Tentative Program

A pdf version of the tentative program is available here.

Rooms for Sessions

We are using the Grande View and Harbor Lights rooms for the different sessions.

Wednesday, January 7, 2026

Registration (8:00 — 9:15)

Opening Remarks, Greetings (9:15 — 9:30)

Keynote Speaker (9:30 — 10:30)

Chair: Martin Golumbic and Frederick Hoffman

Time Description
9:30 Scaling Heterogeneous Network Intelligence
Xingquan Zhu

Coffee Break (10:30 — 10:50)

Morning Sessions (10:50 — 12:20)

Main Track Session 1 - Machine Learning Theory

Chair: Jörg Rothe

Time Description
10:50-11:20 Margin-Based Sparsification for Feature Budged Linear SVM
Lev Reyzin, Shuo Wu
11:20-11:50 Beyond Tsybakov: Model Margin Noise and $H$-Consistency Bounds
Mehryar Mohri, Yutao Zhong

Special session on Data-Driven Decisions: Applied Machine Learning & Optimization - 1

Chair: M. Mine Subasi

Time Description
10:50-11:20 Missing Data Estimation for MR Spectroscopic Imaging via Mask-Free Deep Learning Methods
Tan-Hanh Pham, Sean Subasi, Ovidiu C Andronesi, Kim-Doang Nguyen, and Xianqi Li
11:20-11:50 Pattern-based Survival Analysis and Its Application to Medicine
Travaughn Coren Bain, Ersoy Subasi, and Munevver Mine Subasi
11:50-12:20 A Hybrid Evolutionary Framework for Stratifying Chronic Kidney Disease Risk in African-Americans with Hypertension
Melissa Stradley Moreno, Anna Katja Maria Chesterman, Tiantian Zhang, Ali Tariq, Ersoy Subasi, Munevver Mine Subasi, and Michael Lipkowitz

Lunch (12:20 — 2:15)

On your own.

After Lunch Sessions (2:15 — 3:15)

Main track session 2 - Voting, Knowledge, Logic

Chair: Lev Reyzin

Time Description
2:15-2:45 Complexity of the Possible President Problem in Schulze Voting
Jana Woitaschik; presented by Jörg Rothe
2:45-3:15 Broad Training and Predictability in Knowledge Graph Completion via Formal Concepts
Katie Brodhead

Special session on Data-Driven Decisions: Applied Machine Learning & Optimization - 2

Chair: Xianqi Li

Time Description
2:15-2:45 New Approach to Generate Patterns for Logical Analysis of Data and Its Application to Predict College Retention
Salihah Ahmed Jaafari, Mehdi Mrad, and Munevver Mine Subasi
2:45-3:15 Risk-Conscious and Investor-Centric Stock Portfolio Optimization Models
Sharifa Dafer Alqarni, Mehdi Mrad, and Munevver Mine Subasi

Coffee Break (3:15 — 3:45)

Afternoon Sessions (3:45 — 4:45)

Main track session 3 - Explainable Machine Learning

Chair: Dimitrios Diochnos

Time Description
3:45-4:15 Circuit Representation of Random Forests with Applications to XAI
Chunxi Ji, Adnan Darwiche
4:15-4:45 Scaling the Explanation of Multi-Class Bayesian Network Classifiers
Yaofang Zhang, Adnan Darwiche

Special session on Data-Driven Decisions: Applied Machine Learning & Optimization - 3

Chair: Ersoy Subasi

Time Description
3:45-4:15 Integrating Learned Residual Dynamics with Model Predictive Control
Wenxi Liu, Sirani M. Perera, Aaron Welters, and Xianqi Li
4:15-4:45 Correlation-Threshold Feature Selection for Internet of Things (IoT) Device Identification under Domain Shift
Aniket Dhanawade, Parth Ganeriwala, and Siddhartha Bhattacharyya

Evening

Water Taxi Cruise in Ft. Lauderdale.

Tentative: We will gather in the lobby at 7:00pm, and leave promptly at 7:05pm to walk to a 7:25pm pickup by the Watertaxi.

Thursday, January 8, 2026

Opening Remarks, Greetings (8.45 — 9:00)

Keynote Speaker (9:00 — 10:00)

Chair: Martin Golumbic / Frederick Hoffman

Time Description
9:00-10:00 The Mathematical Foundations of Bidirectional Heuristic Search
Ariel Felner

Coffee Break (10:00 — 10:30)

Morning Session (10:30 — 12:00)

Main track session 4 - Topics in Machine Learning

Chair: Leora Morgenstern

Time Description
10:30-11:00 Lightweight satisfiability solving using dataless neural networks
Andrew Gautier, Piotr Wojciechowski, Sangram K. Jena, K. Subramani, Alvaro Velasquez
11:00-11:30 New results on F-Quantified Linear Programs
A. Subramani, K. Subramani, Piotr Wojciechowski
11:30-12:00 Sensitivity and Uncertainty Quantification in Regression Neural Networks: A Quantum Tensor Network Based Approach
Pragatheeswaran Vipulanandan, Kamal Premaratne, Dilip Sarkar

Special session on Cooperation, Competition, and Complexity in AI Planning and Learning - 1

Chair: Guni Sharon

Time Description
10:30-11:00 Distributionally Robust Markov Games with Average Reward
Yue Wang
11:00-11:30 Is Learning Effective in Dynamic Strategic Interactions? Evidence from Stackelberg Games
Michael Albert
11:30-12:00 Strategic Value and Incentivized Cooperation in Multi-Player Stochastic Games
Alan Kuhnle

Lunch (12:00 — 2:00)

On your own.

After Lunch Sessions (2:00 — 3:00)

Special session on Topics in Math and AI - 1

Chair: Martin Golumbic

Time Description
2:00-2:30 Automated Translation of Regulatory Text into Defeasible Deontic Logic
Leora Morgenstern
2:30-3:00 Control in Computational Social Choice
Jörg Rothe

Cooperation, Competition, and Complexity in AI Planning and Learning - 2

Chairs: Alan Kunhle and Guni Sharon

Time Description
2:00-2:30Planning with Foundation Models: From Service to Assistive Robotics
Shiqi Zhang
2:30-3:00Scaling Tree Search to Domains with Large Belief States
Christopher Solinas

Coffee Break (3:00 — 3:30)

Afternoon Sessions (3:30 — 4:30)

Special session on Topics in Math and AI - 2

Chair: Frederick Hoffman

Time Description
3:30-4:00 Statistical Queries
Lev Reyzin
4:00-4:30 PAC Learning and Label Noise
Dimitrios Diochnos

Cooperation, Competition, and Complexity in AI Planning and Learning - 3

Chair: Alan Kuhnle

Time Description
3:30-4:00Deterministic Policy Gradients in the Era of Soft RL: Mixture Actors and Adaptive Ensembles
Guni Sharon
4:00-4:30HaLLMos: Constitutional AI for Teaching Mathematical Proofs
Vince Vatter

Evening

Banquet in the hotel.

The cash bar will open at 6:30pm, with dinner seating at 7:15.

Keynote Speaker (talk integrated with the banquet)

Chair: Frederick Hoffman

Time Description
TBD Future Directions for Math and AI: in conversation of Jeff Jaffe with Martin Golumbic
Jeff Jaffe

Friday, January 9, 2026

Opening Remarks, Greetings (8.45 — 9:00)

Keynote Speaker (9:00 — 10:00)

Chair: Martin Golumbic / Frederick Hoffman

Time Description
9:00-10:00 Title: AI and AC: the Andrews-Curtis Conjecture
Sergei Gukov

Coffee Break (10:00 — 10:30)

Morning Session (10:30 — 12:45)

Special session on AI and Group Theory - 1

Chair: Sergey Gukov

Time Description
10:30-11:15 AI and classical algorithmic problems in algebra
Alexei Miasnikov
11:15-12:00 RL Algorithmic Mixer
Vladislav Stepanov
12:00-12:45 Automated reasoning for proving non-orderability of groups and quandles
Alexei Lisitsa

Lunch (12:45 — 2:00)

On your own.

After Lunch Session (2:00 — 2:30)

Special session on AI and Group Theory - 2

Chair: Alexei Miasnikov

Time Description
2:00-2:30 Supplementing Knuth-Bendix for groups with Reinforcement Learning
Brett Berger
2:30-3:00 Computing kernel of Burau Representation B4 modulo p using Reinforcement Learning
Borys Holikov
3:00-3:30 Algorithmic Perspectives on the Andrews–Curtis Conjecture
Vazgen Kirakosyan

Coffee Break (3:30 — 4:00)

Afternoon Session (4:00 — 5:30)

Special session on AI and Group Theory - 3

Chair: Elena Bunina

Time Description
4:00-4:30 Extending U-MATH: An Automated University-Level Benchmark for Evaluating Step-by-Step Mathematical Reasoning in LLMs
Winston Lee
4:30-5:00 Computational methods for Studying the Finiteness of Burnside Group with small odd exponent: The case of B(2,5)
Itshak Ginsburg

Adjourn (5:00)