Abhinay Krishna Bodi

       

I build AI and ML systems and know the data behind them. Shipped work at Mastercard and Brane plus AI research and NLP teaching at NYU. Into clean work that creates business impact.

• New York University | Research Scholar

• Mastercard | Data Scientist Intern

• New York University | Teaching Assistant, Natural Language Processing

• Brane Enterprises | Data Scientist

• Brane Enterprises | Software Engineer

• New York University (NYU Courant) | M.S. in Data Science (GPA: 3.8 / 4.0)
• VNR VJIET | B.Tech in Computer Science & Engineering (GPA: 3.9 / 4.0)

• llama.cpp | Merged PR #26536
  • Eliminated redundant audio-encoder chunks for short inputs by replacing 31-second preprocessing padding with the exact 201-sample FFT reflection-padding boundary, halving encoder chunks for affected inputs.

• VIP-MINGLE: Multimodal Interaction Corpus | Accepted at INTERSPEECH 2026
  • Co-authored a multimodal corpus built on transformer-based ASR, extracting aligned audiovisual cues and identifying statistically significant differences in turn-taking and participant enjoyment (p=0.037).

• satavahana Rust Tokio HFT

  • High-frequency options trading engine in async Rust. Streams 1.5GB+/4min tick feeds into lock-free DashMap storage with CPU-pinned workers, an 8-strategy signal pipeline, and Half-Kelly risk allocation.

• creditRisk_Prediction_GNNs PyTorch Geometric FA-GNN LSTM

  • Feature Attention Graph Neural Network with LSTM temporal modeling over dynamic borrower graphs, beating Random Forest and XGBoost baselines at 0.7707 ROC-AUC. Cut data-loading time 94.8% with Polars and explained feature-level risk with SHAP.

• cheque_forensics PyTorch Qwen3-VL NVIDIA DGX

  • Two-stage VLM document forgery pipeline combining a 655.8M C-RADIOv4-H backbone with 30B Qwen3-VL verification, trained on NVIDIA DGX Spark with a 13.2x training speedup at 0.89 precision.

• adaptive-pairwise-preferences Python Bayesian ML Active Learning

  • Bayesian latent factor model for sequential pairwise active feedback on the Netflix Prize dataset (100M+ ratings), cutting required feedback queries by 40% via information-gain maximization.

• ArcPay-Agentic-Financial-System Python Multi-Agent USDC

  • Agent-driven financial execution translating natural language to on-chain USDC payments and equities trading, guarded by a deterministic GuardianAgent liquidity and whitelist risk layer.

• H-ARC_challenge Python LLM Reasoning Program Synthesis

  • Tackles the ARC AGI benchmark through closed-loop hypothesis generation and neural code synthesis with 32B Qwen 2.5 Coder, reaching 11% accuracy against 0% for direct prompting.

• Languages: Python, SQL, R, Rust, C++, TypeScript, Scala, Java, Bash
• AI & ML: PyTorch, TensorFlow, XGBoost, Scikit-learn, SHAP, FAISS, vLLM, LangChain, LangGraph, RAG
• Data Engineering: Spark, PySpark, Kafka, Airflow, PostgreSQL, MongoDB, BigQuery, Snowflake, ClickHouse, Polars, HDFS
• Cloud & DevOps: AWS, GCP, Azure, Docker, Kubernetes, Jenkins, Linux, Git, CI/CD
• Analytics & Visualization: Tableau, Power BI, Excel/VBA, Matplotlib, Seaborn, A/B Testing