Amirerfan E. Profile

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5.0

(29 reviews)

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$80 USD / hour

los angeles,

united states

$80 USD / hour

It's currently 12:31 AM here

Joined October 5, 2019

0 Recommendations

Amirerfan E.

@erfaneshrati

5.0

(29 reviews)

6.6

97%

$80 USD / hour

los angeles,

united states

$80 USD / hour

100%

Jobs Completed

97%

On Budget

100%

On Time

30%

Repeat Hire Rate

Computer Scientist, Ph.D.

I'm Erfan, Ph.D. from University of Southern California. I do part-time projects and will be happy to assist you in: Data Analytics Database Management Data Mining System Admin Machine Learning Deep Learning Computer Vision Python Development Algorithms

See all Machine Learning Experts in United States

Contact Amirerfan E. about your job

Portfolio

On this study, we use traditional and cutting-edge machine learning techniques to determine how similar the reference and test lines in a pregnancy strip are automatically. On real-world photos, this technique gets above 90% accuracy. If the user is unsure about the predictions, the program will prompt them to snap another photo.

Pregnancy Strip Checker AI

Deep Learning Workstation

This project was about training reinforcement learning agents for a leveled field using Ray RLlib. RLlib is an open-source library for reinforcement learning (RL), offering support for production-level, highly distributed RL workloads while maintaining unified and simple APIs for a large variety of industry applications. The project included:
- Support for all RLlib algorithms such as PPO, A3C, A2C, AlphaGo, etc.
- Accelerated GPU processing
- Custom defined rewards and prediction models
- Various logging metrics

Industry-Grade Reinforcement Learning

This project is based on a Raspberry Pi local server and Wi-Fi actuators for controlling of every piece in a house in a smart way.

Smart Home Solutions

Pregnancy Strip Checker AI

Deep Learning Workstation

Industry-Grade Reinforcement Learning

Smart Home Solutions

Reviews

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Showing 1 - 5 out of 29 reviews

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5.0

$1,100.00 USD

Data Analysis

works really good and kind

Python

Data Processing

Data Mining

Xiaocheng S.

@yoghurtshawn

•

1 year ago

5.0

$1,200.00 USD

Project for Amirerfan E. -- 5

nice job. Professional and fast

Kris L.

@kfairik

•

2 years ago

5.0

$20.00 USD

I am working on a Artificial intillegence project and i need someone who can explain me few doubts i have.

He is good at the subject. Delivered intime and made necessary changes when asked for. I Highly recommend him

Machine Learning (ML)

Artificial Intelligence

Deep Learning

Enose B.

@enosearrabelli7

•

2 years ago

4.4

$750.00 USD

Data analytics using Python

Works great and reliable

Ci S.

@regios386

•

2 years ago

5.0

€400.00 EUR

machine learning model

great cooperation, professional technical advice :)

Daniel S.

@investitsystems

•

2 years ago

Experience

Clarifai

Feb 2021 - Present

My job is to improve a no-code/low-code AI platform.

Education

Doctor of Philosophy

University of Southern California,

United States

2017 - 2021

(4 years)

Master of Science

University of Southern California,

United States

2016 - 2018

(2 years)

Publications

BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services

ACM ISLPED

Recent studies have shown the latency and energy consumption of deep neural networks can be significantly improved by splitting the network between the mobile device and cloud. This paper introduces a new deep learning architecture, called BottleNet, for reducing the feature size needed to be sent to the cloud.

Gradient Agreement as an Optimization Objective for Meta-Learning

NeurIPS Meta-Learning Workshop

This paper presents a novel optimization method for maximizing generalization over tasks in meta-learning. The goal of meta-learning is to learn a model for an agent adapting rapidly when presented with previously unseen tasks.

JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services

IEEE Transactions on Mobile Computing

In this paper, we propose an efficient, adaptive, and practical engine, JointDNN, for collaborative computation between a mobile device and cloud for deep neural networks in both inference and training phase.