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 301 (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 ckim@howard.edu.
Any question can be directed to the VIP Director at ckim@howard.edu. 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: eric.seabron@howard.edu |
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: Dr. Md Sami Hasnine (Civil and Environmental Engineering) | 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) hassan.salmani@howard.edu |
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) hassan.salmani@howard.edu |
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) ckim@Howard.edu |
Candidate Projects: (a) Predictive location of underground cable fault; (b) Power converter in space and vehicular systems; (c) Industrial PLC(Programmable Logic Controller) applications; (d) web scrawl for media content analytics Students in all disciplines and all majors are encouraged to inquire about the projects and participation opportunities. EECE101-01 VIP course taking is optional in joining a team.
|
|
| Quantum AI (From Fall 2020) | Dr. Thomas Searles (Physics) (thomas.searles@howard.edu) | 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) (arubaai@howard.edu) |
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)(saurav.aryal@howard.edu) | 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.
