Header logo is
Institute Talks

Human Factors Research in Minimally Invasive Surgery

IS Colloquium
  • 23 May 2019 • 11:00 12:00
  • Caroline G. L. Cao, Ph.D.
  • MPI-IS Stuttgart, Heisenbergstr. 3, Room 2P4

Health care is probably the last remaining unsafe critical system. A large proportion of reported medical errors occur in the hospital operating room (OR), a highly complex sociotechnical environment. As technology is being introduced into the OR faster than surgeons can learn to use them, surgical errors result from the unfamiliar instrumentation, increased motoric, perceptual and cognitive demands on the surgeons, as well as the lack of adequate training. Effective technology design for minimally invasive surgery requires an understanding of the system constraints of remote surgery, and the complex interaction between humans and technology in the OR. This talk will describe research activities in the Ergonomics in Remote Environments Laboratory at Wright State University, which address some of these human factors issues.

Organizers: Katherine J. Kuchenbecker


Uri Shalit - TBA

IS Colloquium
  • 08 July 2019 • 11:15 a.m. 12:15 a.m.
  • Uri Shalit

Organizers: Krikamol Muandet

  • Jinlong Yang
  • PS Aquarium

In the past few years, significant progress has been made on shape modeling of human body, face, and hands. Yet clothing shape is currently not well presented. Modeling clothing using physics-based simulation can sometimes involve tedious manual work and heavy computation. Therefore, a data-driven learning approach has emerged in the community. In this talk, I will present a stream of work that targeted to learn the shape of clothed human from captured data. It involves 3D body estimation, clothing surface registration and clothing deformation modeling. I will conclude this talk by outlining the current challenges and some promising research directions in this field.

Organizers: Timo Bolkart


  • Dr. Urartu Şeker
  • 2P04

Programming cellular devices to deliver proteins or small molecules using synthetic genetic regulation can be employed in many areas such as biomedicine, living therapeutics, living materials and many others. A biological device composed of a cellular sensor coupled with a programmed protein delivery system can lead the formation of a synthetic system that can sense the environmental inputs, carry out calculations and create an output. Using this approach, we have built cellular devices those can sense environmental signals and creates an output in the form of protein secretion. In this talk I will mention about a self-actuated cellular protein delivery system which utilizes logic gate based, and state-machine based operations for sequential protein delivery. Also, I will mention about our recent studies to create synthetic genetic circuits those rely on a sense-response approach. These will include a cellular device for a whole cell biocatalyst and another device for nanomaterial templating.


  • Marilyn Keller
  • Aquarium

Since the release of the Kinect, RGB-D cameras have been used in several consumer devices, including smartphones. In this talk, I will present two challenging uses of this technology. With multiple RGB-D cameras, it is possible to reconstruct a 3D scene and visualize it from any point of view. In the first part of the talk, I will show how such a scene can be streamed and rendered as a point cloud in a compelling way and its appearance improved by the use of external cinema cameras. In the second part of the talk, I will present my work on how an RGB-D camera can be used for enabling real-walking in virtual reality by making the user aware of the surrounding obstacles. I present a pipeline to create an occupancy map from a point cloud on the fly on a mobile phone used as a virtual reality headset. This occupancy map can then be used to prevent the user from hitting physical obstacles when walking in the virtual scene.

Organizers: Sergi Pujades


Unsupervised Learning: Passiv and Active

Talk
  • 11 April 2019 • 16:30 17:30
  • Professor Jürgen Schmidhuber
  • MPI IS lecture hall N0.002

I’ll start with a concept of 1990 that has become popular: unsupervised learning without a teacher through two adversarial neural networks (NNs) that duel in a minimax game, where one NN minimizes the objective function maximized by the other. The first NN generates data through its output actions, the second NN predicts the data. The second NN minimizes its error, thus becoming a better predictor. But it is a zero sum game: the first NN tries to find actions that maximize the error of the second NN. The system exhibits what I called “artificial curiosity” because the first NN is motivated to invent actions that yield data that the second NN still finds surprising, until the data becomes familiar and eventually boring. A similar adversarial zero sum game was used for another unsupervised method called "predictability minimization," where two NNs fight each other to discover a disentangled code of the incoming data (since 1991), remarkably similar to codes found in biological brains. I’ll also discuss passive unsupervised learning through predictive coding of an agent’s observation stream (since 1991) to overcome the fundamental deep learning problem through data compression. I’ll offer thoughts as to why most current commercial applications don’t use unsupervised learning, and whether that will change in the future.

Organizers: Bernhard Schölkopf


  • Jérôme Casas
  • 2P04

Insect chemical ecology is a mature, long standing field, with its own journal. By contrast, insect physical ecology is much less studied and the worked scattered. Using work done in my group, I will highlight locomotion, both in granular materials like sand and at the water surface as well as sensing, in particular olfaction and flow sensing. The bio-inspired implementations in MEMS technologies will be the closing chapter.

Organizers: Metin Sitti


Probabilistic symmetry and invariant neural networks

IS Colloquium
  • 25 March 2019 • 15:30 16:30
  • Benjamin Bloem-Reddy
  • N4.022

In an effort to improve the performance of deep neural networks in data-scarce, non-i.i.d., or unsupervised settings, much recent research has been devoted to encoding invariance under symmetry transformations into neural network architectures. We treat the neural network input and output as random variables, and consider group invariance from the perspective of probabilistic symmetry. Drawing on tools from probability and statistics, we establish a link between functional and probabilistic symmetry, and obtain functional representations of probability distributions that are invariant or equivariant under the action of a compact group. Those representations characterize the structure of neural networks that can be used to represent such distributions and yield a general program for constructing invariant stochastic or deterministic neural networks. We develop the details of the general program for exchangeable sequences and arrays, recovering a number of recent examples as special cases. This is work in collaboration with Yee Whye Teh. https://arxiv.org/abs/1901.06082

Organizers: Isabel Valera


Modularity and transfer in reinforcement learning

Talk
  • 25 March 2019 • 14:00 15:15
  • Herke van Hoof
  • N2 meeting room (N2.025)

Learning new control strategies for (possibly unknown) dynamical systems is a challenging task. Reinforcement learning algorithms typically require 'fresh' data regularly, but obtaining data safely and in sufficient quantities is a challenge on real systems. Thus, it is no surprise that most recent successes have been in domains where massive amounts of data can easily be generated in simulation (e.g., games such as Atari and Go).

Organizers: Georg Martius Mara Cascianelli


  • Sangram Bagh
  • 2P04

The molecular connectivity between genes and proteins inside a cell shows a good degree of resemblance with complex electrical circuits. This inspires the possibility of engineering a cell similar to an engineering device by plugging in genetic logic circuits. This approach, which is loosely defined as synthetic biology is an emerging field of bioengineering, where scientist use electrical and computer engineering principle to re-program cellular functions with a potential to solve next generation challenges in medicine, materials, energy, and space travel. In this talk, we discuss our efforts to create artificial and complex chemical signal processing systems using genetic logic circuits and its applications in building a technology platform for microbial robotics. We further discuss our systems biology effort to understand the effect of microgravity on human and bacterial cells during space travel.

Organizers: Metin Sitti


  • Dr.-Ing. Thomas Seel
  • MPI-IS Stuttgart, seminar room 2P4

Neurological disorders and injuries lead to a loss of sensorimotor function in the central nervous system, which controls the musculoskeletal system. Novel systems and control methods can be employed to create neuroprostheses that restore these functions to an unprecedented degree by two major advances: (1) Long standing limitations of inertial motion tracking are overcome by novel parameter estimation and sensor fusion methods. (2) A recent extension of classic learning control methods facilitates real-time pattern adaptation in artificial muscle recruitment. We review the role of these methods in the development of biomimetic neuroprostheses and discuss their potential impact in a range of further application systems including autonomous vehicles, robotics, and multi-agent networks.

Organizers: Sebastian Trimpe


  • Nikos Athanasiou
  • PS Aquarium

First, a short analysis of the key components of my participation in SemEval 2018, an emotion analysis contest from tweets. Namely, a transfer learning approach used for emotion classification and a context-aware attention mechanism. In my second paper, I explore how brain information can improve word representations. Neural activation models that have been proposed in the literature use a set of example words for which fMRI measurements are available in order to find a mapping between word semantics and localized neural activations. I use such models to predict neural activations on a full word lexicon. Then, I propose a cognitive computational model that estimates semantic similarity in the neural activation space and investigates the relative performance of this model for various natural language processing tasks. Finally, in my most recent work I explore cross-topic word representations. In traditional Distributional Semantic Models -like word2vec- the multiple senses of a polysemous word are conflated into a single vector space representation. In my work, I propose a DSM that learns multiple distributional representations of a word based on different topics. Moreover, we project the different topic representations in a common space and apply a smoothing technique to group redundant topic vectors.

Organizers: Soubhik Sanyal