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Please <a href="http://browsehappy.com/">upgrade your browser</a> to improve your experience.</div><![endif]--><div class="masthead"><div class="masthead__inner-wrap"><div class="masthead__menu"><nav id="site-nav" class="greedy-nav"> <button><div class="navicon"></div></button><ul class="visible-links"><li class="masthead__menu-item masthead__menu-item--lg"><a href="https://kartheekmedathati.github.io/">Kartheek Medathati</a></li><li class="masthead__menu-item"><a href="https://kartheekmedathati.github.io/publications/">Publications</a></li><li class="masthead__menu-item"><a href="https://kartheekmedathati.github.io/cv/">CV</a></li><li class="masthead__menu-item"><a href="https://kartheekmedathati.github.io/talks/">Talks</a></li><li class="masthead__menu-item"><a href="https://kartheekmedathati.github.io/portfolio/">Code</a></li><li class="masthead__menu-item"><a href="https://kartheekmedathati.github.io/markdown/">Resources</a></li></ul><ul class="hidden-links hidden"></ul></nav></div></div></div><div id="main" role="main"><div class="sidebar sticky"><div itemscope itemtype="http://schema.org/Person"><div class="author__avatar"><picture> <source srcset="https://kartheekmedathati.github.io/images/kartheek_profilepic.webp" type="image/webp"> <img src="https://kartheekmedathati.github.io/images/kartheek_profilepic.png" class="author__avatar" alt="Kartheek Medathati" loading="lazy" decoding="async"> </picture></div><div class="author__content"><p class="author__bio">Founder, Handybot | Physical AI for the real world | ex-Amazon & Meta | PhD, INRIA Sophia Antipolis</p></div><div class="author__urls-wrapper"> <button class="btn btn--inverse">Follow</button><ul class="author__urls social-icons"><li><i class="fa fa-fw fa-map-marker" aria-hidden="true"></i> Fremont, CA, USA</li><li><a href="http://www-sop.inria.fr/members/Kartheek.Medathati/"><i class="fas fa-fw fa-link" aria-hidden="true"></i> Website</a></li><li><a href="https://handybot.ai"><i class="fas fa-fw fa-robot" aria-hidden="true"></i> Handybot</a></li><li><a href="mailto:[email protected]"><i class="fas fa-fw fa-envelope" aria-hidden="true"></i> Email</a></li><li><a href="https://www.researchgate.net/profile/N_V_Kartheek_Medathati"><i class="fab fa-fw fa-researchgate" aria-hidden="true"></i> ResearchGate</a></li><li><a href="https://twitter.com/kartheekmnv"><i class="fab fa-fw fa-twitter-square" aria-hidden="true"></i> Twitter</a></li><li><a href="https://www.linkedin.com/in/kartheekmedathati"><i class="fab fa-fw fa-linkedin" aria-hidden="true"></i> LinkedIn</a></li><li><a href="https://github.com/kartheekmedathati"><i class="fab fa-fw fa-github" aria-hidden="true"></i> Github</a></li><li><a href="https://scholar.google.com/citations?user=ajnz0f0AAAAJ&hl=en"><i class="fas fa-fw fa-graduation-cap"></i> Google Scholar</a></li><li><a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=kartheek+medathati"><i class="ai ai-pubmed-square ai-fw"></i> PubMed</a></li></ul></div></div></div><div class="archive"><h1 class="page__title">Publications</h1><style> .pub-scholar { font-size:.9em; color:#555; margin-bottom:1.2em; } .pub-list { margin-top:.5em; } .pub-card { display:flex; gap:1.1em; padding:1.1em 0; border-bottom:1px solid #ececec; align-items:flex-start; } .pub-card:last-child { border-bottom:0; } .pub-card__fig { flex:0 0 220px; display:block; } .pub-card__fig img { width:100%; height:150px; object-fit:contain; background:#fff; border:1px solid #e6e8eb; border-radius:6px; display:block; padding:5px; } .pub-card__body { flex:1 1 auto; min-width:0; } .pub-card--nofig .pub-card__body { flex:1 1 100%; } .pub-card__title { font-size:1.05em; line-height:1.35; margin:0 0 .35em; } .pub-card__title a { text-decoration:none; } .pub-card__title a:hover { text-decoration:underline; } .pub-card__meta { margin:.15em 0 .45em; } .pub-venue { font-style:italic; color:#33414f; font-size:.88em; font-weight:500; } .pub-year { color:#8a8f98; font-size:.85em; margin-left:.35em; } .pub-card__authors { font-size:.85em; color:#555; margin:.15em 0 .35em; } .pub-card__excerpt { font-size:.9em; color:#444; margin:.2em 0 .65em; line-height:1.5; } .pub-card__links { display:flex; flex-wrap:wrap; gap:.45em; } .pub-btn { display:inline-block; font-size:.8em; line-height:1.3; padding:.32em .85em; border-radius:5px; border:1px solid #1f2937; color:#1f2937; text-decoration:none; transition:background .15s,color .15s; } .pub-btn:hover { background:#1f2937; color:#fff; text-decoration:none; } .pub-btn--primary { background:#1f2937; color:#fff; } .pub-btn--primary:hover { background:#374151; } .pub-cite { margin:0; } .pub-cite summary { cursor:pointer; list-style:none; } .pub-cite summary::-webkit-details-marker { display:none; } .pub-cite[open] { flex-basis:100%; } .pub-cite__text { background:#f7f7f7; padding:.7em .8em; border-radius:5px; margin:.5em 0 0; line-height:1.5; font-size:.85em; } .pub-cite__text p { margin:0; } @media (max-width:600px){ .pub-card{flex-direction:column; align-items:flex-start; gap:.6em;} .pub-card__fig{flex-basis:auto; width:100%; max-width:420px;} .pub-card__fig img{width:100%; height:auto;} }</style><div class="pub-list"><div class="pub-card"> <a class="pub-card__fig" href="https://dl.acm.org/doi/10.1145/3379155.3391319"><img src="https://kartheekmedathati.github.io/images/pub-thumbs/cognitive-state-2020.jpg" alt="Figure from: Towards inferring cognitive state changes from pupil size variations in real world conditions" loading="lazy" decoding="async" /></a><div class="pub-card__body"><h3 class="pub-card__title"><a href="https://dl.acm.org/doi/10.1145/3379155.3391319">Towards inferring cognitive state changes from pupil size variations in real world conditions </a></h3><p class="pub-card__meta"><span class="pub-venue">Eye Tracking Research and Applications (ETRA)</span><span class="pub-year">· 2020</span></p><p class="pub-card__authors">N. V. Kartheek Medathati, Ruta Desai, James Hillis</p><p class="pub-card__excerpt">In this paper we study how far we are from estimating cognitive state changes from eye movements in real world environments using a variety of visual search tasks</p><div class="pub-card__links"> <a class="pub-btn pub-btn--primary" href="https://dl.acm.org/doi/10.1145/3379155.3391319">Paper / DOI</a> <a class="pub-btn" href="https://dl.acm.org/doi/pdf/10.1145/3379155.3391319">PDF</a> <details class="pub-cite"><summary class="pub-btn">Cite</summary><div class="pub-cite__text"><p>
1Naga Venkata Kartheek Medathati, Ruta Desai, James Hillis. <em>Towards inferring cognitive state changes from pupil size variations in real world conditions.</em> ETRA ‘20 Full Papers, ACM, 2020, Article 22, 1–10. doi:10.1145/3379155.3391319</p></div></details></div></div></div><div class="pub-card"> <a class="pub-card__fig" href="https://www.nature.com/articles/s41598-017-11373-z"><img src="https://kartheekmedathati.github.io/images/pub-thumbs/recurrent-network-2017.jpg" alt="Figure from: Recurrent network dynamics reconciles visual motion segmentation and integration." loading="lazy" decoding="async" /></a><div class="pub-card__body"><h3 class="pub-card__title"><a href="https://www.nature.com/articles/s41598-017-11373-z">Recurrent network dynamics reconciles visual motion segmentation and integration. </a></h3><p class="pub-card__meta"><span class="pub-venue">Scientific Reports</span><span class="pub-year">· 2017</span></p><p class="pub-card__authors">N. V. K. Medathati, J. Rankin, A. I. Meso, P. Kornprobst, G. S. Masson</p><p class="pub-card__excerpt">This paper characterizes the complex spatio-temporal dynamics of MT neurons</p><div class="pub-card__links"> <a class="pub-btn pub-btn--primary" href="https://www.nature.com/articles/s41598-017-11373-z">Paper / DOI</a> <a class="pub-btn" href="https://www.nature.com/articles/s41598-017-11373-z.pdf">PDF</a> <details class="pub-cite"><summary class="pub-btn">Cite</summary><div class="pub-cite__text"><p>N. V. K. Medathati, J. Rankin, A. I. Meso, P. Kornprobst, G. S. Masson. <em>Recurrent network dynamics reconciles visual motion segmentation and integration.</em> Scientific Reports 7, 11270 (2017). doi:10.1038/s41598-017-11373-z</p></div></details></div></div></div><div class="pub-card"> <a class="pub-card__fig" href="https://doi.org/10.1016/j.cviu.2016.04.009"><img src="https://kartheekmedathati.github.io/images/pub-thumbs/bio-inspired-2016.jpg" alt="Figure from: Bio-inspired computer vision: Towards a synergistic approach of artificial and biological vision" loading="lazy" decoding="async" /></a><div class="pub-card__body"><h3 class="pub-card__title"><a href="https://doi.org/10.1016/j.cviu.2016.04.009">Bio-inspired computer vision: Towards a synergistic approach of artificial and biological vision </a></h3><p class="pub-card__meta"><span class="pub-venue">Computer Vision and Image Understanding</span><span class="pub-year">· 2016</span></p><p class="pub-card__authors">N. V. Kartheek Medathati, Heiko Neumann, Guillaume S. Masson, Pierre Kornprobst</p><p class="pub-card__excerpt">A review paper exploring the mutual benefits of biological and computer vision — how methods used to understand the visual cortex can make machine vision more robust.</p><div class="pub-card__links"> <a class="pub-btn pub-btn--primary" href="https://doi.org/10.1016/j.cviu.2016.04.009">Paper / DOI</a> <a class="pub-btn" href="https://www.sciencedirect.com/science/article/pii/S1077314216300339/pdfft?md5=4f11066c75e759ca85c419f162268056&amp;pid=1-s2.0-S1077314216300339-main.pdf">PDF</a> <details class="pub-cite"><summary class="pub-btn">Cite</summary><div class="pub-cite__text"><p>N. V. Kartheek Medathati, Heiko Neumann, Guillaume S. Masson, Pierre Kornprobst. <em>Bio-inspired computer vision: Towards a synergistic approach of artificial and biological vision.</em> Computer Vision and Image Understanding, Volume 150, 2016, Pages 1–30. DOI: 10.1016/j.cviu.2016.04.009</p></div></details></div></div></div><div class="pub-card"> <a class="pub-card__fig" href="https://www.sciencedirect.com/science/article/abs/pii/S0923596515000673"><img src="https://kartheekmedathati.github.io/images/pub-thumbs/v1mt-2015.jpg" alt="Figure from: What can we expect from a V1-MT feedforward architecture for optical flow estimation?" loading="lazy" decoding="async" /></a><div class="pub-card__body"><h3 class="pub-card__title"><a href="https://www.sciencedirect.com/science/article/abs/pii/S0923596515000673">What can we expect from a V1-MT feedforward architecture for optical flow estimation? </a></h3><p class="pub-card__meta"><span class="pub-venue">Signal Processing: Image Communication</span><span class="pub-year">· 2015</span></p><p class="pub-card__authors">
1Fabio Solari, Manuela Chessa, N. V. Kartheek Medathati, Pierre Kornprobst</p><p class="pub-card__excerpt">We show how a V1-MT neural model can be adapted to handle real sequences.</p><div class="pub-card__links"> <a class="pub-btn pub-btn--primary" href="https://www.sciencedirect.com/science/article/abs/pii/S0923596515000673">Paper / DOI</a> <a class="pub-btn" href="https://hal.inria.fr/hal-01078117/document">PDF</a> <details class="pub-cite"><summary class="pub-btn">Cite</summary><div class="pub-cite__text"><p>Fabio Solari, Manuela Chessa, N. V. Kartheek Medathati, Pierre Kornprobst. <em>What can we expect from a V1-MT feedforward architecture for optical flow estimation?</em> Signal Processing: Image Communication, 39(B):342–354, 2015. doi:10.1016/j.image.2015.04.006</p></div></details></div></div></div><div class="pub-card"> <a class="pub-card__fig" href="https://ieeexplore.ieee.org/document/7362783"><img src="https://kartheekmedathati.github.io/images/pub-thumbs/decoding-mt-2015.jpg" alt="Figure from: Decoding MT motion response for optical flow estimation: An experimental evaluation" loading="lazy" decoding="async" /></a><div class="pub-card__body"><h3 class="pub-card__title"><a href="https://ieeexplore.ieee.org/document/7362783">Decoding MT motion response for optical flow estimation: An experimental evaluation </a></h3><p class="pub-card__meta"><span class="pub-venue">European Signal Processing Conference (EUSIPCO)</span><span class="pub-year">· 2015</span></p><p class="pub-card__authors">M. Chessa, N. V. Kartheek Medathati, G. S. Masson, F. Solari, P. Kornprobst</p><p class="pub-card__excerpt">Decoding the motion energies is of natural interest for developing biologically inspired computer vision algorithms for dense optical flow estimation. Here, we address this problem by evaluating four strategies for motion decoding: intersection of constraints, linear decoding through learned weights on MT responses, maximum likelihood and regression with neural network using multi scale-features.</p><div class="pub-card__links"> <a class="pub-btn pub-btn--primary" href="https://ieeexplore.ieee.org/document/7362783">Paper / DOI</a> <a class="pub-btn" href="https://hal.inria.fr/hal-01215526/document">PDF</a> <details class="pub-cite"><summary class="pub-btn">Cite</summary><div class="pub-cite__text"><p>M. Chessa, N. V. Kartheek Medathati, G. S. Masson, F. Solari, P. Kornprobst. <em>Decoding MT motion response for optical flow estimation: An experimental evaluation.</em> 23rd EUSIPCO, 2015, pp. 2241–2245. doi:10.1109/EUSIPCO.2015.7362783</p></div></details></div></div></div><div class="pub-card"> <a class="pub-card__fig" href="https://www.sciencedirect.com/science/article/abs/pii/S0031320312001537"><img src="https://kartheekmedathati.github.io/images/pub-thumbs/disease-detection-2012.jpg" alt="Figure from: Detection and discrimination of disease-related abnormalities based on learning normal cases" loading="lazy" decoding="async" /></a><div class="pub-card__body"><h3 class="pub-card__title"><a href="https://www.sciencedirect.com/science/article/abs/pii/S0031320312001537">Detection and discrimination of disease-related abnormalities based on learning normal cases </a></h3><p class="pub-card__meta"><span class="pub-venue">Pattern Recognition</span><span class="pub-year">· 2012</span></p><p class="pub-card__authors">K. Sai Deepak, N. V. Kartheek Medathati, Jayanthi Sivaswamy</p><p class="pub-card__excerpt">In this paper, we observe the key challenges for representation and feature extraction schemes to be met for detection of abnormalities by learning normal cases.</p><div class="pub-card__links"> <a class="pub-btn pub-btn--primary" href="https://www.sciencedirect.com/science/article/abs/pii/S0031320312001537">Paper / DOI</a> <a class="pub-btn" href="https://cdn.iiit.ac.in/cdn/cvit.iiit.ac.in/images/JournalPublications/2012/ja_Detectiondiscrimination.pdf">PDF</a> <details class="pub-cite"><summary class="pub-btn">Cite</summary><div class="pub-cite__text"><p>K. Sai Deepak, N. V. Kartheek Medathati, Jayanthi Sivaswamy. <em>Detection and discrimination of disease-related abnormalities based on learning normal cases.</em> Pattern Recognition, 45(10):3707–3716, 2012. doi:10.1016/j.patcog.2012.03.020</p></div></details></div></div></div><div class="pub-card"> <a class="pub-card__fig" href="https://dl.acm.org/doi/10.1145/1924559.1924612"><img src="https://kartheekmedathati.github.io/images/pub-thumbs/texture-projections-2010.jpg" alt="Figure from: Local descriptor based on texture of projections" loading="lazy" decoding="async" /></a><div class="pub-card__body"><h3 class="pub-card__title"><a href="https://dl.acm.org/doi/10.1145/1924559.1924612">Local descriptor based on texture of projections </a></h3><p class="pub-card__meta"><span class="pub-venue">Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP)</span><span class="pub-year">· 2010</span></p><p class="pub-card__authors">N. V. Kartheek Medathati, Jayanthi Sivaswamy</p><p class="pub-card__excerpt">In this paper we propose a novel descriptor that better captures shape information by anal
1ysing texture of the radon transform</p><div class="pub-card__links"> <a class="pub-btn pub-btn--primary" href="https://dl.acm.org/doi/10.1145/1924559.1924612">Paper / DOI</a> <a class="pub-btn" href="http://www-sop.inria.fr/members/Kartheek.Medathati/Medathati_LocalDescriptor_2010.pdf">PDF</a> <details class="pub-cite"><summary class="pub-btn">Cite</summary><div class="pub-cite__text"><p>N. V. Kartheek Medathati, Jayanthi Sivaswamy. <em>Local descriptor based on texture of projections.</em> ICVGIP ‘10, ACM, 2010, pp. 398–404. doi:10.1145/1924559.1924612</p></div></details></div></div></div><div class="pub-card"> <a class="pub-card__fig" href="https://dl.acm.org/doi/10.1145/1882992.1883108"><img src="https://kartheekmedathati.github.io/images/pub-thumbs/retinal-matching-2010.jpg" alt="Figure from: Robust matching of multi-modal retinal images using radon transform based local descriptor" loading="lazy" decoding="async" /></a><div class="pub-card__body"><h3 class="pub-card__title"><a href="https://dl.acm.org/doi/10.1145/1882992.1883108">Robust matching of multi-modal retinal images using radon transform based local descriptor </a></h3><p class="pub-card__meta"><span class="pub-venue">ACM International Health Informatics Symposium (IHI)</span><span class="pub-year">· 2010</span></p><p class="pub-card__authors">Yogesh Babu Bathina, N. V. Kartheek Medathati, Jayanthi Sivaswamy</p><p class="pub-card__excerpt">A novel Radon Transform based descriptor is proposed, which is invariant to illumination, rotation and partially to scale.</p><div class="pub-card__links"> <a class="pub-btn pub-btn--primary" href="https://dl.acm.org/doi/10.1145/1882992.1883108">Paper / DOI</a> <a class="pub-btn" href="http://www-sop.inria.fr/members/Kartheek.Medathati/Medathati_RadonDescriptor_2010.pdf">PDF</a> <details class="pub-cite"><summary class="pub-btn">Cite</summary><div class="pub-cite__text"><p>Yogesh Babu Bathina, N. V. Kartheek Medathati, Jayanthi Sivaswamy. <em>Robust matching of multi-modal retinal images using radon transform based local descriptor.</em> IHI ‘10, ACM, 2010, pp. 765–770. doi:10.1145/1882992.1883108</p></div></details></div></div></div><div class="pub-card"> <a class="pub-card__fig" href="https://ieeexplore.ieee.org/document/5401886"><img src="https://kartheekmedathati.github.io/images/pub-thumbs/tomographic-2009.jpg" alt="Figure from: Synthetic zooming of tomographic images by combination of lattices" loading="lazy" decoding="async" /></a><div class="pub-card__body"><h3 class="pub-card__title"><a href="https://ieeexplore.ieee.org/document/5401886">Synthetic zooming of tomographic images by combination of lattices </a></h3><p class="pub-card__meta"><span class="pub-venue">IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)</span><span class="pub-year">· 2010</span></p><p class="pub-card__authors">N. Dixit, N. V. Kartheek Medathati, J. Sivaswamy</p><p class="pub-card__excerpt">We propose a method for synthetic zooming of tomographic images by applying super resolution technique on reconstructed data via a union of rotated lattices.</p><div class="pub-card__links"> <a class="pub-btn pub-btn--primary" href="https://ieeexplore.ieee.org/document/5401886">Paper / DOI</a> <a class="pub-btn" href="http://www-sop.inria.fr/members/Kartheek.Medathati/Medathati_SyntheticZooming_2009.pdf">PDF</a> <details class="pub-cite"><summary class="pub-btn">Cite</summary><div class="pub-cite__text"><p>N. Dixit, N. V. Kartheek Medathati, J. Sivaswamy. <em>Synthetic zooming of tomographic images by combination of lattices.</em> 2009 IEEE NSS/MIC, pp. 3770–3776. doi:10.1109/NSSMIC.2009.5401886</p></div></details></div></div></div></div></div></div><div class="page__footer"><footer> <!-- start custom footer snippets --> <a href="/sitemap/">Sitemap</a> <!-- end custom footer snippets --><div class="page__footer-follow"><ul class="social-icons"><li><strong>Follow:</strong></li><li><a href="https://twitter.com/kartheekmnv"><i class="fab fa-twitter-square" aria-hidden="true"></i> Twitter</a></li><li><a href="http://github.com/kartheekmedathati"><i class="fab fa-github" aria-hidden="true"></i> GitHub</a></li><li><a href="https://kartheekmedathati.github.io/feed.xml"><i class="fa fa-fw fa-rss-square" aria-hidden="true"></i> Feed</a></li></ul></div><div class="page__footer-copyright">&copy; 2026 Kartheek Medathati. All rights reserved.</div></footer></div>
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