Vishnu Boddeti

Principal Investigator
Google Scholar

I’m an Assistant Professor in the Computer Science Department at Michigan State University. My research is focused at the intersection of Computer Vision, Biometrics and Machine Learning. Specifically, I build computational models for analyzing human attributes, behaviors and interactions from data.

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


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

HERS: Homomorphically Encrypted Representation Search

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


Fairness in Machine Learning Zoo

Computer Vision with Privacy Constraints

Adversarial Representation Learning Zoo