Main Conference location at Indian Institute of Science:
AV Rama Rao Auditorium, Chemical Sciences Building and
IDR Building
Pre-conference events:
9 October, 08:30 am: Campus tour/tree walk
10 October: Visit to Mysore
11 October: Lunch for the Organising team at Shangri-La Bengaluru, followed by shopping in Bangalore
Transportation will be provided to all pre-conference events. Information will be given on site.
Preliminary program. Changes may come.
| Mon, 12 Oct | Time | Activity | Presenter | Location |
|---|---|---|---|---|
| 14:00-15:00 | Tutorial Part 1: Introduction to Tsetlin Machines: Consepts and Foundations | Prof. Ole-Christoffer Granmo, University of Agder | AV Rama Rao Auditorium | |
| 15:00-15:30 | Break | |||
| 15:30-16:30 | Tutorial Part 2: Introduction to Tsetlin Machines: Practical Exercises and Coding | Prof. Linga Reddy Cenkeramaddi, University of Agder Prof. Srinivas Boppu, IIT Bhubaneswar Thrishank Hesaraghatta Shivakumar, IIT Bhubaneswar & University of Agder K Koushik, IIT Bhubaneswar & University of Agder | AV Rama Rao Auditorium | |
| 16:30-17:30 | Research Speed Date Session | AV Rama Rao Auditorium | ||
| 19:30-20:00 | Registration | |||
| 20:00 | Welcome Reception | IDR Building | ||
| Tue, 13 Oct | 08:30-09:00 | Registration | ||
| 09:00-09:10 | Opening Session | AV Rama Rao Auditorium | ||
| 09:10-10:10 | Keynote I: Explainable AI-assisted risk modelling for national financial markets | Prof. David Thomas, University of Southampton | AV Rama Rao Auditorium | |
| 10:10-10:30 | Break | |||
| 10:30-12:10 | Research Session 1: Efficient and Distributed Edge AI | AV Rama Rao Auditorium | ||
| 12:30-14:00 | Lunch | MGH Lawn | ||
| 14:00-16:40 | Research Session 2: New Learning Strategies and Emerging Applications | AV Rama Rao Auditorium | ||
| 16:40-17:00 | Break | |||
| 17:00-18:00 | Keynote II: Tsetlinomics: Simplifying and interpreting multi-omics data using Tsetlin machines | Dr. Mirza Khalid Baig, National Institute of Technology | AV Rama Rao Auditorium | |
| 18:00-18:30 | Break | |||
| 18:30-19:30 | Cultural program by IISc students | |||
| 19:30 | Conference Dinner | IDR Building | ||
| Wed, 14 Oct | 08:30-09:00 | Registration | ||
| 09:00-09:10 | Opening Session | AV Rama Rao Auditorium | ||
| 09:10-10:10 | Keynote III: Towards Trustworthy AI: From Internal Representations to Reliable Behaviour | Dr. Gouthaman K V, Dolby Laboratories Dr. Aveen Dayal, Dolby Advanced Technology Group India | AV Rama Rao Auditorium | |
| 10:10-10:30 | Break | |||
| 10:30-12:30 | Poster Session | |||
| 12:30-14:00 | Lunch | MGH Lawn | ||
| 14:00-16:00 | Research Session 3: Healthcare and Trustworthy AI | AV Rama Rao Auditorium | ||
| 16:00-16:30 | Closing Session | AV Rama Rao Auditorium | ||
Detailed Research Sessions
Session 1: Tsetlin Machines for Efficient and Distributed Edge AI
Chair: Prof. Ambedkar Dukkipati
Session theme: Hardware, edge deployment, latency optimization, and federated TM systems.
| # | ID | Paper title |
| 1 | 3446 | Multi-Pass Systolic Inference Tsetlin Machine Hardware Accelerator |
| 2 | 5496 | Inference Latency-Aware Tsetlin Machine Training |
| 3 | 2775 | Communication-Constrained Federated Tsetlin Machine: Optimizing Payload Size and End-to-End Pipeline Latency |
| 4 | 9080 | VACE: Validation-Free Aggregation Configuration Evaluation for FedTMOS |
| 5 | 8423 | FPGA-based Interpretability Framework for Convolutional Coalesced Tsetlin Machines |
Session 2: New Learning Strategies, Performance Evaluation and Emerging Applications
Chair: Prof. Pathipati Srihari and Prof. Gauri Kalnoor
Session theme: Novel TM learning mechanisms, interpretability improvements, performance evaluation and emerging application domains.
| # | ID | Paper title |
| 1 | 0587 | CRISP: A Framework for Clause-Reconstructed Interpretable NeuroSymbolic Propositions |
| 2 | 3015 | Compressed Recurrent Feedback in Tsetlin Machines: A Reproducible Boolean-FSM Study |
| 3 | 6837 | Clause Sanitization for Compact and Interpretable Tsetlin Machines |
| 4 | 3393 | Semi-supervised Human Activity Recognition Using Pattern-Balanced Cotraining with Tsetlin Machines |
| 5 | 8852 | Malicious URL Detection Using Tsetlin Machines: An Explainable and Energy-Efficient Approach |
| 6 | 8792 | Interpretable Subject Tagging and Text Feature Selection for Norwegian-Language Parliamentary Documents Using Tsetlin Machines |
| 7 | 9535 | Analyzing the Effect of True Markov Blankets on Tsetlin Machine Performance: A Comparative Study with Neural Networks, Random Forests, and Decision Trees |
| 8 | 6940 | Class conditional Booleanizer and Composite Tsetlin Machine for Foot Step Analysis using Pressure Sensors |
Session 3: Explainable Tsetlin Machines in Healthcare and Trustworthy AI
Chair: Prof. Venkanna Udutalapally
Session theme: Interpretable decision support, biomedical discovery, and trustworthy healthcare AI.
| # | ID | Paper title |
| 1 | 5045 | Interpretable Discovery of High-Precision EEG Non-Survivor Subgroups Using Tsetlin Machines |
| 2 | 4045 | Tsetlin Machines Meet Strong Filters: Masquerader Awareness for Explainable Patient Cohort Discovery |
| 3 | 5734 | Interpretable Verification of Boolean Gene Regulatory Networks via Tsetlin Machine Clause Analysis: A Case Study in Alzheimer’s Disease |
| 4 | 9820 | Tsetlin Machine-Based Explainable Infant Skin Exposure Detection Method for Contactless Infant Vital Sign Measurement Systems |
| 5 | 8292 | Gene Signature Discovery for Pancreatic Ductal Adenocarcinoma Using Tsetlin Machine: A Multi-Cohort Transcriptomic Pipeline |
| 6 | 7929 | Tsetlin Machine-Based ECG Signal Quality Assessment Method for Trustworthy Wearable Ambulatory Cardiac Health Monitoring |
Poster Session
Poster Responsible: Prof. M. Sabarimalai Manikandan and Prof. Vishnu Srinivasa Murthy Y
| # | ID | Paper title |
| 1 | 2232 | Quantum-Logic Tsetlin Machines: Interpretable Quantum Machine Learning with Commuting Projector Clauses |
| 2 | 5044 | A Recurrent Multi-Output Tsetlin Machine for Sequential Adaptive Sparse Sampling |
| 3 | 0231 | The Neuroplastic Tsetlin Machine |
| 4 | 1794 | Tsetlin Machine for Non-intrusive Load Monitoring on MCUs |
| 5 | 5905 | Clause Correspondence in Federated Tsetlin Machines: A Systematic Analysis |
| 6 | 6409 | FPGA-Based Tsetlin Machines for Acoustic Insect Classification |
