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.

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: This project aims to develop a networked system which would make workforce and production management adaptable to changing situations. There is a need for efficient workforce management which runs around the product availability and, simultaneously, for seamless production plan following the workforce availability.   A networked co-management system is devised, in which the real-time production control of the field is obtained from a PLC (programmable logic controller) with pumps and valves, while the workforce-status and management is maintained in a software environment, with the two sub-systems connected through a network.   This networked system connects the production control system in the field to the workforce management system so that the production-level and the workforce-status can be both simultaneously managed.



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.
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.

VIP at Howard Main Webpage


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