EDBT 2026 Demo / reviewers in the wild / expert
Qianli Liu
dblp:396/7680
· DBLP profile ↗
5ranked-venue papers
2as first author
5since 2021 · last 2026
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 4 · 2 first-author · 4 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.
| Artificial intelligence
3 papers |
Efficient and distributed learning · 46% Deep learning architectures and training · 27% Language models and text generation · 27% | |
| Computer architecture, parallel and distributed computing, and storage systems
3 papers |
Distributed systems · 70% GPUs and heterogeneous computing · 30% | |
| Computer networks
1 paper |
Edge and fog computing · 100% |
Topics — the 8 heaviest of 10, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation
LLM agents |
1.0 | 1 | 2026 | PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence · INFOCOM 2026 |
Machine learning › Deep learning architectures and training
mixture of experts |
1.0 | 1 | 2026 | Director: Accelerating Distributed MoE Serving via Online Proactive Expert Placement · INFOCOM 2026 |
Distributed systems › distributed machine learning
expert parallelism |
1.0 | 1 | 2026 | Director: Accelerating Distributed MoE Serving via Online Proactive Expert Placement · INFOCOM 2026 |
Machine learning › Efficient and distributed learning
KV cache management |
0.9 | 1 | 2025 | Mell: Memory-Efficient Large Language Model Serving via Multi-GPU KV Cache Management · INFOCOM 2025 |
Machine learning › Efficient and distributed learning › inference serving
large language model serving |
0.9 | 1 | 2025 | Mell: Memory-Efficient Large Language Model Serving via Multi-GPU KV Cache Management · INFOCOM 2025 |
GPUs and heterogeneous computing › multi-GPU computing
GPU cluster |
0.3 | 1 | 2026 | Director: Accelerating Distributed MoE Serving via Online Proactive Expert Placement · INFOCOM 2026 |
Distributed systems › distributed system architecture
interoperability |
0.3 | 1 | 2026 | PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence · INFOCOM 2026 |
GPUs and heterogeneous computing
multi-GPU computing |
0.3 | 1 | 2025 | Mell: Memory-Efficient Large Language Model Serving via Multi-GPU KV Cache Management · INFOCOM 2025 |
Methods — techniques the papers use, named apart from their topics
relaxation-based optimization · 2.0quantization · 2.0prediction · 2.0online migration · 2.0memory management · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Director: Accelerating Distributed MoE Serving via Online Proactive Expert PlacementabstractExpert parallelism has become the prevailing paradigm to serve Mixture-of-Experts (MoE) models. Its efficiency depends on the communication and computation latencies of the GPUs, which are linked to the placement of experts in the GPUs. Existing works for optimizing expert placement focus on leveraging past requests' expert activation patterns. However, they demonstrate deficiencies facing diverse and rapidly changing request patterns, calling for an online, proactive approach. Implementing such an approach requires addressing several challenges: the uncertainty associated with incoming requests' expert activation, the cost of expert migration, and the NP-hard complexity in optimization. Therefore, we present Director, a new distributed MoE serving system that minimizes end-to-end latency via prediction-driven, online expert placement. Director uses either a lightweight cascaded predictor or a low-bit quantized replica for expert activation patterns of incoming requests. An online migration module then enacts the changes with near-zero downtime by executing migrations in compute-bound phases, keeping disruption bounded. At its core, a relaxation-based expert placement optimizer operates under capacity constraints, runs in polynomial time, and achieves a (1+ ϵ) approximation ratio. Finally, we implement a prototype and demonstrate, through extensive experiments, a reduction in end-to-end latency of 11 ~ 55% for popular MoE models (e.g., Mistral, DeepSeek and Qwen) compared to existing work. Qianli Liu, Kaibin Guo, Zicong Hong, Peng Li 0017, Fahao Chen, Song Guo 0001 |
INFOCOM | 1 |
| 2026 | PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence
Zile Wang, Qianli Liu, Kaibin Guo, Zicong Hong, Song Guo 0001 |
INFOCOM | 2 |
| 2025 | Mell: Memory-Efficient Large Language Model Serving via Multi-GPU KV Cache Management
Qianli Liu, Zicong Hong, Peng Li 0017, Fahao Chen, Song Guo 0001 |
INFOCOM | 1 |
| 2025 | Edge-Learning-Based Sensor Allocation Strategy in Internet of Things SystemabstractIn the edge learning process of B5G Internet of Things (IoT) systems, the allocation strategy of edge sensors will directly affect the learning results of the system. This article proposes multiple methods to allocate edge devices that can learn and predict the usage of spectrum data, aiming to improve the efficiency and fairness of edge learning. We design an efficient edge device allocation strategy to enhance the edge learning efficiency and propose a metric called ineffective transmission parameter (ITP) to evaluate its performance. To solve the optimization problem, mathematical analysis is performed and closed-form expressions are obtained. The scenarios considered include: devices with different learning performance and the same learning performance on the same band. We propose three edge device allocation methods: 1) iterative hierarchical Hungarian allocation; 2) bow allocation; and 3) category-divided allocation to ensure the fairness of edge learning among sub-bands. The fairness of the system is measured by evaluating the lowest learning performance in the sub-band. To adapt to the actual scenario, we enhance fairness by introducing band attribute parameters (considering the priority, anti-interference ability, and congestion level of the main users of the sub-band). Simulation results show that the proposed strategy significantly improves the ITP of edge learning in IoT systems, especially in the case of varying band utilization. The fairness scheme improves the overall edge learning fairness of the system. Shanpeng Xiao, Zhiqing Wei, Zhiqiang Wu 0001, Qianli Liu, Weixi Gu |
IEEE Internet Things J. | 4 |
| 2025 | Large language models accurately identify immunosuppression in intensive care unit patientsabstractOBJECTIVE: Rule-based structured data algorithms and natural language processing (NLP) approaches applied to unstructured clinical notes have limited accuracy and poor generalizability for identifying immunosuppression. Large language models (LLMs) may effectively identify patients with heterogenous types of immunosuppression from unstructured clinical notes. We compared the performance of LLMs applied to unstructured notes for identifying patients with immunosuppressive conditions or immunosuppressive medication use against 2 baselines: (1) structured data algorithms using diagnosis codes and medication orders and (2) NLP approaches applied to unstructured notes. MATERIALS AND METHODS: We used hospital admission notes from a primary cohort of 827 intensive care unit (ICU) patients at Northwestern Memorial Hospital and a validation cohort of 200 ICU patients at Beth Israel Deaconess Medical Center, along with diagnosis codes and medication orders from the primary cohort. We evaluated the performance of structured data algorithms, NLP approaches, and LLMs in identifying 7 immunosuppressive conditions and 6 immunosuppressive medications. RESULTS: In the primary cohort, structured data algorithms achieved peak F1 scores ranging from 0.30 to 0.97 for identifying immunosuppressive conditions and medications. NLP approaches achieved peak F1 scores ranging from 0 to 1. GPT-4o outperformed or matched structured data algorithms and NLP approaches across all conditions and medications, with F1 scores ranging from 0.51 to 1. GPT-4o also performed impressively in our validation cohort (F1 = 1 for 8/13 variables). DISCUSSION: LLMs, particularly GPT-4o, outperformed structured data algorithms and NLP approaches in identifying immunosuppressive conditions and medications with robust external validation. CONCLUSION: LLMs can be applied for improved cohort identification for research purposes. Vijeeth Guggilla, Mengjia Kang, Melissa J. Bak, Steven D. Tran, Anna Pawlowski, Prasanth Nannapaneni, Luke V. Rasmussen, Helen K. Donnelly, Ankit Agrawal 0001, David M. Liebovitz, Alexander V. Misharin, G. R. Scott Budinger, Richard G. Wunderink, Theresa Walunas, Catherine A. Gao, Alan R. Hauser, Alec Peltekian, Alexis Rose Wolfe, Alison L. Szabo, Alok N. Choudhary, Amy Ludwig, Anahid Amani Moghadam, Anjana V. Yeldandi, Ankit Bharat, Anna E. Pawlowski, Anthony M. Joudi, Arjun Prakash Tambe, Ashley J. Smith-Nunez, Benjamin D. Singer, Benjamin J. Ulrich, Betty Tran, Cara J. Gottardi, Chiagozie O. Pickens, Clara J. Schroedl, Daniel Meza, Dulce Sarai Garcia, Egon A. Ozer, Elen Gusman, Elisheva D. Shanes, Emily Mower Provost, Emily M. Olson, Erica Marie Hartmann, Erin A. Korth, Estefani Diaz, Estefany R. Guzman, Francisco J. Martinez, Gabrielle Matias, Hiam Abdala-Valencia, Jack T. Sumner, Jacob I Sznajder, Jacqueline M. Kruser, Jakub Glowala, James M. Walter, Jamie H. Rowell, Jason M. Arnold, John Coleman, Jon W. Lomasney, Joseph Isaac Bailey, Judd F. Hultquist, Justin A. Fiala, Justin Starren, Karen M. Ridge, Karolina Senkow, Kathryn A. Helmin, Khalilah L. Gates, Lacy Simmons, Lesley Pinzon, Lindsey D. Gradone, Lisa F. Wolfe, Lucy Luo, Luisa Morales-Nebreda, Manu Jain, Marc Sala, Maxwell Schleck, Melissa H. Ross, Melissa Querrey, Michael J. Cuttica, Michelle Hinsch Prickett, Nandita R. Nadig, Nathaniel Rhodes, Navdeep S. Chandel, Nikolay S. Markov, Peter H. S. Sporn, Qianli Liu, Rachel B. Kadar, Rachel L. Medernach, Ramon Lorenzo-Redondo, Ravi Kalhan, Rebecca K. Clepp, Richard I. Morimoto, Rogan A. Grant, Ruben J. Mylvaganam, Samuel Fenske, Scott A. Laurenzo, Seung Hye Han, Sophia Nozick, Srinivas Panchamukhi, Stephanie C. Eisenbarth, Suchitra Swaminathan, Susan R. Russell, Taylor A. Poor, Thaddeus Cybulski, Theresa A. Lombardo, Thomas Bolig, Thomas Stoeger, Tien Doan, Timothy Rowe, Wan-Ting Liao, Yuan Luo 0001, Yuliana Sokolenko, Ziyan Lu |
J. Am. Medical Informatics Assoc. | 86 |