Nino A. Profile

5.0

(2 reviews)

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$15 USD / hour

skopje,

macedonia

$15 USD / hour

It's currently 4:57 AM here

Joined January 28, 2013

0 Recommendations

Nino A.

@ninoarsov

5.0

(2 reviews)

1.4

100%

$15 USD / hour

skopje,

macedonia

$15 USD / hour

100%

Jobs Completed

100%

On Budget

100%

On Time

N/A

Repeat Hire Rate

Years of expertise in machine learning and writing

Years of experience in machine learning, mathematics, academic writing, programming. Currently, I am a project manager leading projects in document management systems, Ruby on Rails and Business Intelligence, including data warehousing. I have as a machine learning researcher at the Macedonian Academy of Sciences and Arts. Previously, I was a visiting researcher at Stanford University where I worked with the SNAP group (part of the InfoLab). There, I worked on multiple machine learning and data science projects. I have over four years of research experience in machine learning. Extensive knowledge of Python (including various ML libraries), C/C++, Matlab, etc.

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Portfolio

4835791

TOEFL iBT Scorecard (113 / 120)

4835506

Spearman’s ranked correlation as a measure of similarity

4835497

Generating highly accurate prediction hypotheses

4835490

Stacking and stability

4835484

Text retrieval from the Amazon Common Crawl Corpus

4835464

Genetic algorithm for PostgreSQL partitioning in Java

4835791

TOEFL iBT Scorecard (113 / 120)

4835506

Spearman’s ranked correlation as a measure of similarity

4835497

Generating highly accurate prediction hypotheses

4835490

Stacking and stability

4835484

Text retrieval from the Amazon Common Crawl Corpus

4835464

Genetic algorithm for PostgreSQL partitioning in Java

Reviews

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Showing 1 - 2 out of 2 reviews

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5.0

$30.00 SGD

Matlab expert with lower rate

He is very helpful and very patient. When my codes did not worked out the first time, he would look into it and resolved it.

Engineering

Matlab and Mathematica

Algorithm

Machine Learning (ML)

Mathematics

Closed User

@adelaideloh

•

5 years ago

5.0

₹2,500.00 INR

Highly Recommend.

Vamsi Manikanta S.

@vamsisi

•

5 years ago

Experience

Macedonian Academy of Sciences and Arts

Jan 2019 - Present

I am working as a machine learning researcher on various projects related to network science, mining of large graph data, and machine learning in general. My projects involve the stability of machine learning algorithms, machine learning applications in finance, and Bayesian analysis. All of the projects involve extensive data preprocessing and preparation as well as visualization. You can find my papers at http://bit.ly/2Jd8hk2 and https://bit.ly/2VYTB9P

Macedonian Academy of Sciences and Arts

Jan 2015 - Apr 2017 (2 years, 3 months)

Volunteered at the Laboratory for Complex Systems and Networks Research (now called Research Center for Information Technologies). All projects involved developing new machine learning algorithms.

Stanford University

Sep 2016 - Dec 2016 (3 months, 1 day)

Visiting researcher supervised by Prof. Jure Leskovec (cs.stanford.edu/people/jure/) who is heading the SNAP group in the InfoLab at Stanford University. I worked on various research projects in machine learning, including unsupervised node embedding of real-world massive networks, mining of massive gene expression datasets for cancer research, mining of employee databases from the Royal Bank of Canada, helping humans understand why and how machine learning models fail to generalize.

Education

Master of Science (Current GPA: 10.00/10.00) [expected June 2019]

Univerzitet 'Sv. Kiril i Metódij' vo Skopje,

Macedonia

2018 - 2019

(1 year)

Bachelor of Science (GPA: 9.74/10.00)

Univerzitet 'Sv. Kiril i Metódij' vo Skopje,

Macedonia

2011 - 2015

(4 years)

Josip Broz Tito High School (Mathematics) GPA: 5.00/5.00

Macedonia

2007 - 2011

(4 years)

Qualifications

Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University

2014

First author of a paper chosen the best among 66 papers, part of the Research Methodologies in ICT course.

Government of the Republic of Macedonia

2014

Awarded to top Computer Science students in the country.

Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University

2014

Awarded for achieving a total GPA of over 9.50 out of 10.00.

Publications

Weighted Bagging Predictors

MAESTRA — Learning From Massive, Incomplete, Annotated, and Structured Data (2016)

http://bit.ly/2Ty5ExX This poster presents an approach to learn the weights of the members of a bagging ensemble. The goal is to further reduce the error rate of the ensemble.

Collaborative bagging of boosting ensembles

South-East European Forum on Data Science (2016)

http://bit.ly/2SZw7PP This is a poster presented at the South-East European Forum on Data Science in Belgrade, Serbia, 2016. It shows how collaboration between boosting ensembles reduces the error rate.

Stability of decision trees and logistic regression

Machine Learning, Springer (2019) [In review]

Preprint available at: https://arxiv.org/pdf/1903.00816.pdf In this paper, we derive the stability of Decision Trees and Logistic Regression for the first time. We prove that deeper decision trees are more stable. We also show that the stability of logistic regression is not controllable unless the algorithm is regularized beforehand. Our results can be used to improve these algorithms by tightening the stability-based upper bound on their generalization error.

Stacking and stability

Machine Learning, Springer (2019) [In review]

Preprint available at: https://arxiv.org/pdf/1901.09134.pdf In this paper, we analyze the stability of the Stacking algorithm. We algebraically prove that Stacking improves Bagging, and vice versa. The stability can be leveraged to derive and optimze upper bounds on the generalization error of any machine learning algorithm.

A meta-heuristic approach for RLE compression in a column store table

Soft Computing, Springer (2018)

https://link.springer.com/article/10.1007/s00500-018-3081-5 In this paper, we present a genetic algorithm for finding the optimal order of columns in a column-store table in order to maximize the compression rate of the Run-Length encoding (RLE) compression algorithm. We analyze other heuristic algorithms, such as simulated annealing, cuckoo search, particle swarm optimization, Tabu search, and the bat algorithm. Our algorithm is applicable to large column-oriented database systems.

Generalization-aware structured regression towards balancing bias and variance

International Joint Conference on Artificial Intelligence (2018)

https://www.ijcai.org/proceedings/2018/0363.pdf In this paper, we present a novel bias-variance balancing objective function is introduced in order to improve the generalization performance of ensembles for structured regression. Our method is called Generalization-Aware Collaborative Ensemble Regressor (GLACER). GLACER was ∼10-56% and ∼49-99% more accurate for the task of predicting housing prices and hospital readmissions, respectively.

Generating highly accurate prediction hypotheses through collaborative ensemble learning

Scientific Reports, Nature Publishing Group (2017)

https://www.nature.com/articles/srep44649 Supplemenatry Information (PDF): https://go.nature.com/2O87b8d First author. In this paper, we propose a novel collaboration approach between Gentle Boost ensembles. We use Bagging to combine the boosting ensembles and collaboration is driven by stability theory. We provide extensive mathematical proofs to support our approach. On average, our methods were able to decrease the generalization error of the models by 40%.