Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
EDBT 2026 Demo / reviewers in the wild / expert

Sanju Xaviar

dblp:305/9217 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2021
0009-0009-7475-567XORCID · reported

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

Computer networks · 1 · 1 first-author · 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.

Artificial intelligence
1 paper
Efficient and distributed learning · 33% Trustworthy machine learning · 33% Transfer learning and domain adaptation · 33%
Human-computer interaction and pervasive computing
1 paper
Wearable and physiological sensing · 50% Haptics and multimodal interaction · 50%

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

TopicWeightPapersLastEvidence papers
Machine learning › Transfer learning and domain adaptation
domain generalization		 0.512021
Robust and Affordable Deep Learning Models for Multimodal Sensor Fusion · SenSys 2021
Machine learning › Efficient and distributed learning
on-device inference		 0.512021
Robust and Affordable Deep Learning Models for Multimodal Sensor Fusion · SenSys 2021
Machine learning › Trustworthy machine learning
robustness		 0.512021
Robust and Affordable Deep Learning Models for Multimodal Sensor Fusion · SenSys 2021
Haptics and multimodal interaction
multimodal fusion		 0.512021
Robust and Affordable Deep Learning Models for Multimodal Sensor Fusion · SenSys 2021
Wearable and physiological sensing
wearable sensing		 0.512021
Robust and Affordable Deep Learning Models for Multimodal Sensor Fusion · SenSys 2021

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

deep fusion network · 1.0
YearPublicationVenuePosition
2021 Robust and Affordable Deep Learning Models for Multimodal Sensor Fusion
abstract
Deep fusion networks have received considerable attention lately due to the growing adoption of IoT devices, smartphones, and wearables that incorporate multiple sensing modalities, and their promising applications from human activity recognition to smart home automation. Despite recent advances in this area, there are several practical requirements that are often overlooked. Specifically, fusion networks must maintain their performance during momentary and long-term changes in the environment, be robust to sensor data quality issues, and have a reasonable size so that they can be deployed on resource-constrained devices. My PhD research aims to address these challenges by building robust multimodal fusion networks that rapidly generalize to new environments and have a smaller number of trainable weights, hence lower memory and carbon footprints.
Sanju Xaviar
 SenSys1