First Proof, Second Batch: Results
2026-06-10 13:00 - 14:00 (America/New_York) | Virtual
First Proof, Second Batch: Results
Date: June 10, 2026
Time: 1:00–2:00 pm ET
Location: via Webinar
The First Proof Editors present the results of their Second Batch benchmark testing on AI systems.
First Proof Editors

Mohammed Abouzaid (Stanford)
Nikhil Srivastava (UC Berkeley)
Rachel Ward (UT Austin)
Lauren Williams (Harvard)

Big Data Conference 2026
2026-09-03 09:00 - 2026-09-04 17:00 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
Big Data Conference 2026
Dates: Sep. 3–4, 2026
Location: Harvard University CMSA, 20 Garden Street, Cambridge MA & via Zoom
The Big Data Conference features speakers from the Harvard community as well as scholars from across the globe, with talks focusing on computer science, statistics, math and physics, and economics.
Register to attend in person
Register for Zoom Webinar

Confirmed Speakers

SueYeon Chung, Harvard
Bailey Flanigan, MIT
Sergey Ovchinnikov, MIT
Ariel Procaccia, Harvard
Adit Radhakrishnan, MIT
Andrew Sutherland, MIT
Chris Wiggins, Columbia
Rex Ying, Yale
Wei Zhou, Harvard

Organizers

Michael Desai, Harvard
Michael R. Douglas, CMSA
Yannai Gonczarowski, Harvard
Melanie Weber, Harvard

Thursday, Sep. 3, 2026
8:45–9:10 am
Breakfast
9:10–9:15 am
Introductions
9:15–10:15 am
Rex Ying, Yale
A Riemannian Geometry Perspective on Foundation Models
Abstract: In development of foundation models and LLMs, Euclidean space has been the de facto geometric setting for machine learning architectures. However, at a large scale, real-world data often exhibit inherently non-Euclidean structures, such as multi-way relationships, hierarchies, symmetries, and non-isotropic scaling, in a variety of domains, such as languages, vision, and the natural sciences. It is challenging to effectively capture these structures within the constraints of Euclidean spaces. This talk will cover my research that moves beyond Euclidean geometry to maintain the scaling law for the next generation of foundation models. By adopting these geometries, foundation models could more efficiently leverage the structures. Task-aware adaptability that dynamically reconfigures embeddings to match the geometry of downstream applications, could further enhance efficiency and expressivity. This talk will demonstrate the advantages of hyperbolic geometry in building foundation models from individual non-Euclidean components, LLM pre-training / fine-tuning, retrieval-augmented generation, and mixture of expert models. We further present a comprehensive library that greatly simplifies the process of building and adapting foundation models in non-Euclidean. Finally, the talk will enumerate a few promising directions that are being actively explored in this domain.
10:15–10:30 am
Break
10:30–11:30 am
Adit Radhakrishnan, MIT
Toward universal steering and monitoring of AI models
Abstract: Artificial intelligence (AI) models contain much of human knowledge. Understanding the representation of this knowledge will lead to improvements in model capabilities and safeguards. Building on advances in feature learning, we developed an approach for extracting linear representations of semantic notions or concepts in AI models. We showed how these representations enabled model steering, through which we exposed vulnerabilities and improved model capabilities. We demonstrated that concept representations were transferable across languages and enabled multiconcept steering. Across hundreds of concepts, we found that larger models were more steerable and that steering improved model capabilities beyond prompting. We showed that concept representations were more effective for monitoring misaligned content than for using judge models. Our results illustrate the power of internal representations for advancing AI safety and model capabilities.
11:30 am–12:45 pm
Lunch
12:45–1:45 pm
Andrew Sutherland, MIT
Crowdsourcing mathematical databases
Abstract: The L-functions and Modular Forms Database (LMFDB) is one of the largest online repositories of mathematical research data. It contains comprehensive catalogs of mathematical objects that arise in the context of the Langlands program, including number fields, elliptic curves, modular forms, and higher-dimensional analogs of these objects. In cooperation with the Foundation for Science and AI Research (SAIR), we recently ran a competition aimed at solving the inverse Galois problem over Q in degree 24 (IGP24). This goal was achieved by collecting more than 50 million candidate number fields submitted by more than 100 teams, including examples that realize all 25,000 transitive permutation groups of degree 24 as Galois groups. AI tools and computer algebra systems played a central role in this project, both in building the infrastructure to run the contest and in enhancing the capabilities of participants.
1:45–2:00 pm
Break
2:00–3:00 pm
Bailey Flanigan, MIT
Algorithmic Tools for Trading Off Sortition Ideals
Abstract: Citizens’ assemblies and other deliberative minipublics — representative groups of everyday people convened to deliberate on a policy issue and then make recommendations — are now used by governments around the world. Choosing who sits on these panels is the problem of sortition: randomly selecting a small group of citizens that represents the broader population. Sortition has been the subject of substantial computer science research in recent years, and the resulting algorithms are now widely used in practice. This talk will describe the key challenges that arise in the practice of sortition, and the algorithmic tools that have been developed to navigate them optimally.
3:00–3:15 pm
Break
3:15–4:15 pm
Chris Wiggins, Columbia
Prescriptive Learning at Scale: Applied and Deployed Decision-Making
Abstract: Many applications in health and industry require making decisions while learning how the world responds to them: e.g., which article to recommend, which treatment to assign, or when to show a paywall. This problem is prescriptive, what to do rather than what is true. Statistical decision theory has taken up the problem repeatedly since Neyman’s interwar work; recent decades have brought a new wave of results, motivated in large part by the opportunity to make and evaluate decisions at scale online. I will highlight a thread of 21st-century results running through adaptive experimentation, contextual bandits, and off-policy evaluation: they combine advances in supervised learning with fundamental ideas from statistical sampling, and now shape digital products, from content recommendation to marketing, as well as adaptive interventions in health.

Friday, Sep. 4, 2026
8:45–9:15 am
Breakfast
9:15–10:15 am
Ariel Procaccia, Harvard
No Generation Without Representation
Abstract: AI systems and democratic processes are confronting similar challenges around representation. I examine two related questions that cut across both domains. First, how can AI enable democratic processes that handle vast spaces of opinions or statements while ensuring proportional representation of a population’s views? Second, when AI systems themselves provide normative guidance, whose viewpoints do they reflect, and can we make this precise? Drawing on social choice theory, I present formal frameworks and algorithms for both problems, showing that meaningful representation guarantees are feasible and practical.
10:15–10:30 am
Break
10:30–11:30 am
Wei Zhou, Harvard
Biobank-scale genetic discovery: from association testing to global meta-analysis
Abstract: Biobanks linking genomic data with electronic health records provide unprecedented opportunities for genetic discovery for complex human diseases, but they also pose analytical challenges that extend well beyond sample size. Within a biobank, association studies must account for population structure and relatedness, highly unbalanced case–control ratios, rare genetic variants, longitudinal and censored outcomes, and the computational demands of analyzing hundreds of thousands of individuals and millions of genetic variants. Across biobanks, additional challenges arise from differences in ancestry, phenotype definitions, recruitment strategies and genetic effects.In this talk, I will discuss statistical and computational methods developed to address these challenges at successive stages of biobank analysis. These include scalable generalized linear mixed models for binary traits, survival mixed models for censored time-to-event outcomes, and gene- and region-based tests that aggregate rare variants. I will describe statistical approximations and computational strategies that make these analyses feasible at biobank scale while maintaining calibration in the presence of relatedness and highly unbalanced phenotypes. I will then introduce the Global Biobank Meta-analysis Initiative (GBMI) and describe how genetic evidence can be combined across biobanks without sharing individual-level data. Examples from GBMI will illustrate how combining evidence across biobanks can increase statistical power through larger sample sizes and broaden genetic discovery through greater ancestral diversity.
11:30 am–12:45 pm
Lunch
12:45–1:45 pm
Sergey Ovchinnikov, MIT
1:45–2:00 pm
Break
2:00–3:00 pm
SueYeon Chung, Harvard

Fall CMSA Welcome Event
2026-09-03 16:15 - 17:30 | CMSA 20 Garden Street Cambridge, Massachusetts 02138 United States
Fall CMSA Welcome Event
Date: September 3, 2026
Time: 4:15 pm
Location: CMSA Common Room, 20 Garden Street, Cambridge MA

All CMSA and Math affiliates are invited.

Colloquium
2026-09-07 16:30 - 17:30, 2026-09-14 16:30 - 17:30, 2026-09-21 16:30 - 17:30, 2026-09-28 16:30 - 17:30 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States

The Geometry of Machine Learning 2026
2026-09-08 09:00 - 2026-09-11 17:00 | CMSA 20 Garden Street Cambridge, Massachusetts 02138 United States
The Geometry of Machine Learning 2026
Dates: September 8–11, 2026
Location: Harvard CMSA, Room G10, 20 Garden Street, Cambridge MA 02138 & via Zoom Webinar
Register to attend in person
Register for Zoom Webinar
Large language models are presently, and will increasingly, be complemented by other dimensions of intelligence: formal verification and energy-based optimizers, becoming parts of larger ecosystems. Can AIs reason geometrically and can we use geometry to reveal how data is currently processed in NNs? Can AIs reveal the geometry of mathematics, as well as studying geometry as a subject within math. This conference is intended to continue the discussion of these topics.
Confirmed Speakers:

Nada Amin, Harvard
Randall Balestriero, Brown
Michael Brenner, Harvard and Google
Bennet Chow, UCSD
Surya Ganguli, Stanford
Boris Hanin, Princeton
Roi Holtzman, Oxford
Robert Koirala, UCSD
Dmitry Krotov, Dynamical Mind
Slava Krushkal, Virginia
Jared Duker Lichtman, Stanford
Mike Mulligan, UCR, Logical Intelligence
Luca Pesce, Harvard
Gabriel Poesia, U Michigan
Mathew Vanherreweghe, Logical Intelligence
Sean Welleck, CMU (via Zoom)
Mattiew Wyart, JHU

Organizers: Michael R. Douglas (CMSA) and Mike Freedman (CMSA)

Schedule
Tuesday, Sep. 8, 2026
8:15–8:45 am
Breakfast
8:45–9:30 am
Mike Mulligan, UCR, Logical Intelligence
9:45–10:30 am
Boris Hanin, Princeton
10:30–11:00 am
Break
11:00–11:45 am
Slava Krushkal, University of Virginia
12:00–12:45 pm
Robert Koirala and Bennett Chow, UCSD
AI for Ricci flow: Discovery and Formalization

Wednesday, Sep. 9, 2026
8:15–8:45 am
Breakfast
8:45–9:30 am
Surya Ganguli, Stanford
9:45–10:30 am
Mathew Vanherreweghe, Logical Intelligence
10:30–11:00 am
Break
11:00–11:45 am
Michael Brenner, Harvard and Google
12:00–12:45 pm
Mattiew Wyart, JHU
Learn from your own latents, not from tokens
Abstract: Language models need more than a hundred thousand times the data a child does. One explanation is that predicting raw tokens is simply the wrong level: methods like data2vec and JEPA instead train a network to predict its own internal representations, with strong empirical results but no theory of why. Using a hierarchical grammar that models that language and images have a hidden hierarchical structure, we quantify the gain exactly. Token-level learning needs a number of examples growing exponentially with the depth of the hierarchy; latent prediction needs a number independent of it. We also show data2vec performs this hierarchical prediction implicitly, which suggests that explicitly stacking levels – as in H-JEPA – yields little.

Thursday, Sep. 10, 2026
8:15–8:45 am
Breakfast
8:45–9:30 am
Dmitry Krotov, Dynamical Mind
Dense Associative Memory: Physical systems for novel AI architectures
Abstract: Dense Associative Memories are recurrent neural networks with fixed-point attractor states that are described by an energy function. In contrast to conventional Hopfield Networks, which were popular in the 1980s, Dense Associative Memories have a very large information storage capacity, making them appealing tools for many problems in AI. In this talk, I will provide an intuitive understanding and mathematical framework for this class of models and give examples of problems in AI that can be tackled using these new ideas. Specifically, I will explore the relationship between Dense Associative Memories and transformers. I will present a neural network called the Energy Transformer, which unifies energy-based modeling, associative memories, and transformers in a single architecture. I will demonstrate how Energy Transformers can be used for challenging tasks in image processing, solve partial differential equations, and serve as computational modules for energy-based language modeling. I will also discuss an exciting possibility of mapping these models onto analog hardware accelerators, which could enable much more energy-efficient inference compared to GPUs.
9:45–10:30 am
Roi Holtzman, Oxford
10:30–11:00 am
Break
11:00–11:45 am
Sean Welleck, CMU (via Zoom)
12:00–12:45 pm
Gabriel Poesia, University of Michigan (via Zoom)
Friday, Sep. 11, 2026
8:15–8:45 am
Breakfast
8:45–9:30 am
Nada Amin, Harvard
9:45–10:30 am
Randall Balestriero, Brown University
10:30–11:00 am
Break
11:00–11:45 am
Luca Pesce, Harvard CMSA
A spiked perspective on feature learning with gradient-based methods
Abstract: Depth is widely believed to give neural networks a clear computational advantage over shallow models, and making this belief precise is a central problem in learning theory. We study a controlled high-dimensional setting where this can be done. The targets are hierarchical: the relevant structure is distributed across latent subspaces of decreasing dimension, so that a shallow model must resolve all of it simultaneously, while a deep network does not. Each layer forms an intermediate representation, allowing learning to proceed in stages, with every stage reducing the effective dimension of the remaining problem. We analyze this staged mechanism through the gradient descent dynamics of a deep network yielding a sharp separation in sample complexity between shallow and deep architectures. The result is a concrete account of why depth allows such functions to be learned from substantially fewer samples than shallow methods require.
12:00–12:45 pm
Talk tba

Support provided by Logical Intelligence.

Geometry and Quantum Theory Seminar
2026-09-08 16:15 - 18:00, 2026-09-15 16:15 - 18:00, 2026-09-22 16:15 - 18:00 | Science Center 507, 1 Oxford Street, Cambridge, 02138
Joint Math/CMSA Geometry and Quantum Theory Seminar

CMSA Q&A Seminar
2026-09-09 12:00 - 13:00 | Common Room, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States

New Technologies in Mathematics Seminar
2026-09-09 14:00 - 15:00, 2026-09-16 14:00 - 15:00, 2026-09-23 14:00 - 15:00 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
New Technologies in Mathematics Seminar
Speaker: tba

AI for the Working Mathematician
2026-09-09 16:30 - 17:30 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
AI for the Working Mathematician
Speaker: Daniel Litt, University of Toronto
Title: TBA

Geometry and Mathematical Physics Seminar
2026-09-10 16:30 - 17:30 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
Geometry and Mathematical Physics Seminar
Speaker: Brian Williams, Boston University

Member Seminar
2026-09-11 12:00 - 13:00, 2026-09-18 12:00 - 13:00, 2026-09-25 12:00 - 13:00 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States

Lagrangian Floer Theory and Applications Program
2026-09-15 09:00 - 2026-11-21 17:00 | CMSA 20 Garden Street Cambridge, Massachusetts 02138 United States
Lagrangian Floer theory and Applications Program
Dates: September 15–November 21, 2026
Location: CMSA G10, 20 Garden St., Cambridge MA 02138
This thematic program will focus on recent developments in Lagrangian Floer theory and applications. These include the development of family Floer theory, degeneration techniques, Floer homotopy theory in the Lagrangian case, Floer theory in prime characteristic, and applications to problems in singularity theory, geometry and dynamics.
A one-week workshop will be held near the start of the program (September 28 – October 2, 2026), with the same title as the thematic program.
Organizers: Denis Auroux (Harvard), Jonny Evans (Lancaster), and Chris Woodward (Rutgers)

To express interest in attending the workshop or program, and to apply for funding if available, please fill out the form.
Funding from the National Science Foundation may be available for US-based graduate students and postdoctoral fellows.

image: Water Sky Garden, Vancouver
Date: 31 January 2010, 12:57:46
Source: Studio Echelman
Author: Peter Vanderwarker, Studio Echelman

Mathematical Economics Seminar
2026-09-15 14:30 - 15:30 | MA

Ahlfors Lectures: Mohammed Abouzaid
2026-09-16 - 2026-09-17 | Harvard Science Center, 1 Oxford Street, Cambridge, MA, 02138
Ahlfors Lectures
Dates: September 16 & 17, 2026
Time: 4:00–5:00 pm
Location: Science Center Hall A
Speaker: Mohammed Abouzaid (Stanford)
Register to attend in person or online at: https://www.math.harvard.edu/event/ahlfors-lecture-series-mohammed-abouzaid/

September 16, 2026
Title: Framed bordism and nearby Lagrangians
Abstract: The problem of understanding Lagrangian embeddings in symplectic manifolds remains a mystery. The simplest class of symplectic manifolds are the cotangent bundles of smooth manifolds, which is the setting for the most prominent open problem in the subject: Arnold’s nearby Lagrangian conjecture which asserts the connectedness of the space of Lagrangians for which the relative class of the symplectic form vanishes. In particular, the conjecture asserts that all such submanifolds are diffeomorphic. In view of recent work with Alvarez-Gavela, Courte, and Kragh, as well as work of Procelli and Smith, I will use the question as a motivation for joint work with Andrew Blumberg on a bordism-theoretic approach to define bordism-valued refinements of Floer homology.

September 17, 2026
Title: Complex bordism and Hamiltonian fibrations
Abstract: As a consequence of his theory of weights, Deligne proved that smooth complex projective varieties, carrying a smooth morphism to the projective line, have rational cohomology groups that are isomorphic to the tensor product of the cohomology of the fibre with that of the base. In joint work with McLean and Smith, we should that the result not only holds for symplectic manifolds, but is in fact a consequence of an analogous result that holds for complex cobordism. The proof identifies bordism of “stably complex derived orbifolds” as the correct framework in which to formulate these results, and I will discuss joint work with Shaoyun Bai to understand these bordism groups.
The Ahlfors Lecture Series is presented by the Harvard University Math Department in memory of Professor Lars Ahlfors.

CMSA/Tsinghua Math-Science Literature Lecture: Robert Gompf: On classifying smoothings of R^4–from the beginning to the present
2026-09-16 09:00 - 10:30 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
CMSA/Tsinghua Math-Science Literature Lecture
Date: September 16, 2026
Time: 9:00 – 10:30 am ET
Location: CMSA G10, 20 Garden Street & via Zoom Webinar

Please register to attend
In-person registration

Zoom Webinar registration

Speaker: Robert E. Gompf, University of Texas, Austin
Title: On classifying smoothings of R^4–from the beginning to the present
Abstract: The field of 4-manifold topology was born around 1981 with two independent revolutionary breakthroughs, by Fields medalists Michael Freedman and Simon Donaldson. Freedman showed that topological 4-manifolds obeyed the well-developed theory of higher-dimensional manifolds, while Donaldson used nonlinear analysis (gauge theory) to show that smooth 4-manifolds are quite different from their high-dimensional counterparts. The clash between the two theories makes 4-manifold topology uniquely intricate within manifold theory. One of the most startling consequences is that unlike in other dimensions, Euclidean 4-space R^4 admits exotic smoothings. Equivalently, there are smooth 4-manifolds that are homeomorphic, but not diffeomorphic, to R^4. We will trace the classification theory of these manifolds from their discovery to the most recent development (joint with Aristotelis Panagiotopoulos) that they cannot be classified by any countable family of numerical invariants.

Beginning in Spring 2020, the CMSA began hosting a lecture series on literature in the mathematical sciences, with a focus on significant developments in mathematics that have influenced the discipline, and the lifetime accomplishments of significant scholars.

Geometry and Mathematical Physics Seminar
2026-09-17 16:30 - 17:30, 2026-09-24 16:30 - 17:30 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States

CMSA Special Seminar: Deformations to the Complex Plane, Novel Asymptotic Techniques, and the Large t- Asymptotics of the Riemann Zeta Function
2026-09-22 14:00 - 15:00 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
CMSA Special Seminar

Speaker: Thanasis Fokas, University of Cambridge
Title: Deformations to the Complex Plane, Novel Asymptotic Techniques, and the Large t-
Asymptotics of the Riemann Zeta Function
Abstract: The Unified Transform (also known as the Fokas method) is a powerful new method for
solving boundary value problems for linear and for integrable nonlinear PDEs. For linear
PDEs, the relevant transform is based on appropriate deformations of certain integrals from
the real line to the complex plane. After briefly reviewing this transform, it will be shown
that combining this idea with novel asymptotic techniques has recently led to unexpected
and exciting results regarding the large t-asymptotic analysis of the celebrated Riemann zeta
function. The following two results will be discussed. First, a simple formula will be
presented for the difference of the functions defining the error terms in two historic
problems: in Atkinson’s formula and in the formula for the Dirichlet divisor problem; it will
be shown that this difference equals π/ 2 plus a function which is simply related to the
square of the Riemann zeta function. Second, a remarkable integral identity satisfied by the
Riemann zeta function will be presented; this identity is obtained from an earlier identity
derived by the speaker via contour deformation in the complex plane. Making crucial use of
novel asymptotic techniques obtained jointly with Jonatan Lenells, the asymptotic analysis
of this integral equation gives rise to an interesting identity satisfied, for large t, by a sum
generalizing the Dirichlet divisor sum. Also, and more importantly, it gives rise to a specific
integral transform suitable for the large t-asymptotic analysis of the Riemann zeta function.

CMSA Q&A Seminar: Hugh Woodin, Harvard
2026-09-23 12:00 - 13:00 | Common Room, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
CMSA Q&A Seminar
Speaker: Hugh Woodin, Harvard
Title: Truth, proof, and AI

AI for the Working Mathematician
2026-09-23 16:30 - 17:30 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
AI for the Working Mathematician
Speaker: Akhil Mathew, University of Chicago

Workshop on Lagrangian Floer theory and applications
2026-09-28 08:00 - 2026-10-02 17:00 | CMSA 20 Garden Street Cambridge, Massachusetts 02138 United States
Workshop on Lagrangian Floer Theory and Applications
Dates: September 28 – October 2, 2026
Location: CMSA G10, 20 Garden St., Cambridge MA 02138
This workshop is part of the Lagrangian Floer Theory and Applications Program
Registration Form
Speakers will include:

Mohammed Abouzaid, Stanford University
Octav Cornea, University of Montréal
Tobias Ekholm, Uppsala University
Sheel Ganatra, USC
Andrew Hanlon, University of Oregon
Yanki Lekili, Imperial College London
Georgios Dimitroglou Rizell, Uppsala University
Paul Seidel, MIT
Egor Shelukhin, University of Montréal
Vivek Shende, Berkeley/Syddansk

Organizers: Denis Auroux (Harvard), Jonny Evans (Lancaster), and Chris Woodward (Rutgers)

Schedule outline
Monday 9/28/26

9:00 – 9:30
Breakfast

9:30 – 10:30
Talk 1

10:30 – 11:00
Break

11:00 – 12:00
Talk 2

12:00 – 1:30
Lunch

1:30 – 2:30
Talk 3

2:30 – 3:00
Break

3:00 – 4:00
Talk 4

Tuesday 9/29/26

9:00 – 9:30
Breakfast

9:30 – 10:30
Talk 5

10:30 – 11:00
Break

11:00 – 12:00
Talk 6

12:00 – 1:30
Lunch

1:30 – 2:30
Talk 7

2:30 – 3:00
Break

3:00 – 4:00
Talk 8

Wednesday 9/30/26

9:00 – 9:30
Breakfast

9:30 – 10:30
Talk 9

10:30 – 11:00
Break

11:00 – 12:00
Talk 10

12:00 – 1:30
Lunch

1:30 – 2:30
Talk 11

2:30 – 3:00
Break

3:00 – 4:00
Talk 12

Thursday 10/1/26

9:00 – 9:30
Breakfast

9:30 – 10:30
Talk 13

10:30 – 11:00
Break

11:00 – 12:00
Talk 14

12:00 – 1:30
Lunch

1:30 – 2:30
Talk 15

2:30 – 3:00
Break

3:00 – 4:00
Talk 16

6:30
Dinner

Friday 10/2/26

9:00 – 9:30
Breakfast

9:30 – 10:30
Talk 17

10:30 – 11:00
Break

11:00 – 12:00
Talk 18

12:00 – 1:30
Lunch

1:30 – 2:30
Talk 19

2:30 – 3:00
Break

3:00 – 4:00
Talk 20
