Saurabh Mathur
I’m a PhD student working with Prof. Sriraam Natarajan and the Statistical Relational Learning Lab at the University of Texas at Dallas.
Jump to: [Teaching] [Work Experience] [Projects] [Research]
Research
I am interested in problems that involve combining domain knowledge with patterns learned from noisy and uncertain data.
Before joining UTD, I was a Master’s student at Indiana University. I worked with Prof. David Crandall on bayesian uncertainty quantification algorithms for deep neural networks. I completed my M.S. in May 2020.
As an undergraduate student at Vellore Institute of Technology, I worked with Prof. Daphne Lopez on deep models for image caption generation and chatbots. I have interned at Microsoft, Bangalore during my undergrad and Synopsys, Mountain View during my masters.
Publications
-
Modeling multiple adverse pregnancy outcomes: Learning from diverse data sources
Saurabh Mathur, Veerendra P. Gadekar, Rashika Ramola, Peixin Wang, Ramachandran Thiruvengadam, David M. Haas, Shinjini Bhatnagar, Nitya Wadhwa, Garbhini Study Group, Predrag Radivojac, Himanshu Sinha, Kristian Kersting and Sriraam Natarajan
The 22nd International Conference in Artificial Intelligence in Medicine (AIME 2024)
-
Knowledge Intensive Learning of Credal Networks
Saurabh Mathur, Alessandro Antonucci and Sriraam Natarajan
The 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024)
-
On the Robustness and Reliability of Late Multi-Modal Fusion using Probabilistic Circuits
Sahil Sidheekh, Pranuthi Tenali*, Saurabh Mathur*, Erik Blasch and Sriraam Natarajan
The 27th International Conference on Information Fusion (FUSION 2024)
-
Credibility-aware Reliable Multi-Modal Fusion Using Probabilistic Circuits
Saurabh Mathur, Sahil Sidheekh, Pranuthi Tenali, Erik Blasch, Kristian Kersting and Sriraam Natarajan.
The 2nd Workshop of Deployable AI (DAI) at AAAI 2024
-
Learning Credal Conditional Probability Tables with Qualitative Knowledge
Saurabh Mathur, Alessandro Antonucci and Sriraam Natarajan.
The 2nd Workshop of Deployable AI (DAI) at AAAI 2024
-
Neurologic Statistical Prognostication and Risk Assessment for Kids on Extracorporeal Membrane Oxygenation: Neuro SPARK
Neel Shah, Saurabh Mathur, Prashanth Shanmugham, Xilong Li, Ravi R Thiagarajan, Sriraam Natarajan and Lakshmi Raman.
ASAIO Journal 2023
-
Bayesian Learning of Probabilistic Circuits with Domain Constraints
Athresh Karanam*, Saurabh Mathur*, Sahil Sidheekh* and Sriraam Natarajan.
The Sixth Workshop On Tractable Probabilistic Modeling (TPM) at UAI 2023
-
Knowledge Intensive Learning of Cutset Networks
Saurabh Mathur, Vibhav Gogate and Sriraam Natarajan
The Sixth Workshop On Tractable Probabilistic Modeling (TPM) at UAI 2023
-
Knowledge Intensive Learning of Cutset Networks
Saurabh Mathur, Vibhav Gogate and Sriraam Natarajan
The 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023)
-
Exploiting Domain Knowledge as Causal Independencies in Modeling Gestational Diabetes
Saurabh Mathur, Athresh Karanam, Predrag Radivojac, David M. Haas, Kristian Kersting and Sriraam Natarajan
Pacific Symposium on Biocomputing (PSB 2023)
-
Explaining Deep Tractable Probabilistic Models: The sum-product network case
Athresh Karanam*, Saurabh Mathur*, David M. Haas, Predrag Radivojac, Kristian Kersting and Sriraam Natarajan
The 11th International Conference on Probabilistic Graphical Models (PGM 2022)
-
Explaining Deep Tractable Probabilistic Models: The sum-product network case
Athresh Karanam*, Saurabh Mathur*, David M. Haas, Predrag Radivojac, Kristian Kersting and Sriraam Natarajan
The Fifth Workshop On Tractable Probabilistic Modeling (TPM) at UAI 2022
Teaching
-
University of Texas, Dallas
Teaching Assistant
- CS6375 - Machine Learning (Spring 2021)
- CS4375 - Introduction to Machine Learning (Fall 2020)
- CS6301 - Software Defined Networking (Fall 2020)
-
Indiana University, Bloomington
Associate Instructor
- B657 - Computer Vision (Spring 2020)
- B551 - Elements of AI (Fall 2019)
- B456 - Image Processing (Spring 2019)
-
IEEE Computer Society, VIT Chapter
Workshop Instructor
- Game Development with HTML5 (Fall 2016)
- Python Programming (Fall 2016)
- C++ Programming (Spring 2016)
Work Experience
-
Synopsys, Mountain View
R&D Intern
- Developed a neural machine translation system to generate assertions from specifications.
-
Indiana University, Bloomington
Research Assistant
- Visualized 94,000 geotagged Irish folktales using Bayesian topic models under Prof. Roni Khardon.
-
Microsoft, Bangalore
Summer Intern
- Built a movie recommendation engine with an error-rate of 0.87 on the Netflix dataset.
Projects
Speech to text
GitHub, 2019
Deep learning based end-to-end speech recognition system
Semantic and Instance Segmentation
GitHub, 2019
Deep learning and PGM based image segmentation
Automated Image Captioning System
GitHub, 2018
Deep learning based image caption generator to improve accessibility on the web.
VITacademics
GitHub, 2018
A web-server for mobile applications that streamline access to academic information for students at VIT.
Clickbait Detector
GitHub, 2018
Detecting clickbait headlines in the wild with 90% accuracy.
Movie Recommendation Systems
GitHub, 2016
A comparitive study of collaborative filtering algorithms.
graVITas, 2016
GitHub, 2016
Web-portals for VIT University’s annual technical festival.