جستجوی استاد راهنمای دانشگاهی

Marten van Schijndel is an Assistant Professor of Computational Linguistics at Cornell University, affiliated with the Cognitive Science Program and the Department of Linguistics within the College of Arts and Sciences. His research focuses on incremental language processing, comparing human linguistic behavior with computational models like neural networks. He organizes the Computational Psycholinguistic Discussions (C.Psyd) and collaborates with the Cornell Computational Linguistics Lab (CLab) and Cornell NLP Group. Van Schijndel’s work bridges computational modeling and psycholinguistics, probing how humans and models process language incrementally, particularly through methodologies like fMRI and behavioral experiments. His research interests include computational modeling of language processing, neural network representations, and the interplay between syntax, semantics, and discourse. Notably, he investigates ungrounded learning in neural models, exploring what linguistic aspects emerge from statistical patterns alone. Recent contributions include studies on semantic change quantification, phonotactic effects in context, and discourse predictability in Hindi word order. Van Schijndel teaches courses such as Computational Linguistics II and leads seminars on natural language processing. He actively advises students like John R. Starr, Ashlyn Winship, and Zander Lynch, whose work spans experimental paradigms for semantic spaces, implicit arguments in sentence processing, and semantic role localization. His recent activities include co-organizing workshops on event representation (PEER 2025) and presenting at the University of Rochester on event construction from language. Media highlights include research on tracking anti-immigrant hate speech and semantic evolution in French texts.

• Computational Linguistics
• Psycholinguistics
• Neural Network Language Models
• +۳ مورد دیگر

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)

• Multiscale Modeling and Simulation
• Materials Informatics
• Computational Physics
• +۳ مورد دیگر

Helen N. Schwerdt is an Assistant Professor in the Department of Bioengineering at the University of Pittsburgh, affiliated with the Swanson School of Engineering and the Center for the Neural Basis of Cognition (CNBC). Her research focuses on developing implantable tools for probing electrical and chemical neural activity, with a particular emphasis on long-term monitoring in both rodents and primates. Key areas include dopamine signaling in Parkinson’s disease, neural interface design, and chronic neural recording systems. Her lab’s work spans interdisciplinary fields such as bioengineering, materials science, and neuroscience. Notable contributions include scalable carbon fiber electrode arrays for neurochemical monitoring and wireless passive neurorecording systems. Schwerdt’s team collaborates extensively, addressing challenges in neural prosthetics and translational neuroscience. Recent research highlights include the 2023 publication on rodent neurochemical arrays and 2020 studies on dopamine and motor control in primates. The lab actively recruits students and postdoctoral researchers across disciplines, emphasizing training in microfabrication, electrophysiology, and computational neuroscience.

• Neuroscience
• Bioengineering
• Materials Science
• +۶ مورد دیگر

Reza Bosagh Zadeh is an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning, deep learning, and their applications in video classification, healthcare analytics, and distributed algorithms. He specializes in developing scalable computational methods for real-time data processing and has contributed to advancements in neural networks and optimization techniques. Reza's work spans theoretical and applied domains, with notable contributions to TensorFlow frameworks, video summarization systems, and medical imaging analysis. His research often integrates interdisciplinary approaches, leveraging both academic and industrial collaborations. Notable projects include developing machine learning models for glaucoma detection and creating efficient algorithms for large-scale data processing in environments like Apache Spark. His publications emphasize real-time video stream analysis, distributed computing architectures, and practical implementations of deep learning. Reza holds a strong presence in both academic and tech sectors, with contributions to platforms like Twitter's Who-to-Follow system and innovations in edge computing for video surveillance.

• Machine Learning
• Deep Learning
• Video Classification
• +۳ مورد دیگر

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).

• Tissue Engineering
• Biomaterials
• Cell Biology
• +۳ مورد دیگر

Stephanie Riès is an Associate Professor in the Department of Speech, Language, and Hearing Sciences at San Diego State University, where she directs the Laboratory for the Brain Dynamics of Language. Her research specializes in the cognitive neuroscience of human language, examining brain dynamics in language production, control processes, and neural reorganization following brain disorders. Education: BS in Biology from University of Rennes 1 MS in Cognitive Neuroscience from Aix-Marseille University MS in Cellular Biology and Animal Physiology from University of Rennes 1 PhD in Cognitive Neuroscience from Aix-Marseille University Her work integrates electrophysiology (EEG, EMG) and neuroimaging to study language production mechanisms and compensatory plasticity in conditions like stroke, epilepsy, and brain tumors. Key research areas include bilingual interference resolution, speech monitoring mechanisms, and neural reorganization patterns. Recent publications focus on neural hierarchies in speech production, neuroplasticity in language recovery, and advanced brain mapping techniques. Article analyses reveal strong emphasis on electrophysiological methods (sEEG, ERP), bilingual cognition, and clinical translation of neuroscience findings. Dr. Riès leads significant research initiatives through her laboratory, investigating brain-behavior relationships using intracranial recordings and developing novel approaches for speech activity detection across neurological populations.

• Cognitive Neuroscience
• Psycholinguistics
• Speech and Language Disorders
• +۴ مورد دیگر

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.

• Machine Learning
• Cyber Security
• Public Health Prediction
• +۵ مورد دیگر

Alexander Brown is a Lecturer in the Department of Mechanical Engineering at Lafayette College, specializing in dynamic systems and controls. His research bridges robotics, vehicle dynamics, and bio-inspired modeling, with a focus on human-robot and animal-robot interactions. B.S., Pennsylvania State University (2008) M.S., Pennsylvania State University (2012) Ph.D., Pennsylvania State University (2013) Research interests include robotics, vehicle dynamics, and mathematical modeling of physical systems. His work explores interactions between robots, vehicles, humans, and animals, with applications in autonomous driving and ethorobotics. Alexander's recent publications highlight trends in fish-robot interaction studies, autonomous vehicle safety, and simulation platforms like Webots. He emphasizes interdisciplinary collaboration and hands-on student engagement in his research and teaching. Outside academia, he builds and restores motorcycles, surfs in Monmouth County, N.J., and plays music with his band 'Imposters In Rome'.

• Robotics
• Vehicle Dynamics
• Human-Robot Interaction
• +۵ مورد دیگر

Engin Erzin is a Professor at Koç University's College of Engineering, leading the KUIS AI Lab and Multimedia, Vision and Graphics Lab . His research focuses on AI-driven human-centric systems, affective computing, and multimodal interaction analysis. He has contributed extensively to robotics, speech processing, and human-robot interaction through over 70 peer-reviewed publications since 2008. Research interests include: Affective computing and emotion recognition from speech/gestures Human-robot interaction and socially engaging agents Speech-driven animation and gesture synthesis Multimodal data fusion for interaction analysis Deep learning applications in robotics and biomedical engineering Recent work emphasizes: Developing adaptive pHRI controllers for manufacturing tasks Creating engagement measurement frameworks for human-machine interfaces Advancing Turkish speech recognition through self-supervised learning Designing multimodal databases for interaction studies Labs: KUIS AI Lab : Focuses on AI applications in robotics and human-computer interaction Multimedia Lab : Specializes in vision, graphics, and audiovisual analysis

• Artificial Intelligence
• Multimedia Systems
• Computer Vision
• +۶ مورد دیگر

Qiushi Fu is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of Central Florida, focusing on neuromuscular control, human-robot interaction, and biomechanics. His work leverages assistive and rehabilitation technologies to improve motor function and performance. Ph.D. in Biomedical Engineering – Arizona State University M.S. in Mechanical Engineering – State University of New York at Buffalo B.S. in Automation – Tsinghua University Research interests include sensorimotor learning, bioinspired robotics, and rehabilitation prosthetics, integrating neuroscience, robotics, and machine learning. His recent publications emphasize adaptive control in dexterous manipulation, haptic communication, and wearable rehabilitation devices. Fu leads a multidisciplinary lab advancing human movement through technology, with applications in stroke recovery and prosthetic design. His work demonstrates trends in machine learning integration for neural decoding, bioinspired exoskeletons, and collaborative human-robot systems. Though no specific awards are listed, his research has been published in high-impact journals like Frontiers in Neurorobotics and Journal of Neuroscience .

• Neuromuscular Control
• Human-Robot Interaction
• Biomechanics
• +۵ مورد دیگر

Dr. Haydar Aygun serves as Associate Professor in Acoustics and Building Services at the Bioscience and Bioengineering Research Centre, Department of Civil and Building Services Engineering, University of Salford. He holds multiple leadership roles including Course Director for the MSc Environmental and Architectural Acoustics, Diploma in Acoustics and Noise Control, and The Acoustics Apprenticeship programs. With a PhD in Acoustics and Vibration from the University of Hull (2006), his academic foundation includes a MSc in Advanced Materials, Processes and Manufacturing (2002) and a BSc in Mechanical Engineering. Dr. Aygun's research spans multiple critical domains of acoustics: Development and characterization of novel acoustic materials and metamaterials for noise control Duct acoustics and sound propagation through complex media Biomedical applications including bone analysis and respiratory sound detection Vibration control of porous materials using advanced computational methods Environmental noise assessment and building acoustics solutions His publication record shows consistent scholarly output with 46 research items from 2006-2025, demonstrating particular productivity in recent years (7 publications in 2023). His work shows increasing integration of computational methods with experimental validation, expanding applications in medical diagnostics and sustainable building technologies. Dr. Aygun actively contributes to the academic community through multiple service roles: Editorial Board Member for Journal of Applied Acoustics EPSRC Grant Reviewer Reviewer for Journal of Applied Acoustics and Journal of the Acoustical Society of America Deputy leader of Biomedical Acoustics Special Interest Group within UK Acoustics Network As an educator, Dr. Aygun has supervised numerous PhD students and teaches across multiple levels from undergraduate Building Services Engineering to specialized postgraduate Acoustics programs. His consultancy work bridges academic expertise with practical industry applications in noise assessment, building acoustics, and event sound management.

• Acoustic Materials
• Metamaterials
• Biomedical Acoustics
• +۷ مورد دیگر

Emily Gibson is an Associate Professor of Bioengineering at the University of Colorado Denver, affiliated with the CU Anschutz Medical Campus. Her research focuses on developing advanced optical technologies, including multiphoton, superresolution, and structured illumination microscopy, to study neural circuits and advance clinical tools. The Biophotonics Lab, under her leadership, integrates optogenetics and fluorescent sensors to explore nervous system dynamics and non-invasive brain-machine interfaces. Recent projects include NSF-funded work on odor plume navigation and BRAIN Initiative-supported development of miniature microscopes for voltage imaging in freely moving mice. Her team collaborates on clinical neuronavigation via two-photon microscopy and presented innovations like the GRINtrode and SIMscope3D at conferences such as CLEO and the Biophotonics Congress. Students and researchers in her lab include Forest Speed, Tarah Welton, and Gabriel Martinez, among others.

• Biophotonics
• Neuroscience
• Neuroengineering
• +۳ مورد دیگر

Riadul Islam serves as an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC), maintaining his primary office in room 316 of the Information Technology and Engineering (ITE) Building. His academic appointment focuses on hardware design and verification within the institution's engineering framework. His educational qualifications include: Ph.D. in Computer Engineering from UCSC (2017) M.A.Sc. in Electrical and Computer Engineering from Concordia University, Montreal (2011) B.Sc. in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (2007) Professor Islam's research centers on VLSI CAD tools and low-power digital/mixed-signal IC design , with significant contributions to current-mode clock networks, vehicular security systems, and error-robust circuit architectures. His work increasingly integrates machine learning for design automation while exploring neuromorphic computing applications and secure hardware implementations. This multidisciplinary approach bridges traditional IC design with modern AI-driven optimization techniques. Analysis of his 2023-2025 publications reveals three dominant research thrusts: (1) Machine learning applications in early-stage Design Rule Checking (DRC) prediction and clock network optimization, (2) Graph-based intrusion detection systems for automotive networks (particularly CAN bus security), and (3) Event-based vision systems and neuromorphic computing architectures. These areas demonstrate consistent innovation in merging hardware design with AI/ML methodologies for enhanced system reliability and efficiency. He directs the UMBC VLSI and SoC Research Group , which develops energy-efficient clocking networks, secure vehicular communication protocols, and compute-in-memory architectures. The lab maintains active collaboration with industry partners on hardware security and neuromorphic computing initiatives while supporting graduate student research in cutting-edge IC design methodologies.

• VLSI CAD
• Low-power IC Design
• Mixed-signal IC Design
• +۵ مورد دیگر

Dr. Wonbong Choi is a University Distinguished Research Professor at the University of North Texas with joint appointments in the Department of Materials Science and Engineering and Mechanical Engineering. His research focuses on nanomaterials, energy storage systems (particularly lithium-sulfur and zinc-ion batteries), additive manufacturing of composites, and neuromorphic computing devices. He was elected Fellow of the National Academy of Inventors in 2024 for his contributions to materials innovation. Research interests span materials synthesis, electrochemical characterization, and device integration of 2D materials like MXenes and transition metal dichalcogenides. Key areas include: Nanomaterial design for batteries and supercapacitors 3D-printed sensors and structural composites Defect engineering for neuromorphic computing His recent publications highlight consistent themes in MXene synthesis optimization, advanced battery architectures, and multifunctional composites. Article trends show increasing focus on in situ characterization, scalable manufacturing of nanomaterials, and AI-driven materials design. Awards: Fellow, National Academy of Inventors (2024) Collaborates with research groups at Oak Ridge National Laboratory and leads projects on lightweight composites and energy storage systems.

• Materials Science
• Energy Storage
• Nanomaterials
• +۳ مورد دیگر

Yongfeng Zhang is an Associate Professor in the Department of Computer Science at Rutgers University. He is also the Director of the AIOS Foundation . His research focuses on Machine Learning, Data Mining, Recommender Systems, and Explainable AI , with notable contributions to fair and personalized AI, AI for science, and social good. Education and Experience: PhD in CS (2011–2016) from Tsinghua University Postdoc at UMass Amherst (2016–2017) Joined Rutgers as Assistant Professor (2018), promoted to Tenured Associate Professor (2024) Research Interests: Machine Learning, Data Mining, Information Retrieval, Recommender Systems, Natural Language Processing, ML Systems, AI Agents, Explainable AI, Fairness, and AI for Science. Recent Achievements: Received the 2024 ACM SIGIR Test of Time Award, 2024 Presidential Teaching Excellence Award, and multiple NSF grants. He has advised over 15 PhD students and led projects on trustworthy AI, generative recommendation, and causal inference. Awards: 2024 ACM SIGIR Test of Time Award 2021 NSF CAREER Award 2015 Microsoft PhD Fellowship Grants & Labs: Led NSF grants on explainable AI, conversational recommendation, and neural-symbolic AI. Active in the AIOS platform development and multiple research labs.

• Machine Learning
• Data Mining
• Information Retrieval
• +۸ مورد دیگر