicles and microrobots. Best Student Paper Award, 9th Hellenic Conference on AI (SETN 2016) Vlachos has supervised over 30 students at various levels and has participated in multiple national and European research projects in robotics and automatic control. His teaching includes courses such as Computational Mathematics, Robotics, and Robotic Systems. He collaborates extensively with researchers like E. Papadopoulos and K. Blekas. He leads research within the Information Processing and Analysis (I.P.AN.) group, focusing on intelligent perception and control of robotic systems. His lab works on mobile manipulators, haptic devices, mini-robots, and marine platforms, integrating simulation (ROS/Gazebo) with real-world experimentation.

Gianluca AloiView profile

Associate Professor

• Wireless Networks
• Cellular Networks
• Sensor Networks
• +7 more

Gianluca Aloi serves as an Associate Professor in Telecommunications (IINF-03/A) at the Department of Computer Engineering, Modeling, Electronics and Systems (DIMES) of the University of Calabria, Italy. He holds the position of scientific director for the Telecommunications and Information Theory for Advanced Networking Laboratory (TITAN Lab.). Dr. Aloi earned his PhD in Systems Engineering and Computer Science from the University of Calabria in 2003 and became a University Researcher in Telecommunications (ING-INF/03) in 2004 before advancing to his current academic rank. His research expertise spans wireless networks, cellular networks, sensor networks, Internet of Things systems and their interoperability, management of resources and services in the Cloud/Edge and IoT (CEI) Continuum, and management and orchestration of network resources using Artificial Intelligence. His work particularly focuses on UAV-assisted IoT systems for industrial applications, geological hazard monitoring, and maritime environments. Analysis of Dr. Aloi's recent publications (2023-2025) reveals a strong emphasis on applying reinforcement learning and deep learning techniques to solve complex networking challenges. His research shows a clear trajectory toward developing intelligent network architectures that optimize data collection, improve system resilience, and enhance resource management across the edge-to-cloud continuum. Key application areas include smart factories, disaster monitoring, and urban vehicular systems. Dr. Aloi teaches courses including Fundamentals of Telecommunications Networks and Telecommunications Networks for both Electronic Engineering and Computer Engineering programs. He maintains regular reception hours every Tuesday from 9am to 11am or by appointment via email. As scientific director of TITAN Lab, Dr. Aloi leads research initiatives focused on advanced networking technologies, with particular emphasis on developing solutions for next-generation communication systems that integrate artificial intelligence with traditional networking paradigms to address real-world challenges in telecommunications and IoT applications.

Lydia TapiaView profile

Professor

• Robotic Motion Planning
• Computational Biology
• Machine Learning
• +7 more

Lydia Tapia is a Professor and the Department Chair of the Department of Computer Science at the University of New Mexico, School of Engineering. She is a leading researcher in motion planning, robotics, and computational biology, with a strong commitment to diversity and leadership in computing. Education: PhD in Computer Science, Texas A&M University, 2009 BS in Computer Science, Tulane University, 1998 Her research focuses on the development of efficient algorithms for high-dimensional motion simulation in both robotics and molecular systems. She applies adaptive learning techniques to solve computationally intensive problems in robotic navigation, molecular docking, and antibody aggregation. Her work bridges robotics and computational biology, with applications in immunology, neurodegenerative diseases, and human-automation systems. Her recent publications highlight trends in reinforcement learning for motion planning, multi-agent navigation, human-robot collaboration, and the use of deep learning for geometric prediction. These works frequently appear in top venues such as IROS, ICRA, IEEE RA-L, and MIG, reflecting her interdisciplinary impact. Scientific Awards: 2016 Denice Denton Emerging Leader ABIE Award 2016 NSF CAREER Award 2017 CRA-W Borg Early Career Award She actively advises students and postdocs, leads the Tapia Lab, and contributes to major research centers including the Adaptive Motion Planning Research Group, the Center for Evolutionary & Theoretical Immunology, and the New Mexico Center for the Spatiotemporal Modeling of Cell Signaling. She has secured significant research funding and has been instrumental in organizing workshops and initiatives to promote diversity in robotics and computing. Her lab, the Tapia Lab, focuses on complex robotic systems, including molecular robots like proteins, using adaptive learning to solve planning problems. Current projects include human-automation collaboration, resilient learning, robotic task learning, and molecular docking games that leverage human intuition through crowdsourcing.

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +4 more

Joelle Pineau is a Professor at the School of Computer Science, McGill University, Montreal, Canada. Her work spans Machine Learning , Artificial Intelligence , and Reinforcement Learning , with significant contributions to causal inference , ethics in AI , and continual learning . She has led initiatives like the NeurIPS 2019 Reproducibility Program and co-authored over 315 publications. Key Research Themes : Algorithmic fairness, robust policy learning, interpretable models, and AI ethics Recent Trends : Focus on uncertainty-aware systems, multi-task learning, and societal implications of foundation models Scientific Awards : NeurIPS 2019 Reproducibility Program Leadership Advancements in AI Ethics Review Practices Her work intersects Computer Science , Biomedical Research , and Societal Policy , with applications in healthcare, robotics, and knowledge graphs.

• Algorithmic Sequential Decision-Making
• Robust AI
• Safe AI
• +11 more

Ilija Bogunovic is an Assistant Professor (UK Lecturer) in the Department of Electronic and Electrical Engineering at University College London (UCL), Faculty of Engineering Sciences. His research focuses on algorithmic sequential decision-making for robust, reliable, and safe artificial intelligence, with applications in large language model alignment, reinforcement learning, and human feedback integration. His research interests lie at the intersection of machine learning, optimization, and decision theory. He investigates robustness in Bayesian optimization, bandits, and reinforcement learning under adversarial attacks, model misspecification, and distributional shifts. His work spans both theoretical foundations and real-world applications in energy systems, mobility, and AI safety. A central theme is developing algorithms that are provably robust and efficient in uncertain and potentially corrupted environments. The recent publications highlight a strong trend in robust decision-making, particularly in reinforcement learning and preference optimization. Key themes include adversarial robustness in large models, distributional robustness in RL, safe multi-agent systems, and robust Bayesian optimization. The work frequently involves theoretical analysis of regret and sample complexity, combined with practical algorithm design for high-impact applications. Scientific Awards: Google Research Scholar Program Award (Machine Learning & Data Mining), 2023 EPSRC New Investigator Award, 2023 Ilija Bogunovic actively supervises PhD and MSc students, with several of his advisees leading papers accepted to top venues like NeurIPS and ICLR. He has secured competitive research grants, including the EPSRC New Investigator Award, to support his work on robust decision-making. He is building a research team focused on robust and safe AI. He leads a research team and has initiated an online reading group on modern adaptive experimental design and active learning, fostering a collaborative research environment.

• Artificial Intelligence
• Machine Learning
• Artificial Neural Networks
• +19 more

Professor Stephan Chalup is a leading academic in Artificial Intelligence and Machine Learning at the University of Newcastle , affiliated with the School of Information and Physical Sciences and the Data Science and Statistics department. He leads the Interdisciplinary Machine Learning Research Group (IMLRG) and the Newcastle Robotics Lab , where his team has achieved global recognition, including two RoboCup world championships. PhD in Computing Science , Queensland University of Technology (2002) Diplom in Mathematics with Neuroscience , University of Heidelberg His research focuses on artificial neural networks , deep learning , and high-dimensional data analysis , with applications in robotics, computer vision, medical imaging, and architectural analysis. He investigates how biological neural systems inspire robust AI models, particularly in topological data analysis and 4D vision . The recent publications highlight a strong trend in topological and geometric machine learning , with a focus on 4D data analysis , multi-agent reinforcement learning , and robot perception . His work bridges theoretical AI with practical industry solutions in transport, healthcare, and robotics. RoboCup World Champion (2008, 2006) Leadership Excellence, CESE (2024) Supervision Research Excellence Award (2015) Multiple Best Student Paper Awards (2019, 2018, 2011) Chalup has supervised numerous students and led significant research projects, including the ARC Discovery Project on estimating topology of low-dimensional data. His lab fosters interdisciplinary collaboration and innovation, with alumni working in top global tech roles. He is an active keynote speaker and program committee member in major AI conferences. His labs, including the Newcastle Robotics Lab , are equipped with state-of-the-art robots and computing systems, supporting cutting-edge research in humanoid robotics, autonomous navigation, and AI-driven data analysis.

Tom TownsendView profile

Senior Lecturer

• Cyber Security
• IT Infrastructure
• Data Centers
• +7 more

Dr. Tom Townsend serves as a Senior Lecturer at UNSW Canberra within the School of Professional Studies. With 20 years of experience in the higher education sector and federal government consulting, he bridges professional IT leadership with academic instruction in cybersecurity. His educational background includes doctoral research taking a Design Science approach to IT infrastructure and data centers within the context of services availability. His research methodology combines qualitative and quantitative approaches including modeling, scenarios, interviews, and thematic analysis. Townsend's research interests span Cyber Security, Artificial Intelligence (particularly Reinforcement Learning), Serious Games, Wargaming, and IT Infrastructure. His recent work bridges AI, Simulation, and Military Wargaming through interdisciplinary projects connecting Computer Science with Design Science. His publication record demonstrates consistent contributions to fields including data center efficiency (2017-2019) and AI applications in board games (2021-2023), showing an evolving research trajectory from infrastructure to AI applications. As an Education Focused academic, Townsend teaches in the Graduate Certificate in Cyber Security Leadership and Master of Cyber Security (Online) programs, with particular interest in the intersection of Computer Science, Social Sciences, and Management within cybersecurity. Professional highlights include designing recovery programs after cyber incidents at Australian National University, developing strategies for universities and federal government departments, and leading digital transformation programs.