Vladisav Jelisavcic
Verified Expert in Engineering
Machine Learning Developer
Vladisav received his PhD in computer science from Belgrade University—specializing in machine learning with big data. He is passionate about big data and scalable machine learning algorithms and possesses expertise in Python and Java. He has over a decade of experience in programming and machine learning while working in teams of varying sizes. Vladisav enjoys working in innovative and fast-paced startup/academic environments.
Portfolio
Experience
Availability
Preferred Environment
Jupyter, Git, Apache Maven, IntelliJ IDEA
The most amazing...
...algorithm I've created and coded is a novel tool for learning the scale-free Gaussian Markov random fields from big data.
Work Experience
Data Scientist
Censia
- Derived meaningful insights from semi-structured big data in the terabyte range.
- Implemented NLP algorithms for semantic clustering, conflation, and text understanding.
- Designed and scaled-up novel algorithms for scalable machine learning analytics on terabytes of data.
- Visualized insights and patterns.
- Derived and delivered product features from data.
Research Assistant
Mathematical Institute of the Serbian Academy of Sciences and Arts
- Developed a software tool for early aneurysm prediction using neural networks and Apache Spark.
- Worked on a prediction model for analyzing tensiomyography signals.
- Worked as the lead back-end developer for a wiki-based system for a cultural heritage presentation with rich metadata management. The system was written in Java using eXistdb for storage.
- Implemented a module for topic modeling.
- Developed a software framework in Java for complex dynamic form generation based on XSD schema.
- Worked on accelerating and improving scalability of machine learning algorithms for L1 regularized structured prediction.
- Ran the research on novel word embedding methods for various NLP tasks.
Lead Technical Engineer
Magnaf.ai
- Designed and implemented scalable microservice platform for image processing and entity recognition from the text (Python/Node.js, Google Cloud, Docker, TensorFlow, Keras, and Scikit-learn).
- Designed a data processing platform with the semantic matching of extracted entities, based on CQRS and event-sourcing paradigm (Python/TensorFlow, and Elasticsearch).
- Created and optimized REST API using Node.js and Swagger for several bottleneck services.
- Defined data collection, preprocessing, and annotation workflows, and outlined the design for annotation tool custom made for the domain.
- Created deployment scripts and procedures (Docker, Node.js, Google Cloud App Engine, AWS Beanstalk, and ZEIT).
Software Developer
Vitid
- Implemented an ERP system in Java.
- Worked on a business intelligence module.
- Developed a human resources and employee payroll module.
Research Scholar
Center for Data Analytics and Biomedical Informatics | Temple University
- Developed an algorithm for fast learning of a sparse Gaussian Markov Random field from big data.
- Worked on developing novel algorithms for fast structured regression on large heterogeneous graphs.
- Built a large-scale prediction model for sepsis prediction from inpatient hospital data.
Experience
Apache Ignite
https://ignite.apache.org/Decomposition-based Reparameterization for Efficient Estimation of Sparse Gaussian Conditional Random Fields
https://bit.ly/2ZuaAlxWe studied how the reparametrization of the original problem may lead to more efficient estimation procedures. Particularly, instead of representing a problem through a precision matrix, we used its Cholesky factor—its attractive properties allowed inexpensive coordinate descent-based optimization algorithm that is also highly parallelizable.
Learning of Scale-free Networks based on Cholesky Factorization
https://bit.ly/3bzfuW1In this article, we shed light on an alternative objective, where instead of precision, its Cholesky factor is penalized by the 𝐿1 norm. We show that such an objective criterion possesses attractive properties that allowed us to develop a very fast scale-free network estimation via a Cholesky factorization (SNETCH) optimization algorithm based on coordinate descent (which is highly parallelizable and can exploit an active set approach). This is particularly suited for problems with structures that allow sparse Cholesky factor, crucial for scale-free networks.
Evaluation of synthetically generated examples and high-impact apps from a biomedical domain of 900,000+ variables provide evidence that for such tasks the SNETCH algorithm can learn the underlying structure more accurately, and an order of magnitude faster than state-of-the-art approaches based on the 𝐿1 penalized precision matrix.
Fast Sparse Gaussian Markov Random Fields Learning Based on Cholesky Factorization
https://bit.ly/2ZfKaqyWe proposed a novel objective with a regularization term that penalizes an approximate product of the Cholesky decomposed precision matrix. This new reparametrization of the penalty term allows efficient coordinate descent optimization. It results in synergy with an active set approach that results in a fast and efficient method for learning the sparse inverse covariance matrix.
We evaluated the speed and solution quality of the newly proposed SCHL method on problems consisting of up to 24,840 variables. Our approach was several times faster than three state-of-the-art approaches. Applying it to a high impact problem from the health informatics domain, we also demonstrate that SCHL can be used to discover interpretable networks.
Distance-based Modeling of Interactions in Structured Regressions
https://bit.ly/3h6JSIDHere, we relax that assumption and propose a feature that is based on distance and can adapt to ensure that variables have a smaller or larger difference in values. We utilized a Gaussian conditional random field model, where we have extended its originally proposed interaction potential to include a distance term. The extended model is compared to the baseline in various structured regression setups. An increase in predictive accuracy was observed on both synthetic examples and real-world applications, including challenging tasks from climate and healthcare domains.
SNETCH
https://github.com/vladisav/SNETCHSkills
Languages
Python, Java, Octave, C, JavaScript
Frameworks
Spark, Apache Spark, Spring, Hibernate, JPA, Jakarta Server Pages (JSP), JSF
Libraries/APIs
PySpark, NumPy, PyTorch, Pandas, Scikit-learn, TensorFlow, Keras, Spark ML, PubSubJS, MALLET, Mahout, Apache Lucene, Node.js
Tools
Git, Apache Ignite, MATLAB, Jupyter, Plotly, IntelliJ IDEA, Apache Maven, Apache Tomcat
Paradigms
REST, Concurrent Programming, Object-oriented Programming (OOP), Distributed Programming, Asynchronous Programming, Event Sourcing, CQRS
Platforms
Linux, Databricks, Zeppelin, Jupyter Notebook, Docker, AWS Lambda, Amazon Web Services (AWS)
Storage
Elasticsearch, Google Cloud Datastore, Google Cloud Storage, Amazon DynamoDB, Amazon S3 (AWS S3), MySQL, eXist-db, Google Cloud, PostgreSQL
Other
Data Engineering, Machine Learning, Data Analytics, Data Mining, Natural Language Processing (NLP), Big Data, Research, GPT, Generative Pre-trained Transformers (GPT), Serverless, Google Cloud Functions
Education
PhD Degree in Electrical Engineering and Computer Science
University of Belgrade, School of Electrical Engineering - Belgrade, Serbia
Master's Degree in Electrical Engineering and Computer Science
University of Belgrade, School of Electrical Engineering - Belgrade, Serbia
Bachelor's Degree in Electrical Engineering and Computer Science
University of Belgrade, School of Electrical Engineering - Belgrade, Serbia
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