• Integrating AI, Biology, Epidemiology and Citizen-Science for Surveillance and Control of Mosquito-borne Diseases
  
- This highly multi-disciplinary project integrates AI (Computer Vision), Biology, Epidemiology and Citizen Science techniques to generate real-time images of mosquito vectors in nature, identify them automatically, and create accurate and timely risk models for disease spread. End goal is targeted surveillance and control of mosquito vectors. This project is supported by National Science Foundation [Project].


• Multi-disciplinary and Privacy Preserving Techniques to Combat Cyber Risks among Teens
  
- This project integrates data mining techniques, learning algorithms, social science theories and privacy expectations of end users to design context and data driven solutions to model, and detect risks to teens emanating from cyber use. This project is supported by National Science Foundation [Project].


• Assessing Human Behavior from Internet Usage
  
- This project designs techniques for extracting meaningful data emerging from human interactions with emerging technologies like Internet and Smart-Phones, and subsequently design classification algorithms to understand human behavior. Applications of this research are in pervasive mental health care, advancing cyber security and detecting cyber bullying. This project is supported by National Science Foundation [Project].


• Designing Citizen Science Applications for Water Management
  
- Modern smart-phones come equipped with a number of sensors like accelerometers, gyroscopes, humidity, light-intensity sensors and more. This project designs effective algorithms to enable real-time sensing, transmission and processing of such multi-modal sensor data from a number of devices to enable new citizne science applications in the realm of water management, disaster prediction, environmental sustenance and more. This project is supported by National Science Foundation [Project].


• A Multi-disciplinary Framework to Model Cyber Criminals
  
- This project integrates theories from criminology with learning algorithms in computer science to understand the evolution of crimes and criminals in cyber space. Applications of this project lie in understanding group behavior of cyber criminals, early detection and intervention for cyber criminals, and early detection of cyber crimes. This project is supported by National Science Foundation [Project].

• Arup Dey (PhD)
  
     AI for Healthcare

• Hye-Seon Yi (PhD)
  
     AI for Sustainable Societies

• Tanvir Bhuiyan (PhD)
  
     AI for Biology Applications

• Ravi Sharma (PhD)
  
     Mining Social Media

• Jamshidbek Mirzakhalov(MS)
  
     Natural Language Processing

Graduated Students

• Bharti Goel (PhD - 2020) - Data Scientist at Verb Surgical Inc.
  
• Mona Minakshi (PhD - 2020) - Cloud Solutions Engineer at Intel
  
• Anthony Windmon (PhD - 2020) - Senior Model Analyst/ Validator and Assistant Vice President at Citibank
  
• Adel Al Sheri (PhD - 2019) - Assistant Professor at King Abdul Aziz City of Science and Technology (KACST), Saudi Arabia
  
• Soheil Sarmadi (PhD - 2018)- Data/ AI Scientist at EverestLabs
  
• Pratool Bharti (PhD - 2017) - Assistant Professor at Northern Illinois University
  
• Srinivas Thandu (PhD - 2016) - Software Engineer at Amazon
  
• Mark Snyder (PhD - 2014) - Software Development Engineer at Microsoft
  
• Neelanjana Dutta (PhD - 2013) - Cloud R&D Engineer at Intel
  

---

• Sireesha Dadi (MS - 2018) - Software Developer II at Pasco County Sheriff's Office
  
• Karthik Siriyala (MS - 2018)- Software Developer II at Pasco County Sheriff's Office
  
• Surya Kamineni (MS - 2017) - Data Scientist at Cross Country Home Services
  
• Anurag Panwar (MS - 2016) - Data Scientist at Samsung Research America
  
• Jake Bielefeldt (MS - 2014) - Software Engineer at Sandia Natl. Labs
  
• Ashok Bolla (MS - 2014) - Data Scientist at Paypal
  
• Doyal Mukherjee (MS - 2014) - Software Engineer at The Cerner Corp.
  
• Sai Preethi Vishwanathan (MS - 2014) - Software Engineer at The Cerner Corp.
  
• Muhammad Al Mutaz (MS - 2013)
  
• Gerry Howser (MS - 2012) - Faculty at Kalamazoo College
  
• Raghavendra Kotikalapudi (MS - 2011) - Machine Learning Software Engineer at Google
  

• Computing
  
    Nvidia GPU Cluster, Dell T3600 workstations, Dell Latitude E 6330 Laptops, MacBook Pro Laptops


• Embedded Devices
  
    Gumstix motes, Crossbow motes, LabRat robots


• Wearable Devices
  
    Shimmer3 GSR Development Kits, Moto Sport and Fossil Smartwatches


• Smart-Phones
  
    Samsung GALAXY S6, S7, S8, S9, Google Pixel 4, iPhone 11


• Software
  
    MATLAB, SAS, Intel Math Kernel Library

• Enhancing Fidelity of Quantum Cryptography using Maximally Entangled Qubits
  
     Proc. of IEEE Global Communications Conference (GLOBECOM), Taipei, Dec 2020.  [.pdf]


• Designing a Health Coach-Augmented mHealth System for the Secondary Prevention of Coronary Heart Disease Among Women
  
     accepted to appear in IEEE Transactions on Engineering Management, 2020.


• A Framework based on Deep Neural Networks to Extract Anatomy of Mosquitoes from Images
  
     in Scientific Reports 10, 13059 (2020).   [.pdf]


• High-accuracy detection of malaria mosquito habitats using drone-based multispectral imagery and Artificial Intelligence (AI) algorithms in an agro-village peri-urban pastureland intervention site (Akonyibedo) in Unyama Sub-County Gulu District Northern Uganda
  
     Journal of Public Health and Epdemiology, Vol 12/3, July 2020.   [.pdf]


• A Mobile Health Intervention System for Women With Coronary Heart Disease: Usability Study
  
     JMIR Formative Research, Vol 4/6, June 2020.   [.pdf]


• Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques
  
     Proc. of ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS), Guayaquil, June 2020.   [.pdf]


• Learning from Tweets: Opportunities and Challenges to Inform Policy Making During Dengue Epidemic
  
     Proc. of the ACM on Human-Computer Interaction, Vol. 4, CSCW1, May 2020.  [.pdf]


• A Generalized Mechanism beyond NLP for Real-Time Detection of Cyber Abuse through Facial Expression Analytics
  
     Proc. of 16th ACM/EAI International Conference on Mobile and Ubiquitous System: Computing, Networking and Services (MobiQuitous), Houston, Nov 2019.  [.pdf]


• How Smart Your Smartphone Is in Lie Detection?
  
     Proc. of 16th ACM/EAI International Conference on Mobile and Ubiquitous System: Computing, Networking and Services (MobiQuitous), Houston, Nov 2019.  [.pdf]


• HEliOS: Huffman Coding Based Lightweight Encryption Scheme for Data Transmission
  
     Proc. of 16th ACM/EAI International Conference on Mobile and Ubiquitous System: Computing, Networking and Services (MobiQuitous), Houston, Nov 2019.  [.pdf]


• TussisWatch: A Smart-phone System to Identify Cough Episodes as Early Symptoms of Chronic Obstructive Pulmonary Disease and Congestive Heart Failure
  
     IEEE Journal of Biomedical and Health Informatics (J-BHI), Vol 23/4, July 2019.  [.pdf]


• HuMAn: Complex Activity Recognition with Multi-modal Multi-positional Body Sensing
  
     IEEE Transactions on Mobile Computing (TMC), Vol 18/4, April 2019.  [.pdf]


• Leveraging Smartphone Sensors to Detect Distracted Driving Activities
  
     in IEEE Transactions on Intelligent Transportation Systems (T-ITS), Oct 2018.  [.pdf]


• Leveraging Smart-Phone Cameras and Image Processing Techniques to Classify Mosquito Species
  
     15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous), New York, Nov 2018.  [.pdf]


• Watch-Dog: Detecting Self-Harming Activities from Wrist Worn Accelerometers
  
     IEEE Journal of Biomedical and Health Informatics (J-BHI), Vol 22/3, May 2018.  [.pdf]


• On Detecting Chronic Obstructive Pulmonary Disease (COPD) Cough Using Audio Signals Recorded from Smart-Phones
  
     Proc. of 11th International Conference on Health Informatics (HealthInf), Funchal, Portugal, Jan 2018.  [.pdf]


• Click Here for a Complete List