My primary area of interest lies in the design and fabrication of new miniature flapping wing micro air vehicles that could one day possibly challenge the acrobatic flight capabilities of small natural fliers like dragonflies and fruitflies. I am currently studying insect flight kinematics and aerodynamics to help devise equivalent bio-inspired robotic designs and control mechanisms that would allow an MAV to emulate some of the flight characteristics of these incredible natural fliers.
My research interests are in the development of flexible and soft sensors and actuators as well as the design of soft-bodied robots that can couple sensing and actuation.
I study and extend methods for statistical model selection from a static to a dynamic framework. To empirically show reliability and robustness, we apply and test our work by estimating and identifying metabolic or regulatory network models. Moreover investigate causal effects in order to infer relations and connections for example in signalling pathways.
I am interested in understanding deep learning from a mathematical perspective, using mostly differential geometry and group theory, as well as in deriving new algorithms based on this understanding.
I am interested in estimating 3D scene representations from multi-view video sequences. In particular, I focus on combining semantic segmentation, object detection and classification with 3D reconstruction using efficient inference methods.
Bio-inspiration has become an established field in material and computer science as well as robotics. By examining biological characteristics and behaviors we can improve old technologies, develop new ones and combine them with existing methods. But not only technology benefits from the interaction between robotics and biology. Using robotic methods could help to proof biological hypotheses in e.g. morphology and locomotion. So what can we learn from and about the locomotion of spiders?
My research deals with autonomous motion generation for humanoid robots that locomote and interact with their environment through contact interaction.
My research focuses on constrained greedy optimization and structured learning applied to bioinformatics. In particular, I am interested in low rank matrix and tensor problems.
I want to understand how intelligence emerges in machine learning. My main research interests are in deep learning, generative models, learning theory.
My research focus lies at the interplay between control theory and machine learning. I try to make use of both techniques to build probably safe learning agents.
My research focuses on motion estimation from complex videos. I am especially interested in computationally efficient methods to compute the motion, and the interplay between the motion and the 3D structure of a scene.
I am working on reinforcement learning and stochastic optimal control for robotics. In particular, I am interested in finding bounds for approximate solutions, in the sense of probably approximately correct learning.
My research goal is the design and development of bio-hybrid micro-robots and micro-actuators powered by programmed muscle cell contraction
I want to scale-up machine learning using sampling. In particular, I work on coresets, an approach originating from computational geometry: Here we aim to replace massive datasets by a small subset of representative data points while retaining theoretical approximation guarantees.
I am interested in the the questions of how robots can safely explore unknown environments and how they can improve their controllers without failures. In particular, this currently involves topics around Bayesian optimization, robust control, and reinforcement learning.
I have broad interests in topics in learning theory, model/algorithm selection, probabilistic models, (large-scale) optimization & computer vision.
My research is concerned with how robots can learn to act in complex environments based on sensory information. In pursuing this I am interested in a wide range of reinforcement learning techniques and how they can potentially be applied in domains where the amount of training data is very limited.
My interests lie in the statistical-computational trade-offs of large scale learning. The outcome of my research would be a learning method that considers the computational limits and adapts itself to obtain the best statistical accuracy.
My current research interests involve using and extending statistical models to combine heterogeneous biological data sources in order to understand cellular regulation in cancer.
I am interested in feature learning with applications in biomedicine, in particular in models that jointly learn representations either of the underlying biological systems or which can be useful for improving clinical practice.
I'm working on Machine Learning
My research interests are in control and haptic interaction between human and robots in sports and upper limb rehabilitation. My focus is on the design and evaluation of strategies to autonomously adapt robot control and to autonomously configure robot assisted exercises.
I work on the development of novel magnetic microrobot control and fabrication for micro-manufacturing. My current work includes manipulation of sub-mm objects in aqueous environments by capillary attachment and the control of magnetic microrobots with six-degrees-of-freedom.
Many combinatorial algorithms gradually reduce the disorder of inputs while running and finally give a precise output. This information "extraction" power of an algorithm is often related to its robustness on noisy inputs. My research aims at formalizing this insights and combines information theory, combinatorics and statistical physics.
My research aims to solve open problems in 3D scene understanding and semantic interpretation of terrestrial laser scans and to use the obtained knowledge to find elegant new solutions to well- known problems, such as the mesh generation from 3D point clouds.
In broad terms I am interested in how nature solves optimality problems, in particular with regards to dynamics and locomotion. I am currently studying how changes in morphology affect learning and control in both animals and robots.
My research focuses on causal inference and large-scale machine learning and how these fields can benefit from each other. More efficient computation and approximate inference enables novel methods for causality and establishing causal relations inevitably leads to more robust predictions of effects in changing environments.
My research is focused on the intelligent control of technical systems by combining traditional approaches with advances from machine learning. Specifically, I am working on data driven model improvement for the use in model predictive control (MPC).
Magnetic manipulation can control multi-scale robots and the tracking method is as important as a control system. To expand the capabilities of manipulation systems, I am investigating three-dimensional tracking methods using projective x-ray imaging technique and digital holography.
My research activities span applied and theoretical topics. I am broadly interested in the use of machine learning to improve healthcare, which brings me to focus on problems in representation learning, probabilistic modelling and sequential decision making.
My research focuses on dense 3D modelling from images. The goal is to not only reconstruct the geometry but at the same time also reason about the semantic classes and the objects that are present in the scene. I'm also interested in real-time robotics applications of dense 3D reconstruction.
I work at the intersection of machine learning, computer vision and computer graphics. Specifically, I am interested in devising new machine learning techniques for better inference in computer vision models.
I work in rehabilitation robotics and especially on patient-robot interaction with upper arm exoskeletons. We are developing human-machine learning algorithms to innovate robotic arm rehabilitation therapy and improve therapy outcomes for patients.
I work on data-driven learning for dexterous robot manipulation. Specifically: object detection at speed, reactive robotic perception, bootstrapping data-driven methods for grasp planning, leveraging physics simulation, and exploiting sensory experience for decision making
My research mainly focuses on deep learning, structured learning and optimization for big data. I study the theoretical and practical behaviors of learning systems and optimization algorithms, as well as their interactions while working with both large as well as high-dimensional data.
My work focuses on computer vision problems such as multi-view tracking of multiple objects, simultaneous localization and mapping, fast image-based registration for videos and camera pose estimation as well as fast optical flow and depth from stereo.
My research field is Computer Vision, or more specifically dense correspondence search for multi-view depth and flow reconstruction. I am currently working on volumetric particle flow reconstruction from image sequences of multiple cameras in order to estimate the 3D velocity field of fluids.
I am broadly interested in algorithms and theoretical computer science with focus on machine learning and approximation algorithms. My current research focus are computational and statistical tradeoffs in large-scale machine learning based on sketching techniques.
My research interests focus on Bayesian Optimization applied to automatic tuning of feedback controllers for robots.
My research focuses on the question of how users interact with dynamic systems. I want to understand how a user provides feedback and how the controller can learn and automatically adjust its control objective to match user-specific demands.
Can we build artificial systems which learn to understand and reason with natural language in the same effortless ways as ourselves? How is thinking realized in the brain? My main research interest is to build biologically and cognitively inspired machine learning models for Natural Language Processing, which can be easily benchmarked, and perform well on multiple real-world tasks.
I work on robot-assisted Palaeoanthropology. I analyze stone tool artifacts from archaeological sites by microscopy regarding traces of wear and compare this with wear pattern created on stone tool replicas by hypothetical tasks. The use of robots allows to control parameters of the task and to create large reference collections of wear pattern easily.
I am interested in understanding the fundamental principles of robot locomotion and manipulation, how to optimally use its sensory information, and developing planning and control strategies that can leverage the available information to create truly robust and adaptive behaviors
My research focuses on micro-robots that propel through complex biological fluids. I am working on new micro/nano fabrication methods, actuation principles in low Reynolds number hydrodynamics, and exploring potential biomedical applications.
My research focuses on deep learning for dense estimation problems such as optical flow. My broad interests include computer vision and machine learning.
My research interest is in 3D reconstruction and scene understanding/interpretation. I aim to develop systems and algorithms to create high-fidelity 3D models of physical objects and environments. I have build depth panoramas for semantic extraction in indoor settings and work on efficient semantic 3D modelling for large-scale urban areas.
My research in computer vision focuses on predicting age, gender and facial attractiveness of face images in the wild using deep learning. Other works include fine-grained object classification, such as car types, improving non-maximum suppression for object detection as well as image enhancement and single image super resolution.
I explore the connection between 3D reconstruction and semantic data, in particular, how 3D models can be improved if parts of it are segmented into classes or, vice-versa, how 3D world can be segmented based on shapes of objects. My work concentrates on jointly inferring dense 3D geometry and semantic labels from multiple images.
My research focuses on kernel methods and causal inference.
My research interests lie in developing and applying clean, mathematically sound methods to model the behavior of complex relations in high-dimensional data. More specifically, I am interested in learning the physiological states and trajectories of patients from their medical data.
I will work in the area computer vision, machine learning and robotics, and specifically focus on SLAM methods (simultaneous localization and mapping) for medical robots.
I am currently working on magnetic helices as micro-/ nano-propellers, propulsion strategies in biological non-Newtonian environments at low Reynolds numbers, and related applications in biological fluidic systems.
I am interested in the foundations of causal inference and its potential to provide novel insights in neuroimaging. We were the first to provide a comprehensive set of causal interpretation rules for encoding and decoding models in neuroimaging studies.
I am interested in trial-and-error processes of reinforcement learning and how a single rendition of a specific action can shape and improve future renditions. To this aim we study birdsong learning and develop dynamical system models to unravel the influence of reward, variability and sensory target on song learning trajectories.
My current research focuses on causal inference. I try to develop theory and methodology for causal structure learning and estimation of causal effects from purely observational data. I am particularly interested in methodology that scales well to high-dimensional problems and is robust with respect to model misspecification.
My research focuses on applications of reinforcement learning and stochastic optimal control theory for motion control in legged robots.
I investigate the use of Gaussian processes in dual control -- the task of efficiently identifying the relevant parts of a dynamic system while simultaneously controlling it.
I work in computer vision, specifically on visual recognition and tracking of human bodies. I am interested in efficient non-parametric context models and their applications to spatial, temporal and semantic dependencies in the human body.