Alexey Tumanov
Associate Professor
Builds systems for machine learning, including resource management and scheduling for model serving.
Alexey Tumanov's lab at Georgia Tech works on efficient training & inference. 26 papers since 2023, 1 at top venues, mostly CCS. 7 PhD students and 17 master's students. 18 alumni. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 4%
- PhD students
- 7
- MS students
- 17
- alumni listed
- 18
Papers by yearfrom the lab homepage, 2023–2026
- 7 papers2026 · T1 0 · collecting
- 8 papers2025 · T1 1
- 7 papers2024 · T1 0
- 4 papers2023 · T1 0
T1 venuesOtherVenues: CCS
Research focuspaper titles that use each topic's words
- Efficient training & inference15 papers
- model serving0 papers
- resource management0 papers
- scheduling0 papers
- builds systems0 papers
Paper list
From the lab homepage.
20267 papers
- ACM SIGOPS Operating Systems ReviewA Benchmark for Analyzing Cloud Storage Layouts
- To appear in Proc. of International Conference on Learning Representations (ICLR'26), AprilKLAS: Using Similarity to Stitch Neural Networks for an Improved Accuracy-Efficiency Tradeoff
- In Proc. of the European Workshop on Machine Learning and Systems (EuroMLSys'25), co-located with EuroSys'26, Apr 28LayoutBench: Performance Benchmarking of Cloud Storage Layouts for Multimedia Data
- To appear in Proc. of the ACM European Conference on Computer Systems (EuroSys'26), AprilMaya: Optimizing Deep Learning Training Workloads using GPU Runtime Emulation
- To appear in Proc. of ACM Symposium on Cloud Computing (SoCC'26), NovNo Request Left Behind: Tackling Heterogeneity in Long-Context LLM Inference with Medha
- To appear in Proc. of ACM Symposium on Cloud Computing (SoCC'26), NovOn Evaluating Performance of LLM Inference Serving Systems
- In Proc. of The Conference on Empirical Methods in Natural Language Processing (EMNLP'26), Oct 24-29SVD-Attention: Efficiently Analyzing Attention Blocks in Transformers.
20258 papers
- Transactions in Machine Learning Research (TMLR)∇QDARTS: Quantization as an Elastic Dimension to Differentiable Neural Architecture Search
- In Proc. of the Workshop on Machine Learning and Systems (EuroMLSys'25), co-located with EuroSys'25, Rotterdam, Netherlands, March 31Client availability in Federated Learning: It matters!
- ACM SIGOPS Operating Systems ReviewEfficient LLM Inference via Chunked Prefills
- ACM SIGOPS Operating Systems ReviewEMPIRIC: Exploring Missing Pieces in KV Cache Compression for Reducing Computation, Storage, and Latency in Long-Context LLM Inference
- In Proc. of International Conference on Machine Learning (ICML'25), VancouverRocketKV: Accelerating Long-Context LLM Inference via Two-stage KV Cache Compression
- In Proc. of the USENIX Symposium on Networked Systems Design and Implementation (NSDI'25), Philadelphia, USASuperServe: Fine-grained Inference Serving for Unpredictable Workloads
- ACM SIGOPS Operating Systems ReviewToward Weight Sharing Paradigm for Efficient AI: Training and Inference Serving
- CCS (T1)VillainNet: Targeted Poisoning Attacks Against SuperNets Along the Accuracy-Latency Pareto Frontier
20247 papers
- In Proc. of European Conference on Computer Vision (ECCV ), Milano, Italy, OctDεpS: Delayed ε-Shrinking for Faster Once-For-All Training
- In Proc. of 38'th IEEE International Parallel and Distributed Processing Symposium (IPDPS), USAHarmonica: Hybrid Accelerator to Overcome Imperfections of Mixed-signal DNN Accelerators
- Accepted to the ACM Symposium on Cloud Computing (SoCC'24), NovInshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization
- Transactions in Machine Learning Research (TMLR)PLUM: Improving Inference Efficiency By Leveraging Repetition-Sparsity Trade-Off
- In Proc. of European Conference on Computer Vision (ECCV ), Milano, Italy, OctSuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference
- In Proc. of Usenix Symposium on Operating Systems Design and Implementation (OSDI'24), Santa Clara, USA, JulyTaming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve
- In Proc. of Annual Conference on Machine Learning Systems (MLSys'24), Santa Clara, USA, MayVidur: A Large-scale Simulation Framework for LLM Inference
20234 papers
- IEEE Micro Special Issue on TinyMLHardware-Software Co-design for Real-time Latency-accuracy Navigation in TinyML Applications
- In Proc. of On-Device Intelligence Workshop, Machine Learning and Systems (MLSys'23), JunSigned-Binary Networks: Improving Efficiency of Binary Networks by Exploiting Sparsity
- In Proc. of Conference on Machine Learning and Systems (MLSys'23), JunSubGraph Stationary Hardware-Software Inference Co-design
- In Proc. of Machine Learning for Health (ML4H'23), Dec 10TransEHR: Self-Supervised Transformer for Clinical Time Series Data
Where alumni went18 listed
Apply to Georgia TechFall 2027
Georgia Tech Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS
- Fee
- See program page
Not yet confirmed for this intake.
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