جست‌وجوی استاد و پژوهشگر

• Control Systems
• Machine Learning
• Networked Systems
• +۳ مورد دیگر

Siva Seetharaman is an Assistant Professor in the Department of Industrial Engineering at Purdue University's College of Engineering, joining the faculty in 2022. His research bridges control theory, machine learning, and networked systems with critical applications in energy infrastructure and transportation electrification. Affiliated with Purdue's top-ranked engineering programs, he focuses on safety-critical control solutions for real-world cyber-physical challenges. His educational trajectory includes a Ph.D. in Electrical Engineering from the University of Notre Dame, followed by postdoctoral research at Texas A&M University. This foundation supports his interdisciplinary approach to complex system dynamics. Dr. Seetharaman's research centers on risk-aware control synthesis for safety-critical systems, with three core thrusts: (1) Developing risk-tunable control barrier functions for human-robot collaboration and autonomous systems; (2) Creating data-driven energy management frameworks for virtual power plants and grid reliability; (3) Advancing distributed learning methods for stability assessment in large-scale networked systems. His work uniquely integrates formal verification techniques with machine learning to address nonlinear dynamics in transportation electrification and decarbonized energy grids. Analysis of his 15 most recent publications (2021–2025) reveals a decisive shift toward causal safety engineering —evident in risk-tunable control barrier functions and sampling-based safe reinforcement learning—and energy-transportation convergence , particularly in electric roadway charging and heavy-duty vehicle grid impacts. Key technical trends include compositional verification of neural networks (ECLipsE), multi-scale energy datasets, and dissipativity-based modeling for nonlinear systems. His work consistently targets real-world deployment challenges in Texas grid case studies and transportation networks. No scientific awards were documented in the provided materials. No student advisement records or grant funding details were included in the source texts. His collaborative publication pattern suggests engagement with interdisciplinary teams across control theory, machine learning, and power systems domains, though specific mentoring activities remain unreported. While no dedicated laboratory is specified, his research on cyber-physical systems, electric roadways, and grid-transportation integration implies active participation in Purdue's energy and transportation research ecosystems. His focus on synthetic Texas case studies indicates strong ties to regional energy infrastructure initiatives.

Paúl Paucaمشاهده پروفایل

استاد

• Machine Learning
• Image Processing
• Computational Imaging
• +۵ مورد دیگر

Dr. Paúl Pauca is a Professor in the Department of Computer Science at Wake Forest University, where he integrates teaching and research under the teacher-scholar model. His work focuses on applying computational technologies to critical environmental and societal challenges. Ph.D., Computer Science, Duke University (2001) M.S., Computer Science, Wake Forest University (1996) B.S., Mathematics and Computer Science, Wake Forest University (1994) His research spans machine learning , computational imaging , and remote sensing , with emphasis on environmental conservation. Recent projects involve deep learning for change detection in tropical forests, UAV exploration algorithms, and data fusion techniques for ecological monitoring. Publications highlight his interdisciplinary collaborations with biology and environmental science teams. Dr. Pauca contributes to open-source methodologies for high-performance computing in conservation and advises student researchers in the IRSC Lab , which partners with institutions like Dartmouth College and the Center for Amazonian Scientific Innovation in Peru. His grants include funding from The Boeing Company, Air Force Office of Scientific Research, and National Geospatial-Intelligence Agency.

Aniruddha Kembhaviمشاهده پروفایل

دانشیار

• Computer Vision
• Robotics
• Natural Language Processing
• +۵ مورد دیگر

Aniruddha Kembhavi is an Affiliate Associate Professor at the University of Washington's Computer Science & Engineering department and currently serves as Director of Science Strategy at Wayve AI in London, UK. Previously, he led computer vision efforts as Senior Director at Allen Institute for AI (AI2) in Seattle and contributed to Microsoft's Image and Video Search division. His research spans 20+ years in Computer Vision , Robotics , and Embodied AI , focusing on open-source frameworks like AI2-THOR and Molmo. His work emphasizes procedural environment generation , vision-language integration , and 3D asset creation , with large-scale datasets such as Objaverse becoming foundational in 3D computer vision. CVPR 2025 Best Paper (Honorable Mention) CVPR 2023 Best Paper Winner Neurips 2022 Outstanding Paper CoRL 2024 Outstanding Paper IROS 2024 Best Mobile Manipulation Paper ICRA 2024 Best Paper Winner Allen Institute Test Of Time Award 2020 NVIDIA Pioneer Award 2018 His recent publications analyze vision-language models , 3D generation evaluation , and diffusion architectures for unified generation. He actively contributes to community-building as Program Chair for ICCV 2025 and Senior Area Chair for CVPR 2024.

Zuzana Kukelovaمشاهده پروفایل

استادیار

• Minimal problems in computer vision
• Algebraic geometry
• Camera geometry problems
• +۵ مورد دیگر

Zuzana Kukelova is an Assistant Professor and Researcher in the Department of Cybernetics at the Faculty of Electrical Engineering, Czech Technical University in Prague. She leads research in minimal problems for computer vision with applications in camera geometry and distortion modeling. Education: PhD in Mathematical Engineering (Computer Vision), Czech Technical University, Prague (2006-2013) Master Degree (Mgr.) in Computer Science, Comenius University, Bratislava (2000-2005) Rigorous thesis (RNDr.) in Mathematics - Computer Graphics and Geometry Her research focuses on minimal problems in computer vision , specializing in algebraic geometry approaches to solve camera geometry problems. She develops methods for solving systems of polynomial equations using Gröbner basis techniques, with particular expertise in radial distortion calibration and rolling shutter camera models. Her work bridges theoretical mathematics with practical computer vision applications, enabling robust solutions to challenging geometric vision problems. Analysis of her 15 most recent publications reveals dominant trends in minimal solvers for camera geometry (80% of works), with heavy emphasis on radial distortion (65%) and rolling shutter models (45%). Key subfields include polynomial equation solving (100%), camera pose estimation (75%), and homography estimation (40%). Her research consistently demonstrates mathematical innovation applied to real-world vision challenges. Scientific Awards: Saburo Tsuji Best Paper Award (ACCV 2018) Cor Baayen Award (2015) from ERCIM ACCV 2014 Best Paper Honourable Mention CSKI prize for best PhD dissertation (2013) IEEE TPAMI Spotlight paper (2012) As a principal investigator in the Visual Recognition Group, she has secured significant research funding through competitive grants. Her leadership includes co-organizing major conferences (ICCV 2021 Tutorial, CVPR 2019 Tutorial) and serving as Program Chair for 3DV 2020. She maintains active collaborations with leading vision researchers across Europe and North America, evidenced by her extensive publication record in top-tier venues.

Julien Mairalمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Computer Vision
• +۲ مورد دیگر

Julien Mairal is a Senior Research Scientist at Inria Grenoble within the Thoth research team, focusing on machine learning, optimization, and computer vision. His research spans theoretical foundations and practical applications across multiple domains including signal processing, bioinformatics, and neuroimaging. His primary research interests include machine learning , particularly optimization algorithms for large-scale problems, computer vision with applications in image restoration and super-resolution, and kernel methods for structured data. His work demonstrates strong connections between theoretical optimization and practical implementations, as evidenced by his Cyanure toolbox for empirical risk minimization. Analysis of his recent publications reveals a consistent focus on optimization techniques for machine learning, with increasing emphasis on large-scale applications, self-supervised learning, and connections between kernel methods and deep learning architectures. His work shows strong interdisciplinary applications spanning computer vision, neuroscience, and remote sensing. ERC Consolidator Grant (2023) for the Apheleia project ICML Test of Time Award (2019) ERC Starting Grant (2016) for the SOLARIS project Mairal serves extensively in editorial capacities as area chair for major conferences including ICML, NeurIPS, CVPR, and ECCV. He is an associate editor for the Journal of Machine Learning Research (JMLR) and previously served for IEEE PAMI and IJCV. His Cyanure toolbox has become a significant contribution to the field of large-scale optimization in machine learning. His research demonstrates strong connections between theoretical foundations and practical implementations across multiple application domains.

• Artificial Intelligence
• Multi-Agent Path Finding
• Robotics
• +۴ مورد دیگر

Jiří Švancara is a Lecturer at the Department of Theoretical Computer Science and Mathematical Logic (KTIML) within the Faculty of Mathematics and Physics at Charles University in Prague. His academic career focuses on Artificial Intelligence, particularly Multi-Agent Path Finding (MAPF), where he has established himself as a prominent researcher with numerous publications in top-tier conferences. His research interests span across Multi-Agent Path Finding , Robotics , Algorithm Design , and Constraint Satisfaction Problems . Švancara has made significant contributions to the field of MAPF, developing novel approaches for large-scale maps, temporal uncertainty handling, and efficient solving methods. His work bridges theoretical foundations with practical applications in robotics and transportation systems. Analysis of his recent publications reveals a strong focus on improving the scalability and robustness of MAPF algorithms. His research explores graph pruning techniques for large maps, handling temporal uncertainty in path execution, and comparing different objective functions for optimization. He has also investigated applications of MAPF in autonomous intersections and train routing systems, demonstrating the practical relevance of his theoretical work. Best Paper Award for 'Multi-agent Path Finding on Real Robots: First Experience with Ozobots' at IBERAMIA 2018 Švancara actively teaches multiple courses including Introduction to Artificial Intelligence, Propositional and Predicate Logic, and Algorithms and Data Structures. His teaching spans both theoretical foundations and practical implementation, with students engaging in programming assignments that reflect current AI challenges. His research collaborations are extensive, with frequent co-authorship with Roman Barták and other researchers in the field, indicating strong integration within the international AI research community. His laboratory work involves practical implementations of MAPF algorithms, including testing on real robots as evidenced by several publications describing experiments with Ozobots and other robotic platforms. This hands-on approach connects theoretical research with tangible robotic applications, providing valuable validation for his algorithmic contributions.

• Computational Design
• Timber Architecture
• Digital Fabrication
• +۳ مورد دیگر

Hans Jakob Wagner is a Research Group Leader and Doctoral Candidate at the Institute for Computational Design and Construction (ICD) , University of Stuttgart. His work focuses on Computational Wood Architecture , specializing in advanced computational timber building systems and associated robotic fabrication processes. Research Interests : Computational Design for Timber Systems Robotic Fabrication and Assembly Sustainable Construction Methods Hybrid Material Applications (CFRP, Clay, Bamboo) Co-Design Workflows Digital Quality Control in Timber Production Recent Publications explore circular construction with reclaimed timber, adaptive fabrication for bamboo, point-supported timber slabs, and data-informed co-design. His work bridges Computer Science and Structural Engineering through Neural Networks , Augmented Reality , and Agent-Based Modeling . Scientific Awards : Deutscher Holzbaupreis 2025 (Outstanding Scholarship) Best Poster Award (2023) As an ITECH MSc Thesis Advisor , he mentors students in Gradient Density and Terra [In]Cognita projects. His contributions include 15+ peer-reviewed publications and keynote presentations at international conferences like WCTE and IASS.

• Robotics
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Kuan Fang is an Assistant Professor in the Department of Computer Science at Cornell University, specializing in robotics, machine learning, and computer vision. His research focuses on enabling robots to perform complex tasks in unstructured environments through deep learning and scalable algorithms. Education: Ph.D. and M.S. in Computer Science from Stanford University, advised by Fei-Fei Li and Silvio Savarese Bachelor's degree from Tsinghua University Previous roles: Postdoc at UC Berkeley (advised by Sergey Levine), research experience at RAI Institute, Google Brain, Google X Robotics, and Microsoft Research Asia His work emphasizes: Acquisition of versatile skills for visuomotor control via massive data learning Continuous robot improvement through autonomous data generation Boosting generalization by integrating prior knowledge across domains Recent publications span topics in interlimb coordination, diffusion policy learning, visual prompting for reinforcement learning, and language-guided decomposition. While specific scientific awards aren't listed, his work appears in top robotics conferences including RSS, ICRA, IROS, and CoRL. He actively mentors students and maintains collaborations with UC Berkeley, Boston Dynamics AI Institute, and Stanford researchers.

Ido Levinمشاهده پروفایل

استادیار

• Soft Matter
• Material Chemistry
• Physical Chemistry
• +۹ مورد دیگر

Ido Levin is an Assistant Professor at the Department of Chemistry and Department of Mathematics within the Faculty of Science at the University of British Columbia. His research integrates geometrical modeling, responsive materials, and innovative fabrication techniques to design programmable and active materials inspired by natural systems. He combines experimental approaches with analytical and numerical tools to study shape morphing in soft systems, chemo-mechanical active solids, and lipid membrane patterning. Education: B.Sc. in Physics and Mathematics, Hebrew University of Jerusalem (2012) M.Sc. in Physics, Hebrew University of Jerusalem (2014) Ph.D. in Physics, Hebrew University of Jerusalem (2021) His work spans soft matter, material chemistry, and theoretical modeling, focusing on distributed actuation, multi-responsive materials, and geometric frustration. Articles from 2015 to 2024 highlight his contributions to programmable materials, fluid dynamics in bio-inspired structures, and mechanical instabilities in amorphous systems. Scientific Awards: WRF Postdoctoral Fellow (2022-2025) Fulbright Postdoctoral Scholar (2021-2022)

Victor X.D. Yangمشاهده پروفایل

استاد

• Optical coherence tomography
• Machine vision image guidance
• Molecular fluorescence imaging
• +۵ مورد دیگر

Dr. Victor X.D. Yang is a Professor in the Department of Electrical, Computer, and Biomedical Engineering at Toronto Metropolitan University, holding dual qualifications as a PhD engineer and MD neurosurgeon. His pioneering work in biophotonics has led to commercially deployed surgical systems, including a machine vision-guided platform by 7D Surgical used in over 10,000 implants across 2,000 patients globally. He champions engineering solutions that bridge clinical needs with economic viability, emphasizing scalable impact over individual surgical procedures. Education: MD, University of Toronto, 2006 PhD, University of Toronto, 2004 MASc, University of Toronto, 1998 BSc, University of Toronto, 1997 Research Interests: Dr. Yang's work spans Optical coherence tomography , Machine vision image guidance , and Medical robotics , with focus on intraoperative imaging for neurosurgery and spine procedures. His lab develops real-time imaging systems integrating OCT, fluorescence, and augmented reality to enhance surgical precision. He prioritizes translational research where engineering innovations directly address unmet clinical needs while maintaining commercial feasibility. Publication Trends: Recent publications (2017-2019) demonstrate consistent advancement in high-speed intraoperative imaging and robotic surgical systems. Key themes include multimodal image registration (MRI/CT/OCT), microvascular perfusion mapping, and coordinate-based surgical navigation. His work increasingly focuses on error-prevention systems, such as detecting wrong-level spine surgery through real-time anatomical verification. Scientific Awards: Congress of Neurosurgeons Innovator of the Year Award (Team), 2018 George Armstrong Peters Prize, University of Toronto Department of Surgery, 2017 Frost & Sullivan New Product Innovation Award (Team), 2017 Canada Research Chair in Biophotonics and Bioengineering, Tier II, 2008-2018 Advising and Professional Activities: Dr. Yang teaches advanced courses including Emerging Biophotonics and Biomedical Capstone Design, mentoring students in translational device development. He maintains active dual licensure as a Professional Engineer (PEng) and medical practitioner, with memberships spanning SPIE (Lifetime), IEEE, OSA, and major neurosurgical associations. His industry collaboration with 7D Surgical exemplifies his philosophy that engineering impact scales through commercialization. Labs and Teams: Leading a multidisciplinary research group at Toronto Metropolitan University, Dr. Yang's lab operates at the engineering-medicine interface. The team recently developed a GPS-guided surgical robot for spine procedures capable of preventing level-mismatch errors. Current projects focus on integrating machine vision with laser ablation systems and developing augmented reality overlays for complex neurosurgical interventions.

Jean-Paul Kneibمشاهده پروفایل

استاد

• Cosmology
• Gravitational Lensing
• Redshift Surveys
• +۵ مورد دیگر

Jean-Paul Kneib is a Full Professor at the Laboratory of Astrophysics (LASTRO) within the School of Basic Sciences (SB) at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He is also involved in the Space Sustainability initiative at EPFL and has held visiting positions at Caltech and CNRS institutions. His research focuses on cosmology, gravitational lensing, and next-generation spectroscopic surveys like DESI and Euclid. Education : PhD in Astrophysics (1993), Master in Astrophysics, Geophysics & Space Technics (1990), Engineer degree in Aerodynamics & Space Technics (1990), all from Toulouse University and École Nationale Supérieure de l'Aéronautique et de l'Espace. Research Interests : Kneib specializes in cosmology, using galaxy surveys and gravitational lensing to study dark matter and dark energy. His work includes strong lensing in galaxy clusters, redshift surveys for cosmic expansion analysis, and robotic fiber-positioner systems for spectroscopic instruments. He also contributes to space sustainability initiatives addressing orbital debris. Recent Publications emphasize deep learning for radio interferometry, Euclid mission early data, DESI BAO analysis, and novel methods for asteroid detection. Collaborative projects involve JWST observations of high-redshift galaxies and digital twins for CubeSats. Scientific Awards : ERC Advanced Laureate (for project "Light on the Dark") Marie Curie Fellowship Leadership Roles : Co-Lead of ESA Euclid "Strong Lensing" Working Group Co-Principal Investigator for CFHT-Stripe 82 project Developer of Fiber-Robot Positioner System for DESI collaboration

Mikhail Shapiroمشاهده پروفایل

استاد

• Biomedical Engineering
• Synthetic Biology
• Neuroscience
• +۳ مورد دیگر

Mikhail Shapiro is the Max Delbrück Professor of Chemical Engineering and Medical Engineering at the California Institute of Technology , and an Howard Hughes Medical Institute Investigator since 2021. His research laboratory develops acoustic and magnetic biomolecular tools for imaging and controlling cellular function in vivo. Chemical Engineering Faculty Medical Engineering Affiliation Howard Hughes Medical Institute Investigator Research focuses on noninvasive control of biological systems through: Acoustic reporter genes (ARG 2.0) for real-time tumor monitoring Ultrasound neuroimaging via acoustically transparent cranial windows Gas vesicle engineering for cellular manipulation Microrobotics with acoustic hydrogels His 2024-2025 publications demonstrate breakthroughs in: Nonlinear ultrasound for deep-tissue visualization Acoustic percolation switches for targeted drug delivery SEMPER systems for multi-gene expression control Scientific recognition includes: HHMI Investigator (2021-) Vilcek Foundation Prize in Biomedical Innovation Roger Tsien Award for Chemical Biology Packard Fellowship in Science and Engineering

• Control Systems
• Structural Systems Theory
• Biomedical Engineering
• +۵ مورد دیگر

Sérgio Pequito is an Associate Professor in the Department of Electrical and Computer Engineering at Instituto Superior Técnico, University of Lisbon, and a principal investigator at the Evolutionary Systems and Biomedical Engineering Lab (Institute for Systems and Robotics). His research bridges structural control systems theory with applications in neuroscience and biomedicine. Education: PhD in Electrical and Computer Engineering from Carnegie Mellon University and Instituto Superior Técnico (2014); MSc (2009) and BSc (2007) in Mathematics from Instituto Superior Técnico Research focuses on understanding large-scale systems through structural/parametric analysis, with applications to brain dynamics, personalized medicine, and control of complex networks. Key themes include fractional-order systems, distributed consensus, and brain-machine interfaces. Recent publications analyze fractional-order dynamical networks, controllability metrics, and privacy-preserving control algorithms, reflecting his expertise in both theoretical and applied domains. Keywords: Control Systems, Structural Theory, Biomedical Engineering, Neuroscience. Scientific recognition includes the 2016 O. Hugo Schuck Award (Theory Category) and a 2009 IEEE Conference on Decision and Control Best Student Paper Finalist. He leads interdisciplinary research teams and collaborates on neural engineering projects, particularly in epilepsy treatment and cortical activity modeling.

Maximilian Schifferمشاهده پروفایل

دانشیار

• Operations Research
• Management Science
• Machine Learning
• +۷ مورد دیگر

Prof. Dr. Maximilian Schiffer is an Associate Professor at the Professorship of Business Analytics & Intelligent Systems , TUM School of Management , Technical University of Munich. He holds a Ph.D. in Operations Research from RWTH Aachen University (2017) and has previously served as a visiting postdoctoral scholar at Stanford University and a postdoctoral scholar at RWTH Aachen. Education: Ph.D. in Operations Research, RWTH Aachen University (2017) Maximilian’s research focuses on developing operations research and prescriptive analytics methods to address societal mobility and transportation challenges, including electric vehicles , autonomous mobility-on-demand systems , and multi-modal transportation networks . His interdisciplinary work bridges robotics , computer science , and transport engineering . Recent publications highlight advancements in autonomous fleet control , electric vehicle charging infrastructure , and collusion detection in dynamic pricing . Scientific Awards: INFORMS TSL Dissertation Prize GOR Doctoral Dissertation Prize Best Paper Award, IEEE Conference on Intelligent Transportation Systems Maximilian is an associate member of the GERAD research group and serves on the editorial board of OR Spectrum and the TSL Dissertation Prize committee . His lab, the Professorship of Business Analytics & Intelligent Systems , trains students in machine learning, optimization, and future mobility systems.

Brenda L. Bartnik-Olsonمشاهده پروفایل

دانشیار

• Neuroimaging
• Traumatic Brain Injury
• Magnetic Resonance Spectroscopy
• +۴ مورد دیگر

Brenda L. Bartnik-Olson, PhD is an Associate Professor in both the Radiology Department and the Basic Sciences, Biomed Engineering Science Division at Loma Linda University School of Medicine. She also serves as Co-Director of Research for the Radiology Department. With a PhD from the University of Saskatchewan (2002), Dr. Bartnik-Olson has established herself as a leading researcher in neuroimaging and traumatic brain injury, particularly in pediatric populations. Her educational background includes: Doctor of Philosophy, University of Saskatchewan, 2002 Dr. Bartnik-Olson's research program focuses on advanced neuroimaging techniques to study brain metabolism and structural changes following injury. Her work spans multiple areas including: Magnetic Resonance Spectroscopy (MRS) for measuring cerebral metabolism Diffusion Tensor Imaging (DTI) for assessing white matter integrity Hyperpolarized carbon-13 MRI for studying real-time brain metabolism Pediatric traumatic brain injury and concussion Neuroimaging biomarkers for predicting functional outcomes Quantitative susceptibility mapping for measuring cerebral oxygenation Her extensive publication record demonstrates a consistent focus on understanding the metabolic and structural changes following brain injury, with particular emphasis on pediatric populations. Her research often involves multimodal imaging approaches and large collaborative efforts through the ENIGMA Consortium, which has resulted in standardized methodologies for multi-scanner studies and identification of key biomarkers for injury and recovery. Dr. Bartnik-Olson has secured numerous research projects as Principal Investigator or co-investigator, including: Steerable Laser Interstitial Thermotherapy (SLIT) Robot for Brain Tumor Therapy (2023-2026) Personalized Profiles of Pathology in Pediatric Traumatic Brain Injury (2022-2026) Federal Pediatric TBI and DAI: Normal appearing brain is not normal (2024-2025) Neural basis of social cognition deficits in youth with autism and schizophrenia (2020) She serves on the MRI Safety Committee (2014-2021) and is a reviewer for Neurotrauma Reports. Her professional society memberships include the American Association of Physicists in Medicine (2015-2021), National Neurotrauma Society (2009-2021), and International Society for Magnetic Resonance in Medicine (2006-2021), where she has presented her research regularly. Dr. Bartnik-Olson teaches courses including Cross Sectional Radiographic Anatomy and Topics in Rehabilitation, demonstrating her commitment to education alongside her research endeavors.