About me
I am a software engineer at J.P. Morgan. I have experience (research staff, PhD & MSc) in distributed systems and cloud computing and some exposure to data analysis, statistical modeling and machine learning algorithms. I am currently interested in the financial technology (finTech) domain, where I am working on large-scale cloud-native compute distribution of financial risk modelling algorithms. More broadly, I am interested in ML and cloud architectures with applicability to finTech.
In my past work I focused on understanding the characteristics of workloads running in large scale systems, from HPC clusters to clouds, and use them in designing resource management architectures and algorithms that improve the ROI of the infrastructure and the workload performance. My work resulted in prototypes of core-components of the cloud management systems, e.g., schedulers and workload controllers, built on open-source technologies. I also used simulation tools for prototyping and testing scheduling algorithms.
I spend my free time usually outdoors, enjoying a good hike, or making plans for mountain climbing trips.
Key-words to broadly describe the areas of my work
Large-scale distributed systems
Cloud Computing
Performance evaluation and analysis
Data analytics and applied machine learning
Resource management for scalability, reliability and performance/cost optimizations
Technologies and programming languages I’ve worked with
- Programming languages: C, Java, Golang, Python. I’ve also worked sporadically with C++, Scala.
- Linux/Unix: shell scripting
- Web: gRPC, REST, micro-services
- Interactive data analytics/machine learning: Jupyter notebook
- Performance observability: Prometheus, Grafana, Jaeger, DataDog, ElasticSearch
- Cloud orchestration: OpenNebula, OpenStack, Docker, Mesos, Kubernetes
- Public cloud: IBM Cloud, AWS, GCP
- Data storage: relational/mysql, NoSql/Redis, timeseries/InfluxDB
- Data analytics: Spark, Flink, Kafka
- Machine learning: Tensorflow, Kubeflow, Airflow