alireza ( andrew ) ameri
Sensing Hardware Engineer at Apple
sensing hardware @ apple
Apple
https://www.linkedin.com/company/apple
Cupertino, California, United States
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Sensing Hardware Engineer
Lam Research
https://www.linkedin.com/company/lam-research
- Lead advanced sensors designs for Lam Research Equipment Intelligence® platform in Mechatronics team
- Lead embedded (hardware/firmware) system architecture designs for improving current products and introducing new products
- Develop hardware electronics schematics for resource constrained embedded systems that contain different analog/digital sensors and ARM processors
- Design/Prototype/Validate various multi stage power architectures (buck/ boost/ buck-boost) 
- Develop firmware and device drivers in C based on Arm Cortex processors (M0+, and M3) for I2C and SPI devices
- Collaborate in NPI Project transfers to high volume manufacturing and CM’s by developing design and test documents
Fremont, California, United States
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Electrical Engineer IV
Motivo Engineering
https://www.linkedin.com/company/motivoeng
•	Integrated electro-mechanical assemblies with automotive controllers including Motohawk and Raptor using CANBUS
•	Designed technical drawings and harnesses for power distribution and communication interface in electric vehicles 
•	Developed firmware for automotive controllers using Matlab Simulink and CANalyzer
•	Designed custom PCBs from schematic and board layout to fabrication, assembly, and test
•	Ran DFMEA on different electrical sub-systems e.g. power distribution, sensing and control loops
•	Tested and evaluated autonomy sensors including radar, sonar, and 2D/3D camera
•	Analyzed sensor data using python and machine learning for agriculture autonomy applications
United States
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Electrical Engineer
Noribachi
https://www.linkedin.com/company/noribachi
• Architected control system for industrial lighting
   - Designed hardware schematics and PCB including Microchip ATSAM and ATMEGA microcontrollers, RF modules, CANBUS and Ethernet ICs, various types of I2C, SPI, and analog sensors, capacitive touch LCDs
   - Implemented firmware including design of wireless communication protocol, control algorithms, and touch screen user interface (UI) using C/C++
   - Rapid-prototyped digital circuits using breadboard, off-the-shelf modules, and evaluation kits 
   - Implemented LABVIEW user interface and control routines by hardware communication using UDP protocol 
   - Launched a Noribachi IoT product family called PULSE with two products released

• Designed color lighting fixtures
   - Designed PCBs for high power color lighting applications using CREE XML LEDs and LediL color mixing optics
   - Architected various color lighting fixtures using custom designed LED boards and fixtures, and OEM DMX LED drivers
   - Set up color lighting systems with more than 500 DMX addressable LED fixtures in various DMX universes and designed color shows using Nicolaudie lighting solutions

• Designed DMX controller and tester equipment
   - Designed hardware and firmware of DMX panel with multiple capabilities including sending DMX commands, testing DMX device, testing DMX wires, storing up to 10 different shows
Harbor City CA
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Control System Engineer
California State University-Long Beach
https://www.linkedin.com/company/california-state-university-long-beach
• Served as project manager for research on machine learning and its applications in embedded systems at CSULB
   - Collaborated with various researchers to develop a smart traffic control architecture using wireless sensor networks
   - Implemented a variety of machine learning algorithms for vehicle classification using python, scikit-learn, tensorflow 
   - Achieved up to 95% accuracy in identifying different types of vehicles and published the results in IEEE journals

• Taught various seminars and labs in electrical engineering department
   - EE202 - Computer Method Engineering Seminar & Lab
   - EE211 – Electric and Electronic Circuits Seminar & Lab
Long Beach
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Part-Time faculty
California State University-Long Beach
https://www.linkedin.com/company/california-state-university-long-beach
While studying my master's degree, I worked as a research assistant in Networked Embedded Systems Lab at California State University Long Beach under supervision of Dr. Mozumdar. During the last year, I conducted research in various fields including: 

- Wireless sensor networks
- Wearable devices
- Machine learning algorithms

The researches involved many practical efforts including:

- Embedded hardware design using micro-controllers and Arm processors 
- Embedded software design for interfacing with various sensors
- Wireless sensor network protocol design 
- Machine learning algorithm implementations for embedded firmware

The final results of the researches were successfully published in accredited journals and conferences.
Long Beach CA
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Graduate Research/Teaching Assistant
California State University-Long Beach
https://www.linkedin.com/company/california-state-university-long-beach
I Instructed Circuits Lab for undergraduate students. The instructions were about: 

- Fundamentals of analog circuits and electrical components including resistors, capacitors, inductors, etc. 

- Using different lab instruments including Multi-meters, Oscilloscopes, DC power supplies, function generators
Long Beach CA
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Graduate Assistant
AFP Elevator Co.
- Worked on designing control units of elevators
- Tested previous control units PCB boards and verified their functionality 
- Optimized control algorithms of the control units for accomplishing smoother cabin kinematics
- Designed troubleshooting boards which were being used for troubleshooting the control unit boards.
Tehran
Electrical Design Engineer Co-op
Saman Control TAHA
- Worked on electrical hardware and control algorithms implementation of a water show fountain
- Tuned PID algorithms and designed electrical hardware of a 2D CNC Machine prototype 
- Developed an ATMegaX Evaluation Kit with on-board keypad, character LCD, seven segment, indicator LEDs, and push buttons
Tehran
Electrical Design Engineer Co-op
Master of Science (MS)
Electrical Engineering / Embedded Systems
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California State University-Long Beach
https://www.linkedin.com/school/california-state-university-long-beach/
Bachelor of Science (BS)
Electrical Engineering / Control
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Amirkabir University of Technology - Tehran Polytechnic
https://www.linkedin.com/school/amirkabir-university-of-technology---tehran-polytechnic/
High school diploma
3 years of high school at the Allameh Helli high school organized by NODET (National Organization for Exceptional Talents)
Mathematics and Physics
Allameh Helli Tehran - NODET
In this paper, we propose an in-node microprocessor-based vehicle classification approach to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. Our approach for vehicle classification utilizes J48 classification algorithm implemented in Weka (a machine learning software suite). J48 is a Quinlan's C4.5 algorithm, an extension of decision tree machine learning based on ID3 algorithm. The decision tree model is generated from a set of features extracted from vehicles passing over the 3-axis sensor. The features are attributes provided with correct classifications to the J48 training algorithm to generate a decision tree model with varying degrees of classification rates based on crossvalidation. Ideally, using fewer attributes to generate the model allows for the highest computational efficiency due to fewer features needed to be calculated while minimalizing the tree with fewer branches. The generated tree model can then be easily implemented using nested if-loops in any language on a multitude of microprocessors. In addition, setting an adaptive baseline to negate the effects of the background magnetic field allows reuse of the same tree model in multiple environments. The result of our experiment shows that the vehicle classification system is effective and efficient with the accuracy at nearly 100%.
Decision tree-based machine learning algorithm for in-node vehicle classification
Green Energy and Systems Conference (IGESC), 2015 IEEE
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7359454&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7359454
In this thesis, we focused on designing wearable human activity recognition (WHAR) systems. As the first step, we conducted a thorough research over the publications during the recent ten years in this area. Then, we proposed an all-purpose architecture for designing the software of WHAR systems. Afterwards, among various applications of these wearable systems, we decided to work on wearable virtual fitness coach device which can recognize various types and intensities of warm-up exercises that an athlete performs. We first proposed a basic hardware platform for implementing the WHAR software. Afterwards, the software design was done in two phases. In the first phase, we focused on four simple activities to be recognized by the wearable device. We used Weka machine learning tool to build a mathematical model which could recognize the four activities with the accuracy of 99.32%. Moreover, we proposed an algorithm to measure the intensity of the activities with the accuracy of 93%. In the second phase, we focused on eight complex warm-up exercises. After building the mathematical model, the WHAR system could recognize the eight activities with the accuracy of 95.60%.
Wearable Human Activity Recognition Systems
ProQuest LLC
http://pqdtopen.proquest.com/pubnum/1595755.html
Living in an era characterized by the rapid growth of Internet of Things (IoT), people 
can now easily access information about anything at anytime and anywhere. Still today, 
what is missing is that people cannot get relatively basic information of their own health 
without visiting a physician. Moreover, there is no generally acceptable system to provide 
vital health information such as heart rate, skin/core body temperature, respiratory rate, 
blood pressure, etc. In BioMeSensi, we propose an implementation of a wearable system ...
BioMeSensi: a wearable multi-sensing platform for bio-medical applications
International Conference on Information Processing in Sensor Networks (IPSN 2015)
http://dl.acm.org/citation.cfm?id=2742920
In an Aircraft Electric Power Distribution (EPD) system, the power is distributed from generators to loads, sensors and actuators through AC and DC distribution buses using control switches. Over the years, with the increase demands of loads and its power requirement, EPD system must be optimized in order to operate the system efficiently. In this paper, we propose a synthesis tool based on a need-based design method to obtain optimal topology of EPD system considering maximum reliability, continuous connectivity, power requirement, minimal cost, and node failure. We treat the EPD system as an optimization problem by using Mixed-Integer Linear Programming (MILP) to achieve maximum reliability while satisfying a set of constraints.
Improving Reliability of Aircraft Electric Power Distribution System
IEEE Green Energy and Systems Conference (IGESC 2014)
http://ieeexplore.ieee.org.mcc1.library.csulb.edu/xpl/articleDetails.jsp?tp=&arnumber=7018641&queryText%3DImproving+reliability+of+Aircraft+Electric+Power+Distribution+system
Designing backbone network architecture (position of routers) of a distributed wireless sensor network for smart buildings can be a difficult task without the use of computer-aided tools. These tools should provide a robust and efficient solution to the problem with quick response time. However, available synthesis tools for designing wireless sensor networks are very limited, and in most cases, do not have the capability to perform an automatic synthesis of the backbone network. The authors of [1] proposed an interactive design tool based on Dijkstras algorithm, which can assist the rapid design of sensor networks. However, it has a very high execution time when the network size is relatively large (e.g. more than 50 nodes). Moreover, it can produce sub-optimal solutions, by placing a large number of router nodes in the network. In this paper, we present efficient and robust synthesis algorithms which improve the run time with respect to [1] for large networks by as much as 13X (4X on average). The number of routers in these networks was also reduced by as much as 60% (41% on average) in comparison to [1].
Synthesizing Sensor Networks Backbone Architecture For Smart Buildings
IEEE Sensors Journal
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6876125
Designing a distributed wireless sensor network can be a difficult effort without using simulation tools. These tools should provide a robust and efficient solution for the problem with quick response time. However, available simulation tools for designing wireless sensor networks are very limited. Authors in [1] proposed an interactive design tool based on Dijkstra's algorithm which can assist rapid design of sensor network. However, it has a very high execution time when the network size is relatively large, i.e. n 50. Moreover, it produces a sub-optimal solution with large number of router nodes. In this paper, we present efficient and robust synthesis algorithms which exclusively reduce run time for large networks as much as 1000% and also produce a synthesized network with a reduced number of router nodes as much as 150% in comparison to similar previous works.
Optimizing Router Nodes Placement for Designing Distributed Sensor Networks
IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), 2014
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6846187
A campus-wide award granted to only one graduate student
California State University Long Beach
Outstanding Graduate Research Student
2nd place in International Micro Aero Vehicle conference and competition, in the "Indoor exploration competition section" , Germany, Braunschweig.
International Micro Aero Vehicle conference and competition
2nd place in IMAV2012 conference/competition (Indoor exploration section)
2nd place in International Micro Aero Vehicle conference and competition, in the "Indoor exploration competition section" , Netherlands,  t'Harde
International Micro Aero Vehicle conference and competition
2nd place in IMAV2011 conference/competition (Indoor exploration section)
Udacity
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PNFN2EHN
Robotics Software Engineer Nanodegree
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Udacity
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KSXZKCLD
Deep Learning Nanodegree
https://confirm.udacity.com/KSXZKCLD
Coursera Course Certificates
www.coursera.org
NUWGBQJS68AE
Convolutional Neural Networks
https://www.coursera.org/account/accomplishments/verify/NUWGBQJS68AE
Coursera Course Certificates
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GPHK9358P9G4
Structuring Machine Learning Projects
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Coursera Course Certificates
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7QF5UGA23RJ5
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
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Coursera Course Certificates
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Y58ZGL8RHDFU
Neural Networks and Deep Learning
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Coursera Course Certificates
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9SN2JKK48PJK
Robotics: Computational Motion Planning
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Coursera Course Certificates
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FPJQJFCVMCZ4
Control of Mobile Robots
https://www.coursera.org/account/accomplishments/verify/FPJQJFCVMCZ4
Coursera Course Certificates
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T64HJHS2BUE5
Robotics: Aerial Robotics
https://www.coursera.org/account/accomplishments/verify/T64HJHS2BUE5
Board for Professional Engineers, Land Surveyors, and Geologists
157620
Engineer-In-Training (EIT)
University of California, San Diego
C/C++ Programming (120 hours)
Udacity
Intro to Data Science
Udacity
Intro to Hadoop and MapReduce
Coursera Verified Certificates
www.coursera.org
Machine Learning (Stanford University)
https://www.coursera.org/maestro/api/certificate/get_certificate?course_id=971489
https://www.linkedin.com/in/rommelr
San Francisco Bay Area
Rommel Rodriguez
Sr. Recruiter, Hardware + Software for Autonomous Systems
https://www.linkedin.com/in/joshua-misko-60b0b055
San Francisco Bay Area
Joshua Misko
Software Engineer
https://www.linkedin.com/in/matinsaeedkondori
Laguna Niguel, CA
Matin Kondori
CEO at SakoMed, LLC
https://www.linkedin.com/in/krishna-singh-587a6515
Cupertino, CA
Krishna Singh
Wireless Engineering Program Manager | RF System/Application Engineer | Product Engineer | Physical Design Engineer
https://www.linkedin.com/in/daniel-samarin-3655927a
Long Beach, CA
Daniel Samarin
Electrical Engineering Manager at Motivo Engineering
https://www.linkedin.com/in/gifty-sackey-425487a5
San Jose, CA
Gifty Sackey
M365 Enterprise Cloud Experience Program Manager 2
https://www.linkedin.com/in/nahal-tehrany-06498b64
United States
Nahal Tehrany
--
https://cn.linkedin.com/in/angel-chen-8a4475a1
Shandong, China
Angel Chen
Ningyang Sea State Commercial & Trade Co.,Ltd.- Sales Manager
https://www.linkedin.com/in/brendan-hogg-2a4a13161
San Francisco Bay Area
Brendan Hogg
EECS @ UC Berkeley
https://www.linkedin.com/in/qianchang-wang-2018
Los Angeles, CA
Qianchang Wang
System engineer in health sensing
https://www.linkedin.com/in/bobby-coleman-a08a3138
Corona, CA
Bobby Coleman
CNC Programmer at 3D Precision Machine
https://iq.linkedin.com/in/karar-alazawi-971061178
Iraq
karar alazawi
Technician or climber towers في BridgeWave Communications
https://www.linkedin.com/in/albert-torres-793ab875
United States
Albert Torres
--
https://www.linkedin.com/in/chak-lam-chan-08020517
San Jose, CA
Chak-lam Chan
DVT engineer at BridgeWave Communications
https://ir.linkedin.com/in/ali-ramshini-52958184
Iran
ali ramshini
student at Apple
https://www.linkedin.com/in/divyang-joshi
Cleveland, OH
DIVYANGKUMAR JOSHI (E.I.T)
Electrical Engineer at Metro Engineering Solutions LLC
https://www.linkedin.com/in/akanshasaindane
Cincinnati, OH
Akansha Saindane
Electrical Engineer at Honeywell
https://www.linkedin.com/in/joel-amanna
Menomonee Falls, WI
Joel Amanna
Electrical Engineer at Milwaukee Tool
https://www.linkedin.com/in/ajaydillikar
Irvine, CA
Ajay Dillikar
Electrical Engineer at Universal Electronics
Mohammad Mozumdar
      
          



            
              
        
              
  

    
        Alireza is an astonishingly hardworking researcher with whom I have been working around two years. As I met him at the first time, he had an enormous interest in robotics field because of his long term background in this field. However, since I had other active projects in my group related to Embedded Systems on that time, I decided to examine his knowledge and his learning rate in the area of optimization and synthesis tools. Afterward, in an unbelievably short amount of time, he dominated the new area of research independently and I must say he is indeed a fast learner. Specifically, the instances of his knowledge areas which he developed during our researches are optimization techniques (Integer Linear Programming and Mixed Integer Linear Programming), heuristics algorithm in synthesis tools, and big data analysis.  
Embedded Systems Professionals
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Job Openings for OPT/CPT Students - North America
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College Grad Recruiting
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Big Data, Data Science, AI, IoT, Cloud Security & Blockchain
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NODET (Sampad)
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Golden Key International Honour Society
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OPT Students Looking for H1B Sponsorship
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Apache Spark
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Future of Communication (telecom / datacom / autonomous systems), powered by Jakajima
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IEEE-Eta Kappa Nu, Epsilon Theta Chapter at CSULB
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NICHEJOBS.COM Engineering HR Recruiter Business Marketing Advertising Manufacturing Management Sales
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IBM Big Data and Analytics
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Real-Time Embedded Engineering
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Amir-Kabir University of Technology (Tehran Polytechnic) Alumni
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