Find Academic Supervisors

• Wireless Networks
• 5G/6G Technology
• Resource Management
• +3 more

Serkut Ayvasik is a Researcher at the Chair of Communication Networks at Technical University Munich (TUM). He joined TUM in March 2019 as a research and teaching associate, following his M.Sc. in Communications Engineering (2019) and B.Sc. in Electrical and Electronics Engineering (2016) from Middle East Technical University. His research focuses on: Wireless Network Resource Management for heterogeneous latency-critical 5G applications Channel State Information Prediction using depth images Network Slicing and Quality of Service optimization Machine Learning for proactive network configuration Telemedicine Applications in cross-border communication Key article trends include 5G/6G technology , IoT sustainability , digital twins , and haptic feedback systems . He contributes to IEEE and ACM journals, with recent work on Digiot (2025) and OCTOPUS (2024). Collaborations include researchers like Wolfgang Kellerer (Chair), Edwin Babaians , Alba Jano , and Fidan Mehmeti . His work spans projects such as 6G Future Lab Bavaria , DFG GGI QCDE , and ERC FlexNets .

• Atmospheric Aerosols
• Environmental Health
• Air Quality Monitoring
• +7 more

Dr. Zaheer Nasar is a Reader in Atmospheric Aerosols at Cranfield University's School of Aerospace, Transport and Manufacturing. His work focuses on real-time bioaerosol characterization, indoor/outdoor air quality dynamics, and environmental health impacts of particulate matter. He leads the NERC-funded Light-Induced Fluorescence sensor project and contributes to the BioAirNet network (NE/V002171/1) as Co-I. Research Interests Physico-chemical and biological characterization of aerosols Spatio-temporal dynamics of particulate matter (PM) and bioaerosols Quantitative microbial risk assessment (QMRA) methodologies Low-cost air quality sensor networks and machine learning calibration Urban green infrastructure effects on air pollution Policy development in Hindu Kush Himalayan air quality Recent publications emphasize machine learning-enhanced sensor calibration (2024 IEEE paper), wastewater plant bioaerosol risks (2024 Water Research), and urban air quality interventions across the UK and Lahore. He has secured over £1.6M in grants from NERC, STFC, and UKRI GCRF, with significant work on BTEX exposure in Nigeria and SARS-CoV-2 risks in wastewater facilities. Scientific Recognition Fellow of the Higher Education Academy (FHEA) Co-investigator in multiple NERC/UKRI projects Active participant in BSI bioaerosol standards committee As an advisor, he mentors five postgraduate researchers including Reece Dillon and Hathaikarn Tathong, with a strong publication record in journals like Environmental Science: Atmospheres , Risk Analysis , and BJPsych Open . His work bridges environmental science, public health, and policy implementation through interdisciplinary research.

Xiaofeng QianView profile

Associate Professor

• Materials Theory
• Quantum Materials
• 2D Materials
• +7 more

Dr. Xiaofeng Qian is an Associate Professor in the Department of Materials Science & Engineering at Texas A&M University, with joint appointments in Physics and Astronomy, and Electrical & Computer Engineering. His research focuses on materials theory , quantum materials design , and high-throughput computational discovery , particularly for 2D materials and energy applications . Educational Background: Ph.D., Nuclear Science and Engineering, Massachusetts Institute of Technology (2008) B.S., Engineering Physics, Tsinghua University (2001) Research spans first-principles electronic structure methods , nonlinear optical responses , and multiscale modeling of electronic, thermal, and ionic transport. Key areas include quantum spin Hall effect , ferroelectric switching , and machine learning for materials prediction . Notable Awards: Dean of Engineering Excellence Award (2024) Engineering Genesis Multidisciplinary Award (2024) AZZ Faculty Fellow (2021) NSF CAREER Award (2018) Manson Benedict Fellowship (2006) Actively recruiting PhD, MS, and UG researchers with backgrounds in physics, materials science, or computational methods. Collaborates extensively on hybrid AI-materials projects and topological device concepts .

Jürgen BergesView profile

Professor

• Quantum Field Theory
• Non-equilibrium Dynamics
• Heavy-Ion Collisions
• +2 more

Jürgen Berges is a full Professor of Theoretical Physics at Heidelberg University's Faculty of Physics and Astronomy, holding the Chair of Theoretical Physics since 2011. He concurrently serves as an ExtreMe Matter Institute (EMMI) Professor at GSI Helmholtzzentrum for Heavy Ion Research in Darmstadt and acts as Spokesperson for the Steering Board and Principal Investigator in multiple projects within the STRUCTURES Cluster of Excellence. Education includes Habilitation (2003), Doctorate (1997), and Diploma (1994) from Heidelberg University, plus Intermediate Diploma (1990) from Osnabrück University. Research Interests focus on quantum field theory applications in non-equilibrium dynamics , particularly thermalisation phenomena in heavy-ion collisions , nonthermal fixed points , and quantum field simulators . His work integrates topological data analysis for quantum systems within the STRUCTURES Cluster of Excellence (Quantum Structure and Dynamics). Scientific Awards : Karl-Freudenberg Prize (2004) Landesgraduiertenförderung Fellowship (1995-1997) Leadership & Grants : Served as Department Chair (2021-2023), Deputy Chair (2019-2021), and Spokesperson for CRC 1225 (ISOQUANT). Currently Principal Investigator in STRUCTURES Cluster projects A01 (Thermalisation dynamics), B03 (Nonthermal fixed points), and B04 (Quantum field simulators), with prior DOE review panel membership (2018). Labs & Teams : Leads theoretical research groups within Heidelberg's Institute for Theoretical Physics, focusing on infrared phenomena in quantum fields and quantum simulator development through ISOQUANT and STRUCTURES collaborations.

• Glycoengineering
• Biomaterials
• Biotechnology
• +3 more

Prof. Dr. rer. nat. Lothar Elling is a University Professor and director at the Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Germany. His research focuses on biomaterials, glycoengineering, and enzymatic synthesis of carbohydrates and glycoconjugates. Institution: RWTH Aachen University Research Unit: Helmholtz Institute for Biomedical Engineering Academic Rank: Full Professor Prof. Elling's research interests include: Glycoengineering of biomaterials Enzymatic synthesis of glycans Glycosyltransferase immobilization Glycan-protein interactions Biocatalytic cascade reactions Biomedical applications of glycomaterials His recent publications demonstrate strong expertise in: - Automated enzymatic glycan synthesis - Multi-enzyme cascade systems for nucleotide sugar production - Glycosyltransferase engineering - Galectin-targeted glycomaterials - Microgel-based biosensors

Hyosang LeeView profile

Assistant Professor

• Tactile Sensing
• Robotics
• Soft Robotics
• +4 more

Hyosang Lee is an Assistant Professor in the Robotics Section of the Mechanical Engineering Department at Eindhoven University of Technology (TU/e). He holds a PhD from KAIST and has held research positions at the Max Planck Institute and University of Stuttgart. His work focuses on tactile sensing technologies, including artificial skin development, soft robotics, and integration of sensory systems with AI. Bachelor's: Mechanical Engineering, Korea University Master's: Robotics and Mechanical Engineering (double major) PhD: Mechanical Engineering, KAIST (2017) Research interests span tactile sensor design, electrical impedance tomography (EIT), and human-robot interaction. His group emphasizes creating scalable, flexible tactile systems for robots. Recent work includes air pressure sensing for force estimation and biomimetic skin materials. Publications highlight innovations in multi-directional force sensing, soft component technologies, and haptic interfaces for autism therapy. He teaches 'Dynamics and Control of Robotic Systems' and serves on the editorial board of npj Robotics . No formal student advisees are listed, though his lab, the Tactile Sensing and Robotic Skin Group , likely involves graduate researchers. His research contributes to UN Sustainable Development Goals related to health and technology.

Xiaocheng ShangView profile

Associate Professor

• Mathematical Optimization
• Data Science
• Numerical Analysis
• +3 more

Xiaocheng Shang is an Associate Professor in Mathematical Optimisation and Data Science at the University of Birmingham's School of Mathematics. His research focuses on numerical methods for stochastic differential equations, with applications in computational mathematics, statistics, physics, and data science. He is affiliated with the Optimisation and Numerical Analysis Group, Statistics and Data Science Group, and the Institute for Data and AI. Shang holds a PhD in Applied and Computational Mathematics from the University of Edinburgh (2016) and completed postdocs at the University of Edinburgh, Brown University, and ETH Zurich before joining Birmingham in 2019. His academic achievements include fellowships from The Alan Turing Institute, the LMS Emmy Noether Fellowship, and the EUniWell Leadership Fellowship. He has secured funding from EPSRC, the Royal Society, and the Isaac Newton Institute. Shang is actively involved in supervising PhD students and co-organizing research initiatives such as the Data Science and Computational Statistics Seminar. Research interests include structure-preserving integrators, Bayesian sampling techniques, and machine learning applications in dynamical systems. His work bridges numerical analysis, probability theory, and multiscale modeling in materials science. Recent projects involve neural networks for complex dynamical systems and numerical algorithms for deterministic/stochastic systems.

Dominika IgnasiakView profile

Researcher

• Biomechanics
• Musculoskeletal Modeling
• Spinal Surgery
• +3 more

Dr. Dominika Ignasiak is a Researcher affiliated with the Institute of Biomechanics at ETH Zürich. Her work focuses on spinal biomechanics, musculoskeletal modeling, and computational analysis of spinal pathologies. She contributes to understanding the biomechanical implications of surgical interventions, spinal deformities, and age-related changes in spinal alignment and loading. Her research integrates clinical data with advanced musculoskeletal modeling techniques, particularly in predicting postoperative outcomes and assessing spinal load distributions under dynamic conditions. Key areas include spinal stenosis, idiopathic scoliosis, and the biomechanics of spinal fusion surgery. Dr. Ignasiak collaborates on translational studies bridging computational simulations with clinical applications. Her publications emphasize the role of personalized models in optimizing surgical strategies and understanding degenerative spinal conditions. While no formal awards are listed, her contributions to spinal biomechanics research are evident through her active publication record in high-impact journals. Dr. Ignasiak is based at ETH Zürich’s Institute of Biomechanics, where she engages in cutting-edge research and contributes to both academic and clinical advancements in orthopedic biomechanics.

Ye ZhishengView profile

Associate Professor

• Inventory Control
• Reliability Engineering
• Emergency Response Systems
• +4 more

Ye Zhisheng is the Dean’s Chair and Associate Professor in the Department of Industrial Systems Engineering & Management at the National University of Singapore (NUS). His research focuses on reliability engineering, inventory control, emergency response systems, and statistical modeling. He holds a PhD in Industrial and Systems Engineering from NUS, along with a BEng in Material Science and Engineering and a BEco in Economics from Tsinghua University. His work emphasizes practical applications in mission-critical systems, predictive maintenance, and data-driven decision-making. Current research initiatives include optimal maintenance policies for manufacturing systems, degradation analysis of bearings, and federated learning approaches for battery lifecycle prediction. He has pioneered methods for integrating physics-informed neural networks into prognostics and health management (PHM) systems. Key technical contributions span advanced statistical methodologies like sieve estimation for survival data, phase-type distributions modeling, and condition-based maintenance optimization. His interdisciplinary approach bridges operations research, mechanical engineering, and computer science to address complex reliability challenges. Recent projects include resilient consensus-based power grid management and contamination source identification frameworks. Notable collaborations involve developing intelligent cross-domain fault diagnosis systems using transformer networks and advancing the Internet of Federated Things (IoFT) for distributed data analytics. His work has been applied in aerospace, telecommunication infrastructure, and medical emergency response systems.

• Meso-scale materials
• Soft matter physics
• Granular materials
• +7 more

Associate Professor Mohammad Saadatfar is affiliated with the School of Civil Engineering at The University of Sydney. His research focuses on meso-scale materials, combining experiments with simulations to address challenges in environmental science, biomedical engineering, and advanced materials design. Key areas include the study of cellular solids, granular materials, and meta-materials. His work integrates physics, engineering, and biology, with applications to CO₂ geo-sequestration, bone implants, and mechanical meta-materials. He uses X-ray tomography, FE simulations, and topological analysis to explore material behavior. Recent publications span topics like additive manufactured foams, CO₂ flow dynamics in sandstone, and biomimetic wood structures. His contributions highlight interdisciplinary approaches to material science and engineering challenges. No scientific awards or student advisement details are explicitly mentioned in the provided text.

• Neurolinguistics
• Computational Linguistics
• Psycholinguistics
• +4 more

Jon Brennan is an Assistant Professor in the Department of Linguistics at the University of Michigan, affiliated with the College of Literature, Science, and the Arts (LSA). His research focuses on neurolinguistics, computational linguistics, and psycholinguistics, particularly investigating how the brain processes language structure and meaning. He leads the Computational Neurolinguistics Lab, which develops neurocomputational models to study language comprehension mechanisms. Brennan received an NSF Grant for collaborative research with Christophe Pallier (Paris) on neurocomputational models of natural language processing. His work integrates EEG, fMRI, and MEG techniques to decode linguistic features in neural signals. Key research areas include syntax-semantics interfaces, multilingual processing, and developmental disorders like dyslexia. Notable contributions include studies on hierarchical syntactic structure, minimal pairs in language models, and neural correlates of theory of mind in children. Brennan collaborates internationally, exemplified by the US-French NSF-CRCNS grant. He has published extensively on topics like neural decoding of grammatical features, LLM internal representations, and bilingual processing mechanisms. Scientific awards include the NSF Collaborative Research in Computational Neuroscience (CRCNS) Grant (2016). His research bridges computational modeling and experimental neuroscience, aiming to reveal how language mechanisms are implemented in neural systems.

August EvrardView profile

Professor

• Cosmology
• Astrophysics
• Galaxy Clusters
• +2 more

Professor August Evrard is a distinguished academic at the University of Michigan, holding the Arthur F. Thurnau Professorship in Physics and Astronomy. He is affiliated with the Department of Physics within the College of Literature, Science, and the Arts. Known for his contributions to cosmology and astrophysics, he pioneered the Problem Roulette tool, recognized with the Provost's Teaching Innovation Prize. His research focuses on galaxy clusters, dark matter, and cosmological surveys like the Dark Energy Survey (DES) and XXL Survey. He has been honored as an AAS Fellow (2025) and has contributed to advancements in physics education through innovative teaching methods and technologies. In research, Prof. Evrard explores topics such as dark matter halo dynamics, galaxy cluster properties, and weak lensing analyses. His work spans observational cosmology, computational modeling, and multi-wavelength astronomy. Notable projects include studies on galaxy cluster mass distributions, the relationship between X-ray emissions and velocity dispersions, and the application of machine learning to astrophysical data analysis. His contributions to education highlight the integration of AI-driven tools to enhance learning, as seen in initiatives like the Problem Roulette and course recommendation systems. Prof. Evrard's awards include the Provost's Teaching Innovation Prize for Problem Roulette and his AAS Fellowship. His academic leadership and innovative approaches to both research and education solidify his role as a pivotal figure in astrophysics and STEM pedagogy.

Alyson M. BrooksView profile

Associate Professor

• Galaxy Formation and Evolution
• Galactic Structure
• Cosmological Simulations
• +5 more

Alyson M. Brooks is an Associate Professor in the Department of Physics and Astronomy at Rutgers University, part of the School of Arts and Sciences. Her research focuses on galaxy formation and evolution, particularly using cosmological simulations to study dark matter dynamics, stellar abundances, and galactic structures. She holds a PhD in Astronomy from the University of Washington (2008) and has held prestigious fellowships, including the Sherman Fairchild Fellowship at Caltech and the Grainger Postdoctoral Fellowship at UW-Madison. Brooks has been recognized with awards such as the 2015 Alfred P. Sloan Research Fellowship and the 2019 Maria Goeppert Mayer Award from the American Physical Society. Her educational background includes a B.A. in Physics with Astronomy from Macalester College (2000) and an M.S. in Astronomy from the University of Washington (2004). Her research interests span topics like the interplay between baryonic processes and dark matter, chemical evolution in dwarf galaxies, and the role of feedback mechanisms in galaxy formation. Brooks is actively involved in outreach, including founding the RU-PREP program to support undergraduate research and mentoring initiatives. Brooks has authored over 100 refereed publications, with recent work exploring the resolved stellar populations of dwarf galaxies using the James Webb Space Telescope and analyzing dark matter halo properties through simulations. Her scientific contributions have advanced understanding of galaxy kinematics, dark matter interactions, and the evolution of galactic structures across cosmic time.

Justin WanView profile

Professor

• Scientific Computing
• Computer Graphics Simulation
• Medical Image Processing
• +4 more

Justin Wan is a Professor in the Department of Computer Science at the University of Waterloo. His research focuses on scientific computing, medical image processing, computational finance, and machine learning. He holds a Ph.D. from UCLA (1998), an M.A. from UCLA (1995), and a B.Sc. from the Chinese University of Hong Kong (1992). Wan’s work bridges numerical methods, optimization, and deep learning, with applications in financial modeling, medical imaging, and fluid dynamics. His research interests include advanced techniques in scientific computing (e.g., multigrid methods), computer graphics simulation, and medical image enhancement (e.g., CT scan artifact reduction). He has pioneered applications of machine learning to computational finance, including option pricing and hedging using deep neural networks and GANs. His recent work explores denoising diffusion models and multi-agent systems for optimal execution in finance. Publications span topics like volatility surface computation, optimal mass transport for image registration, and parallel solvers for fluid dynamics. His methods address challenges in high-dimensional problems, robust numerical valuation, and scalable algorithms for large datasets. Wan collaborates across disciplines, integrating mathematical rigor with practical engineering solutions.

Samuel ChengView profile

Associate Professor

• Information Theory
• Signal and Image Processing
• Remote Sensing
• +4 more

Dr. Samuel Cheng is an Associate Professor at the Gallogly College of Engineering , University of Oklahoma , specializing in Electrical and Computer Engineering . He holds a Ph.D. in Electrical Engineering from Texas A&M University (2004), preceded by M.S. and M.Phil. degrees from the University of Hawaii and Hong Kong University of Science and Technology. Education: B.S. (University of Hong Kong, 1995), M.Phil. (HKUST, 1997), M.S. (University of Hawaii, 2000), Ph.D. (Texas A&M, 2004) Professional Experience: Senior Research Engineer at Advanced Digital Imaging Research (2004-2005), prior internships at Microsoft Asia and Panasonic Technologies His research focuses on Information Theory , Signal and Image Processing , and Pattern Recognition , with applications in remote sensing, urbanization analysis, and disaster monitoring. His publications span topics including urban impervious surface mapping , nighttime light analysis , and machine learning for environmental data . His work often integrates multi-source datasets (e.g., Landsat, LiDAR, social media) for spatiotemporal modeling. Technical Expertise: Spectral unmixing, machine learning, thermal remote sensing, GIS integration Key Applications: Power outage detection, vegetation-crime correlation, PM2.5 estimation, smart meter data fusion Dr. Cheng holds three US patents in digital watermarking and is affiliated with IEEE, Sigma Xi, and AAAS. His recent articles demonstrate a trend toward leveraging AI for remote sensing challenges and analyzing urbanization impacts on ecosystems.