• I work in the intersection of Human-Computer Interaction and AI. I study Safety and Trust in Online Spaces.

  • “She who laughs most, learns best!”

About Me

Who Am I?

My name is Nouran Soliman (fun fact: Nouran is an Arabic word - نوران - (hear name) in the dual form that means ‘Two Lights’). Currently, I'm pursuing my Ph.D. at CSAIL, MIT, where I'm part of The Haystack Research Group under the supervision of David Karger. I am also mentored by Farnaz Jahanbakhsh, an incoming Assistant Professor at the University of Michigan. My research lies in the intersection of human-computer interaction, social computing, and AI where I focus on safety, trust, and credibility in online communities, particularly in social media platforms. For example, one of the questions I think about a lot is how to facilitate inclusive public discourse while mitigating risks such as harassment and misinformation particularly among early-career individuals and marginalized groups. I design, construct and test novel computing systems incorporating my ideas around enhancing user experiences, safety, trust and interactions on the web, then I conduct experiments and field studies on these systems to evaluate my ideas. I have been investigating key issues around online interactions such as identity disclosure and content moderation within social spaces. I have also worked on ML projects for boosting team productivity and for healthcare. My work experiences include internships at Adobe Research, University of Illinois at Urbana-Champaign, University of California Berkeley, Microsoft Research, and the Allen Institute for AI. I'm honored to have received recognition as an Adobe Research Scholar and Generation Google Scholar. My work has been featured in notable articles by MIT News and Microsoft.

What Methods do I use?


Qualitative & Quantitative Methods

Full Stack Development

Applied ML

My Research Vision

Opportunities and Challenges for Safety and Trust in Online Spaces

My research focuses on safety, trust, and credibility in online communities, especially social media platforms. I study ways to facilitate inclusive public discourse while mitigating risks such as harassment and misinformation propagation. I have explored and designed novel approaches to identity disclosure, such as a novel paradigm called “meronymity” that allows users to selectively reveal verified aspects of their identity to balance privacy and credibility in public interactions. Currently, I am thinking about the application of meronymity to a more generalized conversation, which raises more complex issues around identity verification and content moderation. If we empower users with the autonomy to conceal their identities while sharing potentially controversial opinions, then there is an urgent need for effective moderation to shield those who might be adversely affected. Moderation practices vary widely today. On one hand, we have paternalistic models where control is centralized within the platform itself, as seen on sites like Facebook and Twitter. Alternatively, some platforms adopt a federated model, like Mastodon, where the community takes on the moderation role, guided by established norms and rules. Then there's a hybrid approach, like Reddit, which combines centralized oversight with community-driven moderation. Although these models are operational, they're not without their challenges. The debate between freedom of expression and safety is ongoing. Centralized models tend to prioritize engagement-driven algorithms, which don't always align with user well-being, while community-led moderation demands significant effort and coordination. A common hurdle across all these approaches is the subjective nature of safety and offense; what's considered safe varies greatly from one individual to another. So in this work, I argue that creating safer yet open online environments, while granting users greater agency over their content and consumption, can positively impact online communities, content generation and consumption, mental well-being, and overall user satisfaction. I am designing and building a social media platform to generalize the principles of meronymous communication. This platform will enable verified yet customizable self-descriptions and peer-based identity verifications. It will also incorporate a novel personalized, Trust-Based Human Moderation model to maximize individual agency over safety controls.

My goal is to further study the impact of harnessing artificial intelligence, especially large language models (LLMs), to personalize and automate aspects of such systems. Specifically, I aim to explore whether LLMs could facilitate personalized content moderation and recommendation while avoiding unintended harms. What oversight is necessary? How can we empower users to define “safe/responsible” AI alignment and limitations? I am keenly interested in the democratization of generative AI across social media and its implications for user-generated content (UGC). I believe generative AI holds exceptional potential to enhance online communities by assisting creators, captivating consumers through personalized recommendations, and connecting marginalized communities. However, poorly audited or carelessly deployed models risk jeopardizing platform safety and trust. There are outstanding questions regarding the alignment of generative models with human values, their transparency and accountability, their potential to perpetuate or mitigate biases, and their broader societal impacts. I am eager to think about research questions at the intersection of generative AI, UGC platforms, and issues of safety, trust, governance, bias, and impact assessment within online social spaces.

I am very fascinated by the applications of this research because it touches every human on a daily basis impacting their experiences and shaping the future of online interactions. And as we embrace the democratization of Generative AI and advances in virtual reality, rethinking social media spaces to ensure safety and wellbeing becomes imperative.

My Research

Projects & Publications


Building upon the framework established in my previous work ''Mitigating Barriers to Public Social Interaction with Meronymous Communication,`` this project endeavors to extend the application of meronymous communication beyond academia to a broader social media landscape. Meronymity, as previously defined, entails disclosing verified aspects of one's identity to lend credibility while maintaining a level of anonymity to alleviate social apprehensions in high stake interactions.

The focus of this endeavor lies in integrating meronymity into a general-purpose social media platform that I am building, where users engage in discussions spanning diverse topics. My attention particularly gravitates towards facilitating sensitive conversations that possess the potential to impact minority communities or vulnerable individuals adversely. To ensure the integrity of such discussions, robust moderation mechanisms are imperative. In this context, I advocate for individual-based moderation, wherein users wield agency over the content they encounter. By empowering users to curate their viewing experience based on personal thresholds of sensitivity, I aim to foster an environment conducive to open dialogue while safeguarding against potential harm. Central to my approach is the implementation of a trust-based system for individual content propagation. This personalized moderation strategy acknowledges the nuanced nature of sensitivity across different individuals, thereby enhancing the effectiveness of content management in sensitive discussions. Through this project, I aspire to cultivate a socially inclusive digital space where meronymous communication serves as a catalyst for meaningful discourse while prioritizing the preferences of all participants.


In communities with social hierarchies, fear of judgment can discourage communication. While anonymity may alleviate some social pressure, fully anonymous spaces enable toxic behavior and lack the social context that motivates people to participate and helps them tailor their communication. We explore a design space of meronymous communication, where people can reveal carefully chosen aspects of their identity and also leverage trusted endorsers to gain credibility. We implemented these ideas in a system for scholars to meronymously seek and receive paper recommendations on Twitter and Mastodon. A formative study with 20 scholars confirmed that scholars see benefits to participating but are deterred due to social anxiety. From a month-long public deployment, we found that with meronymity, junior scholars could comfortably ask “newbie” questions and get responses from senior scholars who they normally found intimidating. Responses were also tailored to the aspects about themselves that junior scholars chose to reveal.


In order to help scholars understand and follow a research topic, significant research has been devoted to creating systems that help scholars discover relevant papers and authors. Recent approaches have shown the usefulness of highlighting relevant authors while scholars engage in paper discovery. However, these systems do not capture and utilize users’ evolving knowledge of authors. We reflect on the design space and introduce ComLittee, a literature discovery system that supports author-centric exploration. In contrast to paper-centric interaction in prior systems, ComLittee’s author-centric interaction supports curating research threads from individual authors, finding new authors and papers using combined signals from a paper recommender and the curated authors’ authorship graphs, and understanding them in the context of those signals. In a within-subjects experiment that compares to a paper-centric discovery system with author-highlighting, we demonstrate how ComLittee improves author and paper discovery.


Collaborative project management involves interacting with various tasks in a shared planning space where members add, assign, complete, and edit project-related tasks to have a shared view of the project's status. This process directly impacts how individual team members select, prioritize, and organize tasks on which to focus on a daily basis. However, such coordination and task prioritization can become increasingly challenging for individuals working on multiple projects with big teams. Accordingly, tasks could become at risk and eventually not be completed on time, leading to personal or team losses in many situations. To support task-doers in completing their tasks, we conducted a mixed-methods study focusing on Microsoft Planner---a collaborative project management tool---to understand how users manage their tasks in a team setting, what challenges they encounter, and their preferred solutions. Based on the findings from a qualitative survey with 151 participants and our Planner log data analysis, we further developed a task at risk prediction model using various task characteristics and user actions. Our experimental results suggest that a task at risk can be classified with high effectiveness (accuracy of 89\%). Our work provides novel insights on how users manage their tasks in team task management tools, what challenges they face, how they perceive a task at risk, and how tasks at risk can be modeled. Such an application can significantly improve the user experience in such tools by providing a personal assistant that helps users prioritize their tasks and pay attention to critical situations.


Large online discussions happen extensively on the web to exchange information, insights, humor, diverse opinions and others’ experiences. Archiving conversations available for future retrieval or contribution allows faster and more efficient consumption of information as well as better collaborations. With the growing number of tools and channels becoming available for researchers to engage with the public, and the rising significance of online platforms like science blogs, social media and sub-communities and engaging with audience, developing platforms tailored to guide, encourage and archive academic conversations around research papers is very valuable.


Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost-effective, and robust methods for detection of Alzheimer's dementia (AD). We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect AD and its severity, which is reflected through Mini-Mental State Exam (MMSE) scores. We first evaluate it on the ADReSS challenge dataset, which is a subject-independent and balanced dataset matched for age and gender to mitigate biases, and is available through DementiaBank. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression. To the best of our knowledge, the system further achieves state-of-the-art AD classification accuracy of 88.0% when evaluated on the full benchmark DementiaBank Pitt database. Our work highlights the applicability and transferability of spontaneous speech to produce a robust inductive transfer learning model, and demonstrates generalizability through a task-agnostic feature-space.


We introduce a novel interactive narrative exhibit supporting general public learning about Hip Hop culture and history developed as a collaboration of the MIT Center for Advanced Virtuality, the Universal Hip Hop Museum, and Microsoft and supported by the TunesMap Educational Foundation and internationally known Afrofuturist artists Black Kirby. The exhibit's narrative system is personalized by categorizing users based on evaluating their input data light of a social psychology-based model based in musical identity theory. The system uses user input to determine which interactive narrative and customized music playlist to present to the user. The system has been deployed as the central interactive display within the [R]Evolution of Hip Hop for an exhibit of the Universal Hip Hop Museum. Future work will involve analysis of user feedback data from the thousands of local and international exhibit visitors to determine the impact of personalization on visitor engagement, satisfaction, and learning.


Online social platforms allow users to filter out content they do not like. According to selective exposure theory, people tend to view content they agree with more to get more self-assurance. This causes people to live in ideological filter bubbles. We report on a user study that encourages users to break the political filter bubble of their Twitter feed by reading more diverse viewpoints through social comparison. The user study is conducted using political-bias-analyzing and Twitter-mirroring tools to compare the political slant of what a user reads and what other Twitter users read about a topic, and in general. The results show that social comparison can have a great impact on users’ reading behavior by motivating them to read viewpoints from the opposing political party.

My Journey

Research Timeline

Back from Leave! 🎉

2023 - Present

Went on Leave. 👋🏻

2022 - 2023

Completed my PhD Technical Qualifying Evaluation in NLP, CV, Data Visualization and Theory of Computation.

COVID Happened! Was leveling up in Pandemic Survival. 🦠😷


Received Adobe Scholar Award. 🏆


Interesting/Fun Projects

Selected Projects

HTML5 Bootstrap Template by colorlib.com
2019 | Computer Vision | Class Project (6.869) | Codebase

Exploring Chest X-Ray Classification

This project examines the chest x-ray dataset, CheXpert, using deep learning models that previously produced state-of-the-art results on earlier chest x-ray datasets. I experiment with different versions of an auto-encoder based CNN and Densenet-121 on the CheXpert dataset.

HTML5 Bootstrap Template by colorlib.com
2020 | Data Visualization | Class Project (6.894) | Codebase

COVID-19 Personal Awareness DataVis Game

In this project, I have developed an interactive datavis game to create awareness about taking precautions against spread of COVID-19.

HTML5 Bootstrap Template by colorlib.com
2020 | Data Visualization | Class Project (6.894) | Codebase

Interacting with AAAI 2014 Conference Proceedings Data

This is a datavis class project exploring a dataset of research papers of AAAI 2014 conference.

HTML5 Bootstrap Template by colorlib.com
2016 | Robotics & AI | RoboCup 2D Soccer Simulation League | Codebase

RoboCup 2D Soccer Simulation Multiagent System

I programmed 11 agents that play a simulated soccer match. I participated with this project in the international RoboCup Soccer League.

HTML5 Bootstrap Template by colorlib.com
2015 | Robotics | MATE ROV Robotics Competition

Remotely Operated Vehicle

I designed, built and programmed an underwater robot end-to-end (mechanical design, electrical system, software) to perform missions. I participated with this project in the international MATE ROV Robotics Competition.

HTML5 Bootstrap Template by colorlib.com
2014 | Digital Electronics | Class Project

Traffic Light Controller Circuit

This is a digital electronics class project involving designing and building a Digital Traffic Light Controller in transistor logic. This circuit was challenging to debug!


Selected Talks & Mentions



Community Engagement & Service

Selected Volunteer Work & Teaching

During PhD @ MIT

During Undergrad @ AAST

  • Young Leaders Academy for Robotics Member (2018 - 2019). Tutored 2 WeDo LEGO classes for 15 kids.
  • The Arab Collegiate Programming Championship (ACPC) Volunteer (2018). Contributed to the organization of the biggest competitive programming event in the EMEA Region.
  • Notions Development Academy for Robotics Member (2012 - 2016).
    • Served as one of the management team members of Notions Academy for teaching children and high school students Engineering (robotics, programming, math, etc.)
    • Organized 6 national robotics competitions, hosted orientation events and talks and taught technical materials.
    • Coached 5 teams to international robotics events and science fairs.
    • Outreached to students in remote areas, women and other minority groups to teach them about tech at more than 10 schools around 2 cities.
  • RoboCup National Robotics Competition Volunteer (2014 - 2016). Member of technical committee and judge in annual Mega event and competition.
  • C Programming Tutor (2014). Served 2 classes of 10 students each.
  • MATE ROV Robotics Competition Coach (2014). Mentored high school team of 7 students.
  • IEEE Volunteer (2013). Member of technical committee in ”Building your career” annual Mega event.
Thought Banquet


Coming Soon!

Coming Soon!

Welcome to the Ask Me Anything Zone!

Questions? Answers! 🤔💬

Keep it courteous, keep it classy - let's make this space interesting!

Selected Questions

Coming Soon!
Off the Clock

Some Echos from My World

Grad Schooling

BFF-Teenage Dream of coming to MIT came true, 2024!

Group Picture with the Grads of 2024!

Bitter Sweet Commencement Moments of friends leaving MIT

Presenting @ CHI 2024

Best Paper Award-ing with Amy @ CHI 2024

Best Paper Award-ing with Joseph @ CHI 2024

AI2 @ CHI 2024

Generative AI in User-Generated Content Workshop @ CHI 2024

Lighting Round @ MIT CSAIL Alliances Annual Meeting, 2024.

Poster Session @ MIT CSAIL Alliances Annual Meeting, 2024.

HCI Visit Days 2024

Pasta Making!

HCI Visit Days 2024

Indoor Golf. A second after I got the top score. 😜

WiDS Conference @ Microsoft

CSCW 2023

Catching up with the community.

Soya's Defense, 2023.

Farnaz's Defense, 2023.

Mingling with Pharaonic Soya @ MIT.

CHI 2023.

Lunch with AI2 Summer Interns and PIs.

My Master's Commencement, 2021.

Tarfah's PhD Commencement as well, 2021.

Virtual End-of-Internship Photo @ Microsoft Research, 2021.

Virtual Class Project Presentation, 2020.

Virtual Research Lab Mixers, 2020.

Robotics & Professional Stuff

Generation Google Scholar Conference, London, UK, 2019

Generation Google Scholar Conference, London, UK, 2019

My WeDo LEGO Robotics Class Graduation, 2019

Volunteering in ACPC, 2018

Worked with Ruzena Bajcsy @ UC Berkeley, 2018

Last day of my Adobe Internship with other interns, 2017

Last day of my Adobe Internship with my Manager, 2017

At RoboCup International League with RoboCup Dance Team from Mexico @ China, 2015

National MATE ROV Robotics Competition, 2015

Judging at a local robotics competition, 2014

At RoboCup International League @ Brazil, 2014

Competing in the Soccer World Robotics Olympiads at Sochi, Russia, 2014

Celebrating winning the National World Robotics Olympiads with other great women, 2014

Competing in the International MATE ROV Robotics Competition at Seattle, USA, 2013

Competing in the National MATE ROV Robotics Competition, 2013

Coaching a team of amazing women in Bibliotheca Alexandrina Science & Engineering Fair, 2012

Competing in the Soccer National World Robotics Olympiads, 2012

Competing in the International MATE ROV Robotics Competition at Florida, USA, 2012

Celebrating winning the National First Lego League, 2012

Snippets from Motherland

Alexandria (my home city)

My Fur Babies ❤️❤️

Meet Ginger and Lime.











Baby Ginger


Some Art

I love all forms of art but here is a little face painting I did.

Butterfly Me

Peacock my BFF!

Purple Me

B&W Me

Some Delicious Art

I am not a foodie but I like making good food.

Lamb on top of Saffron Rice with Nuts and Barberry

Lamb Shank with Carrot Sauce

Table spread featuring Egyptian Breaded Chicken Breasts