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Causal Experimental Design at Scale</a></div><div class="description">Wellcome Centre of Human Neuroimaging, UCL - April 2022</div></div><div><div class="title"><a target="__blank__" href="https://drive.google.com/file/d/1FT4lrwEjejqgU8pj955foqM38YBciN0f/view?usp=sharing">Active Inference Seminar</a></div><div class="description">University of Oxford, OATML seminar - Feb 2021</div></div><div><div class="title"><a target="__blank__" href="https://drive.google.com/file/d/1p6l0Ya9FJQKZdXOnulfOx7JEtsXAAXYe/view?usp=sharing">Robust Autonomous Driving</a></div><div class="description">Google-Oxford workshop, November 2020 <br/> Qualcomm, Research talk, November 2020</div></div><div><div class="title"><a target="__blank__" href="https://drive.google.com/file/d/1dWBu_Nro3a3Jwdm8De46tDPPBMDjttsW/view?usp=sharing">Artificial Intelligence in Art &amp; Design</a></div><div class="description">Bartlett School of Architecture, UCL, London - Feb 2019 <br/> Department of Fine Art, U. of Ioannina, Greece - Nov 2019</div></div><div style="margin-top:1.38rem"></div><h3>Publications / Technical Reports</h3><div style="margin-top:1rem"></div><div class="pubItem"><h5 class="year">2025</h5><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/made/banner.png" alt="MADE: Benchmark Environments for Closed-Loop Materials Discovery" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,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" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://arxiv.org/abs/2601.20996" target="_blank">MADE: Benchmark Environments for Closed-Loop Materials Discovery</a></div><div class="authors" style="padding-top:0;padding-bottom:5px">Shreshth A. Malik, Tiarnan Doherty, <b>Panagiotis Tigas</b>, Muhammed Razzak, Stephen J. Roberts, Aron Walsh, Yarin Gal</div><div class="conference">arXiv 2025</div><div class="urls"><span class="urlItem">
1<svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://arxiv.org/abs/2601.20996" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div><div class="pubItem"><h5 class="year">2024</h5><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/bal-pm/banner.png" alt="Deep Bayesian Active Learning for Preference Modeling in Large Language Models" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,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" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://arxiv.org/abs/2406.10023" target="_blank">Deep Bayesian Active Learning for Preference Modeling in Large Language Models</a></div><div class="authors" style="padding-top:0;padding-bottom:5px">Luckeciano C. Melo, <b>Panagiotis Tigas</b>, Alessandro Abate, Yarin Gal</div><div class="conference">NeurIPS 2024</div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://arxiv.org/abs/2406.10023" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div><div class="pubItem"><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/eval-cbed/banner.png" alt="Challenges and Considerations in the Evaluation of Bayesian Causal Discovery" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,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" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://arxiv.org/pdf/2406.03209" target="_blank">
1Challenges and Considerations in the Evaluation of Bayesian Causal Discovery</a></div><div class="authors" style="padding-top:0;padding-bottom:5px">Amir Mohammad Karimi Mamaghan, <b>Panagiotis Tigas</b>, Karl Henrik Johansson, Yarin Gal, Yashas Annadani, Stefan Bauer</div><div class="conference">ICML 2024</div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://arxiv.org/pdf/2406.03209" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div><div class="pubItem"><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/caasl/banner.png" alt="Amortized Active Causal Induction with Deep Reinforcement Learning" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,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" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://arxiv.org/pdf/2405.16718" target="_blank">Amortized Active Causal Induction with Deep Reinforcement Learning</a></div><div class="authors" style="padding-top:0;padding-bottom:5px">Yashas Annadani, <b>Panagiotis Tigas</b>, Stefan Bauer, Adam Foster</div><div class="conference"></div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://arxiv.org/pdf/2405.16718" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div><div class="pubItem"><h5 class="year">2023</h5><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/diff-cbed/banner.png" alt="Differentiable multi-target causal bayesian experimental design" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,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
1HSH5zxL9E1BCtOEQ3SsRnU4b4pW4sR/rgD8RIkt4JiTk7QAAAABJRU5ErkJggg==" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://proceedings.mlr.press/v202/tigas23a/tigas23a.pdf" target="_blank">Differentiable multi-target causal bayesian experimental design</a></div><div class="authors" style="padding-top:0;padding-bottom:5px"><b>Panagiotis Tigas</b>*, Yashas Annadani*, Desi R Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer</div><div class="conference">ICML 2023</div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://proceedings.mlr.press/v202/tigas23a/tigas23a.pdf" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div><div class="pubItem"><h5 class="year">2022</h5><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/geoffectiveness-journal/banner.png" alt="Global geomagnetic perturbation forecasting using Deep Learning" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,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" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://agupubs.onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2022SW003045" target="_blank">Global geomagnetic perturbation forecasting using Deep Learning</a></div><div class="authors" style="padding-top:0;padding-bottom:5px">Vishal Upendran, <b>Panagiotis Tigas</b>, Banafsheh Ferdousi, Teo Bloch, Mark CM Cheung, Siddha Ganju, Asti Bhatt, Ryan M McGranaghan, Yarin Gal</div><div class="conference">Space Weather</div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2022SW003045" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div><div class="pubItem"><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/causal-experimental-design/banner.png" alt="Interventions, Where and How? Experimental Design for Causal Models at Scale" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAIAAACQkWg2AAAACXBIWXMAAAsTAAALEwEAmpwYAAACKUlEQVR4nF2Sy2sTURTG85f4V7jrQlyICzdu3FgQBEXssupCKFKwEqGUiG1FEim2nTxKa10Uahptk04yk1A7iY1TCU3IJDPNGzOThiaT+5gjd6aNod9iuA9+55vz3eOCS1mWNfqllFq2wN4O167hHSG03jAGA0wpwZdCCGGMwYLzc4tSVtdlF2DLTFpY+nA9fRgGAIwxteVcaZryNfTwz7HAAN3QFUXJ5U6+bAQDy09+Z0UAQAhRag3MXrNRKZXUzc0138KY9PMbA9ye4N3xaVHge71+vnDKBUJ9s08IBSCra5HxRzMctwwApXI9vBdjgJg6er+4UqtWlGLx
1o887dWNswzNnp0CD6zsvphYLhRNJkriAf/LWzfDnJdZDIhHf2mZ2JkJbn7zS/j4A9Pt9AEiK8ejuDwCo1Ou+55MRP8cA8eDw3su3MTFFEEKEIEJMc4AQazfKJx68mo0KSZYEpZaTkmF0vsfFRrMpCkK73SaYtFotPrZaq5Yr1foCt57LF87OOqeadhHrUJQSQhAApKWk9901PhqwDzEAqKoqCHaszkNiTACAFzIrwbBh6HZju5nsEYsYszcZDsGFA7GP5uZD9x/PigIvy8d7sfj0xFM5lbo6GqO/NBiYkZ1wTSsj00ykkjN3bvvfvGaAMxW2/gOOb0JMPnN7SloFAKrVWrfbZcBI0RHAdswXlQn3/MGv7N9WS5blUf+rwHDIux0DAHRdV1XVsR0F/gFUeKgs3j6XkAAAAABJRU5ErkJggg==" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://arxiv.org/abs/2203.02016" target="_blank">Interventions, Where and How? Experimental Design for Causal Models at Scale</a></div><div class="authors" style="padding-top:0;padding-bottom:5px"><b>Panagiotis Tigas</b>*, Yashas Annadani*, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer</div><div class="conference">NeurIPS 2022</div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://arxiv.org/abs/2203.02016" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div><div class="pubItem"><h5 class="year">2021</h5><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/causalbald/banner2.png" alt="Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,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" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://arxiv.org/abs/2111.02275" target="_blank">Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data</a></div><div class="authors" style="padding-top:0;padding-bottom:5px">Andrew Jesson*, <b>Panagiotis Tigas</b>*, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal</div><div class="conference">NeurIPS 2021</div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://arxiv.org/abs/2111.02275" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex">
1<svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div><div class="pubItem"><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/peper/banner.png" alt="Exploration and preference satisfaction trade-off in reward-free learning" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,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" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://arxiv.org/abs/2106.04316" target="_blank">Exploration and preference satisfaction trade-off in reward-free learning</a></div><div class="authors" style="padding-top:0;padding-bottom:5px">Noor Sajid, <b>Panagiotis Tigas</b> Alexey Zakharov, Zafeirios Fountas, Karl Friston</div><div class="conference">ICML 2021 Workshop on Unsupervised Reinforcement Learning</div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://arxiv.org/abs/2106.04316" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div><div class="pubItem"><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/sonified/banner.png" alt="Latent Mappings: Generating open-ended expressive mappings using Variational Autoencoders" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,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" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="http://nime2021.org/program/#/paper/170" target="_blank">Latent Mappings: Generating open-ended expressive mappings using Variational Autoencoders</a></div><div class="authors" style="padding-top:0;padding-bottom:5px">Tim Murray-Browne, <b>Panagiotis Tigas</b></div><div class="conference">2021 New Interfaces for Musical Expression (NIME)</div><div class="urls"><span class="urlItem">
1<svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="http://nime2021.org/program/#/paper/170" target="_blank" rel="noreferrer">PDF</a></span></div></div></div></div><div class="pubItem"><h5 class="year">2020</h5><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/geoeffectiveness/geo.png" alt="Global Earth Magnetic Field Modeling andForecasting with Spherical Harmonics Decomposition" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,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" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://ml4physicalsciences.github.io/2020/files/NeurIPS_ML4PS_2020_57.pdf" target="_blank">Global Earth Magnetic Field Modeling andForecasting with Spherical Harmonics Decomposition</a></div><div class="authors" style="padding-top:0;padding-bottom:5px"><b>Panagiotis Tigas</b>*, Téo Bloch*, Vishal Upendran*, Banafsheh Ferdoushi*, Mark C. M. Cheung, Siddha Ganju, Ryan M. McGranaghan, Yarin Gal, Asti Bhatt</div><div class="conference">Machine Learning and the Physical Sciences Workshop - 34th NeurIPS 2020</div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://ml4physicalsciences.github.io/2020/files/NeurIPS_ML4PS_2020_57.pdf" target="_blank" rel="noreferrer">PDF</a></span></div></div></div></div><div class="pubItem"><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/real2sim/real2sim.png" alt="Real2sim: Automatic Generation of Open Street Map Towns For Autonomous Driving Benchmarks" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA4AAAAQCAYAAAAmlE46AAAACXBIWXMAABYlAAAWJQFJUiTwAAADDklEQVR4nB3S606aBwCA4e9qtj/LkjZLqygiMguinBVBhVUUED+EAgpBwAKVk4oDY60KaEVbrId1ppuH1lZdsyxL3GpcsmTZslt5l+y5hke4ubnlh5O3WG0OyuVl/rn9g8PGC4rlJbq1alQ9GlQaI/aHDq5/+53j0zOuP90g/PX3v5ycvWdw2EUmHCIdnMCo1/NAraHXasXhdONyetAazXgn/OgNBl4dHCIsFIu43GP06EzIO1V0KrVMJ55Qq+4wm8jhdnmYioaJJVI0t7QhkUjQGnoR1BoDao0Rt0ek8rTGTD7FWnUDe9SFPK3mfk2BYlPH6moFvcnCg65u1F3dCLJ2OTqTmfW1CpvXL+j4YOTxywzWwkPuvpEjfd/DV3
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" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://ml4ad.github.io/files/papers2020/Real2sim:%20Automatic%20Generation%20of%20Open%20Street%20Map%20Towns%20For%20Autonomous%20Driving%20Benchmarks.pdf" target="_blank">Real2sim: Automatic Generation of Open Street Map Towns For Autonomous Driving Benchmarks</a></div><div class="authors" style="padding-top:0;padding-bottom:5px">Avishek Mandal, <b>Panagiotis Tigas</b>, Yarin Gal</div><div class="conference">Machine Learning for Autonomous Driving Workshop at the 34rd Conference on Neural Information Processing Systems (NeurIPS 2020)</div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://ml4ad.github.io/files/papers2020/Real2sim:%20Automatic%20Generation%20of%20Open%20Street%20Map%20Towns%20For%20Autonomous%20Driving%20Benchmarks.pdf" target="_blank" rel="noreferrer">PDF</a></span></div></div></div></div><div class="pubItem"><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/spatial/spatial.png" alt="Spatial Assembly:Generative Architecture With Reinforcement Learning, Self Play and Tree Search" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAOCAYAAAAmL5yKAAAACXBIWXMAAAsTAAALEwEAmpwYAAACC0lEQVR4nGWRaVPaUBSG8///gXx0GeWLtdYUBZ0GJiyKNqCkOtNRkewkgWwsLuXp5EIdqZl5J3OTe57znvdI4yghjhOS5LPy7/8U5YoSxv9JGo1j8XNZEK9JgMQ7/gxfNZA+OkizjNl8xvzlmclkIiBZlpGm2VqDjyApt5V3yC8GYcBkNmWxWJBNJ7y+vfJnseDl7VWcl7B03UEkZskBE3q6Tvf6GtM06ek9xnEk3CRpynQ2JX/mz3Mx8loGI5FDSrN1jqo2qNfrHBx85fjkmFqthtbpcP/wgKZpGIbBdDYXN
1QIQhhHhKCIIx0xnz2idLvL3EiflCpubm2wUNjgpl6nXG1xdaWITcZoJQC7J8308PxAQy3Y5OpLZ2ysKQKPZolAooChVrn5q6PotyYfivEZyXRfHc8Wct3d3nJ6dcdFucyTLnJ9fUK3VkGWZTveamxudIBy9O/aDEZLjOAiI46KqdXZ3dzk8/Eb78hKlVmV//wuNZpOL9iU3PR0/CPGCAOF8OEQyLRvbziEePxQFpaqgqirbOzuUK8scmq0Wj0998rtD38f1lk1dx0XqD54YGCaGaeO4Q7xhwK/bO5HF1tY2xWKRUqlEpXIqtuC4LpZlC9m2vQS8yzDoDwwRZh6saTloWlds5vf9PYZprbQEWJ8AK+V2H/t9+oMBlmNj2g6GZTEwVgDLEfDcwV+sxi1Spj7g3gAAAABJRU5ErkJggg==" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://drive.google.com/file/d/1MMahskKy9ksirosLOSeuFYBLjP0ZfAtx/view" target="_blank">Spatial Assembly:Generative Architecture With Reinforcement Learning, Self Play and Tree Search</a></div><div class="authors" style="padding-top:0;padding-bottom:5px"><b>Panagiotis Tigas</b>, Tyson Hosmer</div><div class="conference">Workshop on Machine Learning for Creativity and Design at the 34rd Conference on Neural Information Processing Systems (NeurIPS 2020)</div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://drive.google.com/file/d/1MMahskKy9ksirosLOSeuFYBLjP0ZfAtx/view" target="_blank" rel="noreferrer">PDF</a></span></div></div></div></div><div class="pubItem"><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/does-autonomous/auto.png" alt="Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,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" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://arxiv.org/abs/2006.14911" target="_blank">Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?</a></div><div class="authors" style="padding-top:0;padding-bottom:5px">Angelos Filos*, <b>Panagiotis Tigas</b>*, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal</div><div class="conference">37th International Conference on Machine Learning (ICML 2020)</div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://arxiv.org/abs/2006.14911" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex">
1<svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div><div class="pubItem"><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/percieval/brave.png" alt="Percival: Making In-browser Perceptual Ad Blocking Practical with Deep Learning" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAIAAACQkWg2AAAACXBIWXMAAAsTAAALEwEAmpwYAAAB4UlEQVR4nHVSS2hTQRSdhQu70UU3CpKl4AfSaKHid6MlapFqRLOxKi0u66IIolBEtBTclJaCpRt/K+VBEetGsKmK0Wq0gVC1dukHzD9535m5c+S9Rx7vRbwMM3DPOdw7h8MQLqWgKNIhcpuhYhHMF5m6MpruMfUo9I9AATwzb5zqNk4mjP6EkeoWC898KCrwhtLz+3xwr53uspIx+/w+6+JB62jMOhuvndlmatMBjfmP4g4N7MCl3fixirVlcAuC0/fP9HOtlt5ZSW5SgvualqBWVEO78GUJ9RIejWHlvSi8M2eu0Z9f8luumtpC5d8RAVWLamA7Xs+53ZUs8hmRfys+vgTAF7XK8U5ZCgSt7ehGGsc24OsHtZzB3DR/PCHePBWruUoPqw0f9mmtT0vhXtoU9jNoUy5y76aYHAFga3eLW5nxYDygscA1KmRV30bcGUL+FeZnxZNJvvRCv3Wu1MOc3IJHkxFblV6nwTh6OzCagtmEYzev9JW7WKU/Rs16yFa/vETI2evqyHqc6MToaWskWd3DSnHWmLgcEMICzyvHEg9vU2ozejuMBCsfWNeYuUqOHfw4Gg24yVOAKGTl8CHjQtz6tNiWizaBN8eDpd6Qurc3yf+nNZRCL+fKt6Wt/gL97parLZdJcgAAAABJRU5ErkJggg==" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://arxiv.org/abs/1905.07444" target="_blank">Percival: Making In-browser Perceptual Ad Blocking Practical with Deep Learning</a></div><div class="authors" style="padding-top:0;padding-bottom:5px">Zain ul Abi Din, <b>Panagiotis Tigas</b>, Samuel T. King, Benjamin Livshits</div><div class="conference">USENIX ATC, 2020 (Acceptance rate 18.6%)</div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://arxiv.org/abs/1905.07444" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div><div class="pubItem"><h5 class="year">2019</h5><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/rip/rip2.png" alt="Robust Imitative Planning: Planning from Demonstrations Under Uncertainty" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,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" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://ml4ad.github.io/files/papers/Robust%20Imitative%20Planning:%20Planning%20from%20Demonstrations%20Under%20Uncertainty.pdf" target="_blank">Robust Imitative Planning: Planning from Demonstrations Under Uncertainty</a></div><div class="authors" style="padding-top:0;padding-bottom:5px"><b>Panagiotis Tigas</b>*, Angelos Filos*, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal</div><div class="conference">Machine Learning for Autonomous Driving Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada.</div><div class="urls"><span class="urlItem">
1<svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://ml4ad.github.io/files/papers/Robust%20Imitative%20Planning:%20Planning%20from%20Demonstrations%20Under%20Uncertainty.pdf" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div><div class="pubItem"><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/art/banner.jpg" alt="Towards an Autonomous Architecture" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,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" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="http://papers.cumincad.org/data/works/att/acadia19_16.pdf" target="_blank">Towards an Autonomous Architecture</a></div><div class="authors" style="padding-top:0;padding-bottom:5px">Tyson Hosmer, <b>Panagiotis Tigas</b></div><div class="conference">Association for Computer Aided Design In Architecture (ACADIA), 2019 (acceptance rate ~25%) - Emerging Research Award</div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="http://papers.cumincad.org/data/works/att/acadia19_16.pdf" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div><div class="pubItem"><h5 class="year">2014</h5><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/cave-of-sounds/cave.png" alt="The Cave Of Sounds: An Interactive Installation Exploring How We Create Music Together" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,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" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://pdfs.semanticscholar.org/c2c2/ac4850ba15dd8ec3d65b750bf4b93582b564.pdf" target="_blank">The Cave Of Sounds: An Interactive Installation Exploring How We Create Music Together</a></div><div class="authors" style="padding-top:0;padding-bottom:5px">Tim Murray-Browne, Dom Aversano, Susanna Garcia, Wallace Hobbes, Daniel Lopez, Tadeo Sendon, <b>Panagiotis Tigas</b>, Kacper Ziemianin, Duncan Chapman</div><div class="conference">International Conference on New Interfaces for Musical Expression (NIME), 2014</div><div class="urls"><span class="urlItem">
1<svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://pdfs.semanticscholar.org/c2c2/ac4850ba15dd8ec3d65b750bf4b93582b564.pdf" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div><div class="pubItem"><h5 class="year">2012</h5><div style="display:flex;align-items:flex-start;padding-bottom:1.38rem"><div class="paper_image"><div style="position:relative;display:inline-block;overflow:hidden;width:140px;height:140px"><img src="/content/publications/markovjam/banner.png" alt="Markovjam: Real-time jam-session support system" style="display:block;width:100%;height:100%;object-fit:cover" loading="lazy"/><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAMCAYAAABr5z2BAAAACXBIWXMAABYlAAAWJQFJUiTwAAABFUlEQVR4nKVSy46DMAzM/38XZxY4gIRACHGAQ5fyfgTIVOMqFaW72sNaMgnxeDx2ovBPU9eDZVkw9D2mccQ4juj7HsMwwBjzM8FxHNj3XXzbNiGgz/OMaZpkr7WWGP1KpMqyhO/7yLIMSZLA8zyEYYggCOC6LvI8x7quKIpCnIXeCLTWaJpGAqzWNHdZKf/7dpOKdLbC848WjDGI4xhVVYlsz/uS5DRN4TjOCxhFkSigndtQ/FAFk2gcIOfCfjk8a2yDM7EExDNP8YCVCSao67pXC5RNEAntbXDPeF3XQqhIwA2DXElgCS0p59O27RuBLSQElESQXQ/zvFoC2IpVYTFn/MdDssYEKrHO/zPJry/xL7s+pAdyfO1WLr6MQAAAAABJRU5ErkJggg==" alt="" style="position:absolute;top:0;left:0;width:100%;height:100%;object-fit:cover;image-rendering:pixelated;transition:opacity 0.4s ease-out;opacity:1;pointer-events:none;z-index:2" aria-hidden="true"/></div></div><div class="paper"><div class="title"><a href="https://arxiv.org/pdf/1201.6251.pdf" target="_blank">Markovjam: Real-time jam-session support system</a></div><div class="authors" style="padding-top:0;padding-bottom:5px"><b>Panagiotis Tigas</b></div><div class="conference">MSc Thesis (2012)</div><div class="urls"><span class="urlItem"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M4 1h6l4 4v10a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1zm6 1.5V5h2.5L10 2.5zM6 7h4v1H7.5v1H10v1H7.5V12H6V7z"></path></svg> <a href="https://arxiv.org/pdf/1201.6251.pdf" target="_blank" rel="noreferrer">PDF</a></span><span class="urlItem" title="Copy bibtex"><svg class="icon" viewBox="0 0 16 16" fill="currentColor" aria-hidden="true"><path d="M3 6h4v4H5v2H3V6zm6 0h4v4h-2v2H9V6z"></path></svg> <!-- -->bibtex</span></div></div></div></div></main><footer class="layout_footer__nhUaV"><a href="/">ptigas.com</a> / <!-- -->2026<!-- --> / ᐅᒥᖕᒪᒃ</footer>
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Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.