Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ertza Warraich
dblp:299/8878
· DBLP profile ↗
3ranked-venue papers
1as first author
3since 2021 · last 2026
0000-0002-6670-5076ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 2 · 1 first-author · 2 since 2021Security and privacy · 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 architecture, parallel and distributed computing, and storage systems
1 paper |
High-performance computing · 40% Cloud and datacenter computing · 40% Distributed systems · 20% | |
| Network and information security
1 paper |
Systems and software security · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Program analysis · 100% |
Topics — the 8 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing › collective communication
all-reduce |
0.9 | 1 | 2025 | OptiReduce: Resilient and Tail-Optimal AllReduce for Distributed Deep Learning in the Cloud · NSDI 2025 |
Cloud and datacenter computing › resource management
cloud resource management |
0.9 | 1 | 2025 | OptiReduce: Resilient and Tail-Optimal AllReduce for Distributed Deep Learning in the Cloud · NSDI 2025 |
High-performance computing
collective communication |
0.9 | 1 | 2025 | OptiReduce: Resilient and Tail-Optimal AllReduce for Distributed Deep Learning in the Cloud · NSDI 2025 |
Distributed systems › distributed machine learning
distributed deep learning |
0.9 | 1 | 2025 | OptiReduce: Resilient and Tail-Optimal AllReduce for Distributed Deep Learning in the Cloud · NSDI 2025 |
Cloud and datacenter computing › quality of service
tail latency |
0.9 | 1 | 2025 | OptiReduce: Resilient and Tail-Optimal AllReduce for Distributed Deep Learning in the Cloud · NSDI 2025 |
Systems and software security
vulnerability discovery |
0.6 | 1 | 2022 | FuzzUSB: Hybrid Stateful Fuzzing of USB Gadget Stacks · SP 2022 |
Program analysis
static analysis |
0.2 | 1 | 2022 | FuzzUSB: Hybrid Stateful Fuzzing of USB Gadget Stacks · SP 2022 |
Program analysis
symbolic execution |
0.2 | 1 | 2022 | FuzzUSB: Hybrid Stateful Fuzzing of USB Gadget Stacks · SP 2022 |
Methods — techniques the papers use, named apart from their topics
symbolic execution · 1.1static analysis · 1.1state machine extraction · 1.1fuzzing · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Towards Network-Efficient Cross-Regional Inference via Learned Activation CompressionabstractLarge transformer models are increasingly deployed across geographically distributed GPU clusters due to capacity, cost, and locality constraints. When inference is partitioned across sites, intermediate activations must be transmitted over wide area network (WAN) links at each partition boundary, introducing significant communication overhead. We present Feather, a system that reduces this overhead by compressing intermediate activations before transmission and reconstructing them before downstream layers resume execution. Regan McDonald, Marilyn Rego, Ertza Warraich, Annus Zulfiqar, Muhammad Shahbaz 0001 |
SIGCOMM | 3 |
| 2025 | OptiReduce: Resilient and Tail-Optimal AllReduce for Distributed Deep Learning in the Cloud
Ertza Warraich, Omer Shabtai, Khalid Manaa, Shay Vargaftik, Yonatan Piasetzky, Matty Kadosh, Lalith Suresh 0001, Muhammad Shahbaz 0001 |
NSDI | 1 |
| 2022 | FuzzUSB: Hybrid Stateful Fuzzing of USB Gadget StacksabstractUniversal Serial Bus (USB) is the de facto protocol supported by peripherals and mobile devices, such as USB thumb drives and smart phones. For many devices, USB Type-C ports are the primary interface for charging, file transfer, audio, video, etc. Accordingly, attackers have exploited different vulnerabilities within USB stacks, compromising host machines via BadUSB attacks or jailbreaking iPhones from USB connections. While there exist fuzzing frameworks dedicated to USB vulnerability discovery, all of them focus on USB host stacks and ignore USB gadget stacks, which enable all the features within modern peripherals and smart devices. In this paper, we propose FuzzUSB, the first fuzzing framework for the USB gadget stack within commodity OS kernels, leveraging static analysis, symbolic execution, and stateful fuzzing. FuzzUSB combines static analysis and symbolic execution to extract internal state machines from USB gadget drivers, and uses them to achieve state-guided fuzzing through multi-channel inputs. We have implemented FuzzUSB upon the syzkaller kernel fuzzer and applied it to the most recent mainline Linux, Android, and FreeBSD kernels. As a result, we have found 34 previously unknown bugs within the Linux and Android kernels, and opened 8 CVEs. Furthermore, compared to the baseline, FuzzUSB has also demonstrated different improvements, including $ 3\times$ higher code coverage, $ 50\times$ improved bug-finding efficiency for Linux USB gadget stacks, $ 2\times$ higher code coverage for FreeBSD USB gadget stacks, and reproducing known bugs that could not be detected by the baseline fuzzers. We believe FuzzUSB provides developers a powerful tool to thwart USB-related vulnerabilities within modern devices and complete the current USB fuzzing scope. Kyungtae Kim, Taegyu Kim, Ertza Warraich, Byoungyoung Lee, Kevin R. B. Butler, Antonio Bianchi, Jing (Dave) Tian |
SP | 3 |