Aneesh Krishna Email & Phone Number

Buffalo, NY, US

Buffalo, New York, United States

• Enhanced threat detection through the integration of BERT and GloVe models, achieving 99.98% accuracy and a 1.0 F1 score in context-aware interpretation of network traffic patterns and log anomalies.
• Improved attack vector classification accuracy by 20% using packet analysis across all 7 OSI layers, extracting advanced features from log sequences for real-time threat detection.
• Achieved 98.55% accuracy in anomaly detection using a Naive Bayes model with TF-IDF Vectorizer, optimizing feature extraction and classification for system log analysis and network security.

Buffalo, New York, United States

• Increased helpdesk ticket resolution speed by 30% by building a RAG- and LLM-powered assistant that analyzes past solutions to suggest relevant responses for new tickets.
• Enhanced model accuracy by 10% through advanced prompt engineering, including chain-of-thought, few-shot prompting, and metadata filtering, optimizing ticket solution relevance.
• Reduced helpdesk response time by 25% by implementing an LLM system using Hugging Face embeddings, Pinecone vector databases, and Llama 3.1 through Groq for real-time ticket analysis.
• Improved solution quality and team efficiency by collaborating with testing and SDE teams, iterating on model functionality based on feedback to meet helpdesk team expectations.

Mysore, Karnataka, India

• Designed and automated Power BI dashboards and reports to track data migration progress across 90+ sites, using DAX for calculations and providing real-time analytics to stakeholders for data-driven decisions.
• Coordinated User Acceptance Testing (UAT) of reports to ensure a seamless transition into production, enhancing reporting accuracy by 40% through advanced data visualization techniques and stakeholder feedback.
• Streamlined SharePoint site redesign processes by utilizing PowerShell and SQL based scripts, significantly reducing manual intervention by 50% and increasing deployment efficiency for large-scale data migration.
• Led a team of 5 engineers in automating complex data workflows and modernizing legacy SharePoint sites, improving data processing efficiency by 60% through the integration of SPFX, Azure Devops and optimized data.

Master'S Degree, Data Science, 3.6

Bachelor Of Engineering - Be, Information Science/Studies