“Akshay is one of the finest programmers I have worked with and I have tried many. Akshay joined our sharepoint expert team as a fresher and in no time, he became one of the critical team member. At the time of reducing the team size based on project phases, it was no brainers to keep Akshay till end as he was so capable of handling the things single handedly. Akshay is Team player, self motivator and likes to be challenged! He will surely be an asset to any team / company and I recommend him without reservations.”
About
Experienced technologist, architect, and leader. Full-stack software developer with more…
Activity
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Psyched to #welcome our first #newhire to the #teameffort: Marisa Smith 🥳 Marisa is joining as our #developerexperience and #communitymanager 🙌…
Psyched to #welcome our first #newhire to the #teameffort: Marisa Smith 🥳 Marisa is joining as our #developerexperience and #communitymanager 🙌…
Liked by Akshay Kalia
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My father, who lives in India, suffers from heart disease. It has crimped his style but not his spirit. When we first came to know about it, I…
My father, who lives in India, suffers from heart disease. It has crimped his style but not his spirit. When we first came to know about it, I…
Liked by Akshay Kalia
Experience
Education
Courses
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Computer Vision
CSCI-GA 2271
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Data Mining for Business Analytics
INFO-GB.3336
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Fundamental Algorithms
CSCI-GA.1170-001
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Graphics Processing Units (GPUs): Architecture and Programming
CSCI-GA 3033
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Natural Language Processing
CSCI-GA.2590-001
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Operating Systems
CSCI-GA.2250-001
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Programming Languages
CSCI-GA.2110-001
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Realtime & Big Data Analytics
CSCI-GA.3033-006
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Web Search Engines
CSCI-GA 2580
Projects
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Predictive Model for Cancer detection using non-invasive methods
Cornell Health Hackathon 2016 Finalist
- Built a model using various machine learning algorithms to predict the type of cancer using a bio-marker in blood plasma or serum using the technology of "liquid biopsy"
- Worked on a dataset comprising 673 samples with a feature space of 1046 to detect Pancreatic and Ovarian carcinoma based on a bio-marker using Machine Learning algorithms like Linear SVM, Gaussian SVM, Logistic Regression, Naive Bayes, etc.
Stack : Python…Cornell Health Hackathon 2016 Finalist
- Built a model using various machine learning algorithms to predict the type of cancer using a bio-marker in blood plasma or serum using the technology of "liquid biopsy"
- Worked on a dataset comprising 673 samples with a feature space of 1046 to detect Pancreatic and Ovarian carcinoma based on a bio-marker using Machine Learning algorithms like Linear SVM, Gaussian SVM, Logistic Regression, Naive Bayes, etc.
Stack : Python, scikit-learn -
Image (JPEG/PNG) Compression Algorithm
Designed an Image (JPEG/PNG) Compression Algorithm, without using any 3rd party libraries, which can compress photos without any visible loss in quality or dimensions. Currently working on video compression using the same algorithm.
Stack : ASP.NET 4, C#, HTML5, CSS, jQuery -
Chrome Spell Check Plugin
Developed an auto-correct Chrome Extension, which has been given good reviews by leading internet blogs.
Stack : jQuery -
Funny Image Website
Developed funny images sharing website, which has currently over 3.5 million page views per month and active user base. Images are automatically added to the website with help of crawlers and user submissions.
Stack : ASP.NET, C#, HTML5, CSS, jQuery -
Question Answering (QA) System based on Natural Language Processing
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This project deals with building a Question Answering system in Python using NLP techniques like POS, BIO, NER tagging, lexical element extraction using our own algorithms without the use of any external libraries like NLTK and an IR module - SOLR.
The performance of a few modules of the system are as follows:
BIO Tagger: F-1 score of 95.08
NER Tagger: F-1 score of 81.00
Question Classification: accuracy of 80%
- Built POS tagger and trained it on a tagged Wikipedia dump…This project deals with building a Question Answering system in Python using NLP techniques like POS, BIO, NER tagging, lexical element extraction using our own algorithms without the use of any external libraries like NLTK and an IR module - SOLR.
The performance of a few modules of the system are as follows:
BIO Tagger: F-1 score of 95.08
NER Tagger: F-1 score of 81.00
Question Classification: accuracy of 80%
- Built POS tagger and trained it on a tagged Wikipedia dump and generated the model file.
- Built BIO tagger using bigram as well as trigram feature conjunction and trained it against the Wikipedia dump. The tags are assigned using Maxent model.
- Built NER tagger using bigram feature conjunction, external dictionaries and word semantics and trained it against the Wikipedia dump.
- Built Feature File for Question/Answer Type classification and trained it against the TREC dataset.
- Performed stemming and Keyword Extraction
- Did Information Retrieval using SOLR
- Summarized the response from content in Wikipedia which is to be given back to the user
Stack: Python, SOLROther creators -
Cluster Analysis on Restaurant Data to Produce a Rating/Review on Google Maps API
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The analytic grabbed data from Yelp, Google and Zomato and cleaned the data using hive. Analyzed data using mapper and reducer functions, broke it down to different neighborhoods and thus generated a rating for that neighborhood. The result was then plotted on Google Maps using the API.
Stack : Java, Python, Hadoop, Hive, Google APIOther creators
Test Scores
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TOEFL
Score: 112/120
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Graduate Record Examination (GRE)
Score: 323/340
Languages
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English
Full professional proficiency
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Hindi
Native or bilingual proficiency
Recommendations received
3 people have recommended Akshay
Join now to viewMore activity by Akshay
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2020 wasn't all that bad - I helped my mom, Elizabeth Staller, start the family donut business she'd joke about all those years... If you're a…
2020 wasn't all that bad - I helped my mom, Elizabeth Staller, start the family donut business she'd joke about all those years... If you're a…
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Insights on commercial #insurtech from Chris Downer
Insights on commercial #insurtech from Chris Downer
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