Posts by Collection

Portfolio item number 1

Short description of portfolio item number 1

Portfolio item number 2

Short description of portfolio item number 2

Story Understanding in Video Advertisements

Published in British Machine Vision Conference (BMVC), 2018

We make progress in how to understand the dynamic structure of video advertisements. In particular, we present approaches to effectively predict climax and sentiment in ads.

Beyond Floating-Point Ops: CNN Performance Prediction with Critical Datapath Length

Published in IEEE High-Performance Extreme Computing Conference (HPEC), 2020

We investigate how neural network metrics are related to performance on CPU and GPU hardware.

Heartbeat Classification on the Loihi Neuromorphic Processor

Published in IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2021

We conduct a heartbeat classification case study on the Loihi neuromorphic processor, investigating accuracy and latency optimization strategies in artificial-to-spiking neural network conversion. We present benchmarking insights with regards to spiking 1D-CNN versus artificial 1D-CNN performance on state-of-the-art neural network accelerators.

Contrastive View Design Strategies to Enhance Robustness to Domain Shifts in Downstream Object Detection

Published in AAAI Workshop on Practical Deep Learning in the Wild (Practical-DL), 2023

We propose strategies to improve the out-of-domain robustness of contrastively pretrained object detectors. In particular, we augment contrastive views to learn representations that are more robust to appearance-based and out-of-context domain shifts.

Investigating the Role of Attribute Context in Vision-Language Models for Object Recognition and Detection

Published in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024

We analyze if attributes in captions that supervise VL models result in useful representations for fine-grained tasks. We find models have limited fine-grained utility, so we propose a negative sampling strategy in contrastive learning to improve attribute sensitivity.

Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition

Published in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

We analyze how context in class text representations of VL models affects geographical robustness in object recognition. We propose to ensure geographical robustness in soft prompting through a new geography knowledge regularization method.

Quantifying the Gaps Between Translation and Native Perception in Training for Multimodal, Multilingual Retrieval

Published in Empirical Methods in Natural Language Processing (EMNLP) (Short), 2024

We quantify performance gaps between training on captions that come from native German perception and captions that have been either machine-translated or human-translated from English into German. To address these gaps, we further propose and evaluate caption augmentation strategies.

A Case Study in Practical Neuromorphic Computing: Heartbeat Classification on the Loihi Neuromorphic Processor

Published: March 29, 2021

Teaching Assistant Experience

University of Pittsburgh

Courses Taught as Undergraduate TA (2015-2019): Precalculus, Business Calculus, Intermediate Programming Using Java Courses Taught as Graduate TA (2020-2022): Dependable Computer Architecture, Human-Robot Interaction