جستجوی استاد راهنمای دانشگاهی

Simon Aeschbacherمشاهده پروفایل

پژوهشگر ارشد

• Population Genomics
• Speciation
• Local Adaptation
• +۵ مورد دیگر

Simon Aeschbacher is an Independent Research Fellow at the Department of Evolutionary Biology and Environmental Studies, University of Zurich. His work bridges mathematical theory, computational methods, and genomic data to address fundamental questions in evolutionary biology. His educational background includes: M.Sc. in Zoology (2007) and undergraduate studies (2001-2003) at University of Zurich Ph.D. in Evolutionary Biology (2008-2011) at University of Edinburgh/IST Austria under Nick Barton Postdoctoral positions at University of Vienna (2011-2013), UC Davis (2014-2016), and University of Bern (2017) Aeschbacher's research centers on population genomics, specializing in the interplay between gene flow, natural selection, and recombination. His work combines mathematical modeling with genomic analyses to investigate local adaptation, speciation, and human evolutionary history. Key contributions include developing methods for demographic inference and quantifying selection against gene flow. His publication record shows a clear trajectory from theoretical foundations (early work on linkage effects) to applied genomic methodologies (recent development of gIMble for barrier detection). Current research emphasizes human evolution, plant speciation, and hybridization dynamics across diverse taxa. Scientific recognition includes: Swiss NSF Advanced Postdoc.Mobility Fellowship While not explicitly mentioned in the text, his role as Independent Research Fellow implies grant leadership and potential mentoring responsibilities. His work with multiple international collaborators suggests active participation in research networks across Europe and North America.

Leslie Kaelblingمشاهده پروفایل

استاد

• Robotics
• Machine Learning
• Artificial Intelligence
• +۲ مورد دیگر

Leslie Kaelbling serves as the Panasonic Professor in MIT's Department of Electrical Engineering and Computer Science (EECS) and Director of Research for the MIT Quest for Intelligence, while also holding an investigator position at the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her academic background includes a BA in philosophy and PhD in computer science, both earned at Stanford University. Professor Kaelbling's research centers on building intelligent robots capable of operating in uncertain environments through advanced learning, state estimation, and planning techniques. Her work bridges theoretical AI foundations with practical robotics applications, focusing on creating systems that adapt to complex real-world scenarios through probabilistic reasoning and decision-making under uncertainty. Analysis of her recent publications reveals a strong trajectory toward integrating foundation models with robotic planning systems, particularly leveraging vision-language models for constraint inference and diffusion models for task-motion planning. Her research increasingly emphasizes uncertainty quantification, neuro-symbolic representations, and sample-efficient learning methods for long-horizon manipulation tasks. Key scientific recognitions include: Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) Founder and inaugural Editor-in-Chief of the Journal of Machine Learning Research As Director of Research for the MIT Quest for Intelligence, she leads strategic initiatives advancing human and machine intelligence research, fostering cross-laboratory collaborations between CSAIL, neuroscience, and cognitive science groups to develop next-generation AI systems. Her leadership extends to shaping research directions in embodied intelligence and foundational learning algorithms.

Mohammad Sadeghiمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Haptics
• Finite Element Method (FEM) Simulation
• +۳ مورد دیگر

Mohammad Sadeghi is a Research Fellow at the Institute of Mechatronics in Mechanical Engineering, Hamburg University of Technology, specializing in robotics, haptic systems, and sensor design. He holds a Dr.-Ing. degree and maintains active roles in research supervision and collaborative industry projects. His academic foundation includes: M.Sc. in Mechanical Engineering from Ferdowsi University, Mashhad, Iran B.Sc. in Mechanical Engineering from Birjand University, Birjand, Iran Dr. Sadeghi's research integrates robotics with medical applications, focusing on wearable exoskeletons for telerehabilitation, bio-inspired whisker sensors, and magnetoelectric actuator systems. His methodological approach combines finite element simulation, inverse optimization, and machine learning to solve challenges in human-robot interaction and precision sensing. Recent work demonstrates growing emphasis on mixed reality integration for industrial haptic feedback and minimally invasive surgical applications. Publication analysis reveals dual research thrusts: medical robotics (40% of recent output) featuring rehabilitation devices and surgical sensors, and magnetoelectric systems (35%) for bioimaging and precision actuation, complemented by biomaterials and manufacturing optimization studies. Recognition includes: Hamburg Innovation Call for Transfer award (2024) for surgical precision enhancement via whisker sensors He supervises six active theses on topics including magnet-based whisker sensors and mixed reality haptic feedback, with Kyrillos Adeeb's 2024 singularity-avoidance project completed. Industry collaborations with Schaeffler (robotic manufacturing), MBJ GmbH (solar robotics), and bAhead (liquid handling systems) demonstrate translational impact. His experimental work utilizes the institute's Haptics Lab and PHiLsLab for hardware-in-the-loop validation of autonomous systems.

• Reliability of Mechanical Systems
• Structural Dynamics
• Uncertainty Quantification
• +۳ مورد دیگر

David Moens is Full Professor and Chair of the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Technology. He leads the Mecha(tro)nic System Dynamics (LMSD) research group at De Nayer Campus, where his research focuses on reliability engineering, structural dynamics, and computational mechanics. Moens serves in multiple leadership roles including membership in the university's Research Policy Council and heads Subdivision 10 of the De Nayer Campus. His research integrates computational mechanics with uncertainty quantification methods to address challenges in mechanical system reliability. Primary research domains include: Non-deterministic numerical analysis techniques for structural systems Fatigue and lifetime prediction methodologies Spatial uncertainty quantification in composite materials Physics-informed machine learning for engineering applications Robust design optimization under uncertainty Moens' recent publications demonstrate strong emphasis on computational uncertainty frameworks with applications spanning composite pressure vessels, manufacturing process twins, and neural network uncertainty estimation. Research consistently intersects finite element analysis, experimental validation, and emerging machine learning techniques to solve reliability challenges in mechanical systems. He leads significant research initiatives including RISQ (Random and Interval Spatial Uncertainty Quantification, 2025-2027) and projects on hydrogen storage vessel reliability (2024-2028). As primary supervisor for multiple PhD candidates, Moens actively contributes to academic training through courses including Reliability of Mechanical Systems, Dynamic Behavior of Machines, and Design for Safety and Reliability.

Zheyu Jiangمشاهده پروفایل

استادیار

• Process Systems Engineering
• Industrial Decarbonization
• Advanced Separation Processes
• +۳ مورد دیگر

Dr. Zheyu Jiang is an Assistant Professor in the Department of Chemical Engineering at Oklahoma State University , leading the Computational Laboratory for Advanced Manufacturing and Sustainability (CLAMS). His research bridges process systems engineering with digital agriculture and sustainability , focusing on industrial decarbonization , food-water-energy nexus , and explainable AI applications. Education : Ph.D. in Chemical Engineering from Purdue University (2018), B.Ch.E. (Hons) from University of Minnesota (2014) Research Expertise spans multi-scale modeling, physics-informed machine learning, and optimization techniques applied to: AI-driven digital agriculture solutions Decarbonization of chemical processes Advanced separation technologies Process monitoring through digital twins Scientific Contributions include: Pioneering physics-constrained machine learning for soil moisture estimation Developing MOLA (Multi-block Orthogonal LSTM Autoencoder) for industrial process monitoring Advancing decentralized optimization algorithms for renewable energy integration Research Recognition : 2025 NSF CAREER Award ($500k grant for digital agriculture) Featured in AIChE Journal Futures Issue (top 10% downloads) Multiple travel awards and Corteva Agriscience People's Choice Awards Laboratory : CLAMS trains students in computational sustainability, with members like graduate researcher Saba Ghasemi Naraghi and undergraduate success stories such as Tylee Kareck (offered Ph.D. positions at Texas A&M/UT Austin)

Dennis Krupkeمشاهده پروفایل

پژوهشگر

• Human-Robot Interaction
• Bio-inspired Robotics
• Modular Robots
• +۷ مورد دیگر

Dennis Krupke is a Researcher at the Department of Informatics, University of Hamburg, affiliated with the Human-Computer Interaction (HCI) group and the Technical Aspects of Multimodal Systems (TAMS). His work bridges robotics, virtual reality, and human-computer interaction, focusing on natural interfaces for human-robot cooperation. Education: Diploma in Informatics (2014), University of Hamburg Research Interests: Human-Robot Interaction (HRI) Bio-inspired Robotics and Sensor Systems Modular Robotics and Low-cost Prototyping Mixed Reality for Immersive Scenarios Scientific Awards: Finalist, IROS KROS Best Paper Award on Cognitive Robotics (2018) Highly Commended Paper, Industrial Robot Innovation Award (2017) CLAWAR Association Best Technical Paper Award (2015) Best Innovative Robot Award, CLAWAR (2014) Publications & Projects: Co-developed a printable modular robot with Florens Wasserfall Contributed to ROS-Unity integration for VR-based robotics Explored locomotion techniques for modular robots using reinforcement learning

• Biochemistry
• Molecular Imaging
• Cancer Biology
• +۶ مورد دیگر

Kathryn Luker is a Researcher in the Department of Radiology at the University of Michigan Medical School, affiliated with the Biointerfaces Institute. She specializes in molecular imaging of cell signaling in cancer, developing fluorescence and bioluminescence tools for single-cell analysis of tumor microenvironments. BS in Chemistry (University of Kansas, 1987) PhD in Biochemistry & Molecular Biology (Washington University, 1993) Research Interests: Biochemical mechanisms of chemokine/growth factor signaling in cancer progression, autocrine/paracrine signaling, fluorescence/bioluminescence reporters, custom image analysis, and multiscale computational modeling of tumor environments. Her recent work focuses on CXCR4 inhibition in breast cancer immunotherapy, CX43 -mediated tumor-stroma interactions in bone marrow metastases, and machine learning approaches to decode cancer cell heterogeneity. She has received major grants from NIH, Army-DoD, and the W. M. Keck Foundation. Mentoring: Committed to team-based mentorship, she has co-mentored over 70 trainees across biology, medicine, engineering, and computational disciplines.

Romeil Singh Sandhuمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Network Science
• +۴ مورد دیگر

Romeil Singh Sandhu serves as Assistant Professor in Biomedical Informatics at Stony Brook University with adjunct appointments in Computer Science and Applied Mathematics & Statistics, directing the Laboratory for Imaging, Networks, and Control (LINC) from the Health Sciences Center. His academic credentials include: B.S. from Georgia Institute of Technology (2006) M.S. from Georgia Institute of Technology (2009) Ph.D. from Georgia Institute of Technology (2010) Dr. Sandhu's research integrates geometry, statistics, and control theory to advance computer vision (3D reconstruction, satellite pose estimation), network science (hypergraph dynamics, Ricci curvature), and systems biology (protein interaction networks, cellular robustness). His methodological innovations span level-set methods, variational techniques, and curvature-based network analysis with applications in medical imaging and aerospace systems. Analysis of his 2019-2023 publications reveals three dominant trajectories: (1) geometric network analysis using Ricci curvature to quantify biological network fragility; (2) distributed reinforcement learning with communication-efficient multi-agent actor-critic frameworks; and (3) medical/satellite image reconstruction via radar-based variational methods and active surfaces. These threads consistently leverage differential geometry to solve inverse problems in complex systems. The Laboratory for Imaging, Networks, and Control (LINC) develops computational frameworks bridging theoretical mathematics with healthcare and aerospace applications, particularly focusing on shape analysis, network dynamics, and control systems for medical diagnostics and satellite imaging.

Liyuan Chenمشاهده پروفایل

استادیار

• Medical Image Reconstruction
• Medical Image Processing
• Inverse Treatment Planning
• +۳ مورد دیگر

Liyuan Chen, Ph.D., is an Assistant Professor in the Department of Radiation Oncology at the University of Texas Southwestern Medical Center. She earned her doctorate from Hong Kong Baptist University and completed postdoctoral training followed by a clinical residency in UT Southwestern's Medical Physics Residency Program, where she served as chief resident during her second year. Her research focuses on computational approaches in radiation oncology, including: Advanced medical image reconstruction and processing techniques Inverse treatment planning optimization methods Machine learning models for treatment outcome prediction Deep learning applications in cancer imaging and radiotherapy Adaptive radiotherapy protocol development Dr. Chen's recent publications (2020-2025) demonstrate strong emphasis on artificial intelligence applications in oncology, particularly deep learning models for medical image analysis, treatment planning automation, and predictive oncology. Her work consistently addresses clinical challenges in head and neck cancer, lung malignancies, and prostate cancer radiotherapy through innovations in convolutional neural networks, reinforcement learning frameworks, and adaptive treatment protocols.

Vissarion Papadopoulosمشاهده پروفایل

استاد

• Structural Engineering
• Computational Mechanics
• Stochastic Analysis
• +۳ مورد دیگر

Vissarion Papadopoulos is a Professor in the Department of Structural Engineering at the School of Civil Engineering, National Technical University of Athens (NTUA). His office is located at the Statics and Aseismic Research Laboratory, with contact details including email vpapado@central.ntua.gr and phone 210 772 4158. He has maintained an active research profile since at least 2016, focusing on advanced computational methods in structural engineering. His primary research domains include: Structural Engineering and Computational Mechanics Stochastic Analysis and Uncertainty Quantification Multiscale Modeling of Composite Materials Machine Learning for Engineering Simulations Optimization of Structural Systems Thermomechanical Behavior of Advanced Materials Analysis of his recent publications reveals a decisive shift toward integrating deep learning frameworks with traditional computational mechanics. His work demonstrates consistent innovation in accelerating solutions for parametric and transient structural problems through transformer networks, Bayesian inference, and physics-informed neural networks. Key application areas include carbon nanotube reinforced composites, sustainable automotive design, and seismic-resistant structures, with emphasis on enhancing computational efficiency while maintaining accuracy. He is affiliated with NTUA's Statics and Aseismic Research Laboratory, which specializes in structural dynamics, earthquake engineering, and advanced computational methodologies for civil infrastructure analysis and design.

• Machine Learning Theory
• Foundations of Artificial Intelligence
• Operations Research
• +۵ مورد دیگر

Yunzong Xu is an Assistant Professor in the Department of Industrial and Enterprise Systems Engineering (ISE) and Coordinated Science Laboratory (CSL) at the University of Illinois at Urbana-Champaign, with affiliations in the Departments of Computer Science (CS) and Electrical and Computer Engineering (ECE). He holds a Ph.D. in Data, Systems, and Society from MIT (2023) and dual B.S. degrees from Tsinghua University in Economics and Mathematical Sciences (2018). His research focuses on machine learning theory , foundations of AI , operations research , and management science , with specific interests in online learning , deep learning , sequential decision making , and mathematical problems related to markets , incentives , and social good . His work bridges theoretical analysis with applications in dynamic pricing, reinforcement learning, and network revenue management. Recent publications highlight algorithmic complexity in contextual bandits, offline reinforcement learning , and phase transitions in constrained bandit problems. His research has been recognized by awards from INFORMS, Applied Probability Society, and IBM Service Science. Honorable Mention, INFORMS George Nicholson Student Paper Competition (2020) Winner, INFORMS Data Mining Best Theoretical Paper Award (2020) Finalist, Applied Probability Society Best Student Paper Award (2019) Finalist, INFORMS Undergraduate Operations Research Prize (2018) Finalist, IBM Service Science Best Student Paper Award (2021) He teaches graduate courses on Foundations of Modern Machine Learning (IE598) and undergraduate courses in optimization models (IE310). For more information, visit his personal homepage .

• Calculus of Variations
• Nonlinear Partial Differential Equations
• Materials Science
• +۵ مورد دیگر

Prof. Dr. Carolin Kreisbeck is a full professor holding the Chair of Analysis in the Department of Mathematics at the Faculty of Mathematics and Geography, Catholic University of Eichstätt-Ingolstadt. She serves as Dean of the Faculty since October 2023 and maintains active research collaborations across Europe. Her research focuses on nonlinear partial differential equations and calculus of variations, with particular expertise in: Variational multiscale problems and relaxation techniques Applications in materials science (elasticity, plasticity, metamaterials) Nonlocal operators and fractional calculus Image processing and machine learning applications Gamma-convergence and homogenization theory Her recent publications (2022-2025) demonstrate strong emphasis on nonlocal gradient theories, materials modeling, and variational methods for multiscale problems. The works consistently integrate rigorous mathematical analysis with applications in physics and engineering sciences. Awards and recognitions include: NWO TOP Grant for postdoc position (2019-2021) Multiple teaching excellence awards (University of Regensburg 2013, Utrecht BKO 2016) Prestigious scholarships (Westerdijk, TopMath, Oberwolfach) EON Culture Prize Bavaria (2011) She holds significant professional roles including Associate Editor for Advances in Continuous and Discrete Models , GAMM committee leadership, and organization of international mathematics conferences.

Hien Nguyenمشاهده پروفایل

دانشیار

• User Modeling
• Information Retrieval
• Human-Computer Interaction
• +۱۳ مورد دیگر

Hien Nguyen is an Associate Professor in the Department of Computer Science at the University of Wisconsin-Whitewater , where she has been employed since Fall 2005. She holds a Ph.D. in Computer Science from the University of Connecticut (2005), a Master’s degree from the University of Wisconsin-Milwaukee (1999), and a Bachelor of Science in Informatics and Applied Mathematics from Hanoi University of Technology (Vietnam). Her career spans industry experience at 3C Inc. and the United Nations Development Programme before transitioning to academia. Education: B.Sc. Informatics and Applied Mathematics, Hanoi University of Technology M.Sc. Computer Science, University of Wisconsin-Milwaukee Ph.D. Computer Science, University of Connecticut Research Interests: Hien Nguyen’s interdisciplinary research focuses on user modeling , information retrieval , human-computer interaction , collaborative filtering , and human factors . She integrates system-centered and user-centered approaches to improve information retrieval performance through formal knowledge representations in artificial intelligence. Article Trends: While her core research aligns with computer science, recent publications span medical informatics, pharmacology, public health, and environmental science, indicating interdisciplinary collaborations. Topics include genomic studies in Vietnam , transnational caregiving , AI in healthcare , and environmental monitoring systems . Grants: Notable grants include a $200,000 award (2007–2010) from IAPRA/DTO for collaborative problem-solving frameworks and a $50,000 award (2015–2016) from the Office of Naval Research for real-time inverse reinforcement learning . Professional Service: She has served on program committees for conferences like User Modeling and Personalization (2010) and FLAIRS (2008–2016). She has chaired departmental committees at UW-Whitewater and reviewed for journals such as IEEE Transactions on Systems, Man and Cybernetics and Journal of Intelligent Information Systems .

Felix Heideمشاهده پروفایل

استاد

• Computational Imaging
• Computer Vision
• Optics
• +۶ مورد دیگر

Felix Heide is a Professor of Computer Science at Princeton University , where he leads the Princeton Computational Imaging Lab . He also serves as Head of AI at Torc Robotics , focusing on full autonomy stacks for self-driving trucks. His research sits at the intersection of optics , machine learning , and computer vision , addressing imaging challenges in harsh environments like dense fog, ultra-low/high illumination, and scattering media. Ph.D. in Computer Science from the University of British Columbia Postdoctoral research at Stanford University His work on computational imaging spans physics-based vision, non-line-of-sight imaging , end-to-end camera design , and robust sensor fusion . He has pioneered techniques for inverse neural rendering , nanophotonic optics , and light-speed AI through optical computing. His recent papers in Nature Machine Intelligence , Science Advances , and top conferences ( SIGGRAPH , CVPR , ICCV ) focus on: Adverse weather imaging (fog, snow, rain) Multi-sensor fusion (LiDAR, radar, gated cameras) Light transport through scattering media Optical metasurfaces and diffractive optics End-to-end optimization of imaging pipelines Event-based vision and polarization cues He has received prestigious awards including the SIGGRAPH Significant New Researcher Award , Sloan Research Fellowship , and Packard Fellowship . His lab's open-source code and datasets enable real-world applications in autonomous driving, microscopy, and augmented reality.

Christoph Bruneمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Computer Vision
• +۷ مورد دیگر

Christoph Brune is a Full Professor at the University of Twente , affiliated with the Digital Society Institute and TechMed Centre . His research bridges Artificial Intelligence , Deep Learning , and Medical Imaging with applications in Robotics and Computational Fluid Dynamics . Keywords: Machine Learning , Network Analysis , Optimal Transport , Medical Image Segmentation Research Trends : Recent publications focus on physics-informed neural networks , equivariant learning for vascular modeling, and operator learning for PDE-constrained problems. His work emphasizes geometric deep learning , implicit neural representations , and robust AI for clinical settings. Scientific Awards : CMI-NEN seed-money award (2022) for medical imaging innovation Collaborative Networks : Active in graph neural networks , optimal transport , and Barron space theory . Key collaborations include Zenodo dataset contributions and ArXiv preprints with multidisciplinary teams in AI, medicine, and engineering.