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added visitors to our team page
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‎_pages/people.md‎

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<a href="/people/venetia">
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<div class="col-sm mt-3 mt-md-0">
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{% include figure.html people-profile=true path="assets/img/people/venetia.jpeg" class="img-fluid rounded z-depth-1" my-style=true %}
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<div class="caption">
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<b>Venetia Pliatsika</b><br>
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Graduate Research Fellow
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</a>
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<div class="row mt-3">
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<a href="/people/seungeun">
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{% include figure.html people-profile=true path="assets/img/people/seungeun.jpg" class="img-fluid rounded z-depth-1" my-style=true %}
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<a href="/people/tanya">
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{% include figure.html people-profile=true path="assets/img/people/tanya.png" class="img-fluid rounded z-depth-1" my-style=true %}
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<div class="caption">
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<b>Tetiana Zakharchenko</b><br>
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Project Coordinator
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</a>
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<div class="row mt-3">
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<a href="/people/caterina">
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{% include figure.html people-profile=true path="assets/img/people/caterina.jpg" class="img-fluid rounded z-depth-1" my-style=true %}
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<b>Caterina Fuligni</b><br>
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Research Scholar
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Ops Manager and Research Scholar
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<a href="/people/anya">
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{% include figure.html people-profile=true path="assets/img/people/anya.png" class="img-fluid rounded z-depth-1" my-style=true %}
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<a href="/people/tanya">
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{% include figure.html people-profile=true path="assets/img/people/tanya.png" class="img-fluid rounded z-depth-1" my-style=true %}
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<div class="caption">
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<b>Tetiana Zakharchenko</b><br>
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Project Coordinator
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</a>
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<div class="row mt-3">
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<a href="/people/hyunseung">
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{% include figure.html people-profile=true path="assets/img/people/Hyunseung.jpg" class="img-fluid rounded z-depth-1" my-style=true %}
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<div class="caption">
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<b>Hyunseung (Daniel) Hwang</b><br>
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Graduate Research Fellow
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</a>
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<a href="/people/enver">
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<div class="col-sm mt-3 mt-md-0">
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{% include figure.html people-profile=true path="assets/img/people/Enver.jpg" class="img-fluid rounded z-depth-1" my-style=true %}
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<div class="caption">
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<b>Enver Menadjiev</b><br>
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Graduate Research Fellow
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</a>
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<h1 class="category" id="alumni">Alumni</h1>
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<a href="/people/venetia">
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<div class="col-sm mt-3 mt-md-0">
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{% include figure.html people-profile=true path="assets/img/people/venetia.jpeg" class="img-fluid rounded z-depth-1" my-style=true %}
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<div class="caption">
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<b>Venetia Pliatsika</b><br>
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Graduate Research Fellow
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</a>
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{% include figure.html people-profile=true path="assets/img/people/umang.jpg" class="img-fluid rounded z-depth-1" my-style=true %}

‎_pages/people/caterina.md‎

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{% include figure.html people-profile=true path="assets/img/people/caterina.jpg" title="caterina" class="img-fluid rounded z-depth-1" %}
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Caterina Fuligni is a Research Scholar advised by Prof. Julia Stoyanovich. Her research interests lie at the intersection of AI and the social sciences, particularly in education, as well as social and cultural psychology. She also serves as the lab coordinator, streamlining processes, supporting academic programs, organizing research events and talks, and managing the lab's day-to-day operations.
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Caterina Fuligni is a Research Scholar advised by Prof. Julia Stoyanovich. Her research interests lie at the intersection of AI and the social sciences, particularly in education, HR, as well as social and cultural psychology. She also serves as the Operations and Administration Manager at the Center, streamlining processes, supporting academic programs, organizing research events, and managing the lab's day-to-day operations.
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Caterina holds a MSc in Psychology of Intercultural Relations from Lisbon University Institute, ISCTE-IUL (Portugal) and a BS in Educational and Developmental Psychology from the University of Padua (Italy). During her studies, she gained experience working in women's health, youth education, and refugee support. After completing her education, she worked in Human Resources at intergovernmental organizations, such as the European Commission, and at a Tech startup. Her educational and professional pursuits have taken her to live in multiple countries, including Portugal, Brazil, Poland, the UK, and the US.
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Caterina holds a MSc in Psychology of Intercultural Relations from Lisbon University Institute, ISCTE-IUL (Portugal) and a BS in Educational and Developmental Psychology from the University of Padua (Italy). During her studies, she gained experience working in women's health, youth education, and refugee support. After completing her education, she worked in Human Resources at intergovernmental organizations, such as the European Commission, and at a tech startup. Her educational and professional pursuits have taken her to live in multiple countries, including Portugal, Brazil, Poland, the UK, and the US.

‎_pages/people/enver.md‎

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---
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layout: page
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title: Enver Menadjiev
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---
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**PhD Candidate** -- *em6033 (at) nyu.edu*
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{% include figure.html people-profile=true path="assets/img/people/enver.jpg" title="Enver" class="img-fluid rounded z-depth-1" %}
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Enver is a Visiting Scholar at NYU/R/AI under the supervision of [Julia Stoyanovich](/). Being a fourth year PhD candidate at [KAIST](https://www.kaist.ac.kr/en/) advised by [Jaesik Choi](https://sail.kaist.ac.kr/members/jaesik/), his research focuses on explainable AI, uncertainty qualification for time series, and robust prediction methods. With his research, Enver airms to bring meaningful changes to the world. He is also a passionate software engineer, enjoying the development of tangible products.
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Outside of academic activities, Enver is an amateur armwrestler in Korea, enjoying an active lifestyle and good food.

‎_pages/people/hyunseung.md‎

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title: Hyunseung (Daniel) Hwang
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**PhD Candidate**
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{% include figure.html people-profile=true path="assets/img/people/Hyunseung.jpg" title="Hyunseung" class="img-fluid rounded z-depth-1" %}
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Hyunseung (Daniel) Hwang is a PhD candidate in Electrical Engineering at [KAIST](https://www.kaist.ac.kr/en/), specializing in machine learning interpretability and explainability. His research critically investigates explanation techniques that are often taken for granted, exposing their limitations and proposing more robust, transparent alternatives. His major work, XClusters: Explainability-First Clustering, introduces a novel framework that prioritizes interpretability in undersupervised learning by generating time series cluster explanations grounded in domain knowledge and feature attributions. He also studies how tools like SHAP can be subtly manipulated, with implications for fairness and trust in AI systems.
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Explainability of AI applications is crucial in decision-making processes where the risk of incorrect predictions influences individuals, groups, and governments. He seeks to build robust explanations that can lead to decisions with high confidence to prevent such risks.

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