Captain Cargo - AI Delivery Tracking System Problem: Support teams were spending significant time on repetitive delivery status queries that required no human judgment. Process: Built a voice-first agentic AI assistant workflow using FastAPI, conversational AI via Vapi, and structured order status retrieval backed by a circuit breaker and caching layer. Why: Chose voice + API orchestration to eliminate manual touchpoints entirely, with production safeguards so the system degrades gracefully when dependencies go down. Impact: Converted manual status lookups into a fully automated voice flow achieving 94% eval accuracy and 127ms average latency, with 75 tests covering unit, contract, and integration scenarios. Learn More
NYC 311 Demand Forecasting & Optimization Problem: Staffing decisions were reactive, creating persistent mismatch between service demand and available teams. Process: Built a forecasting and optimization pipeline using historical 311 request patterns, time-series modeling, and Bayesian optimization to generate capacity recommendations. Why: Chose probabilistic optimization over static thresholds to make staffing plans robust under demand uncertainty, not just accurate on average. Impact: Produced actionable staffing recommendations that reduced both under-allocation risk and resource waste across service demand cycles. Learn More
GenZ AI Therapist Problem: Generic chatbots fail to sustain emotionally safe, context-aware conversations, especially for younger users who need consistency, not just answers. Process: Designed an agentic AI assistant with conversational memory, empathetic tone shaping, RAG-style context retrieval, and guardrail-aware prompting to keep interactions safe and coherent across turns. Why: Prioritized tone consistency and response safety over feature breadth; in mental wellness, trust is the product. Impact: Achieved measurably more coherent multi-turn conversations with near-zero guardrail violations, creating an experience closer to supportive dialogue than a FAQ bot. Learn More
Brain MRI FLAIR Segmentation Problem: MRI abnormality segmentation workflows were slow and difficult to operationalize end to end. Process: Built a full MLOps pipeline covering data handling, experiment tracking, MobileNetV3-based segmentation, and deployment-ready model serving. Why: Chose a lighter backbone to hit deployable inference speeds on CPU-constrained infrastructure without sacrificing segmentation quality. Impact: Delivered an end-to-end pipeline capable of running inference without GPU dependency, closing the gap between research prototype and clinical deployment readiness. Learn More
1. Problem Framing Before writing a single line of code, I map success metrics, constraints, and stakeholder tradeoffs to make sure we're solving the right problem. Business Goal Mapping Metric Definition Risk Analysis
2. Experimentation I run structured experiments - baselines, ablations, and offline evals - to validate design decisions with evidence before committing to an architecture. Rapid Prototyping Offline Eval Ablation Studies
3. Productionization I ship with observability, fallback behavior, and test coverage built in - because a model that can't be monitored or recovered from failure isn't production-ready. MLOps Monitoring Reliability
Primary focus LLM / GenAI Orchestration, retrieval, prompting, and model adaptation for production-grade AI assistants. 8 Hugging Face LangChain RAG Agentic AI Prompt Engineering Fine-Tuning Model Evaluation MCP Servers
Primary focus MLOps / Production Deployment, observability, and CI/CD practices for reliable AI and backend systems in real environments. 13 Docker Kubernetes AWS Azure GCP MLflow Model Serving Monitoring CI/CD GitHub Actions Prometheus Grafana Jenkins
Primary focus Backend / Data Systems API development, async services, storage layers, and distributed data infrastructure for production workflows. 13 Python SQL FastAPI Flask Django Pydantic REST APIs Async Python PostgreSQL MySQL MongoDB Redis Apache Kafka
Supporting depth Retrieval / Knowledge Systems Vector infrastructure and retrieval evaluation used to improve search quality and grounded generation. 6 Milvus Vector Search Hybrid Search Embedding Models Retrieval Evaluation Knowledge Bases
Supporting depth ML / Applied AI Classical ML and multimodal modeling experience across experimentation, feature work, and domain-specific delivery. 10 PyTorch TensorFlow RL Agents Computer Vision Speech ML Scikit-learn Feature Engineering Experiment Design Causal Inference Time Series
Supporting depth Analytics / Experimentation Data analysis and decision-support tooling for model validation, reporting, and stakeholder communication. 4 Pandas NumPy Power BI A/B Testing
Supporting depth Engineering / Delivery Distributed job orchestration, data processing, and engineering practices that keep systems dependable. 10 Celery RabbitMQ Apache Spark Apache Beam Git Pytest Integration Testing System Design Linux Agile Delivery
Advancing Ischemic Stroke Diagnosis: A Novel Two-Stage Approach for Blood Clot Origin Identification Koushik Sivarama Krishnan, PJ Nikesh, S Gnanasekar, Karthik Sivarama Krishnan arXiv 2023 Cited by 0 Krishnan, K. S., Nikesh, P. J., Gnanasekar, S., & Krishnan, K. S. (2023). Advancing Ischemic Stroke Diagnosis: A Novel Two-Stage Approach for Blood Clot Origin Identification. Cite ArXiv Scholar
Mfaan: unveiling audio deepfakes with a multi-feature authenticity network Koushik Sivarama Krishnan, Karthik Sivarama Krishnan SPCOM 2023 Cited by 12 Krishnan, K. S., & Krishnan, K. S. (2023). Mfaan: unveiling audio deepfakes with a multi-feature authenticity network. Cite Scholar
Benchmarking Conventional Vision Models on Neuromorphic Fall Detection and Action Recognition Dataset Koushik Sivarama Krishnan, Karthik Sivarama Krishnan CCWC 2022 Cited by 12 @INPROCEEDINGS{9720737, author={Krishnan, Karthik Sivarama and Krishnan, Koushik Sivarama}, booktitle={2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)}, title={Benchmarking Conventional Vision Models on Neuromorphic Fall Detection and Action Recognition Dataset}, year={2022}, pages={0518-0523}, doi={10.1109/CCWC54503.2022.9720737}} Cite ArXiv IEEE Scholar
Efficient Super-Resolution For Chest X-rays Koushik Sivarama Krishnan, Karthik Sivarama Krishnan Acta Scientific Computer Sciences 2022 Cited by 6 Krishnan, K. S., & Krishnan, K. S. (2022). Efficient Super-Resolution For Chest X-rays. Acta Scientific Computer Sciences, 4(6). Cite Scholar
Vision Transformer based COVID-19 Detection using Chest X-rays Koushik Sivarama Krishnan, Karthik Sivarama Krishnan ISPCC 2021 Cited by 98 @INPROCEEDINGS{9609375, author={Krishnan, Koushik Sivarama and Krishnan, Karthik Sivarama}, booktitle={2021 6th International Conference on Signal Processing, Computing and Control (ISPCC)}, title={Vision Transformer based COVID-19 Detection using Chest X-rays}, year={2021}, pages={644-648}, doi={10.1109/ISPCC53510.2021.9609375}} Cite ArXiv IEEE Scholar
SwiftSRGAN - Rethinking Super-Resolution for Efficient and Real-time Inference Koushik Sivarama Krishnan, Karthik Sivarama Krishnan ICICyTA 2021 Cited by 18 @INPROCEEDINGS{9689188, author={Krishnan, Koushik Sivarama and Krishnan, Karthik Sivarama}, booktitle={2021 International Conference on Intelligent Cybernetics Technology Applications (ICICyTA)}, title={SwiftSRGAN - Rethinking Super-Resolution for Efficient and Real-time Inference}, year={2021}, pages={46-51}, doi={10.1109/ICICyTA53712.2021.9689188}} Cite ArXiv IEEE Scholar