About
Backend engineer building crypto-native, money-critical infrastructure. I work where financial correctness meets scale: on-chain/off-chain consistency, settlement accuracy, idempotency, and the boring-but-load-bearing systems that move real value safely.
Currently at ether.fi, building backend systems for a multi-billion-dollar liquid restaking protocol.
Previously:
Stader Labs - founding engineer; helped scale liquid-staking infrastructure past $500M+ TVL across multiple chains.
Visa - Senior SDE on payments-scale distributed systems, where reliability and correctness under load were non-negotiable.
Across all of it the throughline is the same: high-throughput systems where getting the money math wrong is not an option. Smart-contract integration, exchange and settlement flows, financial invariants, failure modes, and observability for systems handling real user funds.
Stack: Go, Rust, Solidity, Java, Kafka, distributed systems.
On the side I go deep on low-level performance and correctness in the LLM inference ecosystem (vLLM, NVIDIA KVPress, constrained decoding), contributing open source. Same instincts as distributed systems: throughput, memory, latency, correctness.
Competitive programming: Codeforces Expert (~8000 problems solved).
Activity
5K followers
Experience
Education
Licenses & Certifications
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AI Pair Programming with GitHub Copilot
LinkedIn
IssuedCredential ID 3c0c1d204387f4a5d462e6a48c26fa6257274d1c7745f60887055c131b24fc79 -
Introduction to Artificial Intelligence
LinkedIn
IssuedCredential ID 35e7d395545bda93f82965d5f9cf26affe434d6de58ed0d8c0a59a0ffb789c5e -
Prompt Engineering: How to Talk to the AIs
LinkedIn
IssuedCredential ID c79986434df27df0841f5f8f1027fa9a75d680da3f2b7764942c1efb58eb0524 -
APS Certificate – Germany
Akademische Prüfstelle (APS)
Issued ExpiresCredential ID 05392/25
Volunteer Experience
Publications
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Perplexity Holds, Programs Break: Executable Correctness as a Blind Spot of KV-Cache Compression
Zenodo
See publicationKV-cache compression methods are evaluated almost exclusively with metrics that score token or string overlap, retrieval hits, or an extracted final answer. None of the widely used long-context suites check whether generated output actually runs, or whether a generated tool call is well-formed. We argue this is a systematic blind spot: lexical and retrieval metrics can stay flat while a compressed cache quietly breaks structured generation, because a single dropped or garbled token fails a unit…
KV-cache compression methods are evaluated almost exclusively with metrics that score token or string overlap, retrieval hits, or an extracted final answer. None of the widely used long-context suites check whether generated output actually runs, or whether a generated tool call is well-formed. We argue this is a systematic blind spot: lexical and retrieval metrics can stay flat while a compressed cache quietly breaks structured generation, because a single dropped or garbled token fails a unit test or a schema check but barely moves a substring score. We introduce kv-exec-bench, a small open benchmark that measures the two missing quantities, code unit-test pass@1 and tool-call JSON-Schema validity, under KV compression. It is built on top of NVIDIA's kvpress as a dependency, so any press works without modification. In a deliberately small, CPU-only study on a 0.5B-parameter model, we observe the predicted divergence on tool calling: for the best-behaved presses, the rate of parseable tool calls stays at 100 percent across compression ratios while the rate of schema-valid calls falls by half or more. A parse-only or string-only metric would report this regime as nearly lossless. We release the benchmark and all code under Apache-2.0, and frame this as a measurement-gap report plus a reusable benchmark rather than a large-scale empirical study.
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StragglerPolicy: Straggler-Aware Elastic Membership for Decentralized Training
Zenodo
See publicationDecentralized training methods such as DiLoCo make low-communication language-model training over commodity, geographically distributed hardware practical, and production stacks (e.g. Prime Intellect's prime-diloco/PCCL) already tolerate dead nodes through heartbeat eviction and elastic join/leave. They do not, however, handle the slow-but-alive straggler: a single node running at a fraction of peer throughput stalls every synchronous outer step, because the barrier waits for everyone. We…
Decentralized training methods such as DiLoCo make low-communication language-model training over commodity, geographically distributed hardware practical, and production stacks (e.g. Prime Intellect's prime-diloco/PCCL) already tolerate dead nodes through heartbeat eviction and elastic join/leave. They do not, however, handle the slow-but-alive straggler: a single node running at a fraction of peer throughput stalls every synchronous outer step, because the barrier waits for everyone. We present StragglerPolicy, a membership policy that adds an adaptive per-round soft deadline (median + k*MAD over a rolling arrival-offset history), a partial-participation quorum with sidelining, and a graduated slow-node response (transient slow -> rejoin; persistently slow -> evict). On a persistent-straggler scenario (four workers, one 10x slow), StragglerPolicy is 4.59x faster than a faithful PRIME/PCCL-style baseline in a deterministic discrete-event simulator, raising worker utilization from 0.62 to 0.89. To establish that this is not a simulation artifact, we re-run both policies unchanged on a real torch.distributed/gloo DiLoCo loop; the straggler win reproduces directionally (1.32x faster on real code, slow rank sidelined four times then evicted), and a simulator calibrated to the real run's constants predicts the same ordering. We are explicit about the gap between the simulator's relative speedup and the absolute speedup observed on real hardware, and frame the simulator as a tool for relative comparison of membership policies rather than an absolute-throughput predictor. The simulator, policy, and validation harness are open source.
Courses
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Algorithm Design And Analysis
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Compiler Design
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Computer Architechture
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Computer Networks
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Data Structures
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Database Management Systems
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Machine Learning
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Object Oriented Software Engineering
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Operating Systems
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Projects
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6502 Processor emulator
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See project• Emulated a 6502-processor architecture with all defined pins
definition and processor addressing modes.
• Defined all of the 56 operation codes, Bus connections
emulated with a header file written in C++. -
Bellman Ford Visualization
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See projectAbout
Simulation of Bellman Ford Algorithm using GLUT openGL.Dijkstra’s algorithm is a Greedy algorithm and time complexity is O(VLogV) . Implemented using freeglut libraries with cost matrix as input. can be used on negative weighted graphs too. -
Dark Theme Materials Design Android Apps ( Memo+ and Calculator)
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See projecthttps://github.com/pjdurden/memo-
https://github.com/pjdurden/Calculate-flutter
A materials design dark theme calculator App built as a project for 30daysofflutter (Google) using Flutter SDK.
memo adding and saving app for Android written in Kotlin -
restake-xray
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Open-source Go engine that maps restaking / liquid-restaking-token (LRT) exposure across protocols. Author and maintainer.
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Tic-Tac-Toe using Socket Programming
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See projectA Tic-Tac-Toe game to play against computer using Min-Max
Algorithm further modified with Alpha Beta Pruning an
Artificial Intelligence game search algorithm to predict the
best possible move -
Video/Image Analysis Using Machine Learning
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See projectImplementation of YOLO algorithm to do Video and image
analysis build on Google colab notebook.Implemented
Convolutional Neural Network written in C and CUDA. -
Vidzz Android Application
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See projectAn application to record, share, and browse short videos on
an area-based user feed utilizing Firebase Real-time Database
and Storage for fast, scalable streaming of videos(implemented
cache management).
Honors & Awards
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CodeChef Best Finishes 2020
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Highest Rating - 1890
February Challenge 2021 Div 2: Rank - 52 ( College Rank 2)
January Challenge 2021 Div 3: Rank - 439 (College Rank 12)
CodeChef push_back(2): Rank - 382 (College Rank 6)
January Lunchtime 2021 Div 2: Rank - 636 -
First Runnerup at Codesprint
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Secured rank 2 at Codesprint organised by Developer Student Club of Bharati Vidyapeeth College of Engineering
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