Dev is available for hire
Hire DevDev Sharma
Verified Expert in Engineering
Data Scientist and AI Developer
Location
New York, NY, United States
Toptal Member Since
November 25, 2020
Dev是一名多才多艺的数据科学家和开发人员，专门从事构建异常准确的预测AI模型. He focuses on using statistics, deep learning, 以及数据工程，以制定战略并优化数据在组织中的作用. Dev的专业知识和实践经验得到了纽约哥伦比亚大学应用分析硕士学位的支持, where he also teaches almost all facets of data science at the graduate level.
Portfolio
Columbia University
GPT, Natural Language Processing (NLP)...
Insight Data Science
Amazon Web Services (AWS), Python, PyTorch, Scikit-learn, Word Embedding...
Dotin
LSTM, Python, PyTorch, Classification, Neural Networks, Model Tuning...
Experience
Availability
Part-time
Preferred Environment
Teams, Linux, PyCharm, Visual Studio Code (VS Code)， Slack App, Jupyter Notebook, Slack, MacOS
The most amazing...
...我很自豪地把自己的名字写在这个项目上，那就是与印孚瑟斯和斯坦福实验室合作，登上自然语言理解模型的全球排行榜.
Work Experience
Instructor
2020 - PRESENT
Columbia University
- Instructed graduate students in programming, statistics, databases, front-end development, business intelligence tools, hypothesis testing, machine learning, and other analytical skills.
- 领导并建立了一种协作文化，在这种文化中，我们四人教学人员的每个成员都完全致力于每个学生的成功.
- Consistently achieved high student satisfaction scores (4.5+/5).
Technologies: GPT, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Python, Deep Learning, Machine Learning
Artificial Intelligence Researcher
2020 - 2020
Insight Data Science
- Built an intelligent search product for textbooks that uses ALBERT, a lightweight deep learning model, 将学生的搜索查询转换为结果，比传统的目录方法快100倍. I was the sole developer.
- 通过构建一个容器化的web应用程序(textbookqa)来服务于模型和信息检索器.com) in Docker and AWS.
- 在四周的期限内交付MVP，并向利益相关者展示产品.
Technologies: Amazon Web Services (AWS), Python, PyTorch, Scikit-learn, Word Embedding, Neural Networks, Learning to Rank, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), GPT, Deep Learning, Model Tuning, Machine Learning, Models
Data Scientist (Capstone)
2019 - 2020
Dotin
- 通过建立一个基于长短期记忆(LSTM)的架构，利用调查对象的鼠标移动来帮助识别和收回不公平的调查成本，预测付费调查的有效性，准确率约为76%.
- 完成了我们团队关于验证调查回复的研究的同行评审出版物(arxiv).org/abs/2006.14054). Commercialization of the survey validation product is in progress.
- Worked within an Agile framework in a team of eight.
Technologies: LSTM, Python, PyTorch, Classification, Neural Networks, Model Tuning, Machine Learning, Models, Consulting, Data Science
Machine Learning Intern
2019 - 2019
Infosys
- 将最先进的NLP模型(RoBERTa)与斯坦福大学的切片功能集成在一起，在斯坦福大学的SuperGLUE上取得了最佳结果, a leading NLP benchmark for evaluating general natural language understanding models.
- Placed as the first runner up out of 32 teams in the Annual InStep Hackathon, 通过实施创新的教育内容顺序推荐系统，个性化用户的学习之旅.
- 通过实现神经网络架构(PyTorch)，以95%的准确率和90%的召回率检测欺诈性医疗保健提供者, outperforming the firm’s existing rule-based classifier by around 46%.
Technologies: Transformers, Python, Natural Language Toolkit (NLTK), PyTorch, Word Embedding, Classification, Neural Networks, Natural Language Generation (NLG), Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), GPT, Deep Learning, Models, Machine Learning
Data Science Intern
2018 - 2019
Byteflow Dynamics
- 建立机器学习模型，使用新闻和时间序列数据对未来股票价格表现进行分类，准确率为61%.
- Developed a Python crawler to extract around 5,500 financial news articles on a weekly basis for 100 tickers.
- 通过使用Regex和基于规则的金融词汇清理原始数据，对股票进行情绪分析.
Technologies: Natural Language Toolkit (NLTK), PyTorch, Neural Networks, Consulting, Data Science, Python, Models, Machine Learning
Co-founder | Vice President
2016 - 2018
Ummid A Hope Foundation
- Raised $75,000+ to benefit abandoned girls in Udaipur, India, helping to build the core team and a global network of 1,000+ donors.
- 协调团队会议和团队技术栈，以促进组织的全球拓展.
- 组织了几次当地的筹款活动，以留住现有的捐助者并吸引新的捐助者.
Technologies: Nonprofits, Business Management
Business Analyst
2014 - 2018
Zodiac21 Solutions
- Managed datasets with SQL, Excel, and Tableau to track KPIs, present dashboards, and discover actionable insights.
- 通过领导跨职能团队，将平均客户保留率从35%提高到64%, 由五人组成的团队开发网页和信息亭应用程序，以实现客户对员工的即时反馈.
- 实施和培训50多名员工使用最新的自动化工具来实现数字报告, cloud-based time tracking, and task management.
Technologies: Tableau, SQL
Experience
AskAi
http://github.com/devkosal/askaiA complete question answering application for extracting answers from textbooks. 现代信息检索技术成功地从较小的文档中检索信息. However, when it comes to larger documents, current options fall short.
这个存储库试图解决在大型文档上执行问答的问题. This requires a two-part approach. 在一部分中，ALBERT在斯坦福问答数据集(SQuAD) QA数据集上进行训练. 在另一种方法中，我们使用基于规则的方法将教科书分成多个部分. 然后，我们可以将用户问题嵌入与部分嵌入进行比较，以找到最相关的部分。.
我是唯一的贡献者——从产品概念化到部署——并且存储库目前处于MVP状态.
这个存储库试图解决在大型文档上执行问答的问题. This requires a two-part approach. 在一部分中，ALBERT在斯坦福问答数据集(SQuAD) QA数据集上进行训练. 在另一种方法中，我们使用基于规则的方法将教科书分成多个部分. 然后，我们可以将用户问题嵌入与部分嵌入进行比较，以找到最相关的部分。.
我是唯一的贡献者——从产品概念化到部署——并且存储库目前处于MVP状态.
RoBERTa with Fast.ai
http://medium.com/analytics-vidhya/using-roberta-with-fastai-for-nlp-7ed3fed21f6cImplementing the current state-of-the-art NLP model in fast.ai. 迁移学习的概念对NLP来说仍然是一个新的概念，并且正在以非常快的速度发展. 像RoBERTa这样的模型在SuperGLUE基准测试中在几个不同的NLP任务中表现得非常好. This project facilitates the usage of RoBERTa with fast.ai.
我是唯一的开发人员—从概念化到完成交叉集成—并且集成模型可供使用.
我是唯一的开发人员—从概念化到完成交叉集成—并且集成模型可供使用.
Survey Validation With Mouse Movements
http://github.com/dachosen1/Dotin-Columbia-Capstone-Team-Alpha-Thirty percent of users fill out psychometric surveys falsely. 这对于那些期望有效的调查结果来支付调查费用的组织来说是一个问题. 该项目创建多个模型，以找到优于当前验证方法的方法. In the end, the aim is to reduce survey costs by at least 30%.
This project was built by a team of eight. I took ownership of building the complete pipeline for our LSTM approach, which yielded 80% accuracy and an F1 score of .76 on the validation set. The end deliverables are model weights that can be used locally to test predictions. Future goals for this project are to create an API for the LSTM model, which can be sent requests to identify false survey responses.
This project was built by a team of eight. I took ownership of building the complete pipeline for our LSTM approach, which yielded 80% accuracy and an F1 score of .76 on the validation set. The end deliverables are model weights that can be used locally to test predictions. Future goals for this project are to create an API for the LSTM model, which can be sent requests to identify false survey responses.
Fight Detection
http://github.com/devkosal/fight_detectionA deep learning computer vision model to detect fights in videos. By using five to ten frames from a two-second sample of frames, 我们使用残差神经网络(ResNet)模型提取特征，然后将提取的特征传递给LSTM(从零开始训练)来分类视频中是否发生了打斗. 我的模型能够在平衡数据集上以90%以上的准确率预测战斗是否发生. The accuracy is 71% on surveillance camera footage.
I am the sole contributor. The core development phase is complete and the next step is deployment.
I am the sole contributor. The core development phase is complete and the next step is deployment.
Text Generator Web App
A text generator web app built in under 50 lines of Python, using PyTorch. 在PyTorch中，我们使用transformer库导入预训练的OpenGPT-2模型. 其次，PyViz Panel库用于完全创建web应用程序，仅使用Python.
I am the sole contributor to this app. 它是完整的，旨在教育其他人构建完整的文本生成应用程序.
I am the sole contributor to this app. 它是完整的，旨在教育其他人构建完整的文本生成应用程序.
Education
2018 - 2020
Master's Degree in Applied Analytics
Columbia University - New York, NY, USA
2009 - 2013
Bachelor's Degree in Business Administration
University of Memphis - Memphis, TN, USA
Certifications
SEPTEMBER 2020 - PRESENT
SQL Aptitude Test (http://app.testdome.com/cert/6a938ba738ac4fd587aa1808cc2de863)
TestDome
SEPTEMBER 2020 - PRESENT
Python Aptitude Test (http://app.testdome.com/cert/98109584b10e44f68312e8114cdad0fd)
TestDome
AUGUST 2018 - PRESENT
Introduction to Computer Science and Programming Using Python
Massachusetts Institute of Technology | via edX
Skills
Libraries/APIs
PyTorch, Pandas, Matplotlib, SQLAlchemy, Beautiful Soup, Node.js, React, Scikit-learn, Natural Language Toolkit (NLTK), LSTM, Fast.ai
Tools
NGINX, Tableau
Frameworks
Selenium
Languages
Python, JavaScript, R, Visual Basic for Applications (VBA), SQL, HTML
Platforms
Google Cloud Platform (GCP), Docker, Amazon Web Services (AWS)
Paradigms
Business Intelligence (BI), Data Science
Other
Regular Expressions, Gunicorn, Version Control, Neural Networks, Transformers, BERT, Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNN), Regression, Clustering, SVMs, Models, Model Tuning, Deep Learning, Natural Language Processing (NLP), Learning to Rank, Classification, Word Embedding, Natural Language Generation (NLG), Computer Vision, Computer Science, Business Management, Nonprofits, Teams, Consulting, Machine Learning, GPT, Generative Pre-trained Transformers (GPT)
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