Thursday, January 7, 2027 to Friday, January 8, 2027

Overview

AILA 2027: Leading Statistics and Data Science in the Age of AI

A two-day intensive program that equips academic and industry leaders with the vision, tools, and long-term community needed to drive sustainable AI innovation within their institutions.

The current AI landscape is shifting rapidly, and academic and industry leaders in statistics at all levels—from emerging team leads to senior administrators—are proactively seeking to adapt their research, education, and team management to be AI-aware. The AI Leadership Academy (AILA) is a two-day intensive program designed to facilitate this evolution by focusing on practical applications of AI for research and education, while providing strategies to support team members in AI-aware and/or AI-augmented roles and guiding the development of tailored institutional initiatives.

By fostering a collaborative network that extends beyond the academy through ongoing virtual check-ins, office hours, and a reunion at JSM 2027, we equip participants with the vision, tools, and long-term community necessary to drive sustainable AI innovation within their institutions.

Location: Rice University, Houston, TX

Date: January 7 – 8, 2027

Host: Rice University, Department of Statistics, co-hosted by NISS

Format: Two-day intensive, In person, high-engagement

Cohort: Limited to 30 participants, selected through an application process.

Organized By: ASA Stats Up AI Interest Group, hosted by the Department of Statistics, Rice University

Applications

Applications open October 1, 2026 | Deadline November 5, 2026

Apply Now | How to Apply | Info Session Recording

Announcements

Oct 16, 2026: The recording of the October 15 Virtual Info Session is now available. The session covered the application process in detail. Watch on YouTube

Participants & Mentors

Participant Profile

Academic scholars, educators, and industry leaders in statistics and data science.
Department chairs, program directors, and research executives seeking to pilot AI initiatives in education, research, or institutional administration.
The cohort is intentionally limited (max. 30 academy participants) to ensure high-engagement discourse and mentorship between academic and industry peers.
Participants will be selected through an application process, with the cohort curated to ensure a balance across career stages, leadership roles, and sectors.

Speakers and Mentors

Speakers and mentors will be experienced statistical leaders who have led AI teams and initiatives.

Meet the speakers →

The academy is guided by an Advisory Board of leaders from academia, industry, and the profession.

AI Leadership Learning Objectives

By the end of this two-day academy, participants will be able to:

Strategic Perspective: Articulate the integration of statistical principles into modern AI frameworks and identify opportunities to apply statistical expertise in AI development.

Curriculum Strategy: Consider, adopt, and design approaches to evolve statistical curricula to be AI-aware and promote an AI-inclusive research culture.

Collaborative Leadership: Define practical strategies for managing cross-disciplinary AI research teams.

Advocacy & Communication: Develop clear messaging to communicate the value of rigorous statistical methodology to research design, data-driven inquiries, and uncertainty-aware decision making to diverse AI stakeholders.

Team Mentorship: Identify actionable strategies to support the professional growth and leadership development of team members in AI-focused, AI-augmented, and/or AI-aware roles.

Agenda

Day 1: Thursday, January 7, 2027

Focuses on exploring the intersection of statistics and AI, featuring expert insights, collaborative sharing of research and education challenges, and community-building activities to foster leadership adaptability.

8:00 am – 8:30 am	Intro: Brief welcome and overview of the two-day academy goals and structure.
AI for Research (Morning Session) Exploring current AI advancements in research through expert insights and hands-on investigation.
8:30 – 9:30 am	Leader share + Q&A: "AI for Research in Academia" Rafael Irizarry, Professor and Chair of the Department of Data Science, Dana-Farber Cancer Institute; Professor of Applied Statistics, Harvard University
9:30 – 10:30 am	Leader share + Q&A: "AI for Research in Industry" Hadley Wickham, Chief Scientist, Posit
10:30 – 11:00	Break
11:00 – 12:00	Interactive tutorial: "AI for Research" Shihao Yang, Harold E. Smalley Early Career Professor and Assistant Professor, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech Linjun Zhang, Associate Professor of Statistics, Rutgers University
12:00 – 1:30 pm	Lunch — Pain points share Interactive session sharing common challenges in integrating AI within statistical research and leadership.
1:30 – 2:00 pm	Break
AI for Education Afternoon Strategies for modernizing statistics curricula to effectively integrate AI tools and pedagogy.
2:00 – 3:00 pm	Leader share + Q&A: "AI education leadership" Rebecca Nugent, Fienberg University Professor of Statistics & Data Science and Department Head, Carnegie Mellon University
3:00 – 4:00	Interactive Tutorial: "Teaching with AI, about AI, and along AI: leader upskilling, faculty development, to AI curriculum development" Tian Zheng, Professor of Statistics, Columbia University
4:00 – 4:30 pm	Break
4:30 – 5:30 pm	Walk and Talk Informal networking opportunity during a scenic group walk to the evening restaurant.
5:30 – 7:00 pm	Dinner Relaxed networking dinner to build community and foster deeper professional connections among leaders.
7:00 – 9:00 pm	Improv Training Fun, interactive workshop focused on creativity, adaptability, and collaboration through collaborative theater exercises. Richard Zink, Principal Research Fellow, JMP Statistical Discovery

Day 2

Friday, January 8, 2027

Transition & Synthesis: Building on Day 1 discussions regarding key research pain points and education strategy, Day 2 transitions into actionable design and resource planning for institutional initiatives.

8:00 – 9:00

Networking Breakfast

Informal morning gathering to continue conversations and strengthen peer connections from the academy.

9:00 – 12:00

Design Session Morning

Dedicated time for participants to draft and refine their own AI leadership initiatives.

9:00 – 9:30

Introduction

9:30 – 10:30

Design work session

A mentor will be assigned to each group.

10:30 – 11:00

Break

11:00 – 12:00

Peer review and mentor feedback

12:00 – 1:30

Lunch — Design report out

Presentation session where participants share project designs and receive constructive peer feedback.

1:30 – 2:00

Break

2:00 – 5:00

Resource Discussion

Collaborative review of essential resources to support sustained AI leadership and community growth.

5:00

Academy concludes

Post-Academy

Continue being connected with mentors and peer-collaborators.
Ongoing community: monthly Zoom check-ins for participants and an informal gathering at JSM 2027.

How to Apply

Admission is competitive and curated, with the cohort limited to approximately 30 participants.

Applications Open

October 1, 2026

Online application form

Info Session

October 15, 2026

Watch the recording

Application Deadline

November 5, 2026

7d 8h 34m remaining

Notifications

November 24, 2026

Acceptance decisions sent

Application Details & Requirements

Accommodation

A discounted room block has been reserved for academy participants at a hotel near the Rice University campus.

Hotel ZaZa Houston Museum District

5701 Main Street, Houston, TX 77005

Room block: 20 rooms for 2 nights, Thursday – Friday, January 7 – 8, 2027 (check in January 7, check out January 9).
Group rate: $179 per night (approximately $224 per night including taxes and fees).
Block code: YEI739 — the booking button below applies it automatically; if booking elsewhere on the Hotel ZaZa website, enter this code to receive the special rate.

Book Hotel ZaZa