Find Academic Supervisors

• Quantum Control Theory
• Quantum Dynamics
• Quantum Computing
• +5 more

Xiuhao Deng is an Associate Research Fellow and PhD Supervisor at the Institute of Quantum Science and Engineering, Southern University of Science and Technology (SUSTech), with adjunct positions at Pengcheng Lab and Hefei Lab. He obtained his B.S. in Modern Physics from University of Science and Technology of China (USTC) in 2005, followed by an M.S. in Atomic and Molecular Physics from USTC (2009) and a Ph.D. in Physics from University of California, Merced (2015). Research Interests: Driven quantum systems Quantum control theory and open quantum systems Superconducting and spin qubits Quantum error correction Quantum simulation Quantum computing His work focuses on robust quantum gate engineering, scalable quantum control, and error mitigation in multi-qubit systems. Recent publications emphasize geometric correspondence methods, noise resilience, and hardware optimization. Academic Activities: Organized QIP 2020 (international quantum conference) Reviewer for Phys. Rev. X , Phys. Rev. Lett. , and other journals Transferred to Shenzhen International Quantum Academy in 2025 after tenure at SUSTech

• AI-enabled communications
• Green and energy-efficient communications
• Online optimization and machine learning
• +6 more

E. Veronica Belmega is a Full Professor at ESIEE Paris (Université Gustave Eiffel) and a researcher at the LIGM laboratory in Marne-la-Vallée, France. Previously, she served as an Associate Professor at ENSEA graduate school and Deputy Director of the ETIS laboratory in Cergy. She holds an Engineer Degree from the University Politehnica of Bucharest, M.Sc. and Ph.D. from Université Paris-Sud 11, and an HDR habilitation from Université de Cergy-Pontoise. Her research focuses on AI-driven communication systems, energy efficiency, online optimization, and cyber-physical security, with a strong emphasis on applications in smart grids and IoT. Her work spans securing wireless communications against adversarial attacks, optimizing resource allocation in cognitive radio networks, and leveraging machine learning for GNSS localization and MIMO systems. Notable contributions include game-theoretic frameworks for PMU deployment and energy-efficient NOMA systems. Belmega has received prestigious awards such as the 2021 CY Alliance Award and the L’Oréal-UNESCO fellowship, and serves as an Area Editor for IEEE Trans. on Machine Learning in Communications and Networking. Current projects include a CEA LETI postdoc position on AI localization and a PEPR 5G project. She actively contributes to special issues like the EURASIP JASP on sustainable wireless communications. Belmega’s advising includes PhD student S. Maleki, co-author of her 2024 IEEE SmartGridComm Best Paper Award-winning work.

Hafizul AsadView profile

Lecturer

• Formal Verification
• Hybrid Systems
• Intrusion Detection Systems
• +5 more

Dr. Hafizul Asad serves as a Lecturer in Dependability at City St George's, University of London, leveraging his PhD in Electrical Engineering (City University of London, 2016) and MS in Aerospace Engineering (University of Belgrade, 2008) to advance cybersecurity and formal verification research. His expertise bridges critical infrastructure protection and cyber-physical systems security, with significant contributions to IoT/IIoT security frameworks. His educational journey includes: PhD in Electrical Engineering, City, University of London (2012-2016) MS in Aerospace Engineering, University of Belgrade, Serbia (2007-2008) BSc in Electrical and Electronics Engineering, University of Engineering and Technology Peshawar, Pakistan (1999-2003) Asad's research centers on formal verification of hybrid systems and verifiable intrusion detection mechanisms for interconnected environments. He pioneers provably robust security architectures for IoT/IIoT systems, emphasizing mathematical verification to ensure system resilience against cyber threats. His work integrates diversity principles to create defense-in-depth strategies for critical infrastructure, with recent focus on wind turbine cyber-safety and industrial control system protection. Analysis of his 15 most recent publications (2014-2025) reveals an evolution from aerospace applications and analog circuit verification toward cutting-edge cybersecurity for cyber-physical systems. His 2023-2025 work demonstrates increasing specialization in IoT security and formal methods, while maintaining foundational contributions to diversity-based security architectures established in his 2015-2018 research. No scientific awards or prizes are documented in the provided materials, though he maintains professional standing as a British Computer Society member and Higher Education Academy Associate Fellow. Details regarding doctoral student supervision or specific research grants are not disclosed in the source text. His professional trajectory indicates significant project involvement, including the D3S security project at City University of London (2015-2018) and Rolls-Royce-funded Future Systems Simulator development at Cranfield University (2018-2019), though current laboratory affiliations remain unspecified.

Constantin GrigoView profile

Researcher

• Machine Learning
• Uncertainty Quantification
• Stochastic Differential Equ://grigo.userweb.mwn.de/ueber-mich/
• +2 more

Constantin Grigo is a PhD researcher at the Technical University of Munich (TU Munich), actively engaged in the Continuum Mechanics group. His work focuses on Uncertainty Quantification (UQ) and Machine Learning (ML), particularly for applications in maritime safety, bicycle traffic modeling, and stochastic systems. He has presented his research at major conferences like SIAM UQ and WCCM, and has been recognized with Student Travel Awards from SIAM UQ 2018 and SIAM CSE 2019. Education: Master of Science in Physics, LMU Munich (2015) Bachelor of Science in Physics, LMU Munich (2012) Year abroad at Grenoble INP (2010-2011) Research Interests: Probabilistic machine learning for coarse-graining high-dimensional systems Bayesian model and dimension reduction Stochastic differential equations in heterogeneous media Microscopic traffic simulation for bicycles and autonomous vehicles Digital twin applications for maritime and urban mobility Reduced-order modeling of random materials Selected Awards: SIAM UQ 2018: Student Travel Award Winner SIAM CSE 2019: Student Travel Award Winner His publications span topics such as data-driven scenario specification for autonomous vehicles, bicycle maneuver prediction using neural networks, and physics-constrained surrogates for UQ. He also contributes to open-source simulation tools like SUMO for traffic modeling.

• Fluid Power Engineering
• Hydraulic Systems
• Tribology
• +4 more

Prof. Dr.-Ing. Katharina Schmitz serves as Institute Director and Vice Dean at the Institute for Fluid Power Drives and Systems, RWTH Aachen University. Her leadership within the Production Technology Cluster and extensive contributions to fluid power engineering establish her as a leading authority in mechanical engineering research and education. Her research spans fluid power systems, hydraulic component design, tribology, and physics-informed machine learning applications. She pioneers sustainable propulsion solutions through bio-hybrid fuels research while addressing fundamental challenges in polymer material behavior under hydraulic stresses. Current work focuses on carbon-neutral heavy-duty transportation, physics-based neural networks for lubrication modeling, and advanced control systems for electro-hydraulic actuators. Analysis of her 15 most recent publications reveals a dominant trend toward integrating physics-based modeling with deep learning to solve complex engineering problems. Her team consistently develops novel frameworks for cavitation prediction, flow rate determination, and material compatibility assessment - significantly advancing fluid power system reliability, efficiency, and digitalization. Scientific recognition includes: GfT Förderpreis 2023 for experimental and simulative investigation of partially hydrostatic relieved contacts in variable speed axial piston machines As head of the Institute for Fluid Power Drives and Systems, she leads cutting-edge research in sustainable fluid power technologies. The institute maintains strong industry partnerships while driving innovation in hydraulic component design, digital twins for condition monitoring, and next-generation propulsion systems through its position within RWTH Aachen's Production Technology Cluster.

Ahmedullah AzizView profile

Assistant Professor

• Mixed-Signal VLSI Circuits
• Non-Volatile Memory
• Beyond CMOS Device Design
• +4 more

Ahmedullah Aziz is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, within the Tickle College of Engineering. He earned his Ph.D. in Electrical and Computer Engineering from Purdue University (2019), an M.S. from Pennsylvania State University (2016), and a B.S. from Bangladesh University of Engineering & Technology (2013). His research focuses on mixed-signal VLSI circuits, non-volatile memory, and beyond-CMOS device design, emphasizing emerging technologies like ferroelectrics and spintronics. He leads the NorDIC Lab and has published over 60 articles in journals, conferences, and patents. Education: PhD, Electrical & Computer Engineering, Purdue University, 2019 MS, Electrical Engineering, Pennsylvania State University, 2016 BS, Electrical & Electronic Engineering, BUET, 2013 Research Interests: Aziz explores device-circuit-system co-design techniques, particularly in cryogenic neuromorphic systems, superconducting memory, and ferroelectric-based circuits. His work bridges material innovation with practical applications, aiming to advance energy-efficient and high-performance computing. Awards: EDAA Outstanding Dissertation Award (2019/2020) Outstanding Graduate Student Research Award (Purdue, 2019) Samsung 'Icon' Award (2013) Best Publication Awards (SRC-DARPA STARnet, 2015/2016) Advising & Grants: While specific grants are not detailed, his research has been supported by prestigious institutions. He has advised/co-advised projects in emerging technologies but no listed students. His work extends to reviewing IEEE journals, conference TPC roles, and editorial contributions. Labs & Teams: He directs the NorDIC Lab, focusing on next-generation computing and device innovation.

• Human Factors Engineering
• Neuroergonomics
• Robotic Surgery
• +3 more

Denny Yu is an Associate Professor at the Edwardson School of Industrial Engineering, Purdue University. His work bridges human factors, neuroergonomics, and healthcare safety through advanced sensor systems and AI. Primary Affiliation : Edwardson School of Industrial Engineering, Purdue University Research Themes : Surgical ergonomics, autonomous vehicle human factors, cognitive workload assessment, multimodal physiological sensing Dr. Yu's research focuses on neuroergonomics and human-robot interaction , particularly in surgical and transportation contexts. His team develops sensor-based systems for workload monitoring, including: EEG-eye tracking fusion for situation awareness Wearable exoskeletons for surgical posture support Computer vision tools for lifting task risk analysis Smart infusion pump usability frameworks AI-driven surgical coaching systems Recent publications emphasize deep learning applications in soft tissue deformation estimation and real-time adaptive systems for robotic surgery augmentation. His work spans both occupational health (veterinary surgeons, airport workers) and medical device innovation domains.

Kirsty HassallView profile

Assistant Professor

• Applied Statistics
• Bayesian Modeling
• Agricultural Statistics
• +5 more

Kirsty Hassall is an Assistant Professor in Applied Statistics at the Department of Statistics, University of Warwick, having joined the department in 2024. She has previously worked for nearly 10 years as an applied statistician in the agri-environment sector, bringing extensive practical experience to her academic role. Dr. Hassall holds a PhD in "The temporal and spatial analysis of single cell gene expression" from the University of Warwick (2015). She is a Chartered Statistician with the Royal Statistical Society (2019) and has served as a committee member (2017-2021) and currently as Secretary (2021-present) of the British and Irish Region of the International Biometric Society (IBS-BIR). Her research focuses on developing robust hybrid models that combine empirical, mechanistic, and stochastic elements. With expertise spanning Bayesian networks, spatially coherent clustering techniques, and farm typology development, Dr. Hassall applies statistical methods to solve complex problems in agricultural and environmental science. Her work bridges traditional statistical consultancy (mixed models, multivariate statistics, experimental design) with methodological developments for real-world applications. Analysis of Dr. Hassall's recent publications reveals a strong interdisciplinary focus at the intersection of statistics, agriculture, and environmental science. Her work demonstrates consistent application of advanced statistical techniques to address pressing challenges in food security, sustainable agriculture, and environmental management. Key trends include the development of Bayesian Belief Networks for soil quality assessment, spatial modeling for crop production estimation, and statistical approaches to address gender dimensions in agricultural research. Chartered Statistician (Royal Statistical Society, 2019) Dr. Hassall is actively involved in research supervision and teaching, currently offering the ST344: Professional Data Science course. She has secured funding for PhD research, including a fully funded EPSRC scholarship project titled "Unravelling the sound underground: Detecting and estimating earthworm abundance using low-frequency vibrations." She also organizes the "Statistical Challenges in the Agri-Environmental Sector" workshop at the University of Warwick. Through her extensive publication record and active participation in professional societies, Dr. Hassall has established herself as a researcher who effectively bridges statistical methodology with practical applications in the agri-environmental sector. Her collaborative approach is evident in her numerous multi-institutional research projects across the UK, Africa, and Europe.

• Intelligent Transportation Systems
• Traffic Network Optimization
• Stochastic Demand Modeling
• +3 more

Kun An is a Professor at the Department of Traffic Information and Control Engineering within the College of Transportation Engineering at Tongji University. She has previously held academic positions at Monash University (Senior Lecturer, 2018–2019; Lecturer, 2016–2018) and conducted postdoctoral research at the University of Illinois at Urbana-Champaign (2015–2016) and The Hong Kong University of Science and Technology (2014–2015). Her academic journey includes a PhD in Civil Engineering from HKUST (2014) and a Bachelor's degree from Tongji University (2009). PhD: Civil Engineering, The Hong Kong University of Science and Technology (2014) Bachelor's: Transportation Engineering, Tongji University (2009) Dr. An specializes in intelligent transportation systems, focusing on optimizing complex traffic networks, characterizing traveler behavior in stochastic environments, and advancing electric vehicle sharing infrastructure. Her research spans urban transit planning, carsharing logistics, battery electric bus deployment, and real-time traffic signal optimization, with applications to mitigate rail disruptions and enhance multimodal connectivity. Her publications emphasize solving transport challenges through stochastic programming, game theory, and behavioral analysis. Key themes include autonomous vehicle integration, demand uncertainty modeling, and sustainable mobility solutions. Awards include the Hong Kong PhD Fellowship and multiple best paper recognitions. Hong Kong PhD Fellowship (2010–2014) TRBADB30 Best Paper Nomination (2014) Best Paper at 18th Hong Kong Transportation Annual Meeting (Second Author, 2013)

Anuran MakurView profile

Assistant Professor

• Machine Learning Theory
• Information Theory
• Applied Probability
• +4 more

Anuran Makur is an active Assistant Professor at Purdue University with dual appointments in the Department of Computer Science (College of Science) and the Elmore Family School of Electrical and Computer Engineering (College of Engineering). He is affiliated with the Institute for Control, Optimization and Networks (ICON) and teaches foundational courses in machine learning and data science. His educational background includes a B.S. in Electrical Engineering and Computer Sciences from UC Berkeley (2013, summa cum laude), an S.M. in Electrical Engineering and Computer Science from MIT (2015), and a Sc.D. from MIT (2019). B.S., UC Berkeley, 2013 S.M., MIT, 2015 Sc.D., MIT, 2019 Makur's research bridges theoretical machine learning, information theory, and applied probability. Key interests include ranking/preference learning, optimization for ML, non-parametric inference, information measures, permutation channel limits, broadcasting on graphs, and reliable computation. His work emphasizes fundamental theoretical limits and mathematical rigor in complex systems. Recent publications reveal strong trends in statistical learning theory (40%), information-theoretic methods (35%), and networked systems (25%), with growing emphasis on privacy-aware inference and high-dimensional statistics. His scientific achievements are recognized by prestigious awards: Arthur M. Hopkin Award (UC Berkeley, 2013) Ernst A. Guillemin Master's Thesis Award (MIT, 2015) Jin Au Kong Doctoral Thesis Award (MIT, 2020) Thomas M. Cover Dissertation Award (IEEE, 2021) NSF CAREER Award (2023) While specific advising details aren't public, his research leadership is evident through ICON affiliation and collaborations with MIT's LIDS/IDSS groups. The NSF CAREER grant supports his work on information-theoretic foundations of machine learning. He maintains active roles in theoretical computer science and information theory communities through conference organization and editorial work. Makur leads research within ICON, focusing on control-theoretic approaches to networked learning systems. His work integrates probabilistic modeling with optimization theory, particularly for distributed inference and networked decision-making under uncertainty.

Alain HecqView profile

Professor

• Econometrics
• Time Series Analysis
• Financial Economics
• +5 more

Alain Hecq is a Full Professor in the department of QE Econometrics at the School of Business and Economics, Maastricht University. His research focuses on econometric methodologies, particularly in time series analysis, noncausal models, and financial econometrics. He has contributed significantly to the understanding of volatility dynamics, cryptocurrency markets, and inflation targeting regimes. His work often addresses policy-relevant questions in macroeconomics and financial markets. Key research interests include mixed causal-noncausal autoregressive models, volatility modeling with MARMA-GARCH frameworks, and the application of these techniques to real-world phenomena such as oil price bubbles and cryptocurrency volatility. He has also explored the credibility of central banking policies during crises, such as the Brazilian inflation-targeting regime during the pandemic. His recent work emphasizes methodological advancements in high-dimensional time series analysis, including spectral estimation, hierarchical regularizers for mixed-frequency data, and reduced-rank matrix autoregressive models. These contributions reflect a blend of theoretical rigor and practical applicability in addressing complex economic and financial problems. While no formal awards are listed, his extensive publication record and focus on cutting-edge econometric techniques underscore his scholarly impact. Advising and grant activities are not detailed in the provided information, but his research demonstrates sustained engagement with both academic and policy-oriented audiences.

Zuduo ZhengView profile

Professor

• Traffic Flow Theory and Modelling
• Intelligent Mobility Technologies
• Connected and Automated Vehicles (CAVs)
• +5 more

Professor Zuduo Zheng is a faculty member at the University of Queensland, holding the position of Professor & Deputy TMR TAP Chair in the School of Civil Engineering. His research focuses on traffic flow theory, modeling, simulation, and optimization, particularly in the context of connected and automated vehicles (CAVs), traffic safety, and sustainable infrastructure systems for smart cities and major events like the 2032 Olympic and Paralympic Games. He earned his Doctor of Philosophy from Arizona State University and has served as a DECRA Research Fellow. Currently, he is a member of the Australian Research Council's College of Experts and ranks among the Top 2% of Scientists in Logistics and Transportation (Scopus & Stanford University). His work has led to prestigious awards and editorial roles in journals like Transportation Research Part B and IEEE Transactions on Intelligent Transportation Systems . Research interests include traffic flow dynamics, emerging mobility technologies, strategic transport planning, advanced data analysis techniques, and meta-research methodologies. His lab, the Connected and Automated Vehicle Driving Simulation Lab, utilizes cutting-edge tools like VR simulators and SUMO integration to explore mixed traffic scenarios and safety challenges. Key grants include projects on eco-driving strategies for CAVs, real-time traffic signal systems, and infrastructure design for future mobility. He has supervised numerous PhD and Master’s students, contributing to studies on CAV integration, traffic simulation, and policy analysis. His publications address topics such as road user charging theories, reinforcement learning for eco-driving, and paratransit system improvements. The lab’s facilities, including a mixed reality mobility testbed, support research into transitioning to CAVs and enhancing urban resilience.

Pierre NyquistView profile

Associate Professor

• Probability Theory
• Mathematical Statistics
• Applied Mathematics
• +8 more

Pierre Nyquist is an Associate Professor and docent in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University. His research is sponsored by the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). He is also an elected member of the Young Academy of Sweden for the period 2024-2029 and has served as a scientific ambassador for EURANDOM since November 2021. Dr. Nyquist's research interests lie at the intersection of probability theory, mathematical statistics, and applied mathematics. His main expertise is in probability theory, with a focus on large deviations theory and stochastic numerical methods. He has a general interest in all aspects of probability theory and much of what is categorized as applied mathematics, particularly questions related to partial differential equations, optimization, and stochastic optimal control. Recently, he has become increasingly interested in the mathematical foundations of complex data analysis and modeling, and the interplay with ideas from physics. His current research interests include large deviations, gradient flows and their generalizations, stochastic numerical methods, statistical learning theory, stochastic processes, and random dynamical systems. Pierre Nyquist has received research funding from several prestigious sources including the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). His publications demonstrate a consistent focus on theoretical aspects of probability with applications to computational methods and data analysis, showing increasing integration with machine learning techniques in recent years. elected member of the Young Academy of Sweden (2024-2029) scientific ambassador for EURANDOM (since November 2021) Dr. Nyquist is actively involved in mentoring the next generation of researchers. He currently supervises several PhD students including Cinja Arndt (starting Aug. 2025), Niki Wilhemlson (started Aug. 2024), and Viktor Nilsson (started Aug. 2020). He has previously supervised successful PhD students such as Federica Milinanni (Aug. 2020-May 2025) and Carl Ringqvist (Aug 2015-June 2021). He regularly teaches graduate-level courses including "Modern methods of statistical learning" and has supervised numerous MSc theses on topics ranging from deep learning for time-series radar signals to neural network embedding in insurance pricing. His research group is active in both theoretical developments and practical applications, with current projects spanning from mathematical foundations of probability to applications in machine learning and data science. Dr. Nyquist maintains strong international collaborations, as evidenced by his frequent travel for conferences and research visits to institutions such as Brown University and TU Delft.

• Computer Vision
• Artificial Intelligence
• Multimodal Learning
• +4 more

Ke Yan is an Associate Professor in the Department of Computer Science at the National University of Singapore's College of Design and Engineering. With extensive research output from 2021-2026, their work spans computer vision, artificial intelligence, and multimodal learning systems. National University of Singapore (Primary Affiliation) Collaborations with University of Electronic Science and Technology of China Research ties with Tencent Youtu Lab in Shanghai Research focuses on advancing computer vision techniques, particularly in medical image analysis, vision-language integration, and fault diagnosis systems. Their work bridges theoretical AI development with practical applications in healthcare, industrial systems, and environmental monitoring. Notable contributions include novel approaches to multimodal learning, medical image segmentation with sparse annotations, and improving reliability of large vision-language models. Recent publication trends (2024-2026) demonstrate increasing focus on multimodal systems, with significant contributions to vision-language model reliability, medical AI applications, and efficient transfer learning techniques. Their work frequently addresses critical challenges like hallucination mitigation in large models and sparse data scenarios in medical imaging. As an advisor, they mentor multiple researchers including Junlong Du, Shouhong Ding, and Zhiwen Lin, with whom they frequently collaborate on cutting-edge computer vision projects. Their research group maintains strong industry connections, particularly with Tencent's AI research division. The research is conducted within NUS's computer vision and AI research ecosystem, collaborating with multiple laboratories focused on multimodal intelligence and practical AI deployment in real-world systems.

Fritz BuschView profile

Professor

• Traffic Engineering
• Urban Transportation Systems
• Environmental Engineering
• +4 more

Prof. Dr.-Ing. Fritz Busch holds the Professorship of Traffic Engineering and Control at the Department of Civil, Geo and Environmental Engineering at Technische Universität München (TUM). His research focuses on urban transportation systems, traffic dynamics, and sustainable mobility solutions. He leads projects such as RASCH (RAdSCHnellwege) and RadOnTime, exploring efficient bicycle infrastructure and traffic management strategies. His work integrates advanced simulation techniques, data-driven methodologies, and cooperative vehicle-infrastructure systems to enhance urban mobility resilience and reduce environmental impacts. Educational Background: While specific academic qualifications are not detailed here, his title of Professor Dr.-Ing. indicates a doctoral degree in engineering and significant academic contributions. His professorship at TUM underscores expertise in traffic engineering and control systems. Research Interests: Busch’s research spans macroscopic traffic dynamics, bicycle and pedestrian traffic management, emissions reduction through electric vehicle integration, and the application of smart technologies (e.g., V2X communication) for traffic optimization. His projects often involve interdisciplinary collaboration, combining engineering, data science, and urban planning to address complex mobility challenges. Awards and Recognition: While no specific awards are listed, his prolific publication record and involvement in high-profile TUM initiatives reflect his scholarly impact and leadership in transportation engineering. Advising and Grants: Though specific grants are not mentioned, his active participation in projects like UR: BAN (Urban Road Network) and numerous collaborations suggest extensive grant-funded research. Advising activities are inferred through his role as a professor and project leader but are not explicitly detailed here. Labs and Teams: His work leverages the TUM-VT bicycle simulator and SUMO (Simulation of Urban MObility) tools for traffic simulation studies. He collaborates with interdisciplinary teams on projects such as eco-sensitive traffic management and automated vehicle integration.