Aditya Akella
Professor, Regents Chair in Computer Sciences #1
Builds programmable networks, programmable NICs and hardware acceleration, and systems for big data and machine learning workloads such as serverless computing.
Aditya Akella's lab at UT Austin works on accelerators, chips & architecture and data management & big data. 42 papers since 2023, 31 at top venues, mostly NSDI and ICML. Applications close .
At a glance
- papers, 2023–2026
- 11/ year
- at T1 venues
- 74%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 10 papers2026 · T1 9 · collecting
- 13 papers2025 · T1 8
- 5 papers2024 · T1 5
- 14 papers2023 · T1 9
T1 venuesOtherVenues: NSDI, ICML, EMNLP, ACL, SIGCOMM
Research focuspaper titles that use each topic's words
- Data management & big data5 papers
- Accelerators, chips & architecture0 papers
- programmable0 papers
- hardware acceleration0 papers
- serverless0 papers
Paper list
From the lab homepage.
202610 papers
- EuroSys (T1)Canopy: Property-Driven Learning for Congestion Control
- NSDI (T1)Express Lane to Efficiency and Reliability: Multi-Dimensional Control in Meta's Express Backbone Network
- ACL (T1)Improving the Throughput of Diffusion-based Large Language Models via a Training-Free Confidence-Aware Calibration
- PLDI (T1)MatchBox: A Semantic Foundation for Data Plane Portability
- OSDI (T1)Mimesys: Generating Realistic Executable Testing Environments from Resource Usage Traces
- —OSDI 2026, Seattle, WA
- NSDI (T1)SYMPHONY: Improving Memory Management for LLM Inference Workloads
- NSDI (T1)Towards Performance Robustness for Microservices
- NSDI (T1)UNUM: A New Framework for Network Control
- ICML (T1)When Do Graph Foundation Models Transfer? A Data-Centric Theory
202513 papers
- ML4Sys at NeurIPS , San Diego, CAA Joint Learning Approach to Hardware Caching and Prefetching
- —ASPLOS 2025, Rotterdam, The Netherlands
- IROS (T1)ConfigBot: Adaptive Resource Allocation for Robot Applications in Dynamic Environments
- ICLR (T1)CONGO: Compressive Online Gradient Optimization
- ICML (T1)HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training
- HotOS , Banff, CanadaHow I learned to stop worrying and love learned OS policies
- EMNLP (T1)Large Language Models as Realistic Microservice Trace Generators
- HotNets , College Park, MDMan-Made Heuristics Are Dead. Long Live Code Generators!
- NSDI (T1)MTP: A Transport for In-Network Computing
- ICML (T1)On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention for Long-Context LLM Serving
- —OQueue: Observable Communication in Learning Directed Operating Systems
- NSDI (T1)Portable and High-Performance SmartNIC Programs with Alkali
20245 papers
- NSDI (T1)Cassini: Network-Aware Job Scheduling in Machine Learning Clusters
- SIGMOD (T1)ChainedFilter: Combining Membership Filters by Chain Rule
- EMNLP (T1)FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping
- EMNLP (T1)MOSEL: Inference Serving Using Dynamic Modality Selection
- NeurIPS (T1)Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design
202314 papers
- SoCC , Santa Cruz, CAAuxo: Efficient Federated Learning via Scalable Client Clustering
- NSDI (T1)Better Together: Jointly Optimizing ML Collective Scheduling and Execution Planning using SYNDICATE
- VLDB (T1)Blink-hash: An Adaptive Hybrid Index for In-Memory Time-Series Databases
- SIGCOMM (T1)Darwin: Flexible Learning-based CDN Caching
- ICDCN , Kharagpur, IndiaLearned Load Balancing
- MICRO (T1)LogNIC: A High-Level Performance Model for SmartNICs
- ICML (T1)Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit
- —On a Foundation Model for Operating Systems
- SIGCOMM (T1)Resilient Baseband Processing in Virtualized RANs with Slingshot
- NSDI (T1)RingLeader: Efficiently Offloading Intra-Server Orchestration to NICs
- NSDI (T1)Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning
- ASPLOS (T1)Towards a Machine Learning-Assisted Kernel with LAKE
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UT AustinFall 2027
Computer Science PhD
- Round
- Single round (Dec 15)
- Opens
- GRE
- See program page
- English
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- Fee
- See program page
Not yet confirmed for this intake.
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