Harvard University

Research Fellow

Weizmann Institute of Science

Working at the AI Center of the Weizmann Institute.
Using CNN-s as base for designing state-of-the-art neural networks tuned to process images resembling the variable resolution produced by the human eye. Daily responsibilities include:
 - Working with IBM High-performance-computing servers
 - Using PyTorch to build, train and test neural networks
 - Using Horovod and PyTorch to parallelize models
 - Building, maintaining and using Docker containers on Linux servers
 - Working with and processing large datasets (ImageNet, COCO)
 - Writing scientific reports

Supervisors:
Prof. Shimon Ullman (MIT)
Dr. Daniel Harari (MIT)

Computer Vision Researcher

Worshipful Company of Scientific Instrument Makers

An active participant in the ancient Livery. Frequently attend cultural & technology-related events as a representative in the City of London.
My mentor is Freeman Dr. Barrie Reece.

Apprentice

Bocconi AI & Neuroscience Student Association

Led the creation of the first Bocconi Association focused on AI & Neuroscience. The organization's main goal is to provide undergrads the opportunity to gain real-world experience in AI by working on a pilot research project under a mentor's guidence.

Main contributions include:
 - Worked with the Founder's team to ensure we receive one of the highest student approval ratings for the entire academic year (>500 student signatures)
 - Made key partnerships with multiple companies, including Bending Spoons, Banco BPM, Wenda
 - Made BAINSA one of very few industry-backed student associations, by raising ~5,000 EUR capital
 - Created a website & content management platform (bainsa.xyz)
 - Cooperated with the President & led the execution of 3x advertisement campaigns on multiple social media platforms (LinkedIn, Instagram)
 - Conducted 30+ interviews

Daily responsibilities include:
 - Organizing AI-research-themed events, involving discussion of BAINSA's pilot projects
 - Ensuring all research teams have adequate structure & resources to be effective in their projects
 - Communicating with partners & the rest of the Executive Board

President and Co-Founder

Run4AStrongMind

Created and carried out a charity fundraising campaign for spreading awareness about the relationship between sports and mental health:
 - Raised over 1,000$ in donations
 - Reached 10,000+ people
 - Corresponded with media & gave several interviews on national TV (Nova News, Actualno)
 - Ran a 21K mountain race with over 2000M of altitude climbing in support of the charity
 - Co-directed a short production about the relationship between sports & mental health
 - Used the charity's funds to donate to the largest accessible running community in Bulgaria (5kmrun)

Co-Founder

RwHealth

Led research and development efforts to entirly implement the following systems as part of the company's workflow:
 - Pipeline for OCR of medical documents. Technical stack: image enhancement,
machine learning, NLP, Azure, SQL
 - Telehealth system for transcribing patient-doctor consultations. Technical stack: AWS, Python, SQL
 - Wearable device big-data collection & aggregation. Technical stack: machine learning, Withings API, multi-threading
 - Several education workshops: How2Code Python, Computer Vision Basics

In addition, supported customers on Live environments & kept active communication with multiple technical support teams from external organizations - which outsource some of our services (wearable device hardware, website front-end development ect.).

Tech Lead

Institute of Information and Communication Technologies (IICT) - Bulgarian Academy of Sciences

Worked on two computational biology projects with applications in medical image analysis. Both projects were entirely implemented in Python and MATLAB. Project descriptions:

1. Novel method for skeletal age estimation based on cranial suture analysis
This project’s aim is to objectify and improve the accuracy of age estimation methods based on cranial suture analysis by elaborating an algorithm for automatic assessment of the suture closure degree in cross-section and assessing its relation to aging. For the purpose, volumetric images of dry skulls, generated by an industrial micro-CT system, are used. The images are analyzed by VGG-16, a semantic segmentation neural network, and the C-Means clustering algorithm. 
A number of quantifiable metrics about the degree of suture fusion are recorded. Such metrics have been collected for a large sample and correlated with the AAD (age-at-death) using linear regression. The produced model manages to reduce the error of predicted age estimates on validation samples over 3 times compared to the conventionally used methods. Project article:
https://www.sciencenewsforstudents.org/blog/eureka-lab/ISEF-2019-new-forensic-technique-better-gauge-age-death

2. Super-resolution on medical images
Low image quality is a common problem when dealing with CT data taken in vivo. The radiation exposure is the determining factor for quality, and as biological tissue is very vulnerable in that sense, commonly, little radiation is used. As a result, the fine details on such tomographical data is usually lost or hardly visible. This project aims at improving the readability of selected regions on medical images, by implementing a series of image processing techniques. Those include super-resolution with bi-cubic interpolation, kernel-based deblurring and SIM (Sphere Intersection Method) denoising. The algorithm managed to increase the NIQUE (Naturalness Image Quality Evaluator) score of medical samples over 2 times compared to the original.

Computational Biology Researcher

As part of my participation in the International Summer Science Institute (ISSI) program, worked on genome analysis algorithms based on BLAST. Throughout the duration of my stay, I managed to gather proof suggesting that soil bacteria is adapting to conventionally used soil fertilizers and summarized the results in a scientific report. Main technologies used for the project were Python and MATLAB.

Wolfram

Attended the Wolfram Summer School for student researchers. Throughout my stay, created a decoder for human-generated morse code sound. The implementation involved work with clustering algorithms and frequency thresholding. Entirely implemented in the Wolfram Language.

Thereafter, was selected to partake in the Wolfram Mentorship program. There, I entirely implemented a classifier for animal tracks & was able to successfully distinguish between the four most commonly encountered animal families - canines, felines, rodents and ungulates.

Student Researcher

Participated in a selective programming academy organized by SAP Labs Bulgaria. Throughout the program, explored a variety of programming languages and completed an individual project in Java at the end of the program. The project was an Android game, widely known in the real world as the "Shotgun" game. It's simillar to "Rock, Paper, Scissors" in that it has three main moves, but is a lot more strategic and not luck based. 

The mobile app was presented at the National IT Olympiad and a national competition for mobile applications AppsNPMG. It managed to win first place for the age category at both competitions. The implementation included developing a custom client-server framework in Java, which utilized the TCP protocol and was also programmable by custom plugins. The mobile application was entirely created in Java, using Android Studio.

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