news: add post for SciPy 2026 tutorial

_layouts/team.html

@@ -79,6 +79,23 @@ <h3><a href="{{ team-member.link }}">{{ team-member.name }}</a> <span class="typ
 		{% endfor %}
 	</div>
 
+	<h2>QuantEcon Ambassadors</h2>
+
+	{% assign team-members = site.team-members | where: 'role', 'QuantEcon Ambassador' | sort: 'last_name' %}
+	<div class="row">
+		{% for team-member in team-members %}
+		<div class="col-lg-6 team-item">
+			<div class="member d-flex align-items-start">
+				<div class="pic"><img src="{{ team-member.image }}" class="img-fluid" alt=""></div>
+				<div class="member-info">
+					<h3><a href="{{ team-member.link }}">{{ team-member.name }}</a>{% if team-member.tag %} <span class="type">{{ team-member.tag }}</span>{% endif %}</h3>
+					<p>{{ team-member.content }}</p>
+				</div>
+			</div>
+		</div>
+		{% endfor %}
+	</div>
+
 	<h2>Design and Marketing</h2>
 
 	{% assign team-members = site.team-members | where: 'role', 'Design and Marketing' | sort: 'last_name' %}

_posts/2026-07-14-scipy-2026-tutorial.md

@@ -0,0 +1,22 @@
+---
+layout: post
+title: "Tutorial: Computational Methods for Simulation at SciPy 2026"
+author: Matt McKay
+excerpt: "QuantEcon delivered a four-hour tutorial on accelerating simulations with NumPy and JAX at SciPy 2026 in Minneapolis."
+tag: [workshop]
+---
+
+Google Engineer [Smit Lunagariya](https://smit-create.github.io/intro.html), a QuantEcon Ambassador, delivered a four-hour tutorial on **Computational Methods for Simulation using JAX and NumPy** at [SciPy 2026](https://www.scipy2026.scipy.org/), held at the University of Minnesota in Minneapolis, on July 14, 2026.
+
+The tutorial uses Thomas Schelling's segregation model as its running example — a classic demonstration of how mild individual preferences can lead to extreme aggregate outcomes — and works through how to turn readable but slow Python code into a high-performance simulation:
+
+1. **The basic model** — the dynamics of segregation implemented with Python classes
+2. **NumPy implementation** — rewriting the model with arrays and functions for clarity and speed
+3. **JAX implementation** — translating the model to JAX syntax and concepts
+4. **Further parallelization** — exploiting modern parallel hardware such as GPUs
+
+The techniques carry over to other settings that rely on large-scale simulation, including urban planning and epidemiology.
+
+The materials were prepared by [John Stachurski](https://johnstachurski.net/), [Thomas J. Sargent](http://www.tomsargent.com/), [Smit Lunagariya](https://smit-create.github.io/intro.html), and [Matt McKay](https://github.com/mmcky).
+
+All tutorial materials are freely available [online](https://quantecon.github.io/scipy_tutorial_2026/) and on [GitHub](https://github.com/QuantEcon/scipy_tutorial_2026), including notebooks that can be run on Google Colab with no local setup.

_team-members/smit.md

@@ -3,7 +3,7 @@ name: Smit Lunagariya
 last_name: Lunagariya
 image: https://avatars.githubusercontent.com/u/55887635?v=4
 link: https://github.com/Smit-create
-role: "Research Assistant"
+role: "QuantEcon Ambassador"
 tag: "Lead Developer"
 ---
-Smit Lunagariya is a QuantEcon lead developer and now works at Google. Smit's interests lie in High-performance computing, compilers, and open source scientific computing.
+Smit Lunagariya is a QuantEcon Ambassador and works as an engineer at Google. Smit's interests lie in High-performance computing, compilers, and open source scientific computing.