Vishnu Boddeti

Principal Investigator
Google Scholar

I’m an Associate Professor in the Computer Science Department at Michigan State University. My research is focused on the intersection of Computer Vision, Biometrics, and Machine Learning. Specifically, I work on trustworthy machine learning.

I received my bachelor’s degree from the Indian Institute of Technology, Madras, and my Ph.D. from Carnegie Mellon University, advised by Vijayakumar Bhgavatula. Following my Ph.D., I was a Postdoctoral Fellow in the Robotics Institute at Carnegie Mellon University, where I worked with Takeo Kanade.


AutoFHE: Automated Adaption of CNNs for Efficient Evaluation over FHE

Action Reimagined: Text-to-Pose Video Editing for Dynamic Human Actions

Utility-Fairness Trade-Offs and How to Find Them

CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D Face Generation

The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models

Estimating field parameters in multiphysics governing equations from scarce observations

FairerCLIP: Debiasing CLIP's Zero-Shot Predictions using Functions in RKHSs

Mechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damage

Into the LAION’s Den: Investigating Hate in Multimodal Datasets

Fully Homomorphic Encryption Operators for Score and Decision Fusion in Biometric Identification

On Hate Scaling Laws For Data-Swamps

Discovering Adaptable Symbolic Algorithms from Scratch

On the Biometric Capacity of Generative Face Models

Physics Informed Neural Network for Dynamic Stress Prediction

Spurious Correlations and Where to Find Them

Heat-Assisted Detection and Ranging

MOAZ: A Multi-Objective AutoML-Zero Framework

Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing Services

ProTéGé: Untrimmed Pretraining for Video Temporal Grounding by Video Temporal Grounding

Mitigating Task Interference in Multi-Task Learning via Explicit Task Routing with Non-Learnable Primitives

Revisiting Residual Networks for Adversarial Robustness

Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components

On Characterizing the Trade-off in Invariant Representation Learning

NeuralSI: Structural Parameter Identification in Nonlinear Dynamical Systems

Deep learning paradigm for prediction of stress distribution in damaged structural components with stress concentrations

HEFT: Homomorphically Encrypted Fusion of Biometric Templates

Methods For The Rapid Detection Of Boundary Condition Variations in Structural Systems

Bridging Finite Element and Deep Learning: High-Resolution Stress Distribution Prediction in Structural Components

Towards Transmission-friendly and Robust CNN Models over Cloud and Device

Do learned representations respect causal relationships?

Generating Diverse 3D Reconstructions from a Single Occluded Face Image

HERS: Homomorphically Encrypted Representation Search

3DFaceFill: An Analysis-By-Synthesis Approach to Face Completion

Spatially-Adaptive Image Restoration using Distortion-Guided Networks

Adversarial Representation Learning With Closed-Form Solvers

Multi-objective Coevolution and Decision-making for Cooperative and Competitive Environments

Neural Architecture Transfer

Towards Multi-objective Co-evolutionary Problem Solving

Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification

NSGANetV2:Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search

Imparting Fairness to Pre-Trained Biased Representations

Adversarial Representation Learning With Closed-Form Solvers

MUXConv: Information Multiplexing in Convolutional Neural Networks

On the Global Optima of Kernelized Adversarial Representation Learning

NSGA-NET: Neural Architecture Search using Multi-Objective Genetic Algorithm

On the Intrinsic Dimensionality of Image Representations

Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach

Constrained Sampling: Optimum Reconstruction in Subspace with Minimax Regret Constraint

RankGAN: A Maximum Margin Ranking GAN for Generating Faces

Secure Face Matching Using Fully Homomorphic Encryption

Perturbative Neural Networks

Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance

Efficient K-Shot Learning with Regularized Deep Networks

On the Capacity of Face Representation

Local Binary Convolutional Neural Networks

Face Alignment Robust to Pose, Expressions and Occlusions

Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking

Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption

Emergence of Selective Invariance in Hierarchical Feed Forward Networks

Gesture-based Bootstrapping for Egocentric Hand Segmentation

In Teacher We Trust: Learning Compressed Models for Pedestrian Detection

Stacked correlation filters for biometric verification

Probabilistic deformation models for challenging periocular image verification

Zero-Aliasing Correlation Filters for Object Recognition

Learning scene-specific pedestrian detectors without real data

Face Alignment Refinement

3D Pose-by-Detection of Vehicles via Discriminatively Reduced Ensembles of Correlation Filters

Maximum margin vector correlation filter

Maximum-margin coupled mappings for cross-domain matching

A framework for binding and retrieving class-specific information to and from image patterns using correlation filters

Correlation Filters for Object Alignment

Maximum Margin Correlation Filter: A new approach for localization and classification

Coupled marginal fisher analysis for low-resolution face recognition

Rainmon: an integrated approach to mining bursty timeseries monitoring data

Matching highly non-ideal ocular images: An information fusion approach

Improved Iris Segmentation based on Local Texture Statistics

A comparative evaluation of iris and ocular recognition methods on challenging ocular images

Extended-depth-of-field iris recognition using unrestored wavefront-coded imagery

A biometric key-binding and template protection framework using correlation filters

Extended Depth of Field Iris Recognition with Correlation Filters


Adversarial Representation Learning Zoo

Computer Vision with Privacy Constraints

Fairness in Machine Learning Zoo