Evangelos Chatzipantazis

Hello visitor! I am Evangelos. From the Greek Ev (< εύ) + angelos (< ἄγγελος) which translates to "the messenger of good news". So yes; you are in the right place! I am currently a PhD Student in Computer Science at the legendary Grasp Lab at the University of Pennsylvania. Under the advising of Kostas Daniilidis, I work on Geometric Deep Learning and its applications to 3D Computer Vision.

Before that, I did my undergraduate studies in the field of Electrical Engineering and Computer Science at the National Technical University of Athens, under the supervision of Prof. Petros Maragos, where I worked on spectral methods for image segmentation.

Research

My current research focus lies on Equivariant Deep Learning for 3D Computer Vision. More broadly, I am interested in problems that fuse geometry, statistics and physics especially in the form of inductive biases on deep neural networks. I am also very interested in the use of Artificial Intelligence for Science.

Local shape modeling and SE(3)-equivariance are strong inductive biases to reconstruct scenes of arbitrarily many objects appearing in random poses even when a network is trained on single objects in canonical pose.

We propose a new Transformed Risk Minimization (TRM) framework as an extension of classical risk minimization. Our TRM method (1) jointly learns transformations and models in a single training loop, (2) works with any training algorithm applicable to standard risk minimization, and (3) handles any transforms, such as discrete and continuous classes of augmentations. To avoid overfitting when implementing empirical transformed risk minimization, we propose a novel regularizer based on PAC-Bayes theory. We propose a new parametrization of the space of augmentations via a stochastic composition of blocks of geometric transforms. The performance compares favorably to prior methods on CIFAR10/100. Additionally, we show empirically that we can correctly learn certain symmetries in the data distribution (recovering rotations on rotated MNIST) and can also improve calibration of the learned model.

We propose the Information-aware Graph Block Network (I-GBNet), an Active Information Acquisition adaptation of Graph Neural Networks, that aggregates information over the graph representation and provides sequential-decision making in a distributed manner. Numerical simulations on significantly larger graphs and dimensionality of the hidden state and more complex environments than those seen in training validate the properties of the proposed architecture and its efficacy in the application of localization and tracking of dynamic targets.

Teaching

I am very passionate about teaching. Both from the mentoring perspective and as a means to convey knowledge in a clear, concise manner. I am a big fan of Richard Feynman's teaching techniques.

Class Notes (Co-authored with Prof.Pratik Chaudhari)

Website (under Prof. Jianbo Shi)

Credits for the template Jon Barron