Find Academic Supervisors

Meryem SimsekView profile

Researcher

• 5G Wireless Systems
• Reinforcement Learning
• Heterogeneous Wireless Networks
• +7 more

Meryem Simsek is a researcher at the International Computer Science Institute (ICSI) since 2016. She earned her Dipl.-Ing. (2008) and Ph.D. (2013) in Electrical Engineering from the University of Duisburg-Essen, Germany, as a scholar of the German National Academic Foundation. Her academic career includes a post-doctoral visiting scientist role at Florida International University (2013) and a group leadership position at the Vodafone Chair Mobile Communication Systems at Technical University Dresden (2014). She chairs the IEEE ComSoc Tactile Internet technical sub-committee and serves as secretary of the IEEE P1918.1 working group, while leading the ETSI IP6 work item on IPv6-based Tactile Internet. Education: Dipl.-Ing. in Electrical Engineering and Information Technology, University of Duisburg-Essen (2008) Ph.D. in Reinforcement Learning-Based Inter-Cell Interference Coordination, University of Duisburg-Essen (2013) Her research focuses on 5G wireless systems, resource management in heterogeneous networks, tactile internet standards, and network design optimization. She employs machine learning and reinforcement learning for self-organizing networks and ultra-reliable low-latency communication (URLLC). Her publications highlight trends in integrated access/backhaul networks, 6G xURLLC, serverless mobile computing, and IoT/metaverse convergence. Scientific Awards: IEEE Communications Society Fred W. Ellersick Prize (2015) Dr. Simsek contributes to IEEE P1918.1 standards for tactile internet, leads ETSI IP6 work items, and collaborates on 5G/6G research. She has no formal student advisement records listed but shapes wireless network innovation through her leadership roles.

• Machine Learning
• Deep Learning
• Cognitive AI
• +11 more

Volker Tresp is a Professor at the Ludwig-Maximilians-Universität München (LMU) and a leading researcher in machine learning for relational structured domains . His work bridges cognitive AI , knowledge graphs , and quantum computing . He is a PI in the Munich Center for Machine Learning (MCML) and co-director of the ELLIS program on Semantic, Symbolic, and Interpretable Machine Learning . His research interests focus on temporal knowledge graphs , foundation models , multimodal learning , and quantum machine learning . Recent projects include WebPilot (multi-agent web task execution) and FedBiP (federated learning with diffusion models). His work on PyKEEN and RESCAL has advanced knowledge graph embeddings . Volker Tresp's scientific contributions are recognized through ELLIS Fellowship (2020) , Siemens Inventor of the Year (1996) , and Best Paper Awards at ISWC 2021 and IEEE ICHI 2020 . His students have published extensively at top AI venues like AAAI , CVPR , and ECCV . Awards and Honors: ELLIS Fellow (2020) Siemens Inventor of the Year (1996) Best Paper Award, ISWC 2021 Student Best Paper Award, ISWC 2017 Best Paper Runner-up, PKDD 2005

• Wireless Sensor Networks
• Computer Engineering
• IoT
• +7 more

Dr. Uroš Pešović serves as an Associate Professor in the Department of Computer and Software Engineering at the Faculty of Technical Sciences in Čačak, University of Kragujevac, Serbia. With over 15 years of academic service, he has progressed from teaching associate (2007-2018) to assistant professor (2018-2023) and currently holds the position of associate professor for Computer Engineering (2023-2028). Elementary school "Milica Pavlović" Čačak (1989-1997), average grade 5.00 - holder of Vuk's diploma Secondary Technical School Čačak (1997-2001), automation electrical technician, average grade 5.00 - holder of Vuk's diploma Technical Faculty, Čačak, University of Kragujevac (2001-2006), B.Sc. electrical engineer for computer technology, average grade 9.03 Faculty of Electrical Engineering, Computer Science and Informatics, University of Maribor, Slovenia (2007-2010), Master of Electrical Engineering, Major in Telecommunications, average grade 9.75 Faculty of Electrical Engineering, Computing and Informatics, University of Maribor, Slovenia (2010-2016), doctor of technical sciences, doctoral dissertation "Error probability model for IEEE 802.15.4 wireless transmission with co-channel interference and background noise" Dr. Pešović's research spans wireless sensor networks, IoT applications, computer architecture, and reliability engineering. His work demonstrates a strong focus on practical implementations of theoretical models, particularly in IEEE 802.15.4 wireless communication protocols and their applications in precision agriculture, industrial monitoring, and network communication systems. His expertise bridges hardware and software design, with recent work emphasizing machine learning applications for predictive maintenance and anomaly detection in storage systems. Analysis of his recent publications reveals a clear evolution from foundational wireless communication research toward more applied work in precision agriculture, industrial IoT, and educational technology. His current research emphasizes practical problem-solving in agricultural technology using LoRa and other wireless technologies, predictive maintenance of storage systems using machine learning, and innovative approaches to computer engineering education. Dr. Pešović teaches across all academic levels, including undergraduate courses in Computer Architecture, Organization of Computer Systems, Microcontroller Systems, and Computer Networks, as well as graduate courses in Hardware-Software Design, Intelligent Sensors, and Industrial Communication Networks. At the doctoral level, he teaches courses on integrated hardware-software design, disk performance, and telecommunication systems modeling.

Ulya R. KarpuzcuView profile

Associate Professor

• Physics of Computing
• Energy-Efficient Computing
• Application-Domain Specialized Architectures
• +6 more

Ulya R. Karpuzcu is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota, Twin Cities. Her research spans physics of computing, energy-efficient computing, application-domain specialized architectures, hardware security and reliability, and approximate computing. Her research interests focus on understanding how physical principles shape computing paradigms to address emerging energy problems. Key areas include: Physics of computing and physical limits of computation Energy-efficient computing through novel architectures Hardware security and reliability Approximate computing techniques Spintronics and non-volatile memory computing Quantum computing architectures Dr. Karpuzcu's recent publications show a strong focus on in-memory computing, Ising machines for combinatorial optimization, spintronics-based architectures, and quantum computing. Her work bridges theoretical foundations with practical hardware implementations, particularly exploring energy-efficient solutions through novel computing paradigms that address physical constraints. Her notable awards include: NSF CAREER Award (2016) on 'Trading Communication and Storage for Computation to Enhance Energy Efficiency' NSF SPX grant (2017) on scalable in-memory processing using spintronics NSF CCF grant (2014) on soft (approximate) near-threshold voltage computing NSF XPS grant (2014) on approximate communication to enhance energy efficiency Dr. Karpuzcu actively mentors PhD students and undergraduate researchers, with her lab 'wide open to motivated Ph.D. students and undergraduate researchers.' She has served on technical program committees for major conferences including ISCA, HPCA, MICRO, and ICCAD, and has co-organized workshops on Near-threshold Voltage Computing. She teaches courses on computer architecture, physics of computation, and quantum computing, including developing innovative courses like 'Physics of Computation' inspired by Richard Feynman's lectures.

Su YaorongView profile

Professor

• Photocatalytic Materials
• Energy Photocatalysis
• Organic Semiconductor Materials
• +3 more

Su Yaorong is a Professor at the School of New Materials and New Energy, Shenzhen University of Technology, and a Shenzhen Overseas High-Level 'Peacock Plan' Talent (Category C). He holds a PhD in Electronic Engineering from The Chinese University of Hong Kong and has led significant research projects funded by the National Natural Science Foundation of China and Shenzhen University of Technology's High-Level Talent Research Fund. 2004: Bachelor of Engineering in Polymer Materials Science and Engineering, Wuhan University of Technology 2007: Master of Materials Science, Wuhan University of Technology 2013: PhD in Electronic Engineering, The Chinese University of Hong Kong His research focuses on photocatalytic materials for energy conversion, perovskite optoelectronic materials , and low-power organic semiconductor devices . His work addresses challenges in solar energy utilization, photocatalytic hydrogen production, and flexible electronics. Recent publications highlight advancements in Z-scheme heterojunctions, defect-engineered photocatalysts, and solution-processed dielectrics for organic transistors. Notable scientific awards include: Shenzhen Overseas High-Level Talent (Category C) (2019) Second Prize of Building Materials Science and Technology (2009)

Matthew KraftView profile

Professor

• Economics of Education
• Education Policy Analysis
• School Organizational Capacity
• +3 more

Matthew Kraft is a Professor of Education and Economics at Brown University, with affiliations including the National Bureau of Economic Research (Research Associate), IZA - Institute of Labor Economics (Research Fellow), J-PAL North America (Invited Researcher), and the White House Council of Economic Advisers (Senior Economist, 2024-2025). He leads SustainableED, an initiative examining education systems' responses to climate change. EdD in Quantitative Policy Analysis in Education - Harvard University (2013) MA in International Comparative Education - Stanford University (2003) BA in International Relations - Stanford University (2003) Kraft's research focuses on K-12 educator effectiveness, school organizational capacity, and climate change adaptation in education. His work spans teacher evaluation systems, professional development, and educational interventions like tutoring programs, employing causal inference methodologies. His publications demonstrate expertise in education economics, policy analysis, and quantitative methods. Recent articles indicate Kraft's research trajectory spans teacher labor markets, educational interventions, and teacher-family dynamics. His work increasingly addresses climate change implications for education systems, with grants from the Institute at Brown for the Study of Environment and Society and Brown University Office of Sustainability. Outstanding Public Communication Award (AERA) William T. Grant Scholar Award Brown Dean’s Teaching Excellence Award Palmer O. Johnson Memorial Award National Academy of Education/Spencer Fellowship Kraft has secured over $5.8 million in grants, including NSF funding for math tutoring analysis and William T. Grant Foundation support for mentoring programs. His teaching includes advanced courses in education economics, statistics, and policy analysis.

Donatella SciutoView profile

Professor

• Embedded systems
• Multiprocessor System on Chip
• Reconfigurable systems
• +5 more

Donatella Sciuto is a Full Professor in Computer Engineering at the Department of Electronics and Information of Politecnico di Milano, where she conducts research in System Architectures. She serves as Deputy Director of Education at CEFRIEL, managing executive company education training programs. Her academic career spans multiple leadership roles in professional organizations including the Council of EDA (CEDA) where she served as VP of Finance (2008-2009) and is currently President Elect. Her research focuses on embedded systems design methodologies, with particular expertise in Multiprocessor System on Chip, reconfigurable systems, hardware/software co-design, and design space exploration techniques. She leads the Embedded Systems Design and Design Methodologies research group (sagroup.elet.polimi.it) and has contributed significantly to the field of Electronic Design Automation. Her publication record shows a consistent focus on reconfigurable computing and MPSoC design, with recent work emphasizing dynamic reconfiguration techniques, hardware/software synchronization in multiprocessor systems, and optimization algorithms for heterogeneous architectures. Her research demonstrates a progression from foundational work in embedded systems to more complex system-level design methodologies for modern reconfigurable architectures. She has held editorial positions with prestigious journals including IEEE Transactions on Computers, Journal of Design Automation of Embedded Systems, and IEEE Embedded Systems Letters. She has served on program committees for major conferences including DAC, ICCAD, DATE, and CODES+ISSS, and has held leadership roles including Publications Chair for IEEE/ACM ESWEEK 2007 and Co-General Chair of ESWEEK 2009. Prof. Sciuto has advised more than 10 PhD students and currently mentors 4 PhD candidates at Politecnico di Milano. She teaches courses in Computer Architecture, Operating Systems, and High Performance Processors and Systems. She has published 5 teaching books in Italian across multiple editions and maintains active research collaborations through European and national research projects she coordinates.

Jianfeng XueView profile

Associate Professor

• Reliability analysis of soil structures
• Behaviour of reinforced soil structure
• Landfill waste behaviour
• +6 more

Associate Professor Jianfeng Xue is a faculty member in the School of Engineering and Technology at the University of New South Wales (UNSW) Canberra. He holds the position of Associate Professor in Geotechnical Engineering and is a Fellow member and Chartered Professional Engineer accredited by the Institution of Engineers Australia. With extensive experience in both academia and industry, Professor Xue previously worked at Coffey Geotechnics (Perth), Tongji University, Monash University, and Federation University Australia before joining UNSW Canberra. Professor Xue completed his PhD at University College Dublin in 2007. His educational background has provided a strong foundation for his research and teaching career in geotechnical engineering. Professor Xue's research spans multiple critical areas in geotechnical engineering with a focus on practical applications. His work addresses fundamental challenges in soil mechanics, foundation engineering, and sustainable infrastructure development. He has made significant contributions to understanding soil-structure interaction, reinforced soil systems, and the behavior of landfill waste materials. His research integrates advanced computational methods with experimental approaches to solve complex geotechnical problems. Recent publications demonstrate his continued leadership in areas such as geogrid-reinforced pavements, sustainable construction materials, and numerical modeling of soil behavior under various loading conditions. Chartered Professional Engineer, Institution of Engineers Australia (2014-present) Fellow, Institution of Engineers Australia (2019) Committee member, Institution of Engineers Australia, ACT Civil/Structural Branch (2017-present) Member, International Society for Soil Mechanics and Geotechnical Engineering Secretary, Australian Geomechanics Society, ACT group (2019) Professor Xue actively supervises PhD students including CHAI Fei (focusing on pile capacity estimation using CPT database), XIAO Li (improving pavement performance with geogrid reinforced asphalt), Ziheng Wang (cyclic behavior of reinforced soils), and others. His research portfolio includes significant grants from the Australian Research Council and industry partners, totaling over $1.5 million in the past five years. His laboratory is well-equipped with modern geotechnical testing facilities including monotonic and cyclic triaxial cells, unsaturated triaxial equipment, and large direct shear boxes. He also has access to advanced computational tools including Abaqus, ANSYS, FLAC, Plaxis, and discrete element modeling software.

• Applied Statistics
• Geology
• Materials Science
• +3 more

Dr. Eleanor Stillman is a Senior Lecturer in Probability & Statistics at the School of Mathematical and Physical Sciences, University of Sheffield, where she has been a faculty member since 1987 after completing both her BSc and PhD at the institution. Her career exemplifies deep institutional commitment and interdisciplinary impact across scientific domains. Her educational foundation at Sheffield enabled her specialization in applied statistics, with research focusing on geological and archaeological applications (particle size modeling for sediment transport and cereal landrace analysis) and materials science (composite strength, glaze design, manufacturing process control). She also pioneers statistical education through computer-assisted learning tools and studies on school-university transitions. Analysis of her 2016-2024 publications reveals three dominant threads: archaeological morphometrics (geometric analysis of ancient grains), industrial quality control (multivariate monitoring of tool wear and manufacturing), and biological/educational applications (zebrafish breeding statistics, school outreach programs). This demonstrates remarkable versatility in solving real-world problems from prehistoric agriculture to modern industry. Her professional recognition includes: Chartered Statistician designation from the Royal Statistical Society Dr. Stillman has secured significant research funding as Coinvestigator on EPSRC grants including Simulation Tools for Automated and Robust Manufacturing and Quantitative characterisation of particle clustering in MMCS . She actively shapes statistical practice through service on the University's MASH steering committee and as an Associate Consultant to the Statistical Services Unit for training and ethical review. Within the School, she contributes to the Mathematical and Statistical Modelling research group, which drives collaborative projects bridging theoretical statistics with geological, archaeological, and industrial challenges.

• Climate Finance
• Renewable Energy
• Dynamics of Energy Transition
• +8 more

Dr. Sumit Kothari is a Research Fellow at University College London's Bartlett School of Environment, Energy and Resources, specializing in climate finance and low-carbon transition dynamics. His work bridges financial systems analysis with environmental sustainability, leveraging industry experience from Morgan Stanley and entrepreneurial background in Indian investment startups to address real-world climate finance challenges. His educational foundation includes: PhD in Sustainable Resources and Climate Policy (UCL, 2019-2024) MSc Economics and Policy of Energy and the Environment (UCL, 2016-2017) Post Graduate Diploma in Management (IIM Calcutta, 2002-2004) Bachelor of Commerce (University of Mumbai, 1998-2001) Dr. Kothari's research centers on Climate Finance and Energy Transition Dynamics , employing Complexity Economics to analyze investment networks and risk underwriting in developing countries. His work on Technology Scaling examines financial mechanisms for accelerating renewable adoption, while his expertise in Computational Statistics informs analysis of environmental resource economics and policy design. Key contributions address financial tipping points, equity in climate finance flows, and fossil fuel finance phase-out challenges. His publication record (2021-2025) reveals three dominant trends: 1) Systemic analysis of financial mechanisms driving sustainability transitions, 2) Critical examination of equity gaps in international climate finance, particularly for developing nations, and 3) Application of complexity theory to low-carbon finance markets. These works, published in Nature Communications, Nature Climate Change, and Earth System Dynamics, demonstrate increasing impact with multiple papers exceeding 100 citations. No major scientific awards were identified in the source materials, though his research has influenced policy discussions and been covered by numerous news outlets including Financial Times and Bloomberg. Dr. Kothari's advisory activities focus on sustainable development goals 7, 11, and 13, with collaborations spanning UCL, University of Strathclyde, and industry partners. Current research grants investigate: Financial tipping points for accelerated decarbonization Equity frameworks for international climate finance Complexity modeling of low-carbon investment networks He contributes to UCL's Climate Finance and Technology Scaling research cluster, collaborating with environmental economists and complexity scientists to develop practical financial instruments for the low-carbon transition while maintaining industry connections through his private investment startup cofounding role.

Dongkuan XuView profile

Assistant Professor

• Machine Learning
• Natural Language Processing
• Computer Vision
• +7 more

Dongkuan Xu is an Assistant Professor in the Department of Computer Science at North Carolina State University, leading the Generative Intelligent Computing (GIC) Lab. His research spans Artificial Intelligence , focusing on scalable, efficient, and reliable AI systems for large language models, diffusion models, and generative frameworks. Education Ph.D. in Computer Science, Pennsylvania State University (2022) M.S. in Optimization and Data Mining, University of Chinese Academy of Sciences (2017) B.E. in Information Management and System, Renmin University of China (2014) His research explores test-time adaptation, computational efficiency, and trustworthy AI through uncertainty-aware learning and robust generalization. Applications include Education , Robotics , Agriculture , Networking , and Healthcare . Recent work emphasizes hardware-algorithm co-design and domain-specific AI platforms like Synthora and Gentopia. His publications from 2024–2025 highlight advancements in Vision Transformers , Diffusion Models , Edge Computing , and Trustworthy AI , with grants from NVIDIA , Microsoft , and the National Science Foundation . Awards include the AAAI Best Demonstration Award (2025), NVIDIA Academic Grant (2025), and the Carla Savage Award (2024). He actively mentors undergraduate researchers and co-chairs workshops at top venues like DAC , CVPR , and KDD , aiming to democratize AI access and enhance its reliability across real-world domains.

Jens OpitzView profile

Professor

• Transportation Systems
• Railway Timetabling
• Optimization Algorithms
• +3 more

Jens Opitz is a Professor at the Faculty of Transportation Science at Technical University of Dresden, where he is based at Hettnerstraße 1, 01069 Dresden (Room POT 105). He teaches courses including Theory of Transportation Systems and Statistics, Reliability Theory, Advanced Theory of Transportation Systems, Surface Transport Aviation and Simulations, and Actual Aspects in Optimization of Processes in Transportation and Logistics. His research focuses on transportation systems optimization, particularly in railway operations. Dr. Opitz has developed significant methods for generating synchronized timetables, integrating freight traffic into passenger timetables, and optimizing rail network operations. His work bridges theoretical optimization approaches with practical railway operations challenges, with applications in both passenger and freight rail transport. Dr. Opitz's publication record shows a strong focus on railway timetable optimization, with recurring themes in periodic scheduling, conflict resolution, and capacity utilization. His research has evolved from foundational work on modulo network simplex methods to more recent applications in complex railway station track allocation and integrated freight-passenger timetable planning. The publications demonstrate expertise in both theoretical algorithm development and practical implementation of railway optimization systems. His key research projects include Timetabling, Enhancement of existing methods for the generation of synchronized timetables, Integration of freight traffic into the generation of synchronized timetables, and Algorithms for synchronized timetable in railway traffic. These projects reflect his dedication to improving railway operations through advanced optimization techniques and software implementations like the TAKT program system for automatic routing in railway networks.

• Wireless Communications
• Digital Signal Processing
• MIMO Systems
• +5 more

Risto Wichman is a Professor in the Department of Information and Communications Engineering at Aalto University's School of Electrical Engineering. His research focuses on signal processing techniques for wireless communication systems, spanning physical layer design and MAC layer innovations with applications in next-generation networks. His research interests center on in-band full-duplex communications, multiple antenna systems (including massive MIMO), RF non-ideality compensation, spectrum sharing, cooperative communications, and network topology optimization. He employs advanced methodologies from communication theory, detection and estimation theory, stochastic geometry, random matrix theory, and large system analysis to solve complex wireless challenges. Recent publications (2024-2025) reveal strong emphasis on satellite-terrestrial integration, optical wireless networks, and machine learning-driven resource allocation. Key themes include line-of-sight optimization for positioning/sensing, Doppler effect characterization in LEO networks, full-duplex protocol design, and information harvesting for wireless power transfer. His work bridges theoretical foundations with practical implementations for 5G/6G systems. No scientific awards are documented in the provided materials. Information regarding student advising and research grants is not specified in the available sources. His leadership of the Risto Wichman Research Group drives collaborative work in wireless signal processing. The Risto Wichman Research Group focuses on cutting-edge wireless communication systems, with active projects spanning satellite networks, optical wireless integration, full-duplex technologies, and machine learning applications for network optimization. The group maintains strong industry and academic collaborations while developing solutions for real-world deployment challenges.

Abigail BatemanView profile

Researcher

• Reliability of offshore renewable energy structures
• Performance-based design of piled foundations
• Simplified analytical/semi-analytical solutions to soil-structure interaction problems

Abigail Bateman is a Senior Research Assistant at the University of Southampton's Department of Civil, Maritime and Environmental Engineering. She contributes to the Offshore Renewable Energy (ORE) Supergen Hub and the ROBOCONE project, focusing on geotechnical challenges in offshore energy infrastructure. PhD in Civil Engineering (2024), University of Bristol BEng in Civil Engineering, University of Bristol Research Interests: Reliability of offshore renewable energy structures Performance-based design of piled foundations Simplified analytical/semi-analytical solutions to soil-structure interaction problems Her publications since 2020 demonstrate expertise in nonlinear soil behavior modeling, pile foundation analysis, and offshore structure reliability. Key methods include t-z, p-y, and m-θ curves for predicting pile response in clay soils.

Henry BurtonView profile

Associate Professor

• Structural Engineering
• Earthquake Engineering
• Structural Reliability
• +3 more

Henry Burton is an Associate Professor and Presidential Endowed Chair in Structural Engineering within the Department of Civil and Environmental Engineering at the University of California, Los Angeles (UCLA), where he conducts research on structural safety and disaster resilience. His academic credentials include: Ph.D. in Civil Engineering, Stanford University (2014) M.S. in Civil Engineering, Stanford University (2004) B.S. in Civil Engineering, Morgan State University (2002) Professor Burton's research specializes in earthquake engineering and structural reliability, developing advanced methodologies for seismic performance assessment, enhanced building systems, and community-wide resilience planning. His work integrates computational modeling with practical engineering solutions to mitigate earthquake risks in urban infrastructure. His major recognitions include: NSF CAREER Award (2016) National Science Foundation Next Generation Fellowship (2014) International Conference on Building Resilience Best Paper Award (2014) Stanford University DARE Fellowship (2012) NSF-funded International Conference on Earthquake Engineering Travel Award (2011) Environmental Protection Agency Undergraduate Research Fellowship (2000) He teaches core structural engineering courses including Structural Design and Testing Laboratory (C&EE 135L), Advanced Structural Analysis (C&EE 235A), and Structural Reliability (C&EE 244), while leading a dedicated research group focused on seismic safety innovations as evidenced by his project website and media features in LAist, NSF Science Nation, and UCLA Newsroom.