Research & Expertise
The technical depth behind the practice.
The research background that informs how we design AI systems, evaluate models, and treat data as the foundation rather than an afterthought.
Areas of focus
Where our work has the most depth.
Deep learning and scientific ML
Computer vision, super-resolution, and applied deep learning research, including Hi-C super-resolution for genomics — the kind of work where data structure matters as much as model choice.
RAG, LLMs, and agentic systems
Retrieval pipelines, evaluation harnesses, and tool-using agents designed for grounded answers and traceable reasoning, not demo magic.
Bioinformatics and genomics
Reproducible computational pipelines for high-dimensional biological data, with attention to provenance, scale, and reproducibility.
Data engineering for research
Cloud and HPC-friendly pipelines, orchestration, and storage layouts built to support both research iteration and operational reliability.
Capabilities
A working map of what we can pick up.
These are areas where we can work independently and add real engineering value — not buzzwords we know about, but capabilities we have actually shipped against.
Applied machine learning
- Deep learning and computer vision
- Predictive modeling and forecasting
- Model evaluation and benchmarking
- Responsible AI documentation
LLMs, RAG, and agents
- Retrieval-Augmented Generation systems
- Semantic search and document intelligence
- Prompt and evaluation pipelines
- Agentic and tool-using workflows
Data engineering
- ETL/ELT and orchestration (Airflow-style)
- Cloud and lakehouse architectures
- Spark and distributed processing
- Data quality, lineage, and reproducibility
Scientific and research computing
- Genomics and bioinformatics pipelines
- Hi-C super-resolution and scientific ML
- HPC and cloud-based research workflows
- Privacy-aware handling of sensitive data
Tooling
Stack we are fluent in.
We are pragmatic about tooling. The list below reflects what we use regularly across AI, data engineering, and research computing engagements.
- Python
- PyTorch
- TensorFlow
- scikit-learn
- PySpark
- Databricks
- Airflow
- dbt
- AWS
- GCP
- Kubernetes
- Docker
- FAISS
- pgvector
- LangChain
- LlamaIndex
- Snakemake
- Nextflow
- SLURM
- Bash
Collaborate
Working on something technically hard?
Research collaborations, scientific AI projects, and serious data engineering work are all welcome conversations.