Ritvik Janamsetty

dblp:256/7504 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2026
0009-0000-4442-7354ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2 · 2 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
2 papers
Vehicular, aerial and satellite networks · 50% Content delivery and video streaming · 33% Edge and fog computing · 17%
Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 5 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Vehicular, aerial and satellite networks › satellite networks
LEO satellite networks
1.012026
Prediction-Driven QoE Optimization for Video Calls over LEO Satellite Networks · INFOCOM 2026
Content delivery and video streaming › quality of experience
quality of experience optimization
1.012026
Prediction-Driven QoE Optimization for Video Calls over LEO Satellite Networks · INFOCOM 2026
Vehicular, aerial and satellite networks
satellite networks
1.012026
Prediction-Driven QoE Optimization for Video Calls over LEO Satellite Networks · INFOCOM 2026
Virtual and augmented reality › augmented reality
augmented reality applications
0.712023
Demo Abstract: Edge-based Augmented Reality Guidance System for Retinal Laser Therapy via Feature Matching · IPSN 2023
Virtual and augmented reality › augmented reality
medical augmented reality
0.712023
Demo Abstract: Edge-based Augmented Reality Guidance System for Retinal Laser Therapy via Feature Matching · IPSN 2023

Methods — techniques the papers use, named apart from their topics

feature matching · 2.0edge computing · 2.0prediction · 1.0
YearPublicationVenuePosition
2026 Prediction-Driven QoE Optimization for Video Calls over LEO Satellite Networks
Ritvik Janamsetty, Anlan Zhang, Feng Qian 0001
INFOCOM2
2025 VitalWave: An End-to-End Open-Source High-Frequency Wearable Device and Data Collection Platform
Lauren Lederer, Ali R. Roghanizad, Ritvik Janamsetty, Luke Redmore, Seijung Kim, Krish Bansal, Amy Liu, Benjamin Asomani, Lauren Baur, Cindy Wang, Amy Duan, Aayush Goyal, Jamee Krzanich, Naomi Patel, Arthur Zhao, Jessilyn Dunn
BSN3
2023 Demo Abstract: Edge-based Augmented Reality Guidance System for Retinal Laser Therapy via Feature Matching
abstract
In ophthalmology, retinal laser therapy is a treatment for retinopathy that requires the use of magnifying lens to treat damaged regions of retinal landmarks, hence creating challenges of inverted magnified images and requiring prolonged training. Augmented Reality (AR) can benefit clinicians during retinal laser therapy by guiding them with retinal landmark holograms and contextual information. Though recent developments in AR magnification show that a direct overlay of the magnified scenes can be achieved, retinal laser therapy requires high precision and visual acuity while maintaining the visual perception of the rest of the environment. Therefore, we demonstrate an AR-based selective magnification system that provides contextual and visualization-based guidance to clinicians. An edge-computing architecture is developed for detecting and matching the feature points between the magnified image and color fundus image of the retina to identify the magnified region of retinal landmarks. We showcase how our AR guidance system can assist clinicians during retinal laser therapy.
Sangjun Eom, Ritvik Janamsetty, Majda Hadziahmetovic, Miroslav Pajic, Maria Gorlatova
IPSN2