• Docathon 2026: Honorable Mention ("First-timer", Rank 7 of 33, 17 pts)
• Migrated 5 core documentation modules to MyST Markdown (nn.functional, autograd, extending.func, mkldnn, gradcheck)
• Resolved cross-referencing errors and sphinx build warnings in PyTorch core docs pipeline
• Key PRs: #182925, #182626

• Fixed broken {include} paths rendering 3 empty backend documentation pages
• Documented manual CMake build and ctest workflow for C++ test execution
• Added top-of-page Memory Inspector API usage example with print_data_tabular()
• Key PRs: #19585, #19386, #19387

• Root-Caused Decoder Persistence Collapse: Traced model failure to discarded encoder features and zero-seeded decoder observation nodes. Shipped zero-parameter skip-connection fix (PR #237, #239), lifting held-out forecasting skill from 4% to 20% over persistence.
• Movable Adaptive Meshing Architecture: Shipped DynamicGraphBuilder, RegionalForecaster, and BoundaryNudgingLayer for arbitrary runtime sub-grid weather forecasting.
• Published Negative Result (Issue #238): Ran controlled 3-seed held-out ablation proving a 19k-parameter per-node MLP outperformed 2.27M-parameter GNN meshes; detailed scaling and coupling tradeoffs.
• Co-credited Contributor: Formal credit in graph_weather v1.0.132 release (Zenodo DOI).
• Key PRs: #166, #181, #221, #237, #239

• Fixed fitness function range-query window covering full test duration in krkn-ai
• Synchronized uv.lock with pinned development dependencies across CI runs
• Resolved missing scenario frontmatter weight definitions on docs website
• Key PRs: #378, #374, #476

• Added lower-triangle pairwise population fixation index ($F_{ST}$) heatmap visualization for vector genomics
• Resolved CNV data verification indentation defect preventing silent execution errors
• Key PRs: #969, #895

• Open Climate Fix / graph_weather: Adaptive meshing, dual-graph prototype for regional NWP, dynamic encoder graphs
• KubeEdge / Ianvs: Edge AI benchmarking CI modernization and dependency fixes
• krkn-chaos / krkn-ai: Automated chaos scenario discovery and node label filtering

Automated Root Cause Analysis (RCA) on telemetry via deterministic verification graphs and LLM hypothesis generation.

• Core Invariant: LLM proposes hypotheses; only deterministic code decides.
• Verification Gate: Hexagonal core with stdlib-only domain behind ports. Hypotheses bounded to observed signals; emits cryptographic verdicts: SUPPORTED, REFUTED, UNKNOWN.
• Concurrency & Infrastructure: Async PostgreSQL worker queue using FOR UPDATE SKIP LOCKED for lock-free parallel triage; dependency-free Prometheus /metrics.
• Sealed RCAEval Benchmark:
  • Dev Split (RE2-OB, 88 cases): Top-1: 0.932 | Top-3: 0.989 | MRR: 0.959
  • Held-out Split (RE2-TT, 90 unseen cases): Top-1: 0.767 | Top-3: 0.878 | MRR: 0.833
  • 0.0% invalid citations; safely abstained on 52 of 90 held-out cases rather than emit ungrounded causes.
• Discipline: 304 automated tests; zero leakages; strict typing; Apache-2.0.

git clone https://github.com/AswaniSahoo/Incident-evidence-compiler.git
cd Incident-evidence-compiler && pytest  # 304 passing tests

Evaluated 4-node LangGraph risk intelligence system with split-boundary Model Context Protocol (MCP) servers.

• Production Container: Published on GHCR and indexed on official MCP Registry as io.github.AswaniSahoo/climate-ipcc-rag.
• Split MCP Security Boundary:
  • weather-mcp: Live weather forecast routines + 60+ yr ERA5 Generalized Extreme Value (GEV) hazard statistics (bootstrap 90% CIs).
  • ipcc-rag-mcp: Hybrid BM25 + dense Reciprocal Rank Fusion (RRF) over IPCC AR6 WG I & II reports with page citations.
• Protocol Security: ToolAnnotations (read_only_hint=True, open_world_hint=True); measured client-sandbox credential isolation in MCP Inspector.
• Frozen Held-Out Evaluation (105 Questions):
  • Retrieval: Recall@3: 87.0% | Recall@5: 91.0% | Recall@10: 96.2%
  • Grounding: 94.0% citation validity; 0.0% false answers on refusal confusion matrix; ~$0.001 per grounded report.
• Discipline: 238 unit and integration tests.

docker pull ghcr.io/aswanisahoo/climate-ipcc-rag-mcp:0.1.0

• Evaluated 5 algorithmic intervention tracks (T0 through T4) on credit risk scoring across shared seeded splits with compliance guardrails.
• Published Negative Result: Proved no algorithmic intervention improved disparate impact without violating ECOA / Regulation B non-discrimination mandates.
• Foundation Model Benchmark: 1.6B parameter TabFM failed to outperform tuned GBDT at ~1,000x compute cost.
• Discipline: 235 automated tests covering statistical parity, equalized odds, and audit logs.

• Physics-aware Vision Transformer built completely from scratch in pure PyTorch without using torch.nn.MultiheadAttention.
• Custom spatial-temporal patch embeddings, spherical coordinate encodings, and physics-constrained continuity loss on ERA5 data.
• Empirical Gain: +27% RMSE improvement over operational persistence baseline on 6-hour atmospheric forecasting.
• Discipline: 74 unit tests validating attention matrix shapes, gradient flow, and energy conservation.

• Parameter-efficient fine-tuning (LoRA / PEFT) of LLaMA-3.1-8B for deterministic agentic tool execution.
• Maps unstructured natural language task instructions into type-safe, schema-validated JSON function calls with zero syntax defects.
• Enforces strict argument validation across complex multi-turn execution graphs.

• High-throughput RAG pipeline indexing over 200,000+ CFPB consumer financial complaints using hybrid FAISS vector search and LLMs.
• Latency Benchmark: Quantified latency versus semantic coherence: MiniLM (p95: 41ms) vs BGE (p95: 1356ms).
• Deployed with interactive Streamlit exploration UI and automated cluster analysis.

• SciBERT + TF-IDF ensemble text classification pipeline for genomics and biodiversity literature.
• Achieved 99.5% F1 score on Europe PMC open-access biomedical corpus.
• Discipline: 81 unit tests ensuring deterministic feature tokenization and inference stability.

• Natural language query translation layer for vector genomics data (malariagen_data API).
• Validated 10 of 10 test queries with automated parameter binding across 7 public genomic exploration API methods.
• Bridges domain scientific queries with complex underlying dataframes and visualization routines.