Chandrajit Bajaj

About

Chandrajit Bajaj is a professor at the University of Texas at Austin, holding the Computational Applied Mathematics Chair in Visualization. His research focuses on bioinformatics and computational biology, graphics and visualization, machine learning, and scientific computing. As a faculty member, he contributes to advancing the frontiers of computer science research and education, particularly in the areas of visualization and computational mathematics.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Data Mining
• Engineering physics
• Optoelectronics
• Engineering
• Cardiology
• Medicine
• Materials science

Selected publications

• Materials for emergent silicon-integrated optical computing

  Journal of Applied Physics · 2021 · 41 citations

  scheme. Another approach is quantum computing using photons. Both of these approaches can be realized using silicon photonics, and at the heart of both technologies is an efficient, ultra-low power broad band optical modulator. As silicon modulators suffer from relatively high power consumption, materials other than silicon itself have to be considered for the modulator. In this Perspective, we present our view on such materials. We focus on oxides showing a strong linear electro-optic effect th…

  DOI

• Weighted Random Forests to Improve Arrhythmia Classification

  Electronics · 2020 · 39 citations

  Senior authorCorresponding

  Construction of an ensemble model is a process of combining many diverse base predictive learners. It arises questions of how to weight each model and how to tune the parameters of the weighting process. The most straightforward approach is simply to average the base models. However, numerous studies have shown that a weighted ensemble can provide superior prediction results to a simple average of models. The main goals of this article are to propose a new weighting algorithm applicable for each…

  DOI

• Recipes for when physics fails: recovering robust learning of physics informed neural networks

  Machine Learning Science and Technology · 2023-01-17 · 37 citations

  articleOpen access1st author

  Physics-informed neural networks (PINNs) have been shown to be effective in solving partial differential equations by capturing the physics induced constraints as a part of the training loss function. This paper shows that a PINN can be sensitive to errors in training data and overfit itself in dynamically propagating these errors over the domain of the solution of the PDE. It also shows how physical regularizations based on continuity criteria and conservation laws fail to address this issue an…

  PublisherOA PDFDOI

• DeblurSR: Event-Based Motion Deblurring under the Spiking Representation

  Proceedings of the AAAI Conference on Artificial Intelligence · 2024-03-24 · 7 citations

  articleOpen access

  We present DeblurSR, a novel motion deblurring approach that converts a blurry image into a sharp video. DeblurSR utilizes event data to compensate for motion ambiguities and exploits the spiking representation to parameterize the sharp output video as a mapping from time to intensity. Our key contribution, the Spiking Representation (SR), is inspired by the neuromorphic principles determining how biological neurons communicate with each other in living organisms. We discuss why the spikes can r…

  PublisherOA PDFDOI

• TAssembly: Data-driven fractured object assembly using a linear template model

  Computers & Graphics · 2023-05-09 · 7 citations

  articleSenior author
  PublisherDOI

Recent grants

• US-India Cooperative Research: Interrogative Synthetic Environments

  NSF · $51k · 2000–2005

• Polar sampling and optimization of protein-ligand cocrystal structures

  NIH · $225k · 2016–2018

• ITR: Subnanometer Structure Based Fold Determination of Biological Complexes

  NSF · $750k · 2003–2007

Frequent coauthors

• Valerio Pascucci

  University of Utah

  32 shared

• Guoliang Xu

  Jiangxi University of Finance and Economics

  25 shared

• Yongjie Zhang

  Guangdong Medical College

  24 shared

• Andrew Gillette

  Lawrence Livermore National Laboratory

  23 shared

• Daniel R. Schikore

  21 shared

• Guoliang Xu

  Chongqing University of Posts and Telecommunications

  20 shared

• Guoliang Xu

  20 shared

• Qixing Huang

  The University of Texas at Austin

  17 shared

Education

• Ph.D., Computer Science

  University of California, Los Angeles

  1990

• M.S., Computer Science

  University of California, Los Angeles

  1986

• B.S., Computer Science

  University of California, Los Angeles

  1984

Awards & honors

• 2004 - University of Texas, Faculty Research Award
• 2004 - University of Texas, Dean Research Award
• 2006 - Best Paper, Computer Aided Design
• 2010 - Best Paper, ACM Symposium on Solid and Physical Model…
• 2013 - Fellow of the Institute of Electrical and Electronic…

Similar researchers at University of Texas at Austin

• Aditya Akellah-60
• Scott Aaronsonh-51
• Swarat Chaudhurih-33