Find Academic Supervisors

• Condensed Matter Physics
• Materials Discovery
• Ab Initio Calculations
• +4 more

Fernando Manuel da Silva Nogueira is an Associate Professor at the Department of Physics, Faculty of Sciences and Technology, University of Coimbra. He holds a PhD in Theoretical Physics from the same institution (1999) and has been a faculty member since 1990. His research focuses on materials discovery using ab-initio methods , computational physics, and development of scientific software tools like Octopus and APE . He leads the Condensed Matter Physics group at CFisUC (Centro de Física da Universidade de Coimbra) and has been Director of the Portuguese Physics Olympiad (2007-2018). Education: PhD in Theoretical Physics (1999), University of Coimbra MSc in Theoretical Physics (1993), University of Coimbra BSc in Physics (1990), University of Coimbra Research interests include computational materials science , nonlinear optics , density functional theory , and electronic structure calculations . He has authored 40+ peer-reviewed articles, 3 books, and directed over 25 research projects. His work spans topics like carbon nanotube properties, firefly bioluminescence mechanisms, and high-throughput materials discovery. He has organized 25+ conferences, advised 3 PhD students and 17 MSc students, and contributed extensively to open-source computational physics software. His ORCID is 0000-0003-3125-3660 .

Jacqueline ColeView profile

Associate Professor

• Rehabilitation Engineering
• Regenerative Medicine
• Bone Mechanics
• +5 more

Jacqueline Cole is an Associate Professor in the Department of Biomedical Engineering at North Carolina State University, part of the College of Engineering. Her research focuses on bone mechanics, musculoskeletal development, and regenerative therapies for conditions like brachial plexus birth injury and stroke-related bone deficits. She leads the Orthopaedic Mechanobiology Lab, which employs advanced imaging (e.g., CT, microscopy) and computational modeling to study bone-tissue interactions. Cole teaches courses in orthopedic biomechanics and bone mechanobiology. Education: Ph.D. (2007), M.S. (2004) Mechanical Engineering from Cornell University; B.S. (2001) Mechanical Engineering from Auburn University. Research interests include stroke and obesity effects on bone, brachial plexus injury recovery, and tissue engineering. Awards span teaching (NC State Alumni Outstanding Teacher Award) and research mentorship (Michael Dickey Award). She has pioneered studies on bone-vascular interactions and suture materials for tendon repair. Her lab’s long-term goals involve developing therapies to prevent musculoskeletal dysfunction via mechanobiological approaches. Grants include NIH TIGRR and K12 funding. Recent work explores ischemic stroke’s impact on osteovascular structure and brachial plexus injury’s effects on joint morphology.

Tor SkeieView profile

Professor

• High-Performance Networking
• Distributed Systems
• InfiniBand Technology
• +2 more

Tor Skeie is a Professor at the University of Oslo's Department of Informatics, specializing in networks and distributed systems. His research focuses on high-performance networking, InfiniBand technologies, and adaptive routing systems. Current investigations include automated parameter tuning for reservoir simulations, adaptive routing in InfiniBand hardware, and modeling WiFi quality attenuation. His work develops efficient solutions for virtualized HPC environments and cloud computing infrastructures. Recent publications demonstrate innovations in network modeling, adaptive routing algorithms, and performance analysis of distributed systems. Research collaborations span European projects on high-performance networking infrastructures. Leads the Networks and Distributed Systems (ND) research group investigating fault-tolerant routing, network virtualization, and congestion control mechanisms.

• Artificial Intelligence
• Cloud Computing
• Fog Computing
• +3 more

Sundas Iftikhar is a Teaching Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. She specializes in software engineering education and research focused on artificial intelligence, fog/cloud computing, and task scheduling optimization. Her research spans AI applications in distributed systems , including Energy-efficient computing Quality of Service (QoS) optimization Deep learning for healthcare Cloud-fog hybrid architectures Recent publications analyze AI-based fog/edge computing trends , with emphasis on systematic reviews, taxonomy development, and sustainability. She also explores machine learning for serverless computing and smart home applications through fog infrastructure. Teaching duties include the Software Engineering Project module, where students work in teams to solve real-world problems.

Lon S. SchneiderView profile

Professor

• Alzheimer's Disease Research
• Dementia
• Psychiatry
• +5 more

Lon S. Schneider, MD, MS is Professor of Psychiatry & the Behavioral Sciences at the Keck School of Medicine of the University of Southern California, where he holds the Della Martin Chair in Psychiatry and Neuroscience. He directs the USC California Alzheimer's Disease Center (funded by the California Department of Health Services), the Geriatric Studies Center, and co-directs the clinical core of the USC NIA Alzheimer's Disease Research Center. Dr. Schneider's research focuses on treatment development with novel metabolic and neuroregenerative compounds, outcomes assessment, and approaches to modeling, clinical trials methods and simulations, and in silico screening of medications for slowing Alzheimer's disease. His work spans Alzheimer's disease therapeutics, clinical trial methodology, neuropsychopharmacology, dementia prevention, and neurodegenerative biomarkers. His recent publications demonstrate expertise in amyloid and tau biomarkers, clinical trial design, agitation management in dementia, and cross-cultural studies of cognitive decline. Dr. Schneider serves as an associate editor or editorial board member for several publications and is a member of The Lancet Commission on dementia prevention, intervention, and care. His research has significantly influenced clinical practice and trial design in Alzheimer's disease. Woodward/White, Inc.: The Best Doctors in America, 1992-2009 Fellow of the American College of Neuropsychopharmacology Distinguished Life Fellow of the American Psychiatric Association Dr. Schneider has mentored numerous researchers and clinicians in the field of geriatric psychiatry and Alzheimer's disease research. His work on the CitAD trial examining citalopram for agitation in Alzheimer's dementia represents one of the largest studies of its kind. He has contributed significantly to understanding the relationship between neuropsychiatric symptoms and functional decline in dementia, as well as developing novel measures for Alzheimer's disease prevention trials. His laboratory and clinical research programs focus on identifying biomarkers of disease progression, developing novel therapeutic approaches for Alzheimer's disease, and improving clinical trial methodology for neurodegenerative disorders. Dr. Schneider collaborates extensively with researchers across USC and internationally on multi-center clinical trials and observational studies of dementia.

Ingrid MoermanView profile

Professor

• Wireless Networking
• Cognitive Radio Networks
• Internet of Things
• +4 more

Ingrid Moerman is a part-time Professor at Ghent University and a staff member at the Internet Technology and Data Science Lab (IDLab), a core research group of imec embedded within Ghent University and the University of Antwerp. She coordinates mobile and wireless networking research and leads a team of over 30 researchers at Ghent University, with extensive involvement in European and national funding initiatives. She received her Electrical Engineering degree (1987) and Ph.D. (1992) from Ghent University. Her research spans collaborative networks, cognitive radio, software-defined radio, IoT, LPWAN, and high-density wireless access, emphasizing experimentally-supported development of next-generation wireless systems with practical implementations in spectrum management and real-time control. Recent publications (2024-2025) reveal a strong pivot toward AI-integrated wireless networking, featuring OFDMA scheduling innovations, Wi-Fi 6/7 interference mitigation, and time-sensitive networking for industrial applications. Key trends include 5G/6G convergence, vehicular communication enhancements, and digital twin frameworks for network observability, reflecting her focus on mission-critical industrial use cases. Her accolades include: 9 Best Paper Awards 2 FWO Prizes (Research Foundation - Flanders) IMEC Prize of Excellence 2001 MSc Thesis Award (as promoter) Best Demo/Exhibit Award at ICT 2013 DARPA Spectrum Collaboration Challenge Prize ($750,000) She has coordinated major EU projects (FP7/H2020: CREW, WiSHFUL, eWINE, ORCA) with industry partners, securing substantial funding for experimental wireless research. Her grant portfolio emphasizes collaborative innovation in spectrum sharing and neutral-host architectures for multi-operator environments. At IDLab, she directs advanced wireless testbeds supporting real-world validation of technologies like openwifi and White Rabbit, with active experimentation in time-sensitive networking and spectrum collaboration for industrial IoT deployments.

Maria Grazia De AngelisView profile

Professor

• Polymer Science
• Gas Separation Membranes
• Medical Engineering
• +3 more

Professor Maria Grazia De Angelis holds a Personal Chair in Thermodynamics of Materials and Processes at the University of Edinburgh's School of Engineering. Her research focuses on advanced materials for gas separation membranes, medical engineering innovations like wearable artificial kidneys, and environmental applications such as CO2 capture. Key projects include the development of semicrystalline polymers for hydrogen storage and nanoporous materials for dialysate regeneration. She leads interdisciplinary research teams addressing challenges in sustainable materials and biomedical technologies. Her work combines computational modeling, molecular simulations, and machine learning to design next-generation materials. Notable contributions include breakthroughs in superhydrophobic materials and CO2 capture membranes. Funded projects span EPSRC, Kidney Research UK, and industry partners like Danisco. Collaborations involve global institutions, advancing both fundamental and applied research in materials science.

Jason SzafronView profile

Assistant Professor

• Biomedical Engineering
• Cardiovascular Disease
• Pulmonary Vascular Disease
• +6 more

Jason Szafron is an Assistant Professor in the Department of Biomedical Engineering at Carnegie Mellon University’s College of Engineering. He leads a research group focused on developing computational tools to understand and treat cardiovascular diseases, particularly congenital heart defects, pulmonary vascular diseases, and fetal growth restriction. His work integrates biomechanical modeling with experimental data to improve treatment planning and medical device design. Education: B.S. in Biomedical Engineering, Texas A&M University (2015) M.S. and Ph.D. in Biomedical Engineering, Yale University (2018, 2020) Postdoctoral Research Fellow at Stanford University’s Department of Pediatrics (2023) Research Interests: Simulation tools for cardiopulmonary disease progression Biomechanical modeling of vascular adaptation Computational frameworks for organ-scale growth remodeling Optimization of tissue-engineered vascular grafts His lab combines experimental and computational approaches to study disease mechanisms, with a focus on mechanobiological and immunological factors driving vascular pathology. Publications highlight advancements in pulmonary hypertension modeling, vascular graft design, and open-source diagnostic tools like HemoLens. Recent work emphasizes personalized treatment planning via neural network-based digital twins. Awards: Parker B. Francis Fellow (2022–2025) for pulmonary disease research Labs/Teams: Director of the Szafron Lab at CMU, focused on translational cardiovascular engineering. Collaborates with Stanford University and Mayo Clinic on clinical applications.

SHEN LeiView profile

Researcher

• Multiscale Modeling and Simulation
• Materials Informatics
• Computational Physics
• +3 more

SHEN Lei is a researcher at the National University of Singapore (NUS), affiliated with the Department of Physics. With a PhD in Physics from NUS, he specializes in Multiscale Modeling and Simulation and Materials Informatics , leveraging machine learning and computational methods for advanced materials discovery. Research Focus: Density functional theory, molecular dynamics, finite element analysis, and data-driven design of materials. Teaching: Modules include Mechanics and Waves (PC1433), Applied Quantum Mechanics (PC2130B), and Mechanical Properties of Materials (ESP2109). His work spans spintronics, ferroelectricity, and energy storage materials, with recent publications on interatomic potentials, sliding heterostructures, and battery anodes. He has received the Teaching Commendation Award and declined the Lee Kuan Yew Postdoctoral Fellowship . Notable Trends: Recent articles emphasize machine learning in materials science, van der Waals heterostructures, quantum transport, and medical image analysis. Subfields include Rashba spin-orbit coupling, piezoelectric tensor modeling, and defect-informed neural networks. Scientific Awards: Teaching Commendation Award (AY15/16; AY16/17) Lee Kuan Yew Postdoctoral Fellowship (2014) (declined)

Julie GoughView profile

Professor

• Tissue Engineering
• Biomaterials
• Cell Biology
• +3 more

Julie Gough is a Professor of Biomaterials and Tissue Engineering at the University of Manchester's School of Materials. She leads research in mechanically sensitive connective tissues, including bone, cartilage, and intervertebral discs, focusing on biomaterial-cell interactions and scaffold development. She holds roles as Deputy Director of the EPSRC CDT in Advanced Biomedical Materials and Co-Director of the Advanced Materials in Medicine initiative. Education: BSc Cell and Immunobiology (1993), MSc Molecular Pathology and Toxicology (1994), PhD Biomaterials (1998) Career: Lecturer (2002-2006), Senior Lecturer (2006-2010), Reader (2010-2014), Professor (2014-present) Research interests include hydrogel design, cell-biomaterial interfaces, and bioprinting for tissue repair. She has published over 100 papers and collaborates with academia and industry. Her lab moved to the Henry Royce Institute in 2021, leveraging advanced materials research facilities. Key projects include NIHR Manchester Biomedical Research Centre (Co-Investigator), Advanced Materials for Regenerative Medicine (Co-Lead), and nerve conduit translation. Awards include the Royal Academy of Engineering/Leverhulme Trust Senior Research Fellowship (2012-2013).

Aftab AhmadView profile

Professor

• Machine Learning
• Cyber Security
• Public Health Prediction
• +5 more

Aftab Ahmad is a Professor in the Department of Computer Science at the City University of New York (CUNY), specializing in cybersecurity and machine learning applications. He holds a Doctor of Science from George Washington University. His research focuses on developing machine learning algorithms for cyber threat intelligence (CTI) and public health prediction, along with designing secure generative deep learning models resistant to reverse-engineering. Key research areas include: Cybersecurity frameworks and privacy-preserving architectures Generative adversarial networks (GANs) with embedded security features Biomedical signal analysis and human body channel modeling Secure wireless protocols for critical infrastructure His publication trends emphasize: Privacy metrics and data protection mechanisms Smart grid and IoT security Neuroscience-inspired machine learning models Wireless network vulnerability assessments No scientific awards or grants were explicitly mentioned in the provided texts. He teaches advanced courses in computer security and network forensics at undergraduate and graduate levels. No advising relationships or lab affiliations were detailed in the available information.

Khandaker Mamun AhmedView profile

Assistant Professor

• Computer Vision
• Federated Learning
• Cybersecurity
• +4 more

Dr. Khandaker Mamun Ahmed is an Assistant Professor at The Beacom College of Computer & Cyber Sciences, Dakota State University. He teaches undergraduate and graduate courses in artificial intelligence, algorithms, and data structures. He holds a Ph.D. in Computer Science from Florida International University (2024), an M.Sc. from the same institution (2023), and a B.Sc. in Software Engineering from the University of Dhaka (2016). His research focuses on computer vision, federated learning, cybersecurity, explainable AI, vision-language models, and optimization algorithms. He has contributed to peer-reviewed publications and conference presentations, with notable work in federated learning for IoT, anomaly detection in videos, and AI applications in healthcare and agriculture. Recent articles highlight advancements in federated learning frameworks, AI-driven healthcare systems, and real-time object detection using neural networks. His work also addresses cybersecurity challenges in DevOps pipelines and generative AI for educational datasets. Recipient of the 'Best graduate student in research award' (2022), Dr. Ahmed advises on AI ethics and mentors students through academic-industry collaborations. His research bridges theoretical computer science with practical applications in agriculture, healthcare, and infrastructure monitoring.

• Digital Signal Processing
• Audio Technology
• Acoustics
• +5 more

Prof. Dr.-Ing. André Jakob is a faculty member at Berlin University of Technology , affiliated with the Department VII - Electrical Engineering - Mechatronics - Optometry. His academic role spans teaching and research in digital signal processing, audio technology, and acoustics. Digital Signal Processing Audio Technology Acoustics Active Noise Control His research focuses on active noise control , simulation of moving sound sources , and audio signal processing , with applications in robotics, building acoustics, and medical devices. Publications include advancements in anti-noise window systems , sound source localization , and acoustic measurement techniques . His recent work explores real-time auralization for educational robotics and nonlinear acoustic modeling with neural networks. The 15 most recent articles demonstrate a consistent focus on acoustic simulation , active control systems , and sound propagation modeling , with conference contributions at DAGA, NAG-DAGA, and international acoustics events. Topics range from dental drill noise reduction to active sound design in musical instruments , reflecting interdisciplinary applications. He supervises numerous Master's and Bachelor's theses in areas like real-time signal processing, deep learning for sound recognition, and virtual acoustics. His lab at TU Berlin explores multi-loudspeaker systems , acoustic beamforming , and active noise cancellation for both industrial and consumer applications.

Daniel LaumannView profile

Researcher

• Physics Education
• Science Education
• Educational Technology
• +4 more

Dr. Daniel Laumann is a faculty member at the University of Münster, affiliated with the Department of Physics within the Faculty of Mathematics and Natural Sciences. His primary research focuses on physics education innovation, particularly the integration of digital tools and technologies into teaching. He has actively contributed to advancing methodologies such as augmented reality (AR) and mixed reality (MR) in laboratory settings, mobile learning strategies, and interdisciplinary approaches combining physics with art. His work emphasizes enhancing student engagement through interactive simulations, BYOD (Bring Your Own Device) initiatives, and conceptual understanding in magnetism and optics. Dr. Laumann has published extensively in journals like Physics Education , Journal of Science Education and Technology , and Frontiers in Education , reflecting his commitment to evidence-based pedagogy. He collaborates widely with colleagues in physics education, psychology, and engineering to design effective learning environments. Education: While specific academic degrees aren’t listed in the text, his title 'Dr.' indicates a PhD in Physics or a related field. Research Interests: His work spans educational technology (e.g., AR for polarization experiments), mobile device integration in STEM education, and cognitive aspects of learning (e.g., empathizing/systemizing traits in students). He also explores interdisciplinary projects linking physics principles with artistic light installations, aiming to broaden public engagement with science. Key Projects: Leading initiatives like the MiReQu project (Mixed Reality Learning Environments for Quantum Technologies) and studies on BYOD impacts in classrooms. He co-developed tools such as the Light Spectrum Analyzer app and smartphone-based magnification devices. Grants & Awards: No specific grants or awards are mentioned in the provided texts. Labs/Teams: Actively involved in the Physics Education Research Group at the University of Münster, collaborating with institutions like the Deutsche Physikalische Gesellschaft (DPG) on curriculum development and innovation.

Larry GoldsteinView profile

Professor

• Distributional approximation
• Stein’s method
• High dimensional statistics
• +4 more

Larry Goldstein is a Professor of Mathematics at the University of Southern California, specializing in probability theory, mathematical statistics, and their applications. He holds a Ph.D. in Mathematics from the University of California, San Diego (1984). His research focuses on distributional approximation via Stein’s method, high-dimensional statistics, concentration inequalities, and statistical efficiency, with applications in epidemiology and biomedical monitoring. He has organized and participated in numerous conferences, including the 'Stein’s Method: The Golden Anniversary' in Singapore (2022) and the 'BIRS Stein Conference' in Banff (2022). Goldstein teaches advanced courses such as Probability Theory, Statistical Consulting, and Mathematical Statistics, often incorporating modern computational tools like R. He has led international summer programs at the University of Perugia, Italy, on topics including mathematical statistics and high-dimensional probability. His work bridges theoretical foundations with practical applications, including modeling transdermal alcohol concentration and analyzing complex sampling designs in cohort studies. His contributions to Stein’s method include developing couplings for distributional approximation and concentration inequalities. Goldstein’s teaching emphasizes statistical inference, machine learning, and data analysis, reflecting his dual focus on rigorous theory and real-world problem-solving.