جستجوی استاد راهنمای دانشگاهی

• Human-Computer Interaction
• User Interface Design
• Soft Robotics
• +۱۰ مورد دیگر

Jan Borchers is a full professor of computer science and head of the Media Computing Group, an endowed Chair in the Computer Science Department at RWTH Aachen University. He serves as deputy member of the Faculty Council for Mathematics, Computer Science and Natural Sciences (2024-2026) and previously headed the Computer Science Department's Examination Board from 2015 until February 2025. Borchers established his research group in 2003, pioneering modern HCI academic research and teaching in Germany, and opened Germany's first Fab Lab in 2009. Borchers received his PhD 'summa cum laude' in computer science from Darmstadt University of Technology in 2000. Before joining RWTH Aachen, he held faculty positions at Stanford University and ETH Zurich. His PhD thesis, 'A Pattern Approach to Interaction Design,' became the first book to bring design patterns to HCI. His research focuses on Human-Computer Interaction with particular interest in new user interfaces for soft robotics, textile user interfaces, 3D printing and personal fabrication, augmented reality, wearable and tangible computing, interfaces for software development, deceptive patterns, and interactive guides and exhibits. He is a member of ACM, SIGCHI, SIGCHI Germany, and GI, and introduced the Interactivity format to the CHI conference in 2005. Recent publications reveal a strong emphasis on deceptive patterns/dark patterns in UI design, textile interfaces, and the impact of generative AI on creative teamwork. His work spans from fundamental research in interaction techniques to practical applications in smart homes, accessibility, and children's interfaces, with a consistent focus on usability and user-centered design. IDC 2025 Best Work in Progress: 'If They Have No Choice, They'll Accept!' CHI'25 Student Games Competition Winner: 'The Deceptive Dungeon' RWTH-Wissenschaftsnacht 2024 Best Science Slam: 'Usability: 4 Prinzipien guter User Interfaces' Multiple CHI/UIST Honorable Mentions and Best Paper awards spanning two decades Author of influential books including 'A Pattern Approach to Interaction Design' and 'Arduino In A Nutshell' Borchers has provided extensive consulting, training, and user interface design services to major clients including AirBus, Apple, ARD, Bayer, Children's Museum Boston, Daimler, Handelsblatt, OTIS, Scout24, TEDx, and Deutsche Telekom. He actively organizes the Computer Science Department's weekly Faculty Lunch since 2003 and coordinates the department's public relations and web presence since 2010. He leads the Media Computing Group, which has become a leading German lab in terms of archival publications at CHI, the top international conference in HCI. Since March 2025, he serves as faculty patron for TechLabs Aachen, a student initiative providing practical digital skills training.

• Computational Materials Science
• Condensed Matter Physics
• Nanomaterials
• +۶ مورد دیگر

Paul Erhart is a Professor in Condensed Matter and Materials Theory at the Department of Physics, Chalmers University. He received his PhD from Technische Universität Darmstadt in 2006, followed by postdoctoral and staff positions at Lawrence Livermore National Laboratory from 2007, before joining Chalmers in 2011. His research bridges computational physics, materials science, and machine learning to tackle fundamental problems in materials design and characterization. Dr. Erhart's research focuses on computational materials science with particular emphasis on condensed matter physics, nanomaterials, and quantum materials. His work spans from developing computational methods like machine-learned potentials (GPUMD, neuroevolution potentials) to studying fundamental phenomena in perovskites, 2D materials, thermal transport, and plasmonics. He has pioneered approaches connecting simulation with experimental techniques through correlation functions and has made significant contributions to understanding phase transitions, defect physics, and electronic structure in complex materials systems. Analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional computational physics methods. His work increasingly focuses on developing and applying neuroevolution potentials to study thermal properties, phase transitions, and optical phenomena in materials. There's also a clear emphasis on connecting computational results with experimental observations, particularly in neutron scattering, Raman spectroscopy, and plasmonic sensing applications. His research spans fundamental materials physics to applied areas like hydrogen sensing and sustainable materials development. Dr. Erhart has contributed to numerous software packages essential to the computational materials science community, including WulffPack for Wulff constructions, Dynasor for extracting dynamical structure factors, calorine for neuroevolution potential models, and ICET for alloy cluster expansions. His collaborative work spans multiple institutions and disciplines, reflecting the interdisciplinary nature of modern materials research. His contributions to understanding perovskite materials, thermal transport phenomena, and plasmonic systems have established him as a leading researcher in computational materials science.

Michael Feigمشاهده پروفایل

استاد

• Molecular Dynamics
• Computational Biophysics
• Protein Structure and Dynamics
• +۳ مورد دیگر

Michael Feig serves as Professor in the Department of Biochemistry & Molecular Biology at Michigan State University, leading the Feig Lab within the BioMolecular Science Gateway initiative. His research bridges computational modeling and molecular biology to investigate protein behavior in cellular contexts, with particular emphasis on molecular dynamics simulations and machine learning applications. His academic background includes: Ph.D. (1999) from the University of Houston M.S. (1994) from Technical University of Berlin Feig's research program focuses on computational biophysics of protein systems, specializing in molecular dynamics simulations of crowded cellular environments, bacterial microcompartments, and intrinsically disordered proteins. His lab develops advanced modeling techniques including coarse-grained approaches (COCOMO2) and machine learning frameworks to predict protein properties and conformational landscapes. Current work explores temperature-dependent structural ensembles, enzyme cargo loading mechanisms in engineered microcompartments, and biomolecular condensate physics under shear flow. Analysis of his 15 most recent publications (2024-2025) reveals three dominant research thrusts: (1) integration of deep learning with molecular dynamics for protein structure prediction, (2) engineering of bacterial microcompartments for synthetic biology applications, and (3) fundamental studies of macromolecular crowding effects on diffusion and phase separation. His work consistently emphasizes methodological innovation with biological relevance, notably through enhancements to the CHARMM simulation platform. His scientific recognition includes: Alfred P. Sloan Fellowship (2005) As principal investigator of the Feig Lab, he directs research teams in computational biophysics projects supported by active funding mechanisms. While specific grant details aren't provided, his continuous publication pipeline and lab infrastructure indicate sustained research support. His mentorship spans graduate students in the Cell & Molecular Biology Program, with recent work involving multi-institutional collaborations on bacterial microcompartment engineering and protein phase separation. The Feig Lab operates at the intersection of high-performance computing and molecular biology, maintaining strong connections with experimental groups for method validation. Current initiatives include developing generative models for temperature-dependent protein conformations and investigating cytoplasmic protein capture mechanisms in microcompartments, with potential applications in metabolic engineering and nanobiotechnology.

Matthew Lee Smithمشاهده پروفایل

استاد

• Aging
• Health Behavior
• Chronic Disease Management
• +۶ مورد دیگر

Matthew Lee Smith is a Professor at the Texas A&M School of Public Health , part of Texas A&M University . He is a core faculty member of the Center for Community Health and Aging (CCHA) and the Center for Health Equity and Evaluation Research (CHEER) , and serves as the Director of the Texas Research, Analytics, Innovations, and Research Lab (TRAIL) . Education: Post-Doctoral Fellowship, Health Science Center, Texas A&M University (2010) PhD in Health Education, Texas A&M University (2008) MPH, Indiana University Bloomington (2004) BS in Public Health Education, Indiana University Bloomington (2002) Research Interests: Dr. Smith’s research focuses on aging , chronic disease management , and evidence-based public health interventions . He is particularly interested in health behavior change , health risk assessment , and survey research methodology . His translational work bridges research and practice across healthcare, aging services, and public health systems. He has a strong focus on social determinants of health , including social isolation , caregiving , diabetes self-management , and fall prevention among older adults. His work often targets underserved populations, particularly Black/African American men and rural communities . Scientific Awards: Immunization Neighborhood Champion Award (2024) Responsible Research in Management Award (2023) J. Mayhew Derryberry Award (2022) Consumer Education Program Award (Silver) (2022) Bluebonnet Award (2021) Innovators in Aging Award (2019) Redefining American Healthcare Award (2019) Community ConnecTivity Award (2019) Phillip G. Weiler Award for Leadership in Aging and Public Health (2018) Leadership & Mentorship: Dr. Smith holds leadership roles in several national and state-level initiatives. He is the Co-Director of the Advancing Gerontology through Exceptional Scholarship (AGES) Program and serves on the Steering Committees of the Texas Falls Prevention Coalition , Texas Alzheimer’s Research and Care Consortium , and Texas Social Isolation and Loneliness Coalition . He is also the Director of the Research Scholars & Mentorship Program (RSMP) at the American Academy of Health Behavior. Labs & Centers: He leads the Texas Research, Analytics, Innovations, and Research Lab (TRAIL) , which focuses on developing and evaluating community-based interventions. He is also affiliated with: Center for Community Health and Aging (CCHA) Center for Health Equity and Evaluation Research (CHEER)

Borivoje Dakicمشاهده پروفایل

دانشیار

• Quantum Information Theory
• Quantum Foundations
• Quantum Tomography
• +۵ مورد دیگر

Dr. Borivoje Dakic is an Associate Professor at the University of Vienna , affiliated with the Faculty of Physics and the Quantum Optics, Quantum Nanophysics and Quantum Information department. His research spans foundational and applied aspects of quantum theory. Operational reconstruction of quantum formalism Quantum interference as a resource for communication Tomography of large-scale quantum systems Macroscopic quantum phenomena His work includes scalable verification techniques for quantum devices and collaborations with experimental teams like Philip Walther’s and Markus Aspelmeyer’s groups. He received the Marko Jarić Prize (2025) for his contributions. Recent projects focus on diagnostics of quantum devices (FWF BeyondC SFB), information-theoretic foundations of quantum interference (FWF P36994), and local operations in quantum field theory (Cluster of Excellence QuantA). His research on quantum coherence in networks and macroscopic entanglement challenges traditional assumptions about quantum-classical boundaries. Publications emphasize resource-efficient tomography, device-independent verification, and foundational frameworks for quantum statistics and field theory. Teaching: Quantum Information (2025W), Theory in Quantum Optics (2025S), VCQ Summerschool Labs: Dakić Group at University of Vienna

• Machine Learning
• Artificial Intelligence
• Combinatorial Optimization
• +۱۳ مورد دیگر

Yiming Yang is a Professor at the Language Technologies Institute and Machine Learning Department within the School of Computer Science at Carnegie Mellon University , where he has held faculty positions since 2003. His research spans foundational and applied aspects of machine learning , artificial intelligence , and scientific computing . Professor, Carnegie Mellon University (2003–Present) Associate Professor, Carnegie Mellon University (1996–2003) Yang's research focuses on LLM-based problem-solving agents , combinatorial optimization , and scalable oversight frameworks . His work explores diffusion models, Langevin dynamics, and Fourier neural operators for NP-hard problems, while advancing reinforcement learning techniques for self-play supervision and principle-driven fine-tuning of large language models. Recent publications highlight his contributions to code synthesis , PDE solving , and multi-agent reinforcement learning . Key methodologies include demonstration-guided control, retrieval-augmented reasoning, and test-time scaling laws. His team has developed frameworks like FEEDER for efficient in-context learning and μTransfer-FNO for zero-shot hyperparameter transfer in PDE solvers. Notable scientific achievements include: Best Student Paper Runner Up (2013) Best Theoretical Paper Award (1994) Best Theoretical Paper Award (1993) Yang has mentored over 20 PhD students and postdocs, including Shengyu Feng , Zhiqing Sun , and Aman Madaan , across domains like graph learning , extreme multi-label classification , and language model alignment .

• Medical Imaging
• Molecular Imaging
• Neuroscience
• +۳ مورد دیگر

Steven Meikle is a Professor of Medical Imaging Physics and Head of the Imaging Physics Laboratory at the Brain and Mind Centre, University of Sydney. He also serves as Deputy Director (Preclinical) of Sydney Imaging and Deputy Director of the National Imaging Facility's Sydney node. His expertise spans advanced imaging technologies, with a focus on PET/SPECT instrumentation and molecular imaging. He holds a B.App.Sc.(Hons) from the University of Technology Sydney and a PhD from the University of New South Wales. Research focuses include developing novel PET systems like Open-field PET (for freely moving rodents) and Total Body PET, which enhance imaging sensitivity and enable real-time behavioral studies alongside brain function analysis. Collaborations include Tsinghua University (China) and UC Davis (USA). He leads projects on motion correction, quantitative imaging, and AI-driven analysis. Key achievements include over 180 peer-reviewed publications, editorial roles in Physics in Medicine and Biology , and leadership in professional societies. Awards include IEEE Senior Membership and Australian Institute of Physics Fellowship. Current student projects explore Total Body PET applications, motion correction, and radiopharmaceutical evaluation. Teaching roles include medical physics courses in diagnostic radiography and medical physics programs. He advises on imaging ethics, facility implementation, and translational research bridging basic science and clinical applications.

Andrew Lanمشاهده پروفایل

دانشیار

• Artificial Intelligence in Education
• Educational Data Mining
• Personalized Learning
• +۵ مورد دیگر

Andrew Lan is an Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, where he also serves as the CS Undergraduate Program Director. He was granted tenure by the UMass Board of Trustees in June 2025 and is currently on leave through Spring 2026. His research focuses on developing human-in-the-loop machine learning methods to enable scalable, effective, and personalized learning experiences in education. Dr. Lan received his BS in Physics and Mathematics from the Hong Kong University of Science and Technology, followed by his MS (2014) and PhD (2016) in Electrical and Computer Engineering from Rice University. He completed postdoctoral research at Rice University (2016) and Princeton University's EDGE Lab (2017-2018). His research spans artificial intelligence for education, with particular expertise in educational data mining, knowledge tracing, personalized learning systems, and human-AI collaboration in educational contexts. Dr. Lan's work leverages massive and multimodal learner and content data collected from both traditional classrooms and online learning platforms to develop systems that deliver high-quality, affordable, and personalized learning experiences. He has made significant contributions to areas including computerized adaptive testing, math word problem generation, student affect detection, and automated grading systems. His recent work increasingly focuses on leveraging large language models for educational applications while maintaining rigorous scientific validation of these approaches. Best Student Paper Award at the 2024 AIED Conference (with Alexander Scarlatos) Best Paper Nominee at LAK 2021 Best Student Paper Award at IEEE Big Data 2020 NAEP Math Automated Scoring Challenge Grand Prize Winner Dr. Lan actively mentors graduate students and postdoctoral researchers, with several of his advisees receiving recognition for their work. He has secured substantial funding from the National Science Foundation, including a $90M grant for the SafeInsights project, a secure cyberinfrastructure for educational research. His research group collaborates with institutions including Worcester Polytechnic Institute, University of Pennsylvania, and Rice University. He teaches undergraduate and graduate courses including COMPSCI 240 (Reasoning under Uncertainty) and COMPSCI 590OP (Applied Numerical Optimization), with a focus on the practical application of theoretical concepts in machine learning and artificial intelligence. His educational philosophy emphasizes bridging the gap between theoretical foundations and real-world implementation in educational technology.

Tom Dhaeneمشاهده پروفایل

استاد

• Data-Efficient Machine Learning
• Surrogate Modeling
• Gaussian Processes
• +۳ مورد دیگر

Tom Dhaene is a Full Professor at Ghent University, affiliated with the Department of Information Technology (INTEC-IDLab) within the Faculty of Engineering and Architecture (FEA). He also holds a position at imec, a research and innovation hub in nanoelectronics and digital technologies. Research Unit: Internet Technology and Data Science Lab (IDLab) Academic Rank: Full Professor Affiliations: Ghent University, imec His research focuses on data-efficient machine learning, surrogate modeling, Gaussian processes, Bayesian optimization, and system identification. He has developed widely used software tools such as the SUMO toolbox and ooDACE, and holds 5 U.S. patents. His work bridges theoretical advancements with practical applications in engineering and biomedical domains. Recent publications highlight his contributions to physics-informed machine learning, antenna design, microwave optimization, and healthcare applications. Notably, he explores Bayesian active learning, multi-objective optimization under uncertainty, and efficient modeling techniques for complex systems. Prof. Dhaene's research has been recognized through over 500 peer-reviewed publications and collaborations across academia, industry, and government sectors globally.

Sheryl Graceمشاهده پروفایل

دانشیار

• Aerodynamics
• Fluid Dynamics
• Acoustics
• +۴ مورد دیگر

Sheryl Grace is an Associate Professor of Mechanical Engineering at Boston University, leading the Unsteady Fluid Mechanics & Acoustics Laboratory (UFMAL). Her primary appointment is in the Department of Mechanical Engineering within the College of Engineering. She holds a PhD from the University of Notre Dame. Her research focuses on unsteady aerodynamics, aeroacoustics, and fluid-structure interactions, with applications in aerospace systems, propulsion technologies, and biological acoustics. Notable projects include NASA-funded work on quieter vertical lift vehicles and computational modeling of gerbil hearing mechanics. Professor Grace’s research interests span aerodynamics, fluid dynamics, and acoustics. She develops analytical and computational models to predict sound and vibration generated by unsteady flows interacting with solid structures. Recent studies include noise reduction in aircraft wings, turbine blade fatigue analysis, and acoustic scattering in gerbil ears. Her work bridges theoretical models with practical engineering solutions, emphasizing cost-effective predictive tools for next-generation systems. Her publications highlight advancements in shock-droplet interactions, cavitation modeling, and machine learning applications in aeroacoustics. Collaborative projects include multi-institutional efforts to address urban air vehicle noise challenges. While no explicit awards are listed, her contributions to computational acoustics and fluid dynamics are recognized through extensive peer-reviewed output. Advising and grants: Professor Grace leads the UFMAL lab and has secured funding from agencies like NASA. Her research integrates fluid mechanics, acoustics, and computational methods to address industrial and environmental noise issues. She collaborates across disciplines, including mechanical engineering, aerospace, and biomedical acoustics.

Tara Boroushakiمشاهده پروفایل

استادیار

• Wireless Networking
• Cyber-Physical Systems
• Robotics
• +۳ مورد دیگر

Tara Boroushaki is an incoming Assistant Professor in Electrical & Computer Engineering at Yale University. She completed her Ph.D. at MIT (expected May 2025), advised by Prof. Fadel Adib, with a focus on sensing and mobile technologies. Her research spans wireless networking, robotics, and human-computer interaction, emphasizing multi-modal sensing for environmental perception. Key achievements include the Microsoft Research PhD Fellowship (2022–2024) and the IEEE RFID '23 Best Paper Award. Her work on RF-based 'X-ray vision' has been featured in TEDxMIT and media outlets like the BBC and World Economic Forum. She co-founded Cartesian Systems, deploying sensing technologies in retail and supply chain. Research interests include non-line-of-sight perception, RFID localization, and robotic grasping. She has developed systems like FuseBot and RFusion, highlighted as transformative in MIT's '103 Ways to Make the World Better' initiative.

Luca Demetrioمشاهده پروفایل

استادیار

• Adversarial Machine Learning
• Malware Detection
• Cybersecurity
• +۳ مورد دیگر

Luca Demetrio is an Assistant Professor at the University of Genoa, Italy, specializing in adversarial machine learning and cybersecurity. Previously, he was a Post-doctoral Researcher at the PRA Lab within the Department of Electrical and Electronic Engineering at the University of Cagliari. He holds bachelor's (2015), master's (2017), and Ph.D. (2021) degrees from the University of Genova, with his doctoral thesis focusing on formalizing evasion attacks against security detectors. His research emphasizes enhancing the robustness of machine learning models against adversarial attacks, particularly targeting malware detectors, SQL injection defenses, and Windows security systems. He leads the development of SecML Malware, a Python library for generating adversarial Windows malware, and contributes to the SecML framework. His work has been published in top-tier journals like ACM TOPS and IEEE TIFS. Key research interests include adversarial example generation, malware analysis, and cybersecurity defense mechanisms. He has explored query-efficient attacks on phishing detectors, certified adversarial robustness via randomized smoothing, and robust synthetic data-driven threat detection. His recent studies (2023–2025) address challenges in hardening machine learning models against evasion attacks, adversarial SQL injection countermeasures, and securing autonomous driving systems from adversarial reinforcement learning attacks.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۹ مورد دیگر

Professor Mounim A. El Yacoubi holds positions at Institut Polytechnique de Paris, Institut Mines-Télécom, and Telecom SudParis. His research focuses on AI, machine learning, and deep learning applied to e-Health (neurodegenerative disease detection, diabetes management), biometrics (gait, vein, and handwriting recognition), and smart systems (agriculture, surveillance, robotics). He leads the SAMOVAR CNRS Lab and has supervised 17 PhDs and 30+ master's students. Education: PhD (1996, Université de Rennes 1), HDR (2014, Paris-Saclay University). Experience: Senior Researcher at Parascript (2001–2008), Visiting Scientist at CENPARMI (1997–1998), Associate Professor at PUCPR (1998–2001). Research Interests: AI applications in healthcare, biometrics, pattern recognition, and smart technologies. Recent work includes Alzheimer’s detection via handwriting analysis, diabetes prediction using PPG signals, and palm/vein recognition systems. Grants & Leadership: Program Chair of ICPRAI 2022, ICCPRA 2024. Editor of IEEE Access and journals on cyber-physical intelligence. Authored books on Pattern Recognition and AI.

Zhongying Dengمشاهده پروفایل

پژوهشگر ارشد

• Medical Imaging
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Zhongying Deng is a Research Fellow in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work focuses on advancing medical imaging technologies and computer vision through deep learning and domain adaptation techniques. Key contributions include developing benchmark datasets like TrafficCAM and TrafficMOT for traffic analysis, A-Eval for abdominal organ segmentation, and foundational models for medical AI such as GMAI-VL. His research bridges theoretical advancements in neural networks and practical applications in healthcare and transportation. His research interests span image segmentation, domain adaptation, neural network architectures, and multimodal data integration. Notable projects include FCN+ for enhanced convolutional networks and Brain Foundation Models for neurodegenerative disease analysis. Deng collaborates extensively on interdisciplinary projects, combining mathematical modeling with computational tools to address real-world challenges in medical diagnosis and autonomous systems. Publications emphasize scalable medical image analysis frameworks (e.g., STU-Net, Sa-med2d-20m) and robust domain adaptation methods for cross-dataset performance. His datasets and models are widely recognized for enabling reproducible research and advancing state-of-the-art performance in critical areas like MRI reconstruction and multi-organ segmentation.

Ameer Abdelhadiمشاهده پروفایل

استادیار

• Hardware Acceleration
• Neurotechnology
• Reconfigurable Computing
• +۴ مورد دیگر

Dr. Ameer Abdelhadi is an Assistant Professor in the Department of Electrical and Computer Engineering at McMaster University. His research focuses on application-specific custom-tailored computer architectures, hardware-efficient deep learning, neurotechnology, and reconfigurable computing. He holds a PhD from the University of British Columbia and has held academic positions at the University of Toronto, Imperial College London, and Simon Fraser University, alongside industry experience in semiconductor design. Education: PhD in Computer Engineering (University of British Columbia, 2016). Research Interests: Hardware acceleration for machine learning and neurotechnology Reconfigurable computing and FPGAs/ASICs Asynchronous circuits and synchronization protocols VLSI physical design and CAD algorithms Publications span high-impact venues such as IEEE Journal of Solid-State Circuits, IEEE Hot Chips, and IEEE Micro. Notable achievements include the 2017 Best Paper Award at ASYNC for work on synchronization FIFOs. Teaching includes COMPENG 4DV4 (VLSI System Design) and ELECENG 4OI6B (Engineering Design). His lab focuses on advancing hardware systems for next-generation applications in AI and biomedical engineering.