Publication
Clustering Algorithms: From Start to State of the Art
https://www.toptal.com/developers/machine-learning/clustering-algorithms
Lovro is available for hire
Hire LovroLovro Iliassich
Verified Expert in Engineering
Machine Learning Developer
Rijeka, Croatia
Toptal member since February 20, 2016
Bio
Lovro is a machine learning engineer and data scientist who has worked in AI since starting his PhD in 2006 and brings full-stack software engineering experience. He builds custom machine learning and deep learning models as well as generative AI and multi-agent systems, choosing the right approach for each problem and delivering it end-to-end. Lovro's career spans research at INRIA and on a European Space Agency project, engineering at Amazon, and 20+ client engagements.
Portfolio
Toptal and Toptal Clients
Amazon Web Services (AWS), Keras, OpenCV, Pandas, Python 3, SQL...
Buildfast
Python, Artificial Intelligence (AI)...
Job.com
Python, Natural Language Processing (NLP), Large Language Models (LLMs)...
Experience
- Data Science - 12 years
- Python - 10 years
- Machine Learning - 10 years
- Artificial Intelligence (AI) - 10 years
- Deep Neural Networks (DNNs) - 7 years
- Computer Vision - 5 years
- Natural Language Processing (NLP) - 5 years
- Generative Pre-trained Transformers (GPT) - 3 years
Preferred Environment
Amazon Web Services (AWS), Python, Machine Learning, Data Science
The most amazing...
...project I've worked on was my post-doc work with the European Space Agency: a real-time computer vision system that locates a Mars lander as it lands.
Work Experience
Machine Learning Engineer | Data Scientist | Technical Screener
2017 - PRESENT
Toptal and Toptal Clients
- Delivered 20+ end-to-end AI engagements for clients across finance, healthcare, energy, education, media, and eCommerce, usually as the sole machine learning engineer or the technical lead.
- Contributed to financial markets: built equity return prediction models for a quantitative investment firm, extracting NLP features from earnings-call transcripts and combining them with market and fundamental data in a gradient-boosted ensemble.
- Utilized retrieval-augmented generation: designed RAG systems over large document corpora, including a clinical assistant that extracts values, reference ranges, and chart data from laboratory reports.
- Contributed to multi-agent systems: architected agent-based assistants that decompose a request across specialized agents, including a flight assistant for pilots, coordinating several flight-planning agents behind a single conversational interface.
- Leveraged natural language processing: built an early LLM-based prototype for a global professional services firm that matches employees to internal projects by scoring their CVs against project descriptions.
- Applied computer vision: built a pipeline that detects diagnostic biostrips in photos and quantifies their color response with a trained neural network, and an OCR-based system that grades handwritten mathematics tests.
- Contributed to healthcare and genomics: developed predictive models for early-stage cancer detection and chronic kidney disease risk from genomic data, handling cohorts from small clinical samples to volumes requiring distributed processing in Spark.
- Contributed to forecasting and behavioral modeling: modeled EV charging station usage and user behavior, customer churn, lifetime value, and spending, and built product recommendation systems.
- Developed a convolutional neural network for environmental sound recognition and classification.
- Conducted over 500 technical interviews as a screener for Toptal's artificial intelligence and data science specializations.
Technologies: Amazon Web Services (AWS), Keras, OpenCV, Pandas, Python 3, SQL, Artificial Intelligence (AI), Machine Learning, Algorithms, Scikit-learn, TensorFlow, Python, Computer Vision, Deep Neural Networks (DNNs), Data Science, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Deep Learning, PyTorch, Regression, Regression Modeling, Classification, Neural Networks, Convolutional Neural Networks (CNNs), XGBoost, Visualization, Recommendation Systems, Image Recognition, Predictive Modeling, CatBoost, Language Models, Linux, Optical Character Recognition (OCR), Minimum Viable Product (MVP), Cloud, Data Preprocessing, Feature Analysis, Healthcare, Microsoft Excel, Object-oriented Programming (OOP), Adobe Photoshop, Database Modeling, REST, Jupyter Notebook, APIs, Data Analysis, Statistics, Image Processing, Time Series Analysis, Time Series, Amazon SageMaker, Databricks, Selenium, Google Cloud, REST APIs, PDF, Web Development, ChatGPT, Data Scientist, Data Scraping, Large Language Models (LLMs), Windows, Graphical User Interface (GUI), Agile Software Development, AI Model Training, Algorithm Design, Technical Leadership, PySpark, Spark ML
AI Engineer | Solution Architect
2024 - 2026
Buildfast
- Built the generative AI and agent capabilities of a three-sided marketplace connecting homeowners, crew members, and the companies that hire crews for homebuilding and repair projects.
- Developed an AI video interviewing system that screens and qualifies crew members at scale, replacing manual first-round interviews.
- Built a conversational job search agent that matches crew members to open projects from natural-language queries.
- Generated quotes and project specifications automatically from meeting recordings, turning unstructured client conversations into structured, priced scopes of work.
- Architected the AI services and their integration into the existing marketplace platform.
Technologies: Python, Artificial Intelligence (AI), Generative Artificial Intelligence (GenAI), Large Language Models (LLMs), AI Agents, Multi-agent Systems, Retrieval-augmented Generation (RAG), Prompt Engineering, OpenAI API, Natural Language Processing (NLP), AI Chatbots, Document Processing, Amazon Web Services (AWS), REST APIs, Solution Architecture, Software Architecture, Technical Leadership
Machine Learning Engineer
2023 - 2024
Job.com
- Developed large language model applications for the recruiting industry, including conversational assistants for candidates and recruiters.
- Built retrieval-augmented generation pipelines over job listings and candidate documents.
- Developed candidate-to-job matching for the platform, scoring resumes against job listings with natural language processing models.
Technologies: Python, Natural Language Processing (NLP), Large Language Models (LLMs), RAG Systems, Retrieval-augmented Generation (RAG), AI Chatbots, Chatbots, Generative Pre-trained Transformers (GPT), OpenAI API, Prompt Engineering, Vector Databases, Machine Learning, Deep Learning, Language Models, Data Preprocessing, AI Model Training
Machine Learning Developer
2021 - 2023
Trust & Safety Laboratory Inc.
- Developed CatBoost and neural network models that detect misinformation in social media posts, used by some of the largest social networks to check posts on their own platforms.
- Implemented semantic matching by comparing posts against known misinformation using text embeddings and assessing each match across several axes.
- Implemented multilingual detection for several major languages.
- Developed and maintained the NLP pipeline, processing text from databases and reviews, and scraping web articles and social networks for misinformation content.
Technologies: Python, Machine Learning, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Deep Neural Networks (DNNs), CatBoost, Language Models, Amazon Web Services (AWS), Regression, Regression Modeling, Classification, Neural Networks, XGBoost, Visualization, Deep Learning, PyTorch, Linux, Cloud, Data Preprocessing, Feature Analysis, Jupyter Notebook, Data Analysis, Sentiment Analysis, Data Scientist, Large Language Models (LLMs), Agile Software Development, AI Model Training
Software Development Engineer
2017 - 2018
Amazon
- Built back-end services in Java on AWS for the globalization of Try Before You Buy, Amazon's fashion retail program, extending an offering built for the US to European marketplaces.
- Adapted and deployed the program's services for individual European markets, each with its own locale, catalog, and marketplace rules.
- Developed the customer notification and transactional email services behind the program, including the templating used to localize messages per market.
Technologies: Amazon Web Services (AWS), Full-stack, Java, Linux, Cloud, Agile Software Development
Research Scholar
2015 - 2016
Drexel University
- Researched the mining and modeling of ranked and preference data with the Database Group, developing methods that reconstruct the preferences of a user population from partial input: rankings, partial orders, and pairwise comparisons.
- Implemented a Java library for representing, handling, and mining rank and preference data, used to run the group's experiments on real and synthetic preference datasets.
- Published peer-reviewed papers on the resulting approaches to modeling user preferences.
Technologies: Pandas, Python 3, Data Science, Java, Artificial Intelligence (AI), Machine Learning, Algorithms, Python, Classification, Visualization, Data Preprocessing, Rankings, Jupyter Notebook, Data Analysis, Data Scientist, Research, AI Model Training, Algorithm Design, Machine-learned Ranking (MLR)
Research Engineer
2012 - 2015
INRIA
- Parallelized machine learning algorithms for high-performance computing, including SVM, affinity propagation, gradient descent, boosting, and neural networks.
- Optimized those algorithms in C/C++ for a large-memory (8 TB RAM) NUMA machine, tuning cache behavior, memory-block latency, and process-to-core assignment.
- Released apro (https://github.com/lovro-i/apro), a parallelized Java implementation of the affinity propagation clustering algorithm, as open source out of this work.
- Built a crawler and a category recommender system for Wikipedia on a semantic web project mining RDF and Wikidata.
Technologies: Python 3, Data Science, Java, Machine Learning, Algorithms, MATLAB, Python, C, C++, Classification, Visualization, Linux, Distributed Computing, Data Preprocessing, Parallel Computing, Data Analysis, Statistics, Data Scientist, Data Scraping, Research, Algorithm Design
Assistant Professor
2011 - 2012
Metropolitan University
- Taught undergraduate and graduate courses at the Faculty of Information Technology, including information systems design, web systems, web application development, and distributed systems.
- Developed the university's own information system, including the business process management workflow behind it.
- Authored and published the course material for the university's online courses.
Technologies: Java, UML, Web, University Teaching, Web Development, Research
Post-doc Researcher
2010 - 2011
University of Eastern Piedmont
- Built a real-time navigation system in C/C++ and OpenCV for a European Space Agency project, locating a Mars lander during descent from the live feed of its downward-facing camera.
- Detected and tracked distinctive surface features across the video stream, combining the visual position estimate with lidar and inertial measurement unit data.
- Modeled the Martian surface in Java 3D, simulating illumination at different times of day to generate imagery against which the vision system could be tested.
- Built a lander descent simulator in C/C++ and MATLAB.
Technologies: OpenCV, Java, Artificial Intelligence (AI), Machine Learning, Algorithms, MATLAB, C, C++, Computer Vision, Visualization, Image Recognition, Statistics, Image Processing, Data Scientist, Research, Algorithm Design
PhD Student
2006 - 2010
University of Turin, Department of Computer Science
- Completed a doctorate in data mining and machine learning, working across text mining, sequential pattern mining, and complex network analysis.
- Performed text mining and document classification on public-sector documents for the regional government of Piedmont.
- Researched sequential pattern mining, including recognizing individual users from their keystroke patterns.
- Built usage models for a computational grid from log data, predicting network usage with data mining and complex network analysis.
Technologies: Data Science, Java, Machine Learning, Algorithms, Predictive Modeling, Grid Computing, Statistics, Time Series Analysis, Research
Software Engineer
2001 - 2006
RCUB
- Designed and architected a real-time wide area network monitoring system, covering topology discovery, reporting, and intelligent agent assistance, deployed at large banks and telecommunications companies.
- Designed and implemented the hospital information system now used in about 75% of the hospitals in Serbia, covering medical records, patient scheduling, and clinical workflow, and led the team that built it.
- Worked across the full delivery cycle of these systems, from requirements interviews and UML specification through database design and back-end logic to web and stand-alone clients.
- Designed, architected, and implemented a government information system and a fleet management system.
Technologies: Full-stack, SQL, Java, UML, HTML, Web, Databases, Healthcare, Object-oriented Programming (OOP), Adobe Photoshop, Database Modeling, REST, Web Development, Windows, Graphical User Interface (GUI)
Experience
Earnings-Call NLP for Equity Return Prediction
A quantitative investment firm wanted to capture the signal in what is said on earnings calls, from management's remarks to analysts' questions and the answers they draw, information that conventional market data does not contain.
I built the ingestion and feature pipeline for earnings-call transcripts and applied transformer-based language models to extract sentiment, topic, and tone signals from both management and analyst speech. I then combined these text features with market, fundamental, and time-series inputs in a gradient-boosted ensemble that predicts equity returns.
The firm gained a pipeline that turns unstructured call transcripts into model-ready features alongside its conventional data.
I built the ingestion and feature pipeline for earnings-call transcripts and applied transformer-based language models to extract sentiment, topic, and tone signals from both management and analyst speech. I then combined these text features with market, fundamental, and time-series inputs in a gradient-boosted ensemble that predicts equity returns.
The firm gained a pipeline that turns unstructured call transcripts into model-ready features alongside its conventional data.
EV Charging Demand and User Behavior Modeling
Planning capacity for a network of electric vehicle charging stations requires knowing where, when, and how drivers use them.
I analyzed historical charging-session data and modeled station-level usage patterns, temporal demand, and driver behavior.
The models supported capacity planning and utilization decisions across the network.
I analyzed historical charging-session data and modeled station-level usage patterns, temporal demand, and driver behavior.
The models supported capacity planning and utilization decisions across the network.
Multi-agent Flight Assistant for Pilots
Preparing for a flight means pulling together weather data, NOTAMs, and other planning inputs from separate sources, and preparing for pilot theory exams means working through a large body of study material.
I architected an AI assistant that handles both behind a single conversational interface. Specialized agents retrieve and interpret weather data, NOTAMs, and other flight-planning inputs, while a coordinating agent decomposes the pilot's request, dispatches it to the right agents, and assembles a grounded answer. For exam preparation, the assistant generates and evaluates practice questions grounded in aviation study material.
Pilots get one place to ask about a flight or an exam topic, with answers grounded in current data and source material.
I architected an AI assistant that handles both behind a single conversational interface. Specialized agents retrieve and interpret weather data, NOTAMs, and other flight-planning inputs, while a coordinating agent decomposes the pilot's request, dispatches it to the right agents, and assembles a grounded answer. For exam preparation, the assistant generates and evaluates practice questions grounded in aviation study material.
Pilots get one place to ask about a flight or an exam topic, with answers grounded in current data and source material.
Misinformation Detection in Social Media Posts
Some of the largest social networks needed to check posts on their platforms for misinformation at scale and across languages, before LLMs were practical for the task.
I developed the NLP pipeline and models behind the detection service. Posts were represented as text embeddings and matched semantically against known misinformation; each match was assessed on several axes, and CatBoost and neural network models combined those signals to flag misinforming posts. I implemented detection for several major languages.
The social networks used the service to check posts on their own platforms.
I developed the NLP pipeline and models behind the detection service. Posts were represented as text embeddings and matched semantically against known misinformation; each match was assessed on several axes, and CatBoost and neural network models combined those signals to flag misinforming posts. I implemented detection for several major languages.
The social networks used the service to check posts on their own platforms.
Buildfast: Generative AI for a Homebuilding Marketplace
Buildfast runs a three-sided marketplace connecting homeowners, crew members, and the contracting companies that hire crews. Screening crews, matching them to projects, and turning client meetings into quotes were all manual steps.
As an AI engineer and solution architect, I designed and built three AI capabilities and integrated them into the existing platform: an AI video interviewing system that screens and qualifies crew members, a conversational agent that matches crews to open projects from natural-language queries, and a pipeline that turns meeting recordings into structured, priced quotes and project specifications.
First-round crew interviews no longer need a person, and scopes of work come straight from client conversations.
As an AI engineer and solution architect, I designed and built three AI capabilities and integrated them into the existing platform: an AI video interviewing system that screens and qualifies crew members, a conversational agent that matches crews to open projects from natural-language queries, and a pipeline that turns meeting recordings into structured, priced quotes and project specifications.
First-round crew interviews no longer need a person, and scopes of work come straight from client conversations.
Employee-to-project Matching for Internal Staffing
A global professional services firm wanted to match employees to internal projects using the data already held in its HR database.
In early 2023, when large language models were still new, I helped build a prototype that scores how well an employee fits a project, working in a team of data scientists, engineers, and DevOps developers. Features extracted from CVs, text embeddings, and LLM-generated inputs, all drawn from the firm's HR database, were combined in a regression model that produced the match score.
The prototype was accepted, and the company that engaged me for early development continued to build it.
In early 2023, when large language models were still new, I helped build a prototype that scores how well an employee fits a project, working in a team of data scientists, engineers, and DevOps developers. Features extracted from CVs, text embeddings, and LLM-generated inputs, all drawn from the firm's HR database, were combined in a regression model that produced the match score.
The prototype was accepted, and the company that engaged me for early development continued to build it.
TaxBot: RAG Chatbot for a National Tax Service
A national tax service needed a chatbot that answers taxpayer inquiries from tax legislation and official guidance rather than with generic responses.
I architected and implemented the whole system: a serverless back end on AWS (API Gateway, Lambda, DynamoDB), Qdrant for vector storage and retrieval over tax legislation and guidance, and a React front end. The OpenAI API generates context-aware answers grounded in the retrieved source documents.
I took the project from architecture to a deployed, operational service.
I architected and implemented the whole system: a serverless back end on AWS (API Gateway, Lambda, DynamoDB), Qdrant for vector storage and retrieval over tax legislation and guidance, and a React front end. The OpenAI API generates context-aware answers grounded in the retrieved source documents.
I took the project from architecture to a deployed, operational service.
Early Colorectal Cancer Detection from Genomic Data
Colorectal cancer is far more treatable when caught early, but detecting it from genomic data means working with high-dimensional features against a few samples, where models overfit easily.
I developed predictive models that classify early-stage colorectal cancer from genomic data, relying on careful feature selection, dimensionality reduction, and a validation strategy designed to prevent overfitting.
The approach kept performance estimates honest on data where naive models look deceptively good.
I developed predictive models that classify early-stage colorectal cancer from genomic data, relying on careful feature selection, dimensionality reduction, and a validation strategy designed to prevent overfitting.
The approach kept performance estimates honest on data where naive models look deceptively good.
Clinical Reporting Assistant
Laboratory reports are written for clinicians, so physicians spend time turning raw results into reports their patients can understand.
I built a generative AI assistant that ingests laboratory reports and extracts the clinically relevant information: measured values, their reference ranges, and data presented as charts rather than text. Using retrieval-augmented generation over medical guidelines and report templates, it recommends a patient-facing template and populates it with the extracted findings.
Physicians review and edit a draft instead of composing each report from scratch.
I built a generative AI assistant that ingests laboratory reports and extracts the clinically relevant information: measured values, their reference ranges, and data presented as charts rather than text. Using retrieval-augmented generation over medical guidelines and report templates, it recommends a patient-facing template and populates it with the extracted findings.
Physicians review and edit a draft instead of composing each report from scratch.
Automated Mathematics Test Assessment
Grading handwritten mathematics tests is slow, repetitive work for teachers.
I architected a pipeline that combines computer vision and OCR to locate answer regions on scanned test sheets, recognize handwritten mathematical content, and check it against the expected solutions.
The system scores tests automatically and flags ambiguous answers for human review, so teachers only look at the cases that need their judgment.
I architected a pipeline that combines computer vision and OCR to locate answer regions on scanned test sheets, recognize handwritten mathematical content, and check it against the expected solutions.
The system scores tests automatically and flags ambiguous answers for human review, so teachers only look at the cases that need their judgment.
Member Churn and Lifetime Value Modeling for a Fitness Chain
A large chain of gyms wanted to know which members were likely to leave and what each member was worth.
Over a two-year engagement, I worked directly with the client's PostgreSQL database, writing SQL to assemble member-level datasets and developing models for member churn, customer lifetime value, and outlier detection.
The models gave the business a quantitative basis for retention and member-value decisions.
Over a two-year engagement, I worked directly with the client's PostgreSQL database, writing SQL to assemble member-level datasets and developing models for member churn, customer lifetime value, and outlier detection.
The models gave the business a quantitative basis for retention and member-value decisions.
Automated Color Quantification for Diagnostic Biostrips
Reading a diagnostic biostrip meant a technician judging its color change by eye, a subjective step that was hard to reproduce.
I built a pipeline that takes a JPEG photo and runs end to end with no human input. Classical computer vision techniques detect and isolate the test tube and the biostrip, and a neural network I trained compares the strip's colormap against a reference color spectrum.
Each strip is quantified numerically and graphically, replacing visual judgment with consistent, comparable readings.
I built a pipeline that takes a JPEG photo and runs end to end with no human input. Classical computer vision techniques detect and isolate the test tube and the biostrip, and a neural network I trained compares the strip's colormap against a reference color spectrum.
Each strip is quantified numerically and graphically, replacing visual judgment with consistent, comparable readings.
Mountain Rescue Service of Serbia: Rescue Operations Information System
The Mountain Rescue Service of Serbia, including its ski patrol, manages rescuers, shifts, and licensing, and must document every rescue action in reports that can be used in court or for insurance purposes.
The system began in 2005 as a desktop application for writing and storing rescue reports. In 2020, I rewrote it completely: a serverless AWS back end (Lambda, API Gateway, DynamoDB, Cognito) and a React front end. It now covers rescuers and shifts, rescue actions, licensing, reports for ski resorts, and analysis, and exports reports to PDF.
The system is still growing and gains new features every season.
The system began in 2005 as a desktop application for writing and storing rescue reports. In 2020, I rewrote it completely: a serverless AWS back end (Lambda, API Gateway, DynamoDB, Cognito) and a React front end. It now covers rescuers and shifts, rescue actions, licensing, reports for ski resorts, and analysis, and exports reports to PDF.
The system is still growing and gains new features every season.
Sailweek: Fleet Operations and Crew Management System
http://sailweek.comSailweek organizes weekly yachting tours with fleets of sailboats, which means coordinating skippers, hostesses, boats, and guests every week of the season.
Since 2022, I have architected and developed the company's information system, with a serverless AWS back end (Lambda, API Gateway, DynamoDB, Cognito) and a React front end. It began with the skipper and hostess workflow: staff management, boat assignments, and reporting. Each season adds features, including a boat database, docking arrangements, guest allocation to boats using optimization methods, and generative AI that helps skippers turn their own descriptions of boat damage into consistent, structured reports for the office.
The system keeps operations running smoothly for fleets of up to 60 boats and 500 guests, and continues to grow each season.
Since 2022, I have architected and developed the company's information system, with a serverless AWS back end (Lambda, API Gateway, DynamoDB, Cognito) and a React front end. It began with the skipper and hostess workflow: staff management, boat assignments, and reporting. Each season adds features, including a boat database, docking arrangements, guest allocation to boats using optimization methods, and generative AI that helps skippers turn their own descriptions of boat damage into consistent, structured reports for the office.
The system keeps operations running smoothly for fleets of up to 60 boats and 500 guests, and continues to grow each season.
Heliant: Hospital Information System
https://heliant.rsIn 2003, a hospital needed a system for scheduling patients and keeping their medical records.
As an architect and team lead at the University of Belgrade Computer Center, I designed the system and led the team that built it. The work covered the whole cycle, from requirements interviews and UML specification through database design and back-end logic to the client applications. By 2006, it had grown from a single-hospital tool into a broader hospital information system.
After I left in 2006, it continued to grow and was largely rewritten. Today, it is the hospital information system of choice for about 75% of hospitals in Serbia.
As an architect and team lead at the University of Belgrade Computer Center, I designed the system and led the team that built it. The work covered the whole cycle, from requirements interviews and UML specification through database design and back-end logic to the client applications. By 2006, it had grown from a single-hospital tool into a broader hospital information system.
After I left in 2006, it continued to grow and was largely rewritten. Today, it is the hospital information system of choice for about 75% of hospitals in Serbia.
Mandrago: Mobile Workforce Dispatching Platform
Businesses with a mobile workforce need to dispatch jobs to people in the field and know what happens once those people arrive.
From 2013 to 2015, I co-founded Mandrago and served as its architect, designer, and developer, building the entire stack: infrastructure, back end, web front end, and Android app. The platform handled job assignment, location tracking, check-in and check-out, worker grouping, and bidding. It was designed to adapt to any business with a mobile workforce rather than to one client.
The startup did not find its market niche and was discontinued.
From 2013 to 2015, I co-founded Mandrago and served as its architect, designer, and developer, building the entire stack: infrastructure, back end, web front end, and Android app. The platform handled job assignment, location tracking, check-in and check-out, worker grouping, and bidding. It was designed to adapt to any business with a mobile workforce rather than to one client.
The startup did not find its market niche and was discontinued.
Education
2006 - 2010
Ph.D. in Computer Science
University of Turin, Department of Informatics - Turin, Italy
2001 - 2004
Master of Science Degree in Computer Systems and Networks
University of Belgrade, School of Electrical Engineering - Belgrade, Serbia
1995 - 2001
Bachelor of Science Degree in Computer Science and Technology
University of Belgrade, School of Electrical Engineering - Belgrade, Serbia
Skills
Libraries/APIs
REST APIs, Scikit-learn, TensorFlow, PyTorch, Keras, OpenCV, XGBoost, Pandas, NumPy, Matplotlib, LSTM, Google API, React, Spark ML, jQuery, PySpark, Google Maps API, PayPal API, CatBoost, Stripe API, Stripe, OpenAI API, Google Vision API
Tools
Jupyter, Adobe Photoshop, Algorithm Design, Amazon Simple Queue Service (SQS), ChatGPT, Microsoft Excel, You Only Look Once (YOLO), Claude, Claude Code, Amazon SageMaker, MATLAB, Trello, Amazon Cognito, Amazon Simple Notification Service (SNS), OpenAI Gym
Languages
Python, Java, SQL, Python 3, CSS, HTML5, HTML, UML, C++, JavaScript, C, TypeScript
Paradigms
RESTful Development, Parallel Computing, REST, Model View Controller (MVC), Object-oriented Programming (OOP), Functional Programming, ETL, Distributed Computing, High-performance Computing (HPC), Distributed Programming, Agile Software Development, UI Design, Rapid Prototyping, Test-driven Development (TDD), Kanban, Real-time Systems, Anomaly Detection, Machine-learned Ranking (MLR), Web UX Design
Platforms
Jupyter Notebook, Amazon Web Services (AWS), Amazon EC2, Android, Linux, Windows, Web, Databricks, Google Cloud Platform (GCP), AWS Lambda, Docker, Ubuntu
Storage
Database Modeling, Database Architecture, Amazon S3 (AWS S3), PostgreSQL, MySQL, NoSQL, Amazon DynamoDB, Google Cloud, Databases, Elasticsearch
Frameworks
Android SDK, Play Framework, LangGraph, Selenium, JUnit, Apache Spark, Spark, Streamlit, Serverless Framework
Industry Expertise
Healthcare
Other
Algorithms, Recurrent Neural Networks (RNNs), Unsupervised Learning, Clustering Algorithms, Clustering, Regression Modeling, Regression, Classification Algorithms, Classification, Deep Neural Networks (DNNs), Deep Learning, Convolutional Neural Networks (CNNs), Neural Networks, Computer Vision, Machine Learning, Data Mining, Data Modeling, Data Science, Scientific Computing, Data Visualization, Artificial Intelligence (AI), Natural Language Processing (NLP), Web Development, Software Architecture, Back-end, Data Scientist, Generative Pre-trained Transformers (GPT), Data Preprocessing, Feature Analysis, OpenAI GPT-3 API, OpenAI GPT-4 API, Chatbots, Prompt Engineering, K-means Clustering, AI Chatbots, Forecasting, Long Short-term Memory (LSTM), LSTM Networks, AI Model Training, Full-stack Development, AI Integration, Design, R&D, AI Consulting, AI Programming, Time Series Forecasting, FastAPI, APIs, Full-stack, Data Architecture, Cloud, Recommendation Systems, Image Recognition, Predictive Modeling, Sentiment Analysis, Geospatial Data, Cloud Platforms, Graphical Models, Image Processing, Data Analysis, Time Series, Time Series Analysis, Data Analytics, Data Reporting, Statistics, Visualization, University Teaching, Minimum Viable Product (MVP), BERT, Data Scraping, Distributed Systems, Natural Language Queries, Large Language Models (LLMs), Spanish, Research, Graphical User Interface (GUI), OpenAI, Hierarchical Clustering, Optimization, Mathematics, Retrieval-augmented Generation (RAG), Statistical Analysis, Architecture, Generative Artificial Intelligence (GenAI), Technical Leadership, Document Processing, Dashboards, Startups, Solution Architecture, Product Design, CTO, Consulting, Churn Analysis, RAG Systems, Anthropic, Agentic AI, Web Crawlers, Big Data, Serverless, Data Engineering, OpenStreetMap, Stochastic Modeling, Web Scraping, Signal Processing, Robot Operating System (ROS), Reinforcement Learning, Generative Adversarial Networks (GANs), Web MVC, Mapping, Language Models, Optical Character Recognition (OCR), Amazon API Gateway, PDF, A/B Testing, Predictive Analytics, Clinical Trials, Genomics, Computer Networking, Software Engineering, Monitoring, Electrical Engineering, Digital Electronics, Computer Science, Rankings, Grid Computing, LangChain, DBSCAN, Hugging Face, Deep Reinforcement Learning, Qdrant, ChatGPT API, Vector Databases, Machine Learning Operations (MLOps), Health, Healthtech, Financial Markets, Fintech, PDF Scraping, AI Agents, Multi-agent Systems, Meta Llama, Equity, Equity Market Data, Computer Vision Algorithms, Images, Customer Lifetime Value (CLV), Time Series Data, Linear Optimization, Healthcare Software
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