VIP Program at Howard University (VIPatHOWARD)

Howard University

Washington, DC 20059

VIP Director: Dr. Charles Kim (CKIM@HOWARD.EDU), Professor, Electrical Engineering and Computer Science

*NOTE: From Fall 2023 semester, EE/CpE students could earn 3 credits from VIP courses (EECE101 Faculty-Student Team Project (VIP) (1 cr); EECE201 (1 cr); and EECE 302 (1 cr)) and substitute them for 1 EE/CpE elective course.

* How to Join?:  If you want to  join one of the teams below, contact Project Advisor (via email) or the VIP Director (Dr. Charles Kim) via email


 Any question can be directed to the VIP Director at  Anyone, yeah anyone, can join.


Project Teams

Team Contract Form: (in pdf, docx, forms)

Project Team Advisor/Contact Project Description Sponsor
Optical Metasurfaces
(From Fall 2021)
Advisor: Dr. Eric Seabron (Electrical Engineering) email:

Metasurfaces and Resonators are physical structures that are patterned into crystals to create special quantum-like behavior when it interacts with light; think about how light reflects off a flat CD/DVD disc and creates a rainbow. In this research we study Metasurfaces and Resonators in transparent and reflective crystals to enable new effects which can be used for photonic circuits, waveguides, and sensors. This work doesn’t require prior knowledge in optics but requires a desire to learn new concepts. The research tasks will include:

o   Simulations using Software

o   Data analysis and Statistics

o   Collecting microscopy and optical spectroscopy data

o   Scientific Reading and Writing

Team Members
Capital One
(From Fall 2021)
Advisor: Dr. Imtiaz Ahmed (Electrical Engineering) Algorithmic and Visualization Capabilities for Machine Learning: The objective of the project is to evaluate various algorithmic and visualization tools and insights which would generate various types of visualization to support machine learning-driven analysis of large/complex data sets.  The project utilizes open source tools and publicly available data in the evaluation study.

Team Members
Amazon Freight (From Fall 2021) Advisor: This research aims to develop advanced machine learning and econometric models to tackle current days freight optimization problems. More specifically, this project focuses on the cold chain implementation in the Amazon Freight Inbound (AFI) network. Students from both Civil and Environmental Engineering and Computer Engineering are part of this research team. The project team will help Amazon to create a web-based service for cold chain shippers. The service allows shippers to send their freight pickup requests and then builds optimized execution plans for the Amazon Freight inbound process, including freight pickup, DC cross-dock, and delivery to the destination facilities. The optimization engine generates the most cost-efficient route solutions that meet the time, temperature, capacity, and operational constraints.

Team Members
Memory Forensics (From Fall 2020) Advisor: Dr. Hassan Salmani (Computer Eng)
This project aims to complete a trade study against attack methods which have become increasingly sophisticated, for the tools that provide physical memory coverage against those attacks, by conducting extensive analysis which leads to determination of the best memory forensics tools that provide the best threat intelligence coverage used in identifying and investigating cyber-attacks.

Team Members
Secure Smart Traffic (from Fall 2019) Advisor: Dr. Hassan Salmani (Computer Eng)
The goal of the project is to realize secure smart traffic which is characterized: Driving cars on the road; base stations on the side of the road which gives road data to the cars; cars adjust position and speed among the cars on the road; there are possibilities that the communication between cars and car-station may be hacked; secure smart traffic shields the communication.
Team Members
Forming new teams Advisor: Dr. Charles Kim (Electrical Eng)

Candidate Projects: (a) Predictive location of underground cable fault and  (b) Power converter in space and vehicular systems.

Students in all disciplines and all majors are encouraged to inquire about the projects and participation opportunities.  EECE101-01 or EECE201-01 VIP course taking is optional in joining a team.

Social Sphere Machine
(From Fall 2019)
Advisor: Dr. Charles Kim (Electrical Eng) Media Content Analysis for Event Prediction.  The goal of this project is to scrawl major media webpages and collects significants words regulary which, later, will be analyzed and learned to, hopefully, predict future events. Team members:
(from Fall 2023)
Advisor: Dr. Charles Kim (Electrical Eng)

External Advisor (DC Water): Karen Green (Senior Manager)
Industrial PLC(Programmable Logic Controller) applications in DC Water.  The goal of this project is to collaborate betweem HU and DC Water for candidates for SCADA (Supervisory  Control and Data Acquaition) application positions. Team Members

Quantum AI (From Fall 2020) Dr. Thomas Searles (Physics) ( Quantum Applications:  This project explores quantum computing applications.  One of them includes development of quantum games for experimental test beds for hybrid classical-quantum machine learning algorithms via the IBM-HBCU Quantum Coalition.

Team Members
Solar Powered Vehicle-   (From Fall 2021) Advisor: Dr. Ahmed Rubaai (EE) (

Solar arrays use sunlight as a source of energy to generate DC electricity. Due to the relatively low efficiency of modern day solar cells (~20% maximum) solar powered electronics must be energy efficient for practical use. In the field of embedded systems, some modern day microcontrollers have peripherals and functionalities designed to minimize the amount of energy that is needed to interface with and control systems, making them perfect for solar powered projects. The goal of this project is to design and build a solar powered vehicle that can efficiently maximize solar array energy to charge batteries, as well as minimizing the energy lost through microcontroller control power consumption. 


Team Members
AI Platform (From Spring 2021) Advisor: Dr. Saurav Aryal (CS)( Businesses need help sifting through mountains of documents (unstructured data), finding insights and using these to take action. Manual review is often not practical or viable.  Organizations are using NLP models to review large volumes and data and patterns and anomalies. These findings can then lead to greater insights and actions. However, many current NLP models are trained on generalized data sets and are not domain specific, making them less effective. This project will focus on proposing a method to find a way to adapt generic language models for a domain specific use case such as: healthcare, legal, financial data. This project is carried out in conjunction with Excella as an industry sponsor.

Team Members


VIP Teams of 2019-2020, 2018-2019, 2017-2018, 2016-2017, 2015-2016.

VIP at Howard Main Webpage