Scalable Neural Models for Your Needs
I specialize in building interpretable neural networks, demonstrating that with proper representation, a model can be both high-performing and transparent. My solutions are tailored for domain experts, using techniques like domain-guided losses and architectures to address complex tasks in machine learning, natural language processing, and computational sciences.
For my PhD, I worked on developing fair and accurate neural models for toxicity detection in natural language. I used Multi Task Learning models to learn how both shared and specific tones of toxicity vary across demographic groups. This goes beyond a “one-size-fits-all” model which most often overfits to majority groups and raises algorithmic fairness concerns.
Machine Learning & Neural Networks
Neural SVD Solver for Big Data
- Two-stage neural engine as alternative to randomized SVD techniques
- Explicit memory requirement guided by feature dimension and desired rank
- Fully interpretable model with meaningful outputs and weights
Hyperspectral Unmixing for Mixture Model
- Autoencoder structure (SCA-Net) to perform blind unmixing of mixture model
- Achieves 1000x lower RMSE and SAD scores than reported in state of the art works
- Low-weight network with strict interpretability in terms of model
Streaming Low-rank Model for Generalized Rayleigh
- Improved model for Generalized Rayleigh using low-rank constraint for streaming big data
- Extensions to Minimum Noise Fraction for Denoising and Linear/Kernel Discriminant Analysis
- Achieves around 10x efficiency in time and space compared to state of the art models
Computational Sciences & Imaging
Hyperspectral Imaging Analysis
- End-to-end cancer detection pipelines for biopsy tissue samples
- Low-rank, fast and memory-efficient denoising algorithms with error bounds
- Higher-order feature selection to weed out redundant and irrelevant features
Real-Time CUDA Visual Saliency
- GPU-based real-time implementation for obtaining visual saliency
- Psychovisual saliency models for color images
3D Image Segmentation for Surgical Planning
- Real-time 3D image segmentation assist during facial reconstruction surgery
- Exporting 3D models for surgical planning tools and 3D printing
Camera motion estimation for Cryo-EM images
- Simulated Cryo-EM images with simulated noise from Protein Database structures
- Built 3D virus capsid geometry from 2D image stacks using bundle assignment
- Trained customed ResNet for relative pose estimation of cameras and virus class identification
Natural Language Processing
Multi-Task Learning Toxicity Model
- Conditional MTL model to learn toxicity targeted at different groups
- Improved recall ~8% and ~15% over Independent and SoA MTL models
- Runtime and parameter reductions by ~56% and ~72% over baseline
GAP for Target-Group Detection
- Group-fairness loss function based on Accuracy Parity measure
- Balanced group accuracy around target-group detection
- Group disparity reduced from ~22% to ~8% with minimal accuracy drop
Neural Pareto Optimality for Classification and Search
- Interpretable PINN-based Pareto hypernetwork (SUHNPF) to benchmark non-convex verifiable solutions
- Extension to finding Pareto Front for Accuracy v.s. Fairness and Relevance v.s. Diversity tasks
- Scalable to high dimensional neural problems to trace out an approximate Pareto trade-off front
My Submitted Works
Published and Arxiv
Grouped by Type
Conference
Fairness-Aware Multi-Group Target Detection in Online Discussion [pdf] Gupta, De-Arteaga, Lease. FAccT 2026.
Finding Pareto trade-offs in fair and accurate detection of toxic speech [pdf] Gupta*, Kovatchev*, Das, De-Arteaga, Lease. iConf 2025.
Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection [pdf] Gupta*, Lee*, De-Arteaga, Lease. WWW 2023.
Learning a neural Pareto manifold extractor with constraints [pdf] Gupta*, Singh*, Bollapragada, Lease. UAI 2022.
Pareto Solutions vs Dataset Optima: Concepts and Methods for Optimizing Competing Objectives with Constraints in Retrieval [pdf] Gupta*, Singh*, Das, Lease. ICTIR 2022.
A Streaming model for Generalized Rayleigh with extensions to Minimum Noise Fraction [pdf] Gupta, Bajaj. IEEE International Conference on Big Data 2019.
Correlation, Prediction and Ranking of Evaluation Metrics in Information Retrieval [pdf] (Best Student Paper Award). Gupta, Kutlu, Khetan, Lease. WWW 2023.
Efficient Clustering-based Noise Covariance Estimation for Maximum Noise Fraction [pdf] Gupta, Bajaj. NCVPRIPG, Springer 2017.
A GPU based real-time CUDA implementation for obtaining Visual Saliency [pdf] Gupta*, Agarwal*, Mukhopadhyay, Layek. ICVGIP, ACM 2014.
Psychovisual saliency in color images [pdf] Gupta*, Agarwal*, Layek, Mukhopadhyay. NCVPRIPG, IEEE 2013.
Journal
HOFS: Higher order mutual information approximation for feature selection in R [pdf] Gajowniczek, Wu, Gupta, Bajaj. SoftwareX, Elsevier 2022.
A Fully Automated, Faster Noise Rejection Approach to Increasing the Analytical Capability of Chemical Imaging for Digital Histopathology [pdf] Gupta*, Mittal*, Balla, Bhargava, Bajaj. PloS One 2019.
Arxiv
A Scalable Multi-Task Learning Framework for Modeling Demographic Disagreements in Annotation Tasks [pdf] Gupta, De-Arteaga, Lease. Coming soon on Arxiv
Tail-Net: Extracting Lowest Singular Triplets for Big Data Applications [pdf] Gupta*, Singh*. arXiv preprint 2021.
SCA-Net: A Self-Correcting Two-Layer Auto-encoder for Hyperspectral Unmixing [pdf] Gupta*, Singh*, Lease, Dawson. arXiv preprint 2021.
Hybrid Neural Pareto Front (HNPF): A Two-Stage Neural-Filter approach for Pareto Front Extraction [pdf] Gupta*, Singh*, Lease, Dawson. arXiv preprint 2021.
Streaming Singular Value Decomposition for Big Data Applications [pdf] Gupta*, Singh*, Lease, Dawson. arXiv preprint 2020.
Extracting Optimal Solution Manifolds using Constrained Neural Optimization [pdf] Gupta*, Singh*, Lease. arXiv preprint 2020.
Prevention is Better than Cure: Handling Basis Collapse and Transparency in Dense Networks [pdf] Gupta*, Singh*, Dawson. arXiv preprint 2020.
TIME: A Fully Convolutional Neural Network Architecture with Interpretable Kernels for Dynamic Physical Processes [pdf] Gupta*, Singh*, Lease, Dawson. arXiv preprint 2020.