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
* 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.
Course-Based VIP Projects (link to VIP course webpage)
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. Only 1 VIP course is allowed to take in a given semester
Semester | Courses | Faculty Advisors (Number of students) | Project Description |
Spring 2025 |
EECE101 EECE201 EECE301 |
Dr. James Momoh (2)
Dr. Imtiaz Ahmed (2) Dr. Charles Kim (6) Dr. Eric Seabron (1) |
jmomoh@howard.edu imtiaz.ahmed@howard.edu ckim@howard.edu eric.seabron@howard.edu |
Fall 2024 | EECE101, EECE201, EECE302 |
Dr. James Momoh (4) Dr. Imtiaz Ahmed (3) Dr. Su Yan (1) Dr. Charles Kim (9) |
su.yan@howard.edu |
Spring 2024 | EECE201 |
Dr. James Momoh (1) Dr. Eric Seabron(1) Dr. Imtiaz Ahmed (1) Dr. Su Yan (2) Dr. Hassan Salmani (1) Dr. Charles Kim (7) |
|
Fall 2023 | EECE101 |
Dr. Fadel Lashhab (1) Dr. Imtiaz Ahmed (5) Dr. Su Yan (5) Dr. Hassan Salmani (1) Dr. Charles Kim (5) |
fadel.lashshab@howard.edu hassan.salmani@howard.edu |
Industry-Sponsored VIP Projects
Any students can join (upon faculty advisor's approval) and selected students receive VIP scholarship
Semester | Project Team | Advisor/Contact | Project Description | Sponsor |
Spring 2025 | Intelsat Performance Bot (from Fall 2024) | Dr.Anietie Andy (Computer Science) Email: anietie.andy@howard.edu |
The proposed VIP project is
"Intelsat Performance Bot.”
Intelsat collects and maintains a vast amount of data points
used by Network and Operations to monitor, analyze, and model
operational performance to ensure connectivity service is highly
available for our customers. Users outside Commercial Aviation (CA)
Network and Operations teams often need answers held in the team’s data
and spend significant time in meetings and/or requesting data to answer
fundamental, performance-related questions. The goal of the project is
to make Commercial Aviation (CA) performance data more widely available
by setting up a generative AI-enabled chatbot interface with natural
language processing (NLP) capabilities so users can interact / query
various datasets without expertise. |
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Fall 2024 | Co-Manager (From Fall 2024) | Advisor: Dr. Saurav Aryal (CS) and Dr. Legand Burge(saurav.aryal@howard.edu) |
Production and Workforce Co-Management System |
![]() Team Members |
Fall 2024 | Smart Signal Detector (From Fall 2024) |
Advisor: Dr. Eric Seabron (Electrical Engineering) email:
eric.seabron@howard.edu Co-advisors: Dr. Anietie Andy and Dr. Imtiaz Ahmed |
Smart Signal Detector. We want to classify the modulation type of arbitrary RF signals in a noisy environment by using Machine Learning to make the classification processing more robust, increase, accuracy, and improve processing speed. We propose exploring a variety of AI techniques and feature engineering to improve classification accuracy across a variety of modulation types. Feature engineering may include enhancement to statistical, cyclostationary, frequency domain change, principal component analysis or image (ridge, edge) features. The AI models may explore Convolutional Neural Networks (CNNs), Autoencoders, Transformer, Recurrent Neural Networks (RNN), Random Forest architectures and techniques to improve accuracy and speed. We will benchmark state of the art algorithms, as well as novel software solutions for smart signal detection on compact Intel based NPU hardware systems. |
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Fall 2023 |
DC-Water SCADA (from Fall 2023) |
Advisor:
Dr. Charles Kim
(Electrical Eng) ckim@Howard.edu 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 |
Fall 2021 |
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 |
Fall 2021 | 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 |
Fall 2021 | 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 |
Spring 2021 | 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 |
Fall 2020 | 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 |
Fall 2020 | Quantum AI (From Fall 2020) | Advisors: Dr. Michaela Amoo (CpE) (mamoo@howard.edu) and 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 |
VIP Teams of 2019-2020, 2018-2019, 2017-2018, 2016-2017, 2015-2016.