Selected Publications

We propose Deep Conditional Target Densities (DCTD), a novel and general regression method with a clear probabilistic interpretation. DCTD models the conditional target density p(y|x) by using a neural network to directly predict the un-normalized density from the input-target pair (x, y). This model of p(y|x) is trained by minimizing the associated negative log-likelihood, approximated using Monte Carlo sampling. Notably, our method achieves a 1.9% AP improvement over Faster-RCNN for object detection on COCO, and sets a new state-of-the-art on visual tracking when applied for bounding box regression.
Preprint, 2019

We propose an evaluation framework for predictive uncertainty estimation that is specifically designed to test the robustness required in real-world computer vision applications. Using the framework, we perform an extensive comparison of the popular ensembling and MC-dropout methods on the tasks of depth completion and street-scene semantic segmentation. Our comparison suggests that ensembling consistently provides more reliable uncertainty estimates.
NeurIPS Bayesian Deep Learning Workshop, 2019

In this thesis we study the computer vision problem of 3D object detection, in which objects should be detected from various sensor data and their position in the 3D world should be estimated. We also study the application of Generative Adversarial Networks in domain adaptation techniques, aiming to improve the 3D object detection model’s ability to transfer between different domains.
Master of Science Thesis in Electrical Engineering, 2018

Publications

DCTD: Deep Conditional Target Densities for Accurate Regression
Preprint, 2019

arXiv

Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
NeurIPS Bayesian Deep Learning Workshop, 2019

arXiv Code Slides Video

Automotive 3D Object Detection Without Target Domain Annotations
Master of Science Thesis in Electrical Engineering, 2018

Link to paper Code Slides Video

Blog Posts

When I first got interested in deep learning a couple of years ago, I started out using TensorFlow. In early 2018 I then decided to switch to PyTorch, a decision that I’ve been very happy with ever since…

CONTINUE READING

Projects

Semantic Segmentation for Autonomous Driving.

Website Aiming to Increase Interest in Higher Education Among Youths.

Autonomous/Web Controlled TurtleBot3.

Autonomous Minesweeping System.

TensorFlow Implementation of SqueezeDet.

Autonomous/Web Controlled RC Car.

Deep Learning Demo/Test Platform.

The SE-Sync Algorithm for Pose-Graph SLAM.

Neural Image Captioning for Intelligent Vehicle-to-Passenger Communication.

Control of an Inverted Double Pendulum using Reinforcement Learning.

Web Tool for Analysis and Visualization of Sensor Data.

Autonomous/Web Controlled Raspberry Pi & Arduino Robot.

2D Adventure Game.

Teaching

Uppsala University

Linköping University

Coursework

Uppsala University

Stanford University

  • CS 229 | Machine Learning | 3 Units
  • EE 263 | Introduction to Linear Dynamical Systems | 3 Units
  • EE 278 | Introduction to Statistical Signal Processing | 3 Units
  • EE 310 | Ubiquitous Sensing, Computing and Communication Seminar | 1 Unit
  • AA 274 | Principles of Robotic Autonomy | 3 Units
  • CS 224N | Natural Language Processing with Deep Learning | 3 Units
  • EE 373A | Adaptive Signal Processing | 3 Units
  • EE 203 | The Entrepreneurial Engineer | 1 Unit
  • AA 203 | Introduction to Optimal Control and Dynamic Optimization | 3 Units
  • AA 273 | State Estimation and Filtering for Aerospace Systems | 3 Units
  • CS 547 | Human-Computer Interaction Seminar | 1 Unit
  • EE 380 | Colloquium on Computer Systems | 1 Unit
  • MS&E 472 | Entrepreneurial Thought Leaders’ Seminar | 1 Unit

Linköping University

  • TSEA51 | Switching Theory and Logical Design | 4 Credits
  • TATM79 | Foundation Course in Mathematics | 6 Credits
  • TFYY51 | Engineering Project | 6 Credits
  • TATA24 | Linear Algebra | 8 Credits
  • TATA41 | Calculus in One Variable 1 | 6 Credits
  • TATA42 | Calculus in One Variable 2 | 6 Credits
  • TATA40 | Perspectives on Mathematics | 1 Credit
  • TATA14 | The Language of Mathematics | 4 Credits
  • TFYA10 | Wave Motion | 8 Credits
  • TFFM12 | Perspectives on Physics | 2 Credits
  • TATA43 | Calculus in Several Variables | 8 Credits
  • TDDC74 | Programming: Abstraction and Modelling | 8 Credits
  • TSRT04 | Introduction in Matlab | 2 Credits
  • TATA44 | Vector Analysis | 4 Credits
  • TANA21 | Scientific Computing | 6 Credits
  • TSTE05 | Electronics and Measurement Technology | 8 Credits
  • TATA34 | Real Analysis, Honours Course | 6 Credits
  • TMME12 | Engineering Mechanics Y | 4 Credits
  • TATA45 | Complex Analysis | 6 Credits
  • TMME04 | Engineering Mechanics II | 6 Credits
  • TAOP07 | Introduction to Optimization | 6 Credits
  • TATA53 | Linear Algebra, Honours Course | 6 Credits
  • TAMS14 | Probability, First Course | 4 Credits
  • TSEA28 | Computer Hardware and Architecture Y | 6 Credits
  • TFYA13 | Electromagnetic Field Theory | 8 Credits
  • TATA77 | Fourier Analysis | 6 Credits
  • TAMS24 | Statistics, First Course | 4 Credits
  • TSDT18 | Signals and Systems | 6 Credits
  • TFYA12 | Thermodynamics and Statistical Mechanics | 6 Credits
  • TATM85 | Functional Analysis | 6 Credits
  • TDDC76 | Programming and Data Structures | 8 Credits
  • TSRT12 | Automatic Control Y | 6 Credits
  • TFYA73 | Modern Physics I | 4 Credits
  • TSEA56 | Electronics Engineering - Bachelor Project | 16 Credits
  • TATA66 | Fourier and Wavelet Analysis | 6 Credits
  • TSKS10 | Signals, Information and Communication | 4 Credits
  • TEAE01 | Industrial Economics, Basic Course | 6 Credits
  • TSRT62 | Modelling and Simulation | 6 Credits
  • TSRT10 | Automatic Control - Project Course | 12 Credits
  • TGTU49 | History of Technology | 6 Credits
  • TSEA81 | Computer Engineering and Real-time Systems | 6 Credits
  • TQET33 | Degree Project - Master’s Thesis | 30 Credits

Reading

I categorize and post comments on all research papers I read, and share this publicly on GitHub. Feel free to reach out with any questions or suggested readings, I am always interested in learning about new methods and ideas.