جست‌وجوی استاد و پژوهشگر

Sandeep K Neemaمشاهده پروفایل

استاد

• Embedded Systems
• Design-Space Exploration
• Model Integrated Computing
• +۳ مورد دیگر

Sandeep K Neema is a Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. He serves as Chair of the Executive Council of the Institute of Software Integrated Systems. His research focuses on embedded systems, design-space exploration, and model integrated computing. He holds a Ph.D. from Vanderbilt University, an M.S. from Utah State University, and a B.Tech. from the Indian Institute of Technology. Dr. Neema's educational background includes: Ph.D., Electrical Engineering & Computer Engineering, Vanderbilt University M.S., Electrical Engineering & Computer Engineering, Utah State University B.Tech., Electrical Engineering, Indian Institute of Technology His research interests encompass embedded systems, design-space exploration, and model integrated computing, with a strong emphasis on cyber-physical systems. He explores formal methods, model-based design, and their applications in automotive systems, smart cities, and reliability engineering. His work integrates theoretical frameworks with practical tools to address challenges in system reliability and adaptive software design. Recent publications highlight advancements in artificial general intelligence evaluation, cybersecurity defense mechanisms, and formal methods applied to neural networks and language models. His work also addresses societal-scale control systems and adaptive software frameworks for resource management. These contributions underscore his interdisciplinary approach to solving complex engineering problems. Scientific Awards include: Best Paper Award (2013) In addition to his academic roles, Dr. Neema leads initiatives in the Institute for Software-Integrated Systems. His work often involves collaborative projects and the development of innovative tools for cyber-physical systems design. He is a key member of the Institute for Software-Integrated Systems, contributing to research in model integration, tool development, and cyber-physical systems engineering.

Stefan Ulbrichمشاهده پروفایل

استاد

• Nonlinear Optimization
• PDE-Constrained Optimization
• Optimal Control
• +۳ مورد دیگر

Stefan Ulbrich is a Professor of Nonlinear Optimization at the Department of Mathematics, Technische Universität Darmstadt. His research focuses on optimization theory, algorithms, and applications, particularly in PDE-constrained optimization, optimal control, and numerical methods. He co-authored influential textbooks on nonlinear and PDE-constrained optimization. His work bridges theoretical foundations with practical engineering solutions, addressing challenges in fluid dynamics, electromechanical systems, and energy processes. Key contributions include robust optimization frameworks, sensitivity analysis for complex systems, and model reduction techniques. His research emphasizes interdisciplinary applications, such as electric machine design, industrial robotics, and sustainable energy systems. Research Interests: Nonlinear Optimization and Numerical Methods PDE-Constrained Optimization and Optimal Control Robust Design and Uncertainty Quantification Applications in Engineering and Energy Systems Publications: Ulbrich's work spans over 100 peer-reviewed articles and books. Notable contributions include the textbook *Nichtlineare Optimierung* (with Michael Ulbrich) and research on optimal control of fluid flows, robust optimization of electromechanical systems, and Bayesian model calibration. His recent focus includes parallel-in-time optimization, topology optimization under uncertainty, and exergy-based process optimization in green energy systems. Awards and Recognition: While specific awards are not explicitly listed, his leadership in optimization theory and impactful contributions to applied mathematics and engineering reflect sustained academic excellence.

William Boleyمشاهده پروفایل

استادیار

• Additive Manufacturing
• Functional Printing
• Self-Assembly
• +۳ مورد دیگر

William Boley is an Assistant Professor of Mechanical Engineering at Boston University, with a primary appointment in the Department of Mechanical Engineering and affiliations in Materials Science & Engineering. His research focuses on additive manufacturing, functional printing, self-assembly, and thermal fluid science to develop novel materials and devices for applications in electronics, optics, sensors, and soft robotics. He holds a PhD in Mechanical Engineering from Purdue University. Professor Boley's research integrates materials science, thermal fluid dynamics, and design optimization to create multi-scale architectures in additive manufacturing. His work emphasizes programmable materials, 4D printing, and liquid metal systems for flexible electronics. He has affiliations with Energy, Sustainability & Climate and the Materials by Design initiative. Key honors include the Air Force Office of Scientific Research Young Investigator Program (2020) and the NSF CAREER Award (2021). His contributions span over 30 peer-reviewed articles, with recent work focusing on liquid metal emulsions, neural network-enabled fluid control, and shape-shifting lattice structures. The Additive Assembly Laboratory, led by Boley, explores cutting-edge manufacturing techniques to bridge material synthesis and functional device fabrication.

Josephine Sullivanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Josephine Sullivan is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Division of Robotics, Perception and Learning. She is a core faculty member of Digital Futures, a cross-disciplinary research center addressing global challenges through digital technologies. Her research focuses on AI-driven environmental monitoring, disaster management, and deep learning applications in computer vision and remote sensing. She has supervised projects such as the fusion of radar and optical remote sensing for wildfire monitoring and contributes to initiatives like EO-AI4GlobalChange. Sullivan teaches advanced courses in deep learning, generative models, and machine learning at KTH, emphasizing hands-on training and research integration. Her work bridges theoretical advancements in neural networks with practical solutions for climate-related disasters, leveraging satellite imagery and time-series data. Recent projects highlight innovations in wildfire prediction datasets (Wildfirespreadts) and robust networks for disaster damage assessment (DisasterAdaptiveNet). She collaborates across disciplines at Digital Futures, contributing to labs and teams focused on AI ethics, sustainable technologies, and global change mitigation. Sullivan’s academic leadership includes roles as course examiner and responsible for multiple graduate programs in computer science and engineering.

• Computational Neuroscience
• Neural Network Modeling
• Locomotion Control
• +۳ مورد دیگر

Alexander Kozlov is a Researcher at KTH Royal Institute of Technology's Division of Computational Science and Technology. His work focuses on computational neuroscience, particularly modeling neural networks and locomotor control systems. He has contributed to understanding spinal cord networks, striatal connectivity, and neuromodulation mechanisms through in silico studies. Kozlov teaches courses in machine learning, artificial neural networks, and mathematical modeling of biological systems. His research integrates AI frameworks with biological data, emphasizing GPU-accelerated simulations and large-scale microcircuit analysis. Key contributions include studies on Parkinson's disease impact on neural connectivity and the role of sensory feedback in locomotion. He collaborates with the Science for Life Laboratory in Stockholm and maintains active roles in both peer-reviewed and conference publications. Education background and affiliations are not explicitly detailed in the provided text, but his extensive publication record reflects a deep engagement with interdisciplinary neuroscience and computational biology. Awards or grants are not listed here.

Nir Lipovetzkyمشاهده پروفایل

دانشیار

• AI Planning
• Search
• Learning
• +۶ مورد دیگر

Nir Lipovetzky is an Associate Professor in Artificial Intelligence at the School of Computing and Information Systems, University of Melbourne. He is affiliated with the Agent Lab and Digital Agriculture, Food and Wine Lab. His research focuses on AI planning, search, and applications in autonomous systems, particularly in agriculture. He holds a PhD from Universitat Pompeu Fabra (2012) and a BSc in Computer Science (2004). Education: PhD in Artificial Intelligence, Universitat Pompeu Fabra (2012) MEng in Artificial Intelligence, Universitat Pompeu Fabra (2007) BSc in Computer Science, Universitat Pompeu Fabra (2004) Graduate Certificate in University Teaching, University of Melbourne (2020) Research interests include AI planning, constraint programming, operations research, and intention recognition. He has developed novel width-based planning algorithms and contributed to planning competitions. Recent work includes adaptive goal recognition and reinforcement learning for decision-making under uncertainty. Publications span topics like count-based novelty exploration, process mining for goal recognition, and human-like goal inference models. Awards include the Best Student Paper Award at AAMAS 2024 and recognition in planning competitions. Advises 10+ PhD and Master’s students on topics like exploration methods, agricultural robotics, and multi-agent systems. Supervises Farm.bot, an open-source platform for AI-driven agriculture. Serves as Conference Chair for ICAPS 2025 and has held roles in AAAI, IJCAI, and ICAPS committees.

• Robotics
• Resilient Robotic Autonomy
• Robot Control
• +۵ مورد دیگر

Prof. Konstantinos Alexis is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on resilient robotic autonomy, emphasizing robot control, path planning, and Simultaneous Localization and Mapping (SLAM). He leads major international grants from agencies like DARPA, NSF, and the Norwegian Research Council, advancing autonomous systems for extreme environments such as subterranean exploration and search-and-rescue operations. His work integrates advanced control strategies with field robotics, demonstrated through projects like the DARPA Subterranean Challenge. Key contributions include robust model predictive control for UAVs, contact-based inspection planning, and graph-based exploration path planning. Alexis has organized workshops on aerial robotics at top conferences like ICRA and ICUAS, and his team develops open-source tools for robotics autonomy. Award-winning publications include the IET 2014 Premium Award and multiple best-paper recognitions. His research extends to solar-powered UAVs, thermal-inertial localization, and multi-modal sensor fusion for degraded environments. The Autonomous Robots Lab at NTNU is central to his research activities.

Matt Pharrمشاهده پروفایل

دانشیار

• Materials Science
• Electrochemistry
• Energy Storage
• +۲ مورد دیگر

Matt Pharr is Associate Professor and J. Mike Walker '66 Faculty Fellow in Mechanical Engineering at Texas A&M University, with joint affiliation in Materials Science & Engineering. Researches chemo-mechanical phenomena in energy and biomedical materials. Research focuses on deformation/fracture mechanics of battery materials, soft material behaviors, irradiation effects on polymers, and biomechanics. Recent publications examine multi-scale material properties for battery applications and irradiation effects in medical polymers. Teaching recognition includes Distinguished Achievement Award in Teaching (2022). Research supported by NSF, DoE, and industry partners.

Bhaskar Vajipeyajulaمشاهده پروفایل

استادیار

• Additive Manufacturing
• Materials Science
• Mechanical Engineering
• +۵ مورد دیگر

Bhaskar Vajipeyajula is an Assistant Professor in the Department of Engineering Technology & Industrial Distribution at Texas A&M University's College of Engineering. His research focuses on additive manufacturing processes, material behavior under various conditions, and sustainable construction materials. Key areas include optimizing manufacturing parameters for dissimilar materials, analyzing fracture mechanics in thermoplastics, and developing energy-efficient production systems. He also explores robotics applications in construction and advanced modeling techniques for civil engineering materials like asphalt binders. His work spans interdisciplinary topics such as viscoelastic material modeling, stress concentration in porous solids, and recycling strategies for asphalt binders. He holds a faculty position in the Manufacturing & Mechanical Engineering Technology program, emphasizing practical engineering solutions for industrial challenges. Dr. Vajipeyajula's research trends highlight a focus on bridging materials science with manufacturing innovation, particularly in additive technologies and sustainable infrastructure. He has contributed to over 15 peer-reviewed articles addressing process optimization, material characterization, and energy-efficient systems design in expeditionary environments. His advising and grants activities remain unspecified in the provided text, but his involvement in Texas A&M's College of Engineering indicates active participation in academic and industrial collaborations. The department's research infrastructure supports his work in advanced manufacturing and construction systems.

Hormoz Marzbaniمشاهده پروفایل

دانشیار

• Mechanical Engineering
• Automotive Engineering
• Vehicle Dynamics
• +۷ مورد دیگر

Associate Professor Hormoz Marzbani is affiliated with the School of Engineering at RMIT University. His roles include teaching and research supervision in areas such as Dynamics and Vibration, Vehicle Dynamics, Autonomous Systems, and Mechanical Design. He actively supervises research projects focusing on autonomous vehicle control, safety enhancements, and advanced manufacturing techniques. Research Interests Mechanical Engineering Automotive Engineering Autonomous Systems Motion Sickness Mitigation Additive Manufacturing Infrastructure Integration for Automated Vehicles Recent research trends include optimizing autonomous vehicle trajectories, enhancing safety through infrastructure collaboration, and mitigating motion sickness via ergonomic path planning. His work frequently addresses challenges in control systems, sensor fusion, and experimental validation with scaled prototypes. Supervision & Grants Supervision projects include topics like Autonomous Optimal Vehicle Control , Trajectory Optimization for Active Safety , and 3D Printed Mycelium Composites . His research often intersects with industry needs, leveraging grants for experimental setups and computational modeling. Labs & Collaborations Engages in collaborative projects involving scaled autonomous vehicle testing and infrastructure-based sensor networks, emphasizing real-world application of theoretical advancements.

• Gynecology
• Obstetrics
• Hysterectomy
• +۵ مورد دیگر

Annemette Jørgensen serves as a Clinical Associate Professor at Aalborg University's Faculty of Medicine, Department of Gynaecology and Obstetrics, where she holds the position of Senior Consultant (Ledende overlæge) at Aalborg University Hospital. Her academic career spans multiple disciplines within women's health, with particular expertise in surgical gynecology. Dr. Jørgensen's research focuses primarily on: Hysterectomy techniques and associated complications Minimally invasive and robotic gynecological surgery Population-based cohort studies using Danish national databases Ocular surface research through interdisciplinary collaboration Management of obstetric complications Her publication record demonstrates a strong emphasis on improving surgical outcomes in gynecology, with multiple high-impact studies examining complications across different hysterectomy approaches. Her work combines clinical practice with rigorous epidemiological methods, leveraging the Danish Hysterectomy Database for large-scale analyses. Notably, her research extends beyond traditional gynecology into ophthalmology, showing versatility in medical research methodologies. Dr. Jørgensen has maintained active professional involvement through: Membership in Dansk Hysterektomi Database (DHD) since March 13, 2006 Membership in Hyskobase since March 13, 2006 Her scholarly impact is substantial, with her 2020 robotic surgery paper accumulating 63 Scopus citations and policy references, while her hysterectomy complications research has reached both academic and public audiences through news coverage and significant Mendeley readership. She maintains extensive collaborative networks across Danish medical institutions, particularly within Aalborg University Hospital, contributing to multi-center studies that inform clinical practice in gynecological surgery and obstetrics.

• Aerospace Engineering
• Optimal Control Theory
• Air Traffic Management
• +۵ مورد دیگر

Manuel Fernando Soler Arnedo is a Full Professor in the Aerospace Engineering Department at Carlos III University of Madrid's College of Engineering. His research focuses on Aerospace Engineering with specialization in Optimal Control Theory , Climate Impact Mitigation , and Artificial Intelligence applications for air traffic systems. He leads the Aerospace Engineering Research Group and has contributed extensively to Flight Trajectory Optimization , Weather-Induced Uncertainty , and Network-scale Conflict Resolution . Principal Researcher on EU-funded projects like F4EClim (2024-2027) and KAIROS (2023-2026) Key international collaborations with NEXTOR (UC Berkeley) and ETH Zurich Developed tools like CLIMaCCF V1.0 and ROOST V1.0 for climate-aware flight planning His work integrates Artificial Intelligence with Climate Modeling to address 4D Trajectory Planning , Thunderstorm Avoidance , and Non-CO2 Emission Reduction in aviation. Recent publications emphasize Robust Optimization under meteorological uncertainty and Deep Learning for traffic flow management. He has supervised theses on Data Science for Weather Mitigation and Multi-objective Space Mission Design , with grants from European Commission , Boeing , and Spanish government agencies .

• Spatio-temporal statistics
• Computational statistics
• Geophysics
• +۴ مورد دیگر

Jo Eidsvik is a Professor at the Department of Mathematical Sciences, NTNU. His research focuses on spatio-temporal statistics, computational statistics, and their applications in Earth sciences, geophysics, and data assimilation. He leads projects like the Center for Geophysical Forecasting (SFI-CGF), GEOPARD, and MASCOT. He has authored a seminal book on 'Value of Information in the Earth Sciences' (2017), recognized with a runner-up award by INFORMS. Eidsvik has supervised numerous PhD and MSc students, including current advisees André Olaisen and Oscar Ovanger. He holds editorial roles in 'Mathematical Geosciences' and 'Statistics and Computing'. Education: MSc in Applied Mathematics (University of Oslo, 1997), PhD (NTNU, 2003). Industry experience includes roles at the Norwegian Defense Research Establishment and Statoil. He has been a visiting scholar at Stanford University and Duke University. Research highlights include Bayesian inversion methods, adaptive sampling with autonomous vehicles, and value of information analysis in decision-making. Recent work explores machine learning for irregular time series (ML4ITS) and oceanographic data assimilation. Over 60 MSc theses supervised, emphasizing interdisciplinary approaches. Labs/Teams: Center for Geophysical Forecasting (SFI-CGF), GEOPARD, MASCOT, Games. Grants/Awards: INFORMS Decision Analysis Society award (2017), extensive project funding through SFI and industry. Key Projects: Havvarsel (ocean data), CO₂ storage monitoring, and robotic sampling systems.

Andreas Haeberlenمشاهده پروفایل

استاد

• Distributed Systems
• Networking
• Security
• +۱۰ مورد دیگر

Andreas Haeberlen is a Professor in the Department of Computer and Information Science at the University of Pennsylvania, where he is a member of the Distributed Systems Lab (DSL) and co-director of the NETS program. He is currently on a leave of absence from Penn to lead the new systems group at Roblox Research, focusing on large-scale distributed systems in cloud and metaverse environments. University: University of Pennsylvania School: School of Engineering and Applied Science Department: Department of Computer and Information Science Academic Rank: Professor Email: ahae@cis.upenn.edu His research centers on distributed systems, networking, security, and privacy, with key interests in differential privacy, fault tolerance, secure network provenance, accountability in federated systems, and synchronous data center architectures. He aims to build practical systems that provide strong, provable privacy and security guarantees for real-world applications. The recent publications reflect a strong trend toward privacy-preserving distributed analytics, resilient cyber-physical systems, and secure, accountable federated infrastructures. His work combines techniques from programming languages, operating systems, and distributed computing to address fundamental challenges in scalability, security, and timing predictability. Key themes include bounded-time recovery, differential privacy in federated settings, and secure provenance for network diagnostics. Scientific Awards: Recipient of the Otto Hahn Medal from the Max Planck Society Recipient of the Ford Motor Company Award for Faculty Advising Recipient of the Lindback Award for Distinguished Teaching He has advised graduate students such as Karan Newatia and Robert Gifford, often in collaboration with Linh Thi Xuan Phan. His research is supported by active projects in differential privacy, synchronous data centers, resilient cyber-physical systems, secure network provenance, and accountability. He leads or co-leads major research initiatives that bridge academic innovation with industrial-scale deployment, particularly in cloud and metaverse platforms.

Xuelian Chengمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Computer Vision
• Medical Imaging
• +۴ مورد دیگر

Dr. Xuelian Cheng is an Adjunct Lecturer at the Department of Data Science & AI, Monash Suzhou Research Institute, and a Research Fellow at Monash University Australia. She holds a PhD from Monash University under the supervision of Professors Zongyuan Ge, Mehrtash Harandi, and Tom Drummond. Her research focuses on medical imaging, 3D reconstruction, and interdisciplinary applications of AI in robotic surgery and AR/VR technologies. She has contributed to projects with industry partners like Tencent, IIAI, and Airdoc, resulting in publications at top venues such as MICCAI, CVPR, and Nature. Affiliations: Monash Suzhou Research Institute, Monash University Australia. Teaching: Courses include FIT5047 (Artificial Intelligence), FIT5226 (Multi-Agent Systems), and FIT5216 (Discrete Optimization). Research interests span deep learning for 3D visual perception, automated machine learning (AutoML), and medical image analysis. Her work aligns with UN SDGs related to health and innovation. The MMAI group she co-leads has published extensively in top conferences/journals, including JAMA, The Lancet, and NeurIPS. Advising: Accepting PhD students interested in medical 3D topics, with projects focusing on surgical intelligence and clinical decision support. Labs/Teams: Monash Medical AI (MMAI) Group, collaborating on surgical workflow understanding and endoscopic reconstruction.