Engineering Lead | Software, Data & ML Engineering for Computational Biology
PhD Computer Science · self-taught computational biologist & bioinformatician
📍 Switzerland
I am a software engineer by training, with a PhD in Computer Science & master's degrees in Embedded Systems & Information Systems. For over a decade I have built production-grade software, data platforms & machine-learning systems across life sciences, healthcare, supply chain & IoT.
My career spans academic research, peer-reviewed publishing & industry. That mix is what I bring to the table: the rigour of research, combined with the pragmatism of shipping systems that people rely on every day.
Along the way I turned these skills to biology & taught myself computational biology & bioinformatics. At Debiopharm I lead the data & bioinformatics team & work hands-on across preclinical trial analytics, biomarker discovery, antibody developability for our antibody-drug conjugate (ADC) programs & simulation-based design of bioequivalence trials.
I enjoy building cross-functional teams of software engineers, machine learning engineers, data scientists, bioinformaticians & computational biologists, where each discipline sharpens the others.
I believe in continuous learning: I contribute to open-source software & compete in computational biology challenges. At the heart of my work is a commitment to high-quality software, guided by a passion for clean, pragmatic code.
Embedded systems & IoT
BOLT IoT · TU Berlin · TU Eindhoven · ETH Zürich
Machine learning research
PhD Computer Science · University of Lausanne
Software & data engineering
GenLots · SOPHiA GENETICS
Data, AI & computational biology
Debiopharm
Alongside building & running our data & analytics platforms, from the central data repository to our data warehouses, these are the scientific problems my team & I work on every day, from antibody design to the clinic.
Illustrations use simulated example data.
protein engineering
In-silico developability & design support for antibody-drug conjugate programs: protein language models, structure modelling & PTM-liability scans to rank CMC risk across antibody candidates & engineered formats.
in-vitro pharmacology
Analysis of in-vitro assay data across our programs: cytotoxicity dose-response & IC50, antibody internalization & target binding, with standardized curve fitting & quality control across assays & CROs.
in-vivo pharmacology
Lead the data analysis & visualization for all preclinical efficacy trials at Debiopharm: harmonizing CRO data, computing tumor growth inhibition, AUC & relative tumor volume, & serving interactive dashboards across PDX & CDX mouse models.
translational oncology
Transcriptomics biomarker discovery & validation for clinical-stage oncology programs: RNA-seq QC & batch-effect assessment, differential expression, pathway enrichment, ML gene signatures & omics foundation-model embeddings.
digital pathology
Pipeline for immunohistochemistry whole-slide images: tiling & stain normalization, CNN nuclear segmentation & Ki-67 scoring, benchmarked against a classical colour-deconvolution baseline.
clinical pharmacology
Generative models (VAE, WGAN-GP, physics-informed VAE, copulas) that learn from a small set of real PK profiles, with Monte Carlo trial simulations to test Type I error & optimize trial sample size.
bioinformatics
Built RNA-seq, variant-calling & ctDNA pipelines with HRD, MSI & TMB scoring, & automated transfers from CROs & sequencing vendors into a central omics hub alongside TCGA, CCLE & CPTAC.
Ginkgo Datapoints AbDev Competition · 2025
Predicted 5 developability properties from sequence with ensembles of protein language model embeddings (ProtT5, p-IgGen, ESM-2) & LightGBM / ridge stacking.
Competition →Kaggle · AIRR-ML-25
Predicted immune state from T/B-cell receptor repertoires using k-mer XGBoost baselines & ESM-2 multiple-instance learning.
Competition →Open source
Curated catalogue of foundation models for biopharma R&D: single-cell & genomics, antibody sequence & structure, generative design, small molecules & ADCs.
GitHub →Open source
Curated research on human mobility modeling & next-place prediction, started during my PhD. My most-starred repository.
GitHub →Open source
Transforms ERA5 precipitation data into queryable Parquet with geospatial indexing.
GitHub →Paper code & engineering notes
Code for my SIGSPATIAL & SGX papers, data-driven tech choices for a clinical data warehouse & a PostgreSQL tuning cheat sheet.
github.com/vaibhav90 →
Debiopharm
Debiopharm
SOPHiA GENETICS
GenLots
Distributed Object Programming Lab
ETH Zürich
BOLT IoT
National Institute of Oceanography
Writing clean, tested, maintainable software across the stack, from embedded firmware to web applications & data services.
Designing & building scalable, production-grade platforms from data ingestion to user-facing applications, with focus on system reliability, performance optimization, & maintainability.
Designing & implementing enterprise-scale data repositories, data warehouses, & real-time data processing systems.
Deploying & managing scalable, cloud-native applications & infrastructure.
Building & deploying robust, scalable, & reproducible ML pipelines & services.
Developing & productionizing privacy-aware LLM applications for scientific document analysis & generation.
Applying protein language models, structure predictors & omics foundation models to antibody engineering & biomarker discovery.
Creating ML-driven platforms for predictive analytics & statistical analysis.
Engineering bioinformatics pipelines for harmonizing & analyzing genomics, transcriptomics, & other omics data.
Sequence- & structure-based assessment of antibody developability for ADC programs.
In-vitro assay & preclinical trial analytics, pharmacokinetic studies & simulation for clinical trial design.
Supporting small molecule & linker-payload research with data analysis & molecular modeling.
Developing platforms & strategies to identify & validate novel biomarkers from complex datasets.
Developing & owning regression & performance testing systems for critical data pipelines.
Implementing DevOps-based automation for GxP compliance, infrastructure qualification, & security audits.
Using formal specifications to verify the logical correctness & reliability of complex data systems.
Leading teams of data engineers, bioinformaticians, & software developers.
Defining the technical vision for AI & data platforms in R&D environments.
Evaluating the technology stacks & ML strategies of healthcare startups for investment purposes.
University of Lausanne - Information Systems Department
November 2015 – August 2019
Thesis: Information Systems for Privacy-Aware Machine Learning
ETH Zürich & Eindhoven University of Technology
August 2014 – August 2015
Master thesis at ETH Zürich · Graduated cum laude
Technische Universität Berlin
February 2013 – August 2014
Graduated with Honours
V. Kulkarni, B. Garbinato
ACM SIGSPATIAL - AI and Deep Learning for Geographic Knowledge Discovery, 2017
PDF →V. Kulkarni, A. Mahalunkar, B. Garbinato, J. D. Kelleher
Entropy, 2019
PDF →V. Kulkarni, N. Tagasovska, T. Vatter, B. Garbinato
Neural Information Processing Systems Workshop on spatiotemporal modeling, 2018
Preprint →V. Kulkarni*, A. Moro*, B. Garbinato (*co-primary authors)
ACM SIGSPATIAL Workshop on GeoStreaming, 2016
PDF →D. Naous, V. Kulkarni, C. Legner, B. Garbinato
International Conference on Information Systems (ICIS), 2019
PDF →V. Kulkarni, B. Chapuis, B. Garbinato
ACM SenSys Workshop on Human-centered Sensing, Networking, and Systems, 2017
PDF →V. Kulkarni, B. Garbinato
Advances in Geographic Information Systems (ACM SIGSPATIAL), 2019
PDF →B. Chapuis, A. Moro, V. Kulkarni, B. Garbinato
ACM SIGSPATIAL Workshop on Mobile Geographic Information Systems, 2016
PDF →V. Kulkarni, A. Mahalunkar, B. Garbinato, J. D. Kelleher
28th International Conference on Artificial Neural Networks (ICANN), 2019
PDF →V. Kulkarni, A. Moro, B. Chapuis, B. Garbinato
Advances in Geographic Information Systems (ACM SIGSPATIAL), 2017
Preprint →A. Moro, V. Kulkarni, P. Ghiringhelli, B. Chapuis, K. Huguenin, B. Garbinato
Advances in Geographic Information Systems (ACM SIGSPATIAL), 2019
PDF →A. Hithnawi, V. Kulkarni, S. Li, H. Shafagh
ACM MobiCom workshop in Software Radio Implementation Forum (SRIF), 2016
PDF →V. Kulkarni, A. Moro, B. Chapuis, B. Garbinato
IEEE Conference On Trust, Security & Privacy In Computing & Communications, 2018
Preprint →V. Kulkarni, A. Moro, B. Garbinato
ACM Conference on Mobile Computing and Networking (MobiCom) (Poster), 2016
PDF →