B. Autonomous Vehicles (S&P)
Autonomous vehicles (and existing human-driven vehicles) contain sensors that collect data
about the vehicle’s operation and its surroundings. For example, sensors in self-driving car
include cameras, radar, thermal imaging devices, and light detection and ranging (LIDAR)
devices that collect data about the environment outside the vehicle. This data helps
autonomous vehicles determine the objects it encounters, make predictions about the
environment, and take action based on these predictions. As data privacy and security
represent growing critical concerns, FL enables digital devices toc ollaboratively learn a
shared prediction model while keeping all the training data on the device, decoupling the
ability to do machine learning from the need to store the data in the cloud. However,FL is
not the magic bullet to privacy issues. Even holding an “anonymized” data set on the
cloudcan still put users’ privacy at risk via linkage to other data sets. The research
agenda is to addresssuch privacy concerns when training machine learning models.
Role - Principal Investigator, Funding Agency (2022) - Toyota Infotech, Dollar
Amount - $70,000
Collaborators - Dr. Michael Clifford (Toyota Infotech
Lab, CA), Dr. Sara Sampazzi (University of Florida), Dr. Matt Bishop (UC Davis), Dr. Karl
Levitt (UC Davis), Dr. Miriam Heller.
C. Healthcare Analytics (HEALS) -
The primary goal of the HEALS (Health Empowerment by Analytics, Learning, and Semantics)
project is to apply advanced cognitive computing capabilities to help people understand and
improve their own health conditions. In particular, we are exploring areas including
personalized and mobile medical care, improved healthcare analytics, and new data-based
approaches to driving down the cost of medical care. The HEALS project is a joint IBM-RPI effort with close collaboration and transition.
I'm also interested in the following challenges tied to healthcare data: (1) data resides in
different locations (e.g., hospitals, physician offices', home-based devices, patients’
smartphones); (2) there is a growing availability of data, which makes scalable frameworks
important; and (3) aggregating data in a single database is infeasible or undesirable due to
scale and/or data privacy concerns.
Role - Researcher
Lead Investigator - Dr. Mohammed Zaki (RPI),
Principal Investigator (IBM) - Dr. Ching-Hua Chen, Researchers (RPI) - Dr. Oshani
Seneviraten, Dr. Dan Gruen.
Undergrads - Ruisi Jian. Alumni - Megan Goulet, Lydia Zhou,
Aaron Hill.
D. Exploratory Research -
Privacy and Security of User Platforms - This exploratory project
undertakes different user platforms such as Chat applications, Cloud platform and evaluates
user data privacy conerns. Some of the applications I've evaluated are WhatsApp, Covid-19
apps, cloud platforms, etc.
Insider Threat - Insider threat is one of the most pernicious threat
vector to organizations across the world due to the elevated level of trust and access that
an insider is afforded. This type of threat can stem from both malicious and negligent
users. In this research, we propose a novel approach that uses system logs to detect insider
behavior using Deep Learning models. System logs are modeled as a natural language sequence
and patterns are extracted from these sequences. We create workflows of sequences of actions
that follow a natural language logic and control flow.
Role - Researcher
Collaborators - Dr. Kristine Gloria (Aspen).
Undergrads - Qicheng Ma, Daniel Steven.
E. Ph.D. Thesis -
Threat and attack detection in large networks by identifying systemic anomalous behavior.
Identified anomalous data from a set of “important” nodes (instead of an entire system)
leveraging Graph Analytics and Machine Learning models. Used simulated (NS2) and real attack
dataset - Conficker to prove various hypothesis on systemic cyber attacks.
Advisor - Dr. Jim A. Hendler