プロフィール詳細

プロジェクトを作成

★★★★★

☆☆☆☆☆

★★★★★

☆☆☆☆☆

Dr. Susovan P.に依頼

France

Pure mathematician turned machine learning researcher looking for math and machine learning related projects

プロフィール概要

Machine learning and statistics skills: Classification and regression algorithms (SVM, LDA, LR etc.), cross validation, regularization, kernel methods, ensemble methods: bagging and boosting, dimensionality reduction - PCA, variance threshold. Clustering: K-means, DBSCAN. Deep learning: Multilayer perceptron (MLP), convolutional and recurrent neural networks(CNN, RNN), long short term memoery (LSTM). Sequential models: Hidden Markov Models (HMM), RNN. Advanced probability, statstical tests-hypothesis testing, inference. Advanced mathematics skills: Differential geometry, algebraic topology, complex analysis and Riemann surfaces, real analysis, field and Galois theory, partial differential equations, measure theory, abstract and linear algebra, functional analysis. Programming ans software skills: Python, Matlab, objected oriented programming, NumPy, sk-learn, Pandas, Seaborn, OpenCV, audio signal processing libraries (pydub, pyAudioAnalysis), elementary familiarity with TensorFlow, Linux, LaTeX, Git. Projects: 1) Audio signal processing: Music and speech detection (coding in Python and TensorFlow): Developed a kernelized SVM classifier that can classify music and speech with 98% accuracy using the MFCC features of the audios, and used it to segment recorded call audios into music and speech. Also developed a long short term memory (LSTM) classifier that gave 95% accuracy on similar data. 2) Geolocation data analysis: Determining popular places from GPS and other geolocation related data (coding in Python): constructed a suitable cost function which is an indicator of how popular a co-ordinate of earth's surface is, in the sense that it measures how many trajectories pass at or near that co-ordinate or through the corresponding geohash. By minimizing this cost function, which is further a function of distance to trajectories, I was able to estimate the popularity of a place. 3) Academic research on classification of medical data: Classification of fetal alcohol syndrome patients (coding in MATLAB): Performed linear discriminant analysis (LDA) on 175 precentral and central sulcal curves, with a 120-dimensional feature space consisting of Fourier coefficients of the curves. Estimated the performance of this classifier by a 10-fold cross validation to obtain classification accuracy of 76%.

専門分野

• ☆ Cluster Analysis
• ☆ Tensorflow
• ☆ LaTex
• ☆ Hypothesis

サービス

• Data & AI

Data & AI Statistical Analysis, Algorithm Design-Non ML

職務経験

R&D data scientist in Audio Signal Processing

Batvoice

4月 2019 - 7月 2019

Data Scientist

Connecthings

7月 2018 - 7月 2018

学歴

PhD in Mathematics

Rutgers University

1月 2019 - 1月 2019

認定資格

• 認定資格の詳細は未入力です。