# Anurag Khandelwal

> Anurag Khandelwal is an Associate Professor of Computer Science at Yale University, where he leads the NOVA Lab. His research spans computer systems, networks, architecture, and security, including memory disaggregation, systems for AI and LLM serving, secure cloud systems, and serverless computing.

Canonical website: https://www.anuragkhandelwal.com/
Email: anurag.khandelwal@yale.edu

## Biography

Anurag Khandelwal is an Associate Professor of Computer Science at Yale University, where his group focuses broadly on problems in computer systems, networks and security. He received his Ph.D. from UC Berkeley, where he was advised by Ion Stoica. His work has been recognized by an NSF CAREER award, two NetApp faculty fellowships, a Roberts Innovation Award, paper and artifact distinctions at USENIX Security’20, ISCA’23, EuroSys’24, ISCA’25, ASPLOS'26, and MobiSys'26, and IEEE Micro Top Picks selections in 2023 and 2025.

## Research directions

### Memory Disaggregation

Scaling resources at server granularity wastes resources when compute and memory needs are mismatched, increasing both costs and carbon emissions. Memory disaggregation separates compute and memory into shared network-connected pools to improve resource efficiency, capacity, and elasticity. We are rethinking the cloud software and hardware stack to realize memory disaggregation:

- Scalable cache coherence for disaggregated shared memory pools: [MIND](https://www.anuragkhandelwal.com/papers/mind/) (SOSP'21), [CORD](https://www.anuragkhandelwal.com/papers/cord/) (ISCA'25), [Soul](https://www.anuragkhandelwal.com/papers/soul/) (OSDI'26)
- Fair sharing across disaggregated memory resources: [Karma](https://www.anuragkhandelwal.com/papers/karma/) (OSDI'23), [Spirit](https://www.anuragkhandelwal.com/papers/spirit/) (SOSP'25)
- Low-latency, high-throughput access to disaggregated memory: [PULSE](https://www.anuragkhandelwal.com/papers/pulse/) (ASPLOS'25), [Mage](https://www.anuragkhandelwal.com/papers/mage/) (SOSP'25)
- Understanding memory performance: [BulletTime](https://www.anuragkhandelwal.com/papers/bullettime/) (ISCA'26), [CounterPoint](https://www.anuragkhandelwal.com/papers/counterpoint/) (ASPLOS'26)

Our work on scalable cache coherence for disaggregated memory has been incorporated into [NVIDIA's Vera Rubin Architecture](https://developer.nvidia.com/blog/inside-nvidia-rubin-gpu-architecture-powering-the-era-of-agentic-ai/#accelerated_scale-up_communications).

### Secure cloud systems

As more privacy-sensitive applications move storage and computation to the cloud, encrypted data and secure enclaves can still leak sensitive information through access patterns. Existing defenses are often too expensive in bandwidth or storage to deploy widely. Our research studies these real-world access-pattern vulnerabilities and designs more efficient protections against them.

- Protecting against access pattern vulnerabilities: [Pancake](https://www.anuragkhandelwal.com/papers/pancake/) (USENIX Sec'20), [Shortstack](https://www.anuragkhandelwal.com/papers/shortstack/) (OSDI'22), [Weave](https://www.anuragkhandelwal.com/papers/weave/) (OSDI'25)
- Understanding access pattern vulnerabilities: [Length Leakage](https://www.anuragkhandelwal.com/papers/length-leakage/) (USENIX Sec'24), [Found in Translation](https://www.anuragkhandelwal.com/papers/fit/) (USENIX Sec'25)

### Systems for AI

Today’s AI serving systems waste substantial time and resources because they treat requests as independent and unpredictable, even though real workloads contain rich recurring structure in both arrival patterns and prompt content. We are building cloud AI serving platforms that treat workload structure as a first-class systems primitive:

- Scheduling for low-latency, high-throughput AI inference: [Shepherd](https://www.anuragkhandelwal.com/papers/shepherd/) (NSDI'23), [TimelyLLM](https://www.anuragkhandelwal.com/papers/timelyllm/) (MobiSys'26)
- Caching attention state across prompts for low-latency inference: [PromptCache](https://www.anuragkhandelwal.com/papers/promptcache/) (MLSys'24)

[PromptCache](https://www.anuragkhandelwal.com/papers/promptcache/) has been adopted in Gemini, OpenAI, and Anthropic for reusing attention states across LLM prompts.

### Storage and processing stacks for automated data

Emerging applications that rely on automated data sources --- ranging from smart vehicles to brain implants --- require processing, storing, and serving massive volumes of semantically rich data. We are developing systems for efficient ingestion of data without compromising query and processing performance by exploiting properties specific to machine-generated data:

- High-throughput compressed storage of high-dimensional data: [Trinity](https://www.anuragkhandelwal.com/papers/trinity/) (EuroSys'24)
- Distributed system for scalable Brain-Computer Interfacing (BCI): [SCALO](https://www.anuragkhandelwal.com/papers/scalo/) (ISCA'23), [MICRO Top Picks'23](https://www.anuragkhandelwal.com/papers/scalo-toppicks/)
- Distributed monitoring & diagnosis for high speed networks: [Confluo](https://www.anuragkhandelwal.com/papers/confluo/) (NSDI'19)

### Serverless Systems (past)

Serverless analytics workloads increasingly demand fine-grained, rapidly changing compute and memory resources, but existing cloud systems manage them too coarsely, forcing a tradeoff between performance and utilization under bursty, time-varying demand. We built a serverless analytics stack that treats elasticity and workload-aware multiplexing as first-class primitives:

- Position papers: [Berkeley View on Serverless](https://www.anuragkhandelwal.com/papers/berkeley-view-serverless/) (Tech Report), [Le Taureau](https://www.anuragkhandelwal.com/papers/sigmodserverless/) (SIGMOD'20), [What Serverless Should Become](https://www.anuragkhandelwal.com/papers/serverless-next/) (CACM'21)
- Enabling fast and cost-effective analytics over serverless functions: [Caerus](https://www.anuragkhandelwal.com/papers/caerus/) (NSDI'21), [Jiffy](https://www.anuragkhandelwal.com/papers/jiffy/) (EuroSys'22)

### Queries on compressed data (past)

As datasets grow beyond DRAM capacity, maintaining interactive query performance becomes difficult because spilling to slower secondary storage increases latency and lowers throughput. We developed systems that address this challenge using a fundamentally new approach: enabling rich query execution directly on compressed data, reducing the need to fully decompress or rely on large DRAM footprints.

- Enabling queries on compressed data: [Succinct](https://www.anuragkhandelwal.com/papers/succinct/) (NSDI'15), [ZipG](https://www.anuragkhandelwal.com/papers/zipg/) (SIGMOD'17), [Queries on Compressed Data](https://www.anuragkhandelwal.com/papers/phdthesis/) (PhD Thesis)
- Dynamic storage-performance tradeoff in data stores: [BlowFish](https://www.anuragkhandelwal.com/papers/blowfish/) (NSDI'16)

## Real-world impact

- 2025: [CORD](https://www.anuragkhandelwal.com/papers/cord/) incorporated in the design of [NVIDIA's Vera Rubin architecture](https://developer.nvidia.com/blog/inside-nvidia-rubin-gpu-architecture-powering-the-era-of-agentic-ai/#accelerated_scale-up_communications).
- 2024: [PromptCache](https://www.anuragkhandelwal.com/papers/promptcache/) adopted in [Gemini](https://ai.google.dev/gemini-api/docs/caching?lang=python), [OpenAI](https://platform.openai.com/docs/guides/prompt-caching), and [Anthropic](https://www.anthropic.com/news/prompt-caching) for reusing attention states across LLM prompts.
- 2023: The ideas underlying [SCALO](https://www.anuragkhandelwal.com/papers/scalo/) have been tested by Yale Neurosurgery in FPGA-based emulation frameworks for interfacing with the human brain in the operating room.
- 2021: [MIND](https://www.anuragkhandelwal.com/papers/mind/) is the foundational system and central pillar of the multi-institution $20-million NSF-funded AI Institute, [Athena](https://athena.duke.edu/what-we-do/edge-powered-ai-systems).
- 2019: [Confluo](https://www.anuragkhandelwal.com/papers/confluo/) employed in [Alibaba Cloud](https://medium.datadriveninvestor.com/high-speed-querying-with-confluo-from-riselab-445bed3f7f9a) for consuming and analyzing telemetry data.
- 2015: [Succinct](https://www.anuragkhandelwal.com/papers/succinct/) employed by [Databricks](https://www.databricks.com/blog/2015/11/10/succinct-spark-from-amplab-queries-on-compressed-rdds.html) [customers](https://spark-packages.org/package/amplab/succinct) to query compressed RDDs.

## Awards and honors

- Best Paper Award, ACM ASPLOS 2026: "CounterPoint: Using Hardware Event Counters to Refute & Refine Microarchitectural Assumptions"
- Best Paper Award Runner-Up, Best Artifact Award Runner-Up, ACM MobiSys 2026: "TimelyLLM: Time-sensitive LLM Serving System for Physical-I/O Limited Agents"
- IEEE Micro Top Picks in Computer Architecture, 2025: "CORD: Low-Latency, Bandwidth-Efficient and Scalable Release Consistency via Directory Ordering"
- Distinguished Artifact Award, ACM ISCA 2025: "CORD: Low-Latency, Bandwidth-Efficient and Scalable Release Consistency via Directory Ordering"
- NetApp Faculty Fellowship, 2024: To develop GPU-native vector storage and indexing for accelerating AI workloads.
- Best Student Paper Award, ACM EuroSys 2024: "TRINITY: A Fast Compressed Multi-attribute Data Store"
- IEEE Micro Top Picks in Computer Architecture, 2023: "SCALO: An Accelerator-Rich Distributed System for Scalable Brain-Computer Interfaces"
- Best Paper Award, ACM ISCA 2023: "SCALO: An Accelerator-Rich Distributed System for Scalable Brain-Computer Interfaces"
- Roberts Innovation Award, 2023: For innovations in data center resource disaggregation.
- NSF CAREER Award, 2021: For developing in-network memory management for disaggregated data centers.
- NetApp Faculty Fellowship, 2021: For developing elastic and compressed disaggregated memory for edge data centers.
- Distinguished Paper Award, USENIX Security 2020: "Pancake: Frequency Smoothing for Encrypted Data Stores"
- Madan Sundar Sahu Scholarship 2010, 2011, Indian Institute of Technology, Kharagpur: For outstanding academic performance during 2009-2010 and 2010-2011.

## Publications

Each paper has a landing page with its abstract, venue, DOI, PDF, and BibTeX.

- [Bringing Distributed Ordering to Heterogeneous Memory Consistency](https://www.anuragkhandelwal.com/papers/cord-toppicks/). IEEE Micro, 2026.
- [Efficient and Scalable Synchronization via Generalized Cache Coherence](https://www.anuragkhandelwal.com/papers/soul/). USENIX OSDI, 2026.
- [BulletTime: Time Dilation for High-Fidelity Tracing](https://www.anuragkhandelwal.com/papers/bullettime/). ACM ISCA, 2026.
- [TimelyLLM: Time-sensitive LLM Serving System for Physical-I/O Limited Agents](https://www.anuragkhandelwal.com/papers/timelyllm/). ACM MobiSys, 2026. Best Paper Award Runner-Up, Best Artifact Award Runner-Up.
- [CounterPoint: Using Hardware Event Counters to Refute & Refine Microarchitectural Assumptions](https://www.anuragkhandelwal.com/papers/counterpoint/). ACM ASPLOS, 2026. Best Paper Award.
- [Spirit: Fair Allocation of Interdependent Resources in Remote Memory Systems](https://www.anuragkhandelwal.com/papers/spirit/). ACM SOSP, 2025.
- [Scalable Far Memory: Balancing Faults and Evictions](https://www.anuragkhandelwal.com/papers/mage/). ACM SOSP, 2025.
- [Found in Translation: A Generative Language Modeling Approach to Memory Access Pattern Attacks](https://www.anuragkhandelwal.com/papers/fit/). USENIX Security, 2025.
- [Weave: Efficient and Expressive Oblivious Analytics at Scale](https://www.anuragkhandelwal.com/papers/weave/). USENIX OSDI, 2025.
- [CORD: Low-Latency, Bandwidth-Efficient and Scalable Release Consistency via Directory Ordering](https://www.anuragkhandelwal.com/papers/cord/). ACM ISCA, 2025. Distinguished Artifact Award, IEEE Micro’s Top Picks in Computer Architecture 2025.
- [PULSE: Accelerating Distributed Pointer-Traversals on Disaggregated Memory](https://www.anuragkhandelwal.com/papers/pulse/). ACM ASPLOS, 2025.
- [Exploring Intelligent Dynamic Resource Provisioning for Elastic Massive MIMO vRAN](https://www.anuragkhandelwal.com/papers/elasticmimo/). ACM HotMobile, 2025.
- [Length Leakage in Oblivious Data Access Mechanisms](https://www.anuragkhandelwal.com/papers/length-leakage/). USENIX Security, 2024.
- [Prompt Cache: Modular attention reuse for low-latency inference](https://www.anuragkhandelwal.com/papers/promptcache/). MLSys, 2024.
- [Distributed Brain-Computer Interfacing With a Networked Multi-accelerator Architecture](https://www.anuragkhandelwal.com/papers/scalo-toppicks/). IEEE Micro, 2024.
- [Trinity: A Fast Compressed Multi-attribute Data Store](https://www.anuragkhandelwal.com/papers/trinity/). ACM EuroSys, 2024. Best Student Paper Award.
- [Karma: Resource Allocation for Dynamic Demands](https://www.anuragkhandelwal.com/papers/karma/). USENIX OSDI, 2023.
- [SCALO: An Accelerator-Rich Distributed System for Scalable Brain-Computer Interfacing](https://www.anuragkhandelwal.com/papers/scalo/). ACM ISCA, 2023. Best Paper Award, IEEE Micro’s Top Picks in Computer Architecture 2023.
- [Shepherd: Serving DNNs in the Wild](https://www.anuragkhandelwal.com/papers/shepherd/). USENIX NSDI, 2023.
- [Prefetching Using Principles of Hippocampal-Neocortical Interaction](https://www.anuragkhandelwal.com/papers/cls/). ACM HotOS, 2023.
- [Shortstack: Distributed, Fault-tolerant, Oblivious Data Access](https://www.anuragkhandelwal.com/papers/shortstack/). USENIX OSDI, 2022.
- [Jiffy: Elastic Far-memory for Stateful Serverless Analytics](https://www.anuragkhandelwal.com/papers/jiffy/). ACM EuroSys, 2022.
- [Caerus: Nimble Task Scheduling for Serverless Analytics](https://www.anuragkhandelwal.com/papers/caerus/). USENIX NSDI, 2021.
- [What Serverless Computing Is and Should Become: The Next Phase of Cloud Computing](https://www.anuragkhandelwal.com/papers/serverless-next/). Communications of the ACM, 2021.
- [MIND: In-Network Memory Management for Disaggregated Data Centers](https://www.anuragkhandelwal.com/papers/mind/). ACM SOSP, 2021.
- [Pancake: Frequency Smoothing for Encrypted Data Stores](https://www.anuragkhandelwal.com/papers/pancake/). USENIX Security, 2020. Distinguished Paper Award.
- [Le Taureau: Deconstructing the Serverless Landscape & A Look Forward](https://www.anuragkhandelwal.com/papers/sigmodserverless/). ACM SIGMOD, 2020.
- [Queries on Compressed Data](https://www.anuragkhandelwal.com/papers/phdthesis/). University of California, Berkeley, 2019.
- [Cloud Programming Simplified: A Berkeley View on Serverless Computing](https://www.anuragkhandelwal.com/papers/berkeley-view-serverless/). UC Berkeley Technical Report, 2019.
- [Confluo: Distributed Monitoring and Diagnosis Stack for High-speed Networks](https://www.anuragkhandelwal.com/papers/confluo/). USENIX NSDI, 2019.
- [ZipG: A Memory-efficient Graph Store for Interactive Queries](https://www.anuragkhandelwal.com/papers/zipg/). ACM SIGMOD, 2017.
- [BlowFish: Dynamic Storage-Performance Tradeoff in Data Stores](https://www.anuragkhandelwal.com/papers/blowfish/). USENIX NSDI, 2016.
- [Attacking Data Center Networks from the Inside](https://www.anuragkhandelwal.com/papers/attacking-dcn/). Microsoft Research Technical Report, 2015.
- [Succinct: Enabling Queries on Compressed Data](https://www.anuragkhandelwal.com/papers/succinct/). USENIX NSDI, 2015.

## Teaching

- CPSC 422/522: Operating Systems (Spring 2026, Spring 2025, Spring 2024)
- CPSC 433/533: Computer Networks (Spring 2022, Spring 2021, Spring 2020)
- CPSC 438/538: Big Data Systems (Fall 2025, Fall 2023, Fall 2021, Fall 2020)

## Prospective students

Anurag Khandelwal is looking for motivated graduate students and postdoctoral researchers. See https://www.anuragkhandelwal.com/group/ for current lab members.

## Primary pages

- Profile and news: https://www.anuragkhandelwal.com/
- Publications: https://www.anuragkhandelwal.com/publications/
- Research impact and awards: https://www.anuragkhandelwal.com/impact/
- NOVA Lab: https://www.anuragkhandelwal.com/group/
- Biography: https://www.anuragkhandelwal.com/bio/
- CV (PDF): https://www.anuragkhandelwal.com/cv/cv.pdf
- Full text for language models, including every abstract: https://www.anuragkhandelwal.com/llms-full.txt

## Authoritative identity links

- Yale Engineering: https://engineering.yale.edu/research-and-faculty/faculty-directory/anurag-khandelwal
- Google Scholar: https://scholar.google.com/citations?user=LlfHFqUAAAAJ&hl=en
- ORCID: https://orcid.org/0000-0002-2199-6391
- DBLP: https://dblp.org/pid/162/1936
- Semantic Scholar: https://www.semanticscholar.org/author/2712026
- GitHub: https://github.com/anuragkh
- Wikidata: https://www.wikidata.org/wiki/Q130845533