Mohan Vamsi Adluru
Zurich, Switzerland | Nationality: Indian | Permit: B (Student)
Professional Summary
Applied AI researcher and engineer at the intersection of life sciences and intelligent systems. Completing an M.Sc. in Applied Computational Life Sciences at ZHAW (July 2026), with a research focus on biomedical NLP, knowledge graphs, and evidence-grounded AI systems designed for clinical use. Backed by 3+ years of production software engineering experience, with a consistent track record of taking data-driven systems from concept to deployment. Driven by the belief that AI should amplify human expertise in solving critical health challenges.
Research interests: Biomedical NLP, Knowledge Graphs, Explainable AI, Clinical Decision Support, LLM Evaluation
Work Experience
Databiomix (Industry Partner)
MSc Thesis Researcher
- Developing an evidence-based decision-support system for inflammatory bowel disease, structuring and integrating biomedical literature using modern AI techniques including retrieval-augmented methods and knowledge representations (NDA-covered project).
- Designing a modular pipeline connecting biological data with literature-derived evidence, enabling transparent and traceable clinical insights rather than opaque model outputs.
Eatomics / Dishcovery
Mobile Application Developer (Startup Collaboration)
- Built the full-stack mobile application in React Native to support a personalised dietary recommendation engine for a food-tech startup, enabling users with allergens and dietary restrictions to discover safe, curated restaurant options and menus.
- Designed the data schema in Supabase/PostgreSQL linking customer dietary profiles (allergens, intolerances, preferences) with restaurant and menu data, laying the structured foundation for downstream personalised recommendations; implemented secure multi-provider authentication and Row Level Security policies throughout.
Lumel Technologies – xViz
Associate Product Developer
- Took end-to-end ownership of 9 of 16 Power BI visuals shipped to production, driving each from initial concept through design, implementation, and release, directly contributing to Microsoft AppSource Certification and 92% enterprise client adoption across the product suite.
- Reduced front-end rendering latency by 35% through algorithmic refactoring and optimised data-binding strategies using D3.js, Highcharts, and AG Grid; resolved 300+ production issues via systematic root-cause analysis across large enterprise datasets and cross-browser environments.
- Contributed to agile sprint planning, peer code reviews, and technical documentation within a cross-functional product team, maintaining a consistent quarterly release cadence.
Solarillion Foundation
Undergraduate Research Assistant
- Developed a critical-state-detection mechanism for deep RL agents under adversarial attack; attacking <1% of states reduced agent performance by 40%+, while the proposed long-term impact classifier reduced compute time by 80.3% over prior methods, published at IEEE ICMLA 2021.
- Built a two-stage flight delay prediction pipeline using SMOTE, Random Forest, and XGBoost for delay classification and duration estimation (R²=0.95, RMSE=16.42, MAE=11.45).
Education
Zurich University of Applied Sciences (ZHAW)
M.Sc. Applied Computational Life Sciences — GPA: 4.6 / 6.0 Expected: July 2026
- Clinical NLP & LLM Fine-tuning: Replicated a hospital course summarisation pipeline benchmarking Clinical-T5, LLaMA2-13B, and GPT-4 on the MIMIC-IV-BHC dataset; QLoRA fine-tuning improved BERT F1 by +6.3% on Clinical-T5 (0.584 → 0.647) and +7.1% on LLaMA2-13B (0.612 → 0.683), with LLaMA2 outperforming the specialised clinical model post-tuning.
- Knowledge Graph Extraction: Fine-tuned Mistral-7B on the NewsKG21 dataset for subject–predicate–object triple extraction; evaluated three training-data strategies (gold standard, spaCy-filtered, combined), achieving best F1 of 0.211 with the combined approach using Alpaca-style prompting and LoRA on HPC infrastructure.
- Biomedical Pipelines & Computational Optimisation: Built an NLP pipeline over Medline XML and ChEBI ontology for chemical NER and entity resolution; developed a genetic algorithm optimising insulin dosage, meal intake, and exercise timing for Type 2 diabetes management, achieving 100% time-in-range (70–180 mg/dL) with zero hypo- or hyperglycaemic events in simulation.
Panimalar Engineering College
B.E. Computer Science and Engineering — GPA: 7.96 / 10
- Built a real-time Remote E-Proctoring System using NLP for behavioural anomaly detection and OpenCV for computer-vision-based malpractice identification during online exams, integrated with Amazon S3 for scalable evidence storage.
External Engagements & Achievements
ZHAW × Roche Hackathon 2025 | 2nd Place Winner
Sep 2025- Prototyped an AI-driven remote patient monitoring solution for diabetes management aligned with Roche's Hospital at Home initiative, integrating wearable data analytics and predictive modelling; secured 2nd place across all competing teams.
Sustainability RAG System | SDSC Workshop – Canton of Zurich
Feb 2026- Participated in an SDSC-facilitated industry workshop building a RAG system for EU CSRD compliance querying; hands-on experimentation with hybrid retrieval strategies (BM25, dense vector, HyDE, LLM reranking), multi-agent architectures (RAG Agent, ReAct ToolAgent, LLM Router), and evaluation frameworks (RAGAS, MRR, NDCG, Hit Rate).
HealthTech Summer School | Biodesign & Digital Health Innovation
Jul 2025- Acted as CTO in a multi-disciplinary team; led SOTA analysis of AI diagnostic technologies, conducted patent landscape and gap analysis, and assessed regulatory pathways, from clinical immersion to final industry pitch.
Innosuisse Business Concept Programme | Startup Campus Switzerland
Feb – May 2025- Completed a 12-week Innosuisse-recognised business concept acceleration programme as part of the founding team of KIM; led technical feasibility scoping, evaluated sensor-based and computer vision approaches for species identification, and pitched the solution architecture before a jury.
Technical Skills
Languages & Core Tools: Python | SQL | R | TypeScript | Shell Scripting | Git/GitHub | Docker | Conda
ML & LLMs: PyTorch | Scikit-learn | Hugging Face Transformers | LLM Fine-tuning (QLoRA, LoRA, Unsloth) | Prompt Engineering | Agentic AI
NLP & Biomedical AI: spaCy | scispaCy | PubMedBERT | NER | Relation Extraction | Ontology Processing | Clinical NLP | RAG | Knowledge Graphs | SPARQL | Information Retrieval
Data & Visualization: Pandas | NumPy | Matplotlib | Power BI | PostgreSQL + pgvector | ChromaDB | Neo4j | OpenCV
Tools & Infra: FastAPI | REST APIs | React Native | React | Supabase | LangChain | MCP | HPC Clusters | RAGAS | Agile/Scrum
Languages
Telugu Native | Tamil Native | English C2 (Fluent) | Hindi C1 (Fluent) | German A2 | French A1
Publications
- "Critical State Detection for Adversarial Attacks in Deep Reinforcement Learning," 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), Pasadena, CA, USA, 2021.