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

  • [C10]

    Fairness-Aware Multi-Group Target Detection in Online Discussion [pdf] Gupta, De-Arteaga, Lease. FAccT 2026.

  • [C9]

    Finding Pareto trade-offs in fair and accurate detection of toxic speech [pdf] Gupta*, Kovatchev*, Das, De-Arteaga, Lease. iConf 2025.

  • [C8]

    Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection [pdf] Gupta*, Lee*, De-Arteaga, Lease. WWW 2023.

  • [C7]

    Learning a neural Pareto manifold extractor with constraints [pdf] Gupta*, Singh*, Bollapragada, Lease. UAI 2022.

  • [C6]

    Pareto Solutions vs Dataset Optima: Concepts and Methods for Optimizing Competing Objectives with Constraints in Retrieval [pdf] Gupta*, Singh*, Das, Lease. ICTIR 2022.

  • [C5]

    A Streaming model for Generalized Rayleigh with extensions to Minimum Noise Fraction [pdf] Gupta, Bajaj. IEEE International Conference on Big Data 2019.

  • [C4]

    Correlation, Prediction and Ranking of Evaluation Metrics in Information Retrieval [pdf] (Best Student Paper Award). Gupta, Kutlu, Khetan, Lease. WWW 2023.

  • [C3]

    Efficient Clustering-based Noise Covariance Estimation for Maximum Noise Fraction [pdf] Gupta, Bajaj. NCVPRIPG, Springer 2017.

  • [C2]

    A GPU based real-time CUDA implementation for obtaining Visual Saliency [pdf] Gupta*, Agarwal*, Mukhopadhyay, Layek. ICVGIP, ACM 2014.

  • [C1]

    Psychovisual saliency in color images [pdf] Gupta*, Agarwal*, Layek, Mukhopadhyay. NCVPRIPG, IEEE 2013.

    • Journal

  • [J2]

    HOFS: Higher order mutual information approximation for feature selection in R [pdf] Gajowniczek, Wu, Gupta, Bajaj. SoftwareX, Elsevier 2022.

  • [J1]

    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

  • [A8]

    A Scalable Multi-Task Learning Framework for Modeling Demographic Disagreements in Annotation Tasks [pdf] Gupta, De-Arteaga, Lease. Coming soon on Arxiv

  • [A7]

    Tail-Net: Extracting Lowest Singular Triplets for Big Data Applications [pdf] Gupta*, Singh*. arXiv preprint 2021.

  • [A6]

    SCA-Net: A Self-Correcting Two-Layer Auto-encoder for Hyperspectral Unmixing [pdf] Gupta*, Singh*, Lease, Dawson. arXiv preprint 2021.

  • [A5]

    Hybrid Neural Pareto Front (HNPF): A Two-Stage Neural-Filter approach for Pareto Front Extraction [pdf] Gupta*, Singh*, Lease, Dawson. arXiv preprint 2021.

  • [A4]

    Streaming Singular Value Decomposition for Big Data Applications [pdf] Gupta*, Singh*, Lease, Dawson. arXiv preprint 2020.

  • [A3]

    Extracting Optimal Solution Manifolds using Constrained Neural Optimization [pdf] Gupta*, Singh*, Lease. arXiv preprint 2020.

  • [A2]

    Prevention is Better than Cure: Handling Basis Collapse and Transparency in Dense Networks [pdf] Gupta*, Singh*, Dawson. arXiv preprint 2020.

  • [A1]

    TIME: A Fully Convolutional Neural Network Architecture with Interpretable Kernels for Dynamic Physical Processes [pdf] Gupta*, Singh*, Lease, Dawson. arXiv preprint 2020.