aakash bhatia
Data Scientist at Schlumberger
data scientist | carnegie mellon university
hi , data scientist interest statistical machine learning , deep learning product development . past experience building reliable solution data visualization , database design web application leverage output machine learning algorithm . help keep research mathematical background coming optimization principle coupled statistic . -general purpose programming : python , java , bash , shell scripting . -data mining analytics : python ( numpy , panda , nltk , scikit-learn , kera tensorflow backend , fasttext ) -data visualization : python ( matplotlib , seaborn ) , r ( ggplot2 ) -deep learning framework : pytorch , tensorflow , kera -big data : hadoop , spark -devops deployment : docker , linux bash , maven , database ( querying , b-tree indexing etc ) -os : linux -cloud service : google cloud platform , amazon web service , microsoft azure course helped build strength : @ cmu - statistical machine learning & ii ( 46-926 & 46-927 ) - deep learning ( 11-785 ) - machine learning ( 10-601 ) - cloud computing ( 15-619 ) - data structure application programmer ( 08-722 ) - java application programmer ( 08-671 ) number publication : 3 ( 2 journal 1 conference ) email : aakashb1 @ alumni.cmu.edu
Schlumberger
https://ae.linkedin.com/company/schlumberger
- Developed an end-to-end self-supervised anomaly detection model to identify contextual outliers in multi-variate time series data of daily high-volume rock properties. The model was a bi-directional denoising LSTM autoencoder using pytorch to predict the properties 
- Developed an interpretation based (regression) product with key metrics saving operational costs.
Houston, Texas Area
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Data Scientist
Carnegie Mellon University
https://ae.linkedin.com/company/carnegie-mellon-university
-Implemented a bag of words classifier (SVM) and achieved an average F score of 0.72 across the given set of documents.
-Trained a linear bag of words model fastText and achieved an average F score of 0.79.
- Applied t-SNE to visualize word representation in a 2D vector space to cross validate the learning using skip-gram architecture.

Published the work in Elsevier.
Greater Pittsburgh Area
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Graduate Student Researcher
Master of Science - MS 
Fall 2018 (Semester Three):
-Deep Learning: Concepts of Convolutions for spatial features and recurrence for temporal features in a neural network.
-Cloud Computing: Leveraging Map-Reduce / Hadoop on a cloud based platform along with EMR. Files and Databases, Containers, Machine Learning on the Cloud (GCP)

Spring 2018 (Semester Two):
- Data Structures : LinkedList, Stacks, HashMap, Binary Search Trees, Red Black Trees.
- Java and Object Oriented Programming.
- ML & Data Science: Concepts of over-fitting, bias-variance trade-off, bagging, boosting . GLM's,  Neural Networks

Fall 2017 (Semester One)
- Worked through branch & bound, approximating piecewise functions, bi-level optimization, travelling salesman problem, optimization under uncertainity, robust optimization and metaheuristic algorithm.
-Implemented genetic algorithm to optimize a job shop scheduling problem (NP-hard)
3.81
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Carnegie Mellon University
https://ae.linkedin.com/school/carnegie-mellon-university/
Bachelor’s Degree
A 4 year undergraduate level study which revolved around the field of programming, operations research and mathematics. Successfully completed my final year project based on control and optimization in Matlab/ Simulink.
GPA, 3.2
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Malaviya National Insitute of Technology Jaipur
https://ae.linkedin.com/school/mnitjaipur/
• Represented CMU Nationals team in the American College Cricket Nationals held in Miami, 2018
CMU Cricket Club
Team Member
Maintenance work order records provide valuable insights into chemical plants and production efficiency. These records are manually created in computerized management systems for routine and emergency maintenance. However, since the records are manually created, recording errors are not uncommon. The resulting datasets are additionally imbalanced, i.e., they have significantly more instances of certain classes than other minority classes. It is very challenging to use such datasets for classification and prediction of future events. In this paper, we propose a modeling framework that uses derivative-free optimization (DFO) to optimize the performance of classification models based on datasets that may be imbalanced. We apply our modeling framework to 15 real-world work order datasets. We also evaluate ten mixed-integer box-bounded DFO solvers for their ability to optimize machine learning models from industrial datasets. Compared to standard solutions, our results show dramatic improvements in the prediction accuracies of the models.
Industrial text analytics for reliability with derivative-free optimization
Elsevier
https://www.sciencedirect.com/science/article/abs/pii/S0098135419307781
Electroencephalography (EEG) is a method to record the electrical signals in the brain. Recognizing the EEG patterns in the sleeping brain gives insights into the understanding of sleeping disorders. The dataset under consideration contains EEG data points associated with various physiological conditions. This study attempts to generalize the detection of particular patterns associated with the Non-Rapid Eye Movement (NREM) sleep cycle of the brain using a machine learning model. The proposed model uses additional feature engineering to incorporate sequential information for training a classifier to predict the occurrence of Cyclic Alternating Pattern (CAP) sequences in the sleep cycle, which are often associated with sleep disorders.
A machine learning model for identifying cyclic alternating patterns in the sleeping brain
arXiv
https://arxiv.org/abs/1804.08750
The Money Table was a graduate student competition where we play the angel investor and have the chance to put real Pittsburgh startups in the hot seat. We were assigned to an interdisciplinary team to learn about early-stage investor capital and evaluate real-world startups.
University of Pittsburgh Innovation Institute
Finalist-Money Table Competition
11-785 : Deep Learning
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06-815 : Process Systems Engineering (Project: Genetic Algorithms for Job Shop Scheduling)
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08-671 : Java for Application Programmers
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08-722 : Data Structures for Application Programmers
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10-601 : Machine Learning
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15-619 : Cloud Computing
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CS 109  : Data Science (Harvard University)
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Course: Deep Learning (11-785), Prof. Bhiksha Raj

- Performed N-way classification using a 34 layer ResNet CNN architecture.
- Extracted unique features as embeddings from the network to compute the similarity in two audio recordings.

Dataset:  Librispeech
Trained on pyTorch on p2.xlarge instance in AWS.
Convolutional Neural Networks for Speaker Verification
- Implemented a 3 layer BiLSTM network to generate new sentences based on Shakespeare corpora.
- Applied 10% teacher forcing to train the network and learn from previously predicted words.

Trained on pyTorch on p2.xlarge instance in AWS.
Recurrent Neural Networks for language model generation
Course: Deep Learning (11-785), Prof. Bhiksha Raj

- Trained a decoder consisting of 3 layers of bi-pyramidal LSTMS's along with a character level encoder consisting of 3 layered LSTM cells.
- Produced state of the arts results as published in Listen, Attend and Spell publication (levenshtein distance of 20)

Dataset: LJ Speech 
Trained on pyTorch on p2.xlarge instance in AWS.
Speech to Text using Attention Networks
Performed analytics to find trending topics from Wikipedia page views (i.e. trends from March 8 to April 6, 2018). Implemented MapReduce program to process and aggregate the 30-day dataset from March 8 to April 6, 2018 (36GB compressed, 128GB uncompressed).

-Implemented using Java, Bash on t2.micro instance in AWS.
Big Data Analysis using MapReduce architecture (Hadoop)
Classify Cancer Patient EMR Records to identify the primary site of cancel cells supported by UPMC.

1) Created a bag of words model using a custom dictionary built from NCI thesaurus.
2) Trained a Naïve Bayes classifier and achieved an F score of 0.9032 on the text classification assignment to identify the type of cancer for different EMRs.
Natural language processing of cancer patient’s EMR records
Sponsored by Robotics Institute (School of Computer Science, Carnegie Mellon University) and Philips Respironics.
1) Performed feature engineering to extract useful information from two datasets containing 17 million data points
2) Trained a logistic regression model to identify CAP phase occurrence with an accuracy of 58%.
Detection of Long-Term Oscillatory Patterns in the Sleeping Brain
https://github.com/aakashb1/hackAuton---brain-sleeping-pattern
1) Implemented genetic algorithm (population based metaheuristic) on a job shop scheduling problem (NP-hard) in MATLAB.
2) Minimized the execution time by a factor of 25% as when compared to the previously best known solution obtained from temporal decomposition.
Job Shop Scheduling using Genetic Algorithm and Approach by Localization
AlgoExpert
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AlgoExpert - 100 Questions Completion
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Coursera
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Deep Learning Specialization
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Convolutional Neural Networks
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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
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Coursera
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Neural Networks and Deep Learning
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Coursera
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Sequence Models
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Coursera
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Structuring Machine Learning Projects
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https://ma.linkedin.com/in/mohamedmouatacim
الرباط-سلا-زمور-زعير, المغرب
Mohamed Mouatacim
Artificial Intelligence and Data Science engineer at Intelcia IT Solutions
https://ae.linkedin.com/in/aditi-verma-a5ba15213
دبي
Aditi Verma
--
https://ae.linkedin.com/in/adireddy-lingireddy-550a92183
الإمارات العربية المتحدة
Adireddy Lingireddy
Data Scientist | Machine Learning | Deep Learning | Python | R | SQL | SAS | Tableau | Credit Risk|  Consumer Insights
https://ae.linkedin.com/in/abdul-hameed-bm-b8bb4217b
الإمارات العربية المتحدة
Abdul Hameed BM
Actively Looking for an Accounts, Finance job in UAE
https://ae.linkedin.com/in/susan-sidie-abb310220
دبي, الإمارات العربية المتحدة
susan sidie
Estudiante en Birla Institute of Technology and Science, Pilani
https://ae.linkedin.com/in/sumat-varshney-2072191ba
دبي, الإمارات العربية المتحدة
Sumat Varshney
Account Manager at Capgemini
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منطقة فروتسواف الحضرية
Diana G.
Data Scientist w Capgemini
https://ae.linkedin.com/in/samivulla-sulaiman-825a17110
الإمارات العربية المتحدة
Samivulla Sulaiman
Accountant at Shameem Ibrahim Engineering Turning LLC
https://ae.linkedin.com/in/samuel-david-dadepogue-5a29171ba
دبي
Samuel David Dadepogue
Student at Birla Institute of Technology and Science, Pilani
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ميدون
YAN WANG
Data Scientist Capgemini
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ميلانو
Ivan Bongiorni
Senior Data Scientist at Capgemini
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دبي, الإمارات العربية المتحدة
Hans Christian Ekne
Data Scientist | Consultant | Investor | Economist
https://ae.linkedin.com/in/youssef-kabbaj-b22973108
دبي, الإمارات العربية المتحدة
Youssef Kabbaj
Senior Graphic Designer / Powerpoint specialist at Capgemini Morocco
https://ae.linkedin.com/in/fatima-farooq-89385313
الإمارات العربية المتحدة
Fatima Farooq
Data Scientist, IT Operations and Management, Buisness Analysis
https://pl.linkedin.com/in/miko%C5%82aj-kozyra-511730182
منطقة فروتسواف الحضرية
Mikołaj Kozyra
Junior Data Scientist w Capgemini
https://no.linkedin.com/in/andreas-stusvik-haug-877a5317a
أوسلو
Andreas Stusvik Haug
Data Scientist at Capgemini
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بانغالور
Kumari Savitha Sree
Data Scientist at Capgemini
https://pk.linkedin.com/in/ashar-mehmood-156909109
ناحية بايتختي اسلام‌آباد, باكستان
Ashar Mehmood
Data Scientist and Deep Learning Engineer
Rakesh Shankar
      
          



            
              
        
              
  

    
        I see Aakash as a hard-working person with a blend of qualities like highly self-motivated, analytical, curious and a very good team player. Aakash joined as a trainee in the organization and was technically sound in his work. I had supervised his work around cost-estimation of petro chemical brown field projects. To get started off, Aakash had worked in bringing all the scattered data in our repository to one file by creating a master database. In later courses of the project the same database was used to estimate prices of equipment and bulk that were not available to us through vendors. During the project, he displayed good intuition about learning from data and was quick to understand the factors that truly affected the pricing of the equipment and piping. Aakash is also a good orator and takes considerable effort to deliver presentations.
I am sure he will be asset to any organization. I wish him all the best for his career and future endeavors.
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Hiring below roles for Abu Dhabi, UAE1. Big Data Engineer2. Big Data AdminInterested candidate can share their resume on…
تمت المشاركة من قبل Aakash Bhatia
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Just unlocked my first accomplishment of completing 100 questions at AlgoExpert.https://lnkd.in/eVZFjQ_A
تم إبداء الإعجاب من قبل Aakash Bhatia
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Want something better than k-means? I'm thrilled to announce our SOTA k-medoids algorithm from NeurIPS 2020, BanditPAM, is now publicly available!…
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دلهي, الهند
Aakash Bhatia
Business Manager at Banyan netfaqs pvt lyd
https://in.linkedin.com/in/aakash-bhatia-a24b2b200
نويدا
Aakash Bhatia
Digital Marketing Specialist || Social Media Ads Expert || Media Buyer || ROI Hacker
https://www.linkedin.com/in/aakash-bhatia2201
هيرندون, VA
Aakash Bhatia
AWS Engineer at Amazon Web Services (AWS)
https://in.linkedin.com/in/aakashbht
ديهرادون
Aakash Bhatia
Director at Laundromania Fabric Care, Serial Entrepreneur, Co-Founder (MAD)
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الهند
Aakash Bhatia
SDE @InsideView, A Demandbase Company | Scaler certified | DAIICT passout
Chemical Industry Professionals - Supply Chain
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Abu Dhabi Indian School Alumni
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Jobs for Chemical Engineers in Indian
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Logistics and Supply Chain professionals
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