CV

Generative AI & LLM Systems

Career Break / Independent Study

• Deepened expertise in modern LLM architectures and efficiency — FlashAttention, RoPE, scaling laws, KV-cache, speculative decoding, and fast inference techniques.
• Studied agentic system design (tool use, MCP) and evaluation strategies for LLM/agentic systems; built small prototypes to test concepts.

Senior AI Engineer

Flinn.ai

• Led development of AI-driven product features including data extraction from medical research papers and multilingual complaint monitoring.
• Partnered with product and backend teams to scope problems, define success metrics, and deliver end-to-end features within an agile product lifecycle.
• Role eliminated due to company-wide strategic shift.

Parental Leave (Maternity)

• Stayed engaged with research literature and maintained technical skills.

Research Scientist

Amazon AI

• Led development of a scalable noise-removal/data-quality pipeline processing billions of tokens to improve training data for LLMs.
• Wrote research plans and technical documentation; presented results and recommendations to senior science/engineering leadership.
• Mentored Master’s/Ph.D. interns and collaborated on research deliverables.

Machine Learning Scientist

German Research Center for Artificial Intelligence (DFKI)

• Delivered ML research and engineering in BMBF-funded projects XAINES (Explainable AI) and DeFuseNN (vision-language systems).
• Supervised interns and BSc/MSc students; provided technical mentorship and project guidance.

Machine Learning Scientist Intern

NVIDIA Research

• Built a document understanding pipeline combining table/cell detection, tabular structure retrieval, and OCR for financial documents.

Applied Scientist Intern

Amazon Alexa

• Developed a method for generating diverse synthetic training data to improve intent classification and slot labeling for task-oriented NLU.

Research Intern / Master's Thesis

SAP AI Research

• Designed and implemented an evaluation metric for Visual Question Answering (VQA) models.

Ph.D.

TU Kaiserslautern

Computer Science

• Grade: Sehr Gut 1.0 (highest on 1.0–5.0 scale)
• Thesis: Building Natural Language Generation and Understanding Systems in Data-Constrained Settings
• Work on VQA, interpretability, and conversational AI

Visiting Researcher

University of Copenhagen

Natural Language Processing

• Developed an unsupervised post-editing algorithm to generate fluent fact-checking explanations

M.Sc.

TU Kaiserslautern

Computer Science — Minor in Economics

• Grade: Sehr Gut 1.5

Semester Abroad

Kyushu University

Computer Science

• Explainable AI to analyze behavior of deep CNN architectures for image recognition

B.Tech

Guru Nanak Dev Engineering College

Computer Science & Engineering

• Grade: First Division with Distinction

Search and Learn: Improving Semantic Coverage for Data-to-Text Generation

EaSe: A Diagnostic Tool for VQA Based on Answer Diversity

Can Pre-training Help VQA with Lexical Variations?

Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems

How Do Convolutional Neural Networks Learn Design?

AAAI-22 Scholarship by Hitachi

• Awarded by Hitachi to attend AAAI 2022.

AI Newcomer 2021 Award

• Recognized by the German Informatics Society and BMBF, Germany.

EU-Cost STSM Grant

• Awarded by EU-Cost Action to work on the Multi3generation project at CopeNLU, University of Copenhagen.

Best Student Paper Award

• Awarded by the International Conference on Pattern Recognition (ICPR) for “How Do Convolutional Neural Networks Learn Design?”