Related Publications

Welcome to the Publications section of the SustainSys NSERC CREATE program.

Here, we highlight the academic and scientific contributions of our Collaborating Project PI Team and their supervised CREATE-funded trainees.

Publications listed here represent a wide range of interdisciplinary research outcomes supported by the program, showcasing collaborative advancements in sustainable data systems.

(Last updated: October 21, 2026)

2026

A. Fuad, S. Vashisth, O. Balmau, and B. Kemme. PostLearn: Towards A Learned Index For PostgreSQL. ACM SIGOPS Operating Systems Review, vol. 60, no. 1, pp. 105–110, 2026.

A. Fuad, S. Vashisth, O. Balmau, and B. Kemme. PostLearn: Towards A Learned Index For PostgreSQL. Proceedings of the International Workshop on Challenges and Opportunities of Efficient and Performant Storage Systems (CHEOPS@EuroSys), pp. 20–25, 2026.

M. Braga, J. Pinto, R. Nouaji, O. Michaud, B. Kemme, O. Balmau, C. Brito, and R. Macedo. A principled approach for energy-efficient training via phase-aware GPU frequency tuning. arXiv preprint, arXiv:2609.24205, 2026.

S. Vashisth, O. Michaud, B. Kemme, and O. Balmau. A Pragmatic Approach to Learned Indexing in RocksDB: Targeted Optimizations with Minimal System Modification. arXiv preprint, arXiv:2605.23815, 2026.

C.-E. Bilodeau-Savaria, J. K. Nidzwetzki, S. Scherzinger, and B. Kemme. Unfair by design: eBPF-based scheduling of mixed database workloads. arXiv preprint, arXiv:2605.02377, 2026.

D. Aarao Reis Arturi, C. Köhnen, G. Fletcher, B. Kemme, and S. Scherzinger. Seeing the Trees for the Forest: Leveraging Tree-Shaped Substructures in Property Graphs. arXiv preprint, arXiv:2603.12476, 2026.

R. Omar, A. Orogat, I. Abdelaziz, O. Mangukiya, P. Kalnis, and E. Mansour. Chatty-KG: A Multi-Agent AI System for On-Demand Conversational Question Answering over Knowledge Graphs. Proceedings of the ACM on Management of Data (Proc. ACM Manag. Data), vol. 4, no. 1, pp. 18:1–18:26, 2026.

S. Fathollahzadeh, E. Mansour, and M. Boehm. ReSequel: Robust LLM-assisted Query Rewriting and Optimization using Templatization and Sampling. Proceedings of the VLDB Endowment (PVLDB), vol. 19, no. 10, pp. 2880–2893, 2026.

H. Abdallah, I. Abdelaziz, P. Kalnis, and E. Mansour. Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphs. Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics (EACL), pp. 575–592, 2026.

W. Afandi, H. Abdallah, A. Aboulnaga, and E. Mansour. An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs. Proceedings of the IEEE International Conference on Data Engineering (ICDE), pp. 1675–1688, 2026.

A. Orogat, A. Rostam, and E. Mansour. Understanding Multi-Agent LLM Frameworks: A Unified Benchmark and Experimental Analysis. arXiv preprint, arXiv:2602.03128, 2026.

A. Orogat and E. Mansour. Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory. arXiv preprint, arXiv:2605.26252, 2026.

R. Nouaji, S. Bitchebe, R. Macedo, and O. Balmau. MinatoLoader: Accelerating Machine Learning Training Through Efficient Data Preprocessing. Proceedings of the European Conference on Computer Systems (EuroSys), pp. 2037–2053, 2026.

S. Abuzakuk, O. Balmau, J. Chen, A.-M. Kermarrec, R. Pires, R. Prabhu, and M. de Vos. The Cost of Expertise: Understanding MoE Decode Performance. Proceedings of the Workshop on Machine Learning and Systems (EuroMLSys@EuroSys), pp. 466–472, 2026.

N. Bore, P. Mishra, C. Adam, E. de Lara, and O. Balmau. FOLD: Fuzzy Online Deduplication for Very Large Evolving Datasets via Approximate Nearest Neighbor Search. arXiv preprint, arXiv:2606.03001, 2026.

J. Chen, J. She, Y. Yuan, R. Ghosh, K. Gupta, Q. Ho, X. Liu, and O. Balmau. DeltaServe: Host-Agnostic Co-Serving of Inference and Fine-Tuning for LLMs. arXiv preprint, arXiv:2607.28848, 2026.

J. Liu, G. Bowker-Bafna, Y. Zhang, and N. Enright Jerger. CAPA: Manufacturing Carbon Estimation for Advanced-Packaged Architectures. Proceedings of the International Symposium on Computer Architecture (ISCA), pp. 211–227, 2026.

I. Uwizeyimana, O. Sun, and N. Enright Jerger. A Heterogeneous Mapping of a Speech Neuroprosthesis Application. Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 220–231, 2026.

V. Kariofillis, I. Uwizeyimana, S. Ahmad, T. Manku, R. P. Pothukuchi, A. Bhattacharjee, and N. Enright Jerger. CPU Bottlenecks in Rack-Stand Brain-Computer Interfaces: A Phase-Aware Characterisation. Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 647–649, 2026.

X. Zeng, B. Xiong, S. Wang, Y. Wang, X. Wang, C. Zhang, H.A. Jacobsen, J. Guo, and C. Zhong. Synergistic coupling resolves the scale dilemma: Hierarchical atom-motif guidance for function-aware molecular prediction. Expert Systems with Applications, vol. 326, 132735, 2026.

Y. Mao, H. Takata, M. Bachras, Y. Zhang, S. Zhang, G. Zhang, and H.A. Jacobsen. Epoch-based Optimistic Concurrency Control in Geo-replicated Databases. Proceedings of the ACM on Management of Data (Proc. ACM Manag. Data), vol. 4, no. 3, pp. 175:1–175:27, 2026.

Y. Zhang, Y. Mao, and H.A. Jacobsen. Fugue: Online Elasticity for Distributed Stateful Stream Processing. Proceedings of the VLDB Endowment (PVLDB), vol. 19, no. 11, pp. 3288–3301, 2026.

H. Liu, Y. Li, I. Sabek, X. Zhou, and H.A. Jacobsen. How Can Quantum Computing and Databases Meet "in Practice"? From Algorithms to Systems. Proceedings of the VLDB Endowment (PVLDB), vol. 19, no. 12, pp. 4906–4911, 2026.

M. Dang'ana, Y. Zhang, and H.A. Jacobsen. Ksurf-Drone: Attention Kalman Filter for Contextual Bandit Optimization in Cloud Resource Allocation. IEEE Transactions on Cloud Computing, vol. 14, no. 2, pp. 432–444, 2026.

Y. Zhang and H.A. Jacobsen. Krysha: Cost-Efficient Resource Orchestration for Geo-Distributed Serverless Microservices. Proceedings of the ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), pp. 319–333, 2026.

S. Zhang, M. Bachras, Y. Zhang, Y. Mao, and H.A. Jacobsen. REMON: Remote External Memory Over the Network. Proceedings of the IEEE International Conference on Data Engineering (ICDE), pp. 2335–2348, 2026.

Y. Tian, Y. Bai, Y. He, Z. Chong, Y. Gao, Y. Li, and H.A. Jacobsen. SRE-Marathon: A Continuous, Change-Driven Benchmark for Autonomous Site Reliability Agents. arXiv preprint, arXiv:2609.33023, 2026.

Y. Gao, Y. Tian, Y. Li, C. Zhang, and H.A. Jacobsen. ORCA: Observability-Grounded Program Repair for Microservice Incidents. arXiv preprint, arXiv:2608.17018, 2026.

C. Zhang, Y. Li, Y. Tian, M. Bachras, and H.A. Jacobsen. When Agentic Executions Fail: Detecting and Localizing Runtime Faults from Telemetry. arXiv preprint, arXiv:2608.14680, 2026.

Y. Tian, Y. Liu, Z. Chong, Z. Huang, Y. Li, and H.A. Jacobsen. GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices. arXiv preprint, arXiv:2608.08968, 2026.

S. Zhang, Y. Mao, Y. Zhang, G. Zhang, J. Karimov, and H.A. Jacobsen. REMOP: REmote-Memory-aware OPerator Optimization. arXiv preprint, arXiv:2606.19576, 2026.

H. Wang, Z. Huang, Y. Tian, K. Dzeparoska, H.A. Jacobsen, and A. Leon-Garcia. FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detection. arXiv preprint, arXiv:2605.22779, 2026.

J. Clark, Y. Su, S. M. R. Pial, Y. Tian, L. Gniedziejko, H.A. Jacobsen, Y. Chen, and T. Xu. SREGym: A Live Benchmark for AI SRE Agents with High-Fidelity Failure Scenarios. arXiv preprint, arXiv:2605.07161, 2026.

Y. Li, Y. B. Yilmaz, M. Silver, Z. Vernec, and H.A. Jacobsen. Quantum Hypergraph Partitioning. arXiv preprint, arXiv:2605.02635, 2026.

2025

Y. Zheng, C. Sacré, M. Shahrad, O. Lipchitz, Y. Gu, B. Kemme. G-View: View Management for Graph Databases. Proceedings of the VLDB Endowment (PVLDB), vol. 18, no. 6, pp. 1730-1742, 2025.

M. Helali, Y. Luo, T. J. Ham, J. Plotts, A. Chaugule, J. Chang, P. Ranganathan, and E. Mansour. Reliable and Cost-Effective Exploratory Data Analysis via Graph-Guided RAG. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 16536–16553, 2025.

A. Aly, E. Mansour, and A. Youssef. OCR-APT: Reconstructing APT Stories from Audit Logs using Subgraph Anomaly Detection and LLMs. Proceedings of the ACM Conference on Computer and Communications Security (CCS), pp. 261–275, 2025.

S. Fathollahzadeh, E. Mansour, and M. Boehm. Demonstrating CatDB: LLM-based Generation of Data-centric ML Pipelines. Companion of the ACM SIGMOD International Conference on Management of Data (SIGMOD Companion), pp. 87–90, 2025.

S. Fathollahzadeh, E. Mansour, and M. Boehm. CatDB: Data-catalog-guided, LLM-based Generation of Data-centric ML Pipelines. Proceedings of the VLDB Endowment (PVLDB), vol. 18, no. 8, pp. 2639–2652, 2025.

R. Omar, O. Mangukiya, and E. Mansour. Dialogue Benchmark Generation from Knowledge Graphs with Cost-Effective Retrieval-Augmented LLMs. Proceedings of the ACM on Management of Data (Proc. ACM Manag. Data), vol. 3, no. 1, pp. 31:1–31:26, 2025.

G. Sehgal and S. Salihoğlu. NaviX: A Native Vector Index Design for Graph DBMSs With Robust Predicate-Agnostic Search Performance. Proceedings of the VLDB Endowment (PVLDB), vol. 18, no. 11, pp. 4438–4450, 2025.

A. Chakraborty and S. Salihoğlu. Robust Recursive Query Parallelism in Graph Database Management Systems [Experiment, Analysis & Benchmark]. Proceedings of the VLDB Endowment (PVLDB), vol. 18, no. 11, pp. 4465–4477, 2025.

L. Bigon, J. Tagliabue, and S. Salihoğlu. DAG lakehouse planning with an ephemeral and embedded graph database. Workshops of the VLDB Endowment (VLDB Workshops), 2025.

R. Adão, Z. Wu, C. Zhou, O. Balmau, J. Paulo, and R. Macedo. Keigo: Co-designing Log-Structured Merge Key-Value Stores with a Non-Volatile, Concurrency-aware Storage Hierarchy. Proceedings of the VLDB Endowment (PVLDB), vol. 18, no. 9, pp. 2872–2885, 2025.

O. Balmau, A.-M. Kermarrec, R. Pires, A. L. Espírito Santo, M. de Vos, and M. Vujasinovic. Accelerating MoE Model Inference with Expert Sharding. Proceedings of the Workshop on Machine Learning and Systems (EuroMLSys), pp. 192–199, 2025.

S. Bitchebe and O. Balmau. MaLV-OS: Rethinking the Operating System Architecture for Machine Learning in Virtualized Clouds. arXiv preprint, arXiv:2508.03676, 2025.

O. Balmau, M. Böhm, A. Klimovic, P. Pietzuch, and P. Tözün. Resource‑Efficient Machine Learning (Dagstuhl Seminar 24311). Dagstuhl Reports, vol. 14, no. 7, pp. 153–169, 2025.

I. Uwizeyimana and N. Enright Jerger. Carbon-Aware Server Replacement. Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2025.

P. Elvinger, F. Strati, N. Enright Jerger, and A. Klimovic. Understanding GPU Resource Interference One Level Deeper. Proceedings of the ACM Symposium on Cloud Computing (SoCC), pp. 687–694, 2025.

J. Li, M. C. Jeffery, and N. Enright Jerger. A Performance Model for Disintegrated Manycores. IEEE Computer Architecture Letters, vol. 24, no. 2, pp. 389–392, 2025.

M. Zhang, F. Eliassen, A. Taherkordi, H.A. Jacobsen, Y. Li, and Y. Zhang. Self-Determination Theory and Deep Reinforcement Learning for Personalized Energy Trading in Smart Grid. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 55(6), 4216-4229, 2025.

M. Bachras and H.A. Jacobsen. Environmental Footprints of Query Processing: A Vision for Sustainable Database Architectures. Proceedings of the VLDB Endowment (PVLDB), vol. 18, no. 11, pp. 4064–4072, 2025.

M. Bachras and H.A. Jacobsen. Beyond Performance: Measuring the Environmental Impact of Analytical Databases. arXiv preprint, arXiv:2504.18980, 2025.

G. Zhang, S. Zhang, M. Bachras, Y. Zhang, and H.A. Jacobsen. Cabinet: Dynamically Weighted Consensus Made Fast. Proceedings of the VLDB Endowment (PVLDB), vol. 18, no. 5, pp. 1439–1452, 2025.

S. M. Danish, M. M. Shabir, K. Zhang, H.A. Jacobsen, and S. A. Hassan. A Blockchain-Based Privacy-Preserving Charging Station Reservation and Payment Scheme for Electric Vehicles. Distributed Ledger Technologies: Research and Practice, vol. 4, no. 3, pp. 24:1–24:24, 2025.

P. A. Noreña Cardona, E. Suescún, C. M. Zapata Jaramillo, B. Scaunasu, G. Elliott, H.A. Jacobsen, and D. Mosquera. Continuous learning of event-based systems by using pre-conceptual schemas. Journal of Systems and Software, vol. 230, 112529, 2025.

M. Dang'ana and H.A. Jacobsen. Ksurf+: Attention Kalman Filter for Prediction Under Highly Variable Cloud Workloads. IEEE Transactions on Cloud Computing, vol. 13, no. 4, pp. 1259–1274, 2025.

S. Motepalli and H.A. Jacobsen. Decentralization in PoS Blockchain Consensus: Quantification and Advancement. IEEE Transactions on Network and Service Management, vol. 22, no. 4, pp. 2930–2943, 2025.

S. Ji, J. Yao, W. Wang, J. Wei, and H.A. Jacobsen. Efficient Parallel Boolean Expression Matching. ACM Transactions on Database Systems, vol. 50, no. 4, pp. 16:1–16:41, 2025.

Y. Zhang and H.A. Jacobsen. Mocha: Scalable and Compliant Function Scheduling for Federated Serverless Computing. Proceedings of the ACM/IFIP Middleware Conference, pp. 398–412, 2025.

Y. Zhang and H.A. Jacobsen. Decentralized and Policy-Aware Serverless Orchestration for the Federated Web. Companion Proceedings of the ACM Web Conference (WWW), pp. 1539–1543, 2025.

H. Woisetschläger, R. Zhang, S. Wang, and H.A. Jacobsen. MESS+: Dynamically Learned Inference-Time LLM Routing in Model Zoos with Service Level Guarantees. Advances in Neural Information Processing Systems (NeurIPS), 2025.

D. Sow, H. Woisetschläger, S. Bulusu, S. Wang, H.A. Jacobsen, and Y. Liang. Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining. Proceedings of the International Conference on Learning Representations (ICLR), 2025.

G. M. Jones and H.A. Jacobsen. Analyzing Common Electronic Structure Theory Algorithms for Distributed Quantum Computing. Proceedings of the IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 368–373, 2025.

L. De Martini, J. Tahir, A. Margara, C. Doblander, S. Frischbier, R. Mayer, and H.A. Jacobsen. The DEBS 2025 Grand Challenge: Real-Time Monitoring of Defects in Laser Powder Bed Fusion (L-PBF) Manufacturing. Proceedings of the ACM International Conference on Distributed and Event-based Systems (DEBS), pp. 223–228, 2025.

N. Merkel, D. Stoll, R. Mayer, and H.A. Jacobsen. An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training. Proceedings of the International Conference on Extending Database Technology (EDBT), pp. 171–184, 2025.

J. Vatter, M. L. Rochau, R. Mayer, and H.A. Jacobsen. Experiment & Benchmark Paper: To What Extent Does Quality Matter? The Impact of Graph Data Quality on GNN Model Performance. Workshops of the VLDB Endowment (VLDB Workshops), 2025.

F. Mulitze, H. Woisetschläger, and H.A. Jacobsen. MAR-FL: A Communication Efficient Peer-to-Peer Federated Learning System. arXiv preprint, arXiv:2512.05234, 2025.

Y. Tian, Y. Liu, Z. Chong, Z. Huang, and H.A. Jacobsen. GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis? arXiv preprint, arXiv:2508.12472, 2025.

P. Kananian and H.A. Jacobsen. Adversarial Robustness in Distributed Quantum Machine Learning. arXiv preprint, arXiv:2508.11848, 2025.

S. Prakash, H.A. Jacobsen, and V. K. Prasad. A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning. arXiv preprint, arXiv:2504.02191, 2025.

J. Geng, Q. Li, H. Woisetschläger, Z. Chen, Y. Wang, P. Nakov, H.A. Jacobsen, and F. Karray. A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models. arXiv preprint, arXiv:2503.01854, 2025.

P. Kananian and H.A. Jacobsen. Adversarial Robustness of Partitioned Quantum Classifiers. arXiv preprint, arXiv:2502.20403, 2025.

J. Vatter, M. Zayats, M. Martínez Galindo, V. López, R. Mayer, H.A. Jacobsen, and H. T. Lam. WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks. arXiv preprint, arXiv:2502.19986, 2025.

2024

M. Schiedermeier, J. Kienzle, and B. Kemme. Give me some REST: A Controlled Experiment to Study Effects and Perception of Model-Driven Engineering with a Domain-Specific Language. Proceedings of the ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS), pp. 214–225, 2024.

F. Farhour, A. Abdellatif, E. Mansour, and E. Shihab. A Weak Supervision-Based Approach to Improve Chatbots for Code Repositories. Proceedings of the ACM on Software Engineering, vol. 1, pp. 2378–2401, 2024.

A. Aly, S. Iqbal, A. Youssef, and E. Mansour. MEGR-APT: A Memory-Efficient APT Hunting System Based on Attack Representation Learning. IEEE Transactions on Information Forensics and Security, vol. 19, pp. 5257–5271, 2024.

M. Helali, N. Monjazeb, S. Vashisth, P. Carrier, A. Helal, A. Cavalcante, K. Ammar, K. Hose, and E. Mansour. KGLiDS: A Platform for Semantic Abstraction, Linking, and Automation of Data Science. Proceedings of the IEEE International Conference on Data Engineering (ICDE), pp. 179–192, 2024.

H. Abdallah, W. Afandi, P. Kalnis, and E. Mansour. Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling. Proceedings of the IEEE International Conference on Data Engineering (ICDE), pp. 1833–1846, 2024.

A. Mhedhbi, A. Deshpande, and S. Salihoğlu. Modern Techniques for Querying Graph-Structured Databases. Foundations and Trends in Databases, vol. 14, no. 2, pp. 72–185, 2024.

A. Usta, C. Liu, and S. Salihoğlu. Analysis of Open Government Datasets from a Data Design and Integration Perspective. Proceedings of the 27th International Conference on Extending Database Technology (EDBT), pp. 345–358, 2024.

S. Sahu and S. Salihoğlu. Optimizing Differential Computation for Large-Scale Graph Processing. Proceedings of the 7th Joint Workshop on Graph Data Management Experiences and Systems (GRADES/NDA), Article No. 4:1–4:9, 2024.

P. Mishra, N. Bore, B. Ramprasad, M. Thiessen, M. Gabel, A. da Silva Veith, O. Balmau, and E. de Lara. Falcon: Live Reconfiguration for Stateful Stream Processing on the Edge. Proceedings of the IEEE/ACM Symposium on Edge Computing (SEC), pp. 234–248, 2024.

D. Teguia, J. Chen, S. Bitchebe, O. Balmau, and A. Tchana. vPIM: Processing‑in‑Memory Virtualization. Proceedings of the 25th International Middleware Conference, pp. 417–430, 2024.

R. Nouaji, S. Bitchebe, and O. Balmau. SpeedyLoader: Efficient Pipelining of Data Preprocessing and Machine Learning Training. Proceedings of the 4th Workshop on EuroMLSys (EuroMLSys '24), pp. 65–72, 2024.

J. Zhao, R. Shu, L. Qu, Z. Yang, N. Enright Jerger, D. Chiou, P. Cheng, and Y. Xiong. SmartNIC-Enabled Live Migration for Storage-Optimized VMs. Proceedings of the ACM SIGOPS Asia-Pacific Workshop on Systems (APSys), pp. 45–52, 2024.

J. Zhao, R. Shu, K. Lim, Z. Fan, T. Anderson, M. Gao, and N. Enright Jerger. Arcus: SLO Management for Accelerators in the Cloud with Traffic Shaping. arXiv preprint, arXiv:2410.17577, 2024.

A. Plotnik, K. Ganesan, N. Enright Jerger, and M.C. Jeffery. Intergenerational Embodied Carbon. Proceedings of the Hot Topics in Ethics, Systems, and AI (HotEthics), 2024.

B. Dabak, M. Glenn, J. Liu, A. Buck, S. Yang, R. Calderbank, N. Enright Jerger, et al. Low-Energy Line Codes for On-Chip Networks. arXiv preprint, arXiv:2405.14783, 2024.

J. Tahir, R. Mayer, C. Doblander, and H.A. Jacobsen. How Reliable Are Streams? End-to-End Processing-Guarantee Validation and Performance Benchmarking of Stream Processing Systems. Proceedings of the VLDB Endowment (PVLDB), vol. 18, no. 3, pp. 585–598, 2024.

N. Merkel, P. Toussing, R. Mayer, and H.A. Jacobsen. Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study. Proceedings of the VLDB Endowment (PVLDB), vol. 18, no. 2, pp. 293–307, 2024.

Y. Mao, G. Zhang, Z. Liu, P. Nasirifard, S. Tijanic, and H.A. Jacobsen. Making CRDTs Not So Eventual. Proceedings of the VLDB Endowment (PVLDB), vol. 18, no. 2, pp. 349–362, 2024.

M. Najafi, T. M. Qadah, M. Sadoghi, and H.A. Jacobsen. DIBA: A Re-Configurable Stream Processor. IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 9, pp. 4550–4566, 2024.

S. Ji, Z. Tang, W. Wang, H. Li, J. Yao, and H.A. Jacobsen. Ripple: Large-Scale Service and Configuration Management in the Cloud. Proceedings of the ACM/IFIP Middleware Conference, pp. 354–366, 2024.

S. Ji, Z. Tang, W. Wang, H. Li, J. Yao, and H.A. Jacobsen. Ripple: Large-Scale Service and Configuration Management in the Cloud. Middleware Demos/Posters/Doctoral Symposium, pp. 19–20, 2024.

K. Shao, R. Wang, Y. Hao, L. Hu, M. Chen, and H.A. Jacobsen. Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition. Proceedings of the ACM International Conference on Multimedia (ACM Multimedia), pp. 5692–5701, 2024.

G. M. Jones and H.A. Jacobsen. Distributed Quantum Computing for Chemical Applications. Proceedings of the IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 155–160, 2024.

H. Woisetschläger, A. Erben, S. Wang, R. Mayer, and H.A. Jacobsen. Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly. Proceedings of the Workshop on Data Management for End-to-End Machine Learning (DEEM@SIGMOD), pp. 39–50, 2024.

H. Woisetschläger, A. Erben, R. Mayer, S. Wang, and H.A. Jacobsen. FLEdge: Benchmarking Federated Learning Applications in Edge Computing Systems. Proceedings of the ACM/IFIP Middleware Conference, pp. 88–102, 2024.

A. Erben, R. Mayer, and H.A. Jacobsen. How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study. Proceedings of the VLDB Endowment (PVLDB), vol. 17, no. 6, pp. 1214–1226, 2024.

R. Zhang, H. Woisetschläger, S. Wang, and H.A. Jacobsen. MESS+: Energy-Optimal Inferencing in Language Model Zoos with SLAs. arXiv preprint, arXiv:2411.00889, 2024.

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