Hello! I am a PhD candidate in the Computer & Information Science & Engineering at the
University of Florida. I am interested in exploring ways to make Machine Learning trustworthy, fair, and
safe. I have explored explainability and fairness aspects of Machine Learning to make it fair in different areas such as justice and computer vision. I hope to
continue exploring this area in future.
I majored in Computer Science from Amirkabir University of Technology (AUT) and graduated with a
minor in Mathematics in 2014. At AUT, I worked on research projects about use of reinforcement learning in
mobile networking. In my free time, I enjoy reading biographies, cross stich and ride my bike.
I have diverse background in domains such as Machine Learning/AI, Human Computer
Interaction, and Software Development
Deep Learning Framework: Tensorflow/Keras (using frequently), PyTorch (using if
Other: python, NLP, Computer vision, reinforcement learning
Technologies: Dialog fow,Affinity Diagrams, Balsamique, Invision, prototyping,
Skills: Qualitative analysis, Quantitative analysis, Hypothesis Testing, Survey Design
Java, Agile methodologies, Heroku, Vercel, Object-Oriented Design
I have diverse work experience in academics, non-profit organizations and Tech
startups related to Machine Learning/AI.
Built strong communication with other data scientists for better assessment of techniques in the ML pipeline by leveraging strong knowledge of ML reporting and fairness paradigm
Built object detection models for different type of objects in the city environment for 3D modeling of images. Formulated styleGAN method to generate realistic pictures of different type of trees to establish 3D models of jungles or state parks using national data points.
- Evaluated the current differential privacy effort happening at U.S. Census Bureau and bolded the current problems.
- Investigated the current development of content tracing app and their consequences for Public privacy.
Manage and execute multiple projects related to machine learning, artificial intelligence, algorithmic fairness, and human computer interaction. Collaborate with researchers from different disciplines, including epidemiology, consumer sciences, and philosophy to provide in-depth technical information for software development process.
- Analyzed time series data to find the existing trend of using Stimuli drug in Schools.
- Designed the research agenda and most efficient ways to visualize the data using Tableau.
- Predicting the readmission rate for patients who over utilize the insurance. Using unstructured four-year ER visits data, I was able to improve the accuracy by %10.
Ethical and fairness study of Predictive Policing
Fairness-Aware Methodology in Juvenile Recidivism
Sentiment and Trust in AI
Mobile Decision Aid (MODA)
Trust and QOS optimization in adhoc networks
Outstanding International Student Award at UF|2020
Cornell Summer School on Designing Technology for Social Impact Scholarship|Summer 2021
Bank of America Travel Award to attend Grace Hopper Celebration|Fall 2020
Among the three reciepents of Media Democracy Fund fellowship|Summer 2020
Google travel award for BPDM conference at Howard University|Febraury 2019
Gartner Graduate Fellowship CISE department at UF|March 2020
Induction to AEL Top Graduate Student Honor Society | Fall 2019
Concepts: Neural networks, structure of ML projects, CNN, RNN
Concepts: MRI segemntation, transfer learning, cox survival analysis
Concepts: Data cleaning, problem solving, critical thinking, data ethics, and
As time progress, autonomous vehicles may be a common mode
of transportation, and companies like Lyft and Uber will
adopt them in place of human drivers. In this paper, the barriers to adopting autonomous vehicles in
rural areas are discussed by examining the current struggles of rural communities with respect to
finance, transportation infrastructure, policy, and demographics. This project resulted into one paper
in ISTAS conference 2021 and one open piece paper in Technology and Society Magazine. stay Tuned!
Application Quest, known as AQ, could be used in domains
such as HR and admissions to reduce the implicit bias and
human error. Using unsupervised learning methods, AQ will
select the most holistic and representative sample to
increase diversity. We conducted experiments to compare AQ
with other state-of-art undersampling methods. The paper is
accepted in the International Conference of Machine Learning and Data Mining.
Risk assessment tools are used throughout the nation to treat and rehabilitate juvenile delinquents.
However, Racial disparity is a significant problem in these tools, which leads to a harsher sentencing
process for adolescents of color. Prior research has shown that the neural network outperformed the
other existing methods in predicting recidivism by far. PACT data is used for indicating the
recidivism in the Florida Juvenile Justice Department. This proposal aims to develop a methodology to
assess the predictive performance and fairness of the machine learning methods used in juvenile
recidivism prediction. We use ML explainability combined with data analysis techniques to explain the
existing disparities. Moreover, we aim to find fair learning representation based on the current
sensitive attributes and their proxies. Lastly, we will use interpretable ML to provide
interpretations of how the performance could be improved while preserving fairness.
Dynamicity and infrastructure-less nature of MANETs expose
the routing in such networks to a variety of attacks, and
moreover, make the conventional fixed policy routing
algorithms inefficient. To deal with the routing challenges
and varying behavior of malicious nodes in such networks,
employing reinforcement learning algorithms and proper trust
models seem promising. In this paper, we introduce a
cognition layer in parallel and interacting with the network
layer which comprises two cognitive processes: path learning
(routing) and trust learning. The first process is based on
machine learning algorithms and the latter is based on trust
management. We compare our algorithm, TQOR, with a well
known trust-based routing protocol, TQR, in terms of three
measures of performance. The simulation results show better
end-to-end delay and communication overhead which further
improve as time progresses, without sacrificing the data
packet delivery ratio.
Over the past 20 years, researchers have investigated the potential of Virtual Reality (VR) to enhance
rehabilitative therapies by improving motor control, supporting motivation, and offering analgesic
effects. Prior work indicates that patient adherence to prescribed in-home regimens has significant
impact on recovery time. Though Connected Health Technologies and Virtual and Augmented Reality
(AR/VR) may maximize in-home adherence and recovery, questions about design and deployment remain. We
designed a first-person Augmented Reality (AR) experience to elicit user and practitioner perspectives
about AR for rehabilitative contexts. We found significant differences between patient and
practitioner-report of regimen adherence. We also identified key attitude barriers to adopting VR/AR
for clinical practice which may impact support for in-home VR/AR use. Findings from these studies
inform directions for future research and development about the use of VR/AR in a therapeutic context.
This work recieved the best student award in the annual Meeting of Human Factors and Ergonomics
Society in 2019.
Conversational Voice User Interfaces (VUIs) help us in
performing tasks in a wide range of domains these days.
While there have been several efforts around designing
dialogue systems and conversation flows, little information
is available about technical concepts to extract critical
information for addressing the users’ needs. AI could help
us in extracting dialogue information and address user
needs. We developed an AI-based mobile-decision-aid (MODA)
that predictively models and addresses users’ decision
strategies to facilitate users’ in-store shopping decision
process. Here we share our design and subsystems to make our
research reproducible. To make our research reproducible, the
code of backend server and dialogflow agent used will be
published on my github!
AI and Machine Learning gained so much popularity recently. However, there are many conversations
about the disparities and biases coupled with these technologies.In this project, we conducted user
studies to learn more about the sentiment and trust of participants toward AI-powered technologies. It
resulted into a paper which is accepted to ISTAS 2020. It is currenlty in press.
Machine Learning has become a popular tool in a variety of ap-plications in criminal justice,
including sentencing and policing. Media hasbrought attention to the possibility of predictive
policing systems causing dis-parate impacts and exacerbating social injustices. However, there is
little aca-demic research on the importance of fairness in machine learning applicationsin policing.
Although prior research has shown that machine learning modelscan handle some tasks efficiently, they
are susceptible to replicating systemicbias of previous human decision-makers. While there is much
research on fairmachine learning in general, there is a need to investigate fair machine
learningtechniques as they pertain to the predictive policing. Therefore, we evaluatethe existing
publications in the field of fairness in machine learning and pre-dictive policing to arrive at a set
of standards for fair predictive policing. Wealso review the evaluations of ML applications in the
area of criminal jus-tice and potential techniques to improve these technologies going forward. Weurge
that the growing literature on fairness in ML be brought into conversa-tion with the legal and social
science concerns being raised about predictivepolicing. Lastly, in any area, including predictive
policing, the pros and consof the technology need to be evaluated holistically to determine whether
andhow the technology should be used in policing. This paper is accepted into the Artificial
Inteligance and Law journal 2021. Stay tuned!!