Faculty Candidate
Petter Nilsson
Postdoctoral Scholar,
Aaron Ames group, CalTech

Faculty Candidate: Petter Nilsson

GHC 6115

Areas of Interest: Improving design practices and advancing the capabilities of autonomous systems Host: Stephen Smith Admin Contact: Keyla Cook keylac@andrew.cmu.edu

Faculty Candidate
Assistant Research Professor
Robotics Institute,
Carnegie Mellon University

Multimodal Computational Behavior Understanding

Emotions influence our lives. Observational methods of measuring affective behavior have yielded critical insights, but a persistent barrier to their wide application is that they are labor-intensive to learn and to use. An automated system that can quantify and synthesize human affective behavior in real-world environments would be a transformational tool for research and for [...]

Faculty Candidate
Systems Scientist
Robotics Institute,
Carnegie Mellon University

Faster, Safer, Smaller: The future of autonomy needs all three

Gates-Hillman Center 8102

Abstract In this talk I will start with state estimation as my PhD work. Very often, state estimation plays a crucial role in a robotic system serving as a building block for autonomy. Challenges are to carry out state estimation in 6-DOF, in real-time at high frequencies, with high precision, robust to aggressive motion and [...]

Faculty Candidate
Yuke Zhu
Ph.D. candidate
Department of Computer Science, Stanford University

Faculty Candidate: Yuke Zhu

Gates Hillman Center 6115

Talk: Closing the perception-action loop Abstract: Robots and autonomous systems have been playing a significant role in the modern economy. Custom-built robots have remarkably improved productivity, operational safety, and product quality. However, these robots are usually programmed for specific tasks in well-controlled environments, unable to perform diverse tasks in the real world. In this talk, I will [...]

Faculty Candidate
Deepak Pathak
Ph.D. candidate
Computer Science, UC Berkeley

Self-Directed Learning

Newell-Simon Hall 3305

Abstract: Generalization, i.e., the ability to adapt to novel scenarios, is the hallmark of human intelligence. While we have systems that excel at recognizing objects, cleaning floors, playing complex games and occasionally beating humans, they are incredibly specific in that they only perform the tasks they are trained for and are miserable at generalization. In [...]

Faculty Candidate
Jiajun Wu
Ph.D. student
Electrical Engineering and Computer Science, Massachusetts Institute of Technology

Learning to see the physical world

Newell-Simon Hall 3305

Abstract: Human intelligence is beyond pattern recognition. From a single image, we're able to explain what we see, reconstruct the scene in 3D, predict what's going to happen, and plan our actions accordingly. In this talk, I will present our recent work on physical scene understanding---building versatile, data-efficient, and generalizable machines that learn to see, reason about, and interact [...]

Faculty Candidate
Jun-Yan Zhu
Postdoctoral Researcher
MIT CSAIL

Learning to Synthesize Images

Gates Hillman Center 6115

Abstract: People are avid consumers of visual content. Every day, we watch videos, play games, and share photos on social media. However, there is an asymmetry – while everybody is able to consume visual content, only a chosen few (e.g., painters, sculptors, film directors) are talented enough to express themselves visually. For example, in modern [...]

Faculty Candidate
Angjoo Kanazawa
BAIR postdoctoral researcher
UC Berkeley

Faculty Candidate: Angjoo Kanazawa

Gates Hillman Center 6115

Title: Perceiving Humans in the 3D World Abstract: Since the dawn of civilization, we have functioned in a social environment where we spend our days interacting with other humans. As we approach a society where intelligent systems and humans coexist, these systems must also interpret and interact with humans that reside in the 3D world. [...]

Faculty Candidate
Abe Davis
Postdoctoral Researcher
Stanford University

Augmenting Imagination: Capturing, Modeling, and Exploring the World Through Video

Gates Hillman Center 6115

Abstract: Cameras offer a rich and ubiquitous source of data about the world around us, providing many opportunities to explore new computational approaches to real-world problems. In this talk, I will show how insights from art, science, and engineering can help us connect progress in visual computing with typically non-visual problems in other domains, allowing [...]

Faculty Candidate
Matthias Niessner
Professor
Visual Computing Lab, Technical University of Munic

AI-Driven Videos Synthesis and its Implications

Gates Hillman Center 6115

Abstract: In this talk, I will present my research vision in how to create photo-realistic digital replica of the real world, and how to make holograms become a reality. Eventually, I would like to see photos and videos evolve to become interactive, holographic content indistinguishable from the real world. Imagine taking such 3D photos to [...]

Faculty Candidate
Angela Dai
Postdoctoral Fellow
Technical University of Munich

Understanding 3D Scans

Gates Hillman Center 6115

Abstract: With recent developments in both commodity range sensors as well as mixed reality devices, capturing and creating 3D models of the world around us has become increasingly important. As the world around us lives in a three-dimensional space, such 3D models will not only facilitate capture and display for content creation but also provide [...]

Faculty Candidate
Systems Scientist
Robotics Institute,
Carnegie Mellon University

Faculty Candidate: Wenshan Wang

Newell-Simon Hall 4305

Title: Towards General Autonomy: Learning from Simulation, Interaction, and Demonstration Abstract: Today's autonomous systems are still brittle in challenging environments or rely on designers to anticipate all possible scenarios to respond appropriately. On the other hand, leveraging machine learning techniques, robot systems are trained in simulation or the real world for various tasks. Due to [...]