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Machine Learning and Analytics

Talita Perciano

Talita Perciano
Talita Perciano
Research Scientist
Scientific Data Division
Phone: +1 510 486 5060
1 Cyclotron
Mail Stop 59R3103 - 059-3034B
Berkeley, California 94720 us

Talita Perciano is a Research Scientist at Lawrence Berkeley National Laboratory. She is a member of both the Machine Learning and Analytics group and the Computational Biosciences group. She conducts research in the areas of machine learning, quantum image processing, image analysis and high-performance computing motivated by the incredible challenges around scientific data generated by computational models, simulations, and experiments. Her research focuses on mathematical foundations for new methods, on the implementation of scalable methods, and on platform-portability. Her goal is to develop powerful, mathematically-grounded, scalable algorithms that meet the requirements needed to analyze current and future scientific datasets acquired in user data facilities. She has built a diverse collaboration network throughout the years in fields such as materials science, biosciences, geosciences, among others. She earned her doctorate in Computer Science from the University of São Paulo in 2012.


More info about research projects

Journal Articles

Gregory Wallace, Zhe Bai, Robbie Sadre, Talita Perciano, Nicola Bertelli, Syun'ichi Shiraiwa, Wes Bethel, John Wright, "Towards fast and accurate predictions of radio frequency power deposition and current profile via data-driven modelling: applications to lower hybrid current drive", Journal of Plasma Physics, August 18, 2022, 88:895880401, doi: 10.1017/S0022377822000708

M. G. Amankwah, D. Camps, E. W. Bethel, R. Van Beeumen, T. Perciano, "Quantum pixel representations and compression for N-dimensional images", Nature Scientific Reports, May 11, 2022, 12:7712, doi: 10.1038/s41598-022-11024-y

S. Zhang, R. Sadre, B. A. Legg, H. Pyles, T. Perciano, E. W. Bethel, D. Baker, O. Rübel, J. J. D. Yoreo, "Rotational dynamics and transition mechanisms of surface-adsorbed proteins", Proceedings of the National Academy of Sciences, April 11, 2022, 119:e202024211, doi: 10.1073/pnas.2020242119

M. Avaylon, R. Sadre, Z. Bai, T. Perciano, "Adaptable Deep Learning and Probabilistic Graphical Model System for Semantic Segmentation", Advances in Artificial Intelligence and Machine Learnin, March 31, 2022, 2:288--302, doi: 10.54364/AAIML.2022.1119

C Varadharajan, AP Appling, B Arora, DS Christianson, VC Hendrix, V Kumar, AR Lima, J Müller, S Oliver, M Ombadi, T Perciano, JM Sadler, H Weierbach, JD Willard, Z Xu, J Zwart, "Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?", Hydrological Processes, January 1, 2022, 36, doi: 10.1002/hyp.14565

Li Zhou, Chao Yang, Weiguo Gao, Talita Perciano, Karen M. Davies, Nicholas K. Sauter, "Subcellular structure segmentation from cryo-electron tomograms via machine learning", PLOS Journal of Computational Biology, April 2, 2020, submitte, doi: doi:

RJ Pandolfi, DB Allan, E Arenholz, L Barroso-Luque, SI Campbell, TA Caswell, A Blair, F De Carlo, S Fackler, AP Fournier, G Freychet, M Fukuto, D Gürsoy, Z Jiang, H Krishnan, D Kumar, RJ Kline, R Li, C Liman, S Marchesini, A Mehta, AT N Diaye, DY Parkinson, H Parks, LA Pellouchoud, T Perciano, F Ren, S Sahoo, J Strzalka, D Sunday, CJ Tassone, D Ushizima, S Venkatakrishnan, KG Yager, P Zwart, JA Sethian, A Hexemer, "Xi-cam: a versatile interface for data visualization and analysis", Journal of Synchrotron Radiation, 2018, 25:1261--1270, doi: 10.1107/S1600577518005787

Benedikt J Daurer, Hari Krishnan, Talita Perciano, Filipe RNC Maia, David A Shapiro, James A Sethian, Stefano Marchesini, "Nanosurveyor: a framework for real-time data processing", Advanced structural and chemical imaging, 2017, 3:7,

T Perciano, D Ushizima, H Krishnan, D Parkinson, N Larson, DM Pelt, W Bethel, F Zok, J Sethian, "Insight into 3D micro-CT data: Exploring segmentation algorithms through performance metrics", Journal of Synchrotron Radiation, 2017, 24:1065--1077, doi: 10.1107/S1600577517010955

M Farmand, R Celestre, P Denes, ALD Kilcoyne, S Marchesini, H Padmore, T Tyliszczak, T Warwick, X Shi, J Lee, YS Yu, J Cabana, J Joseph, H Krishnan, T Perciano, FRNC Maia, DA Shapiro, "Near-edge X-ray refraction fine structure microscopy", Applied Physics Letters, 2017, 110, doi: 10.1063/1.4975377

T Perciano, F Tupin, R Hirata, RM Cesar, "A two-level Markov random field for road network extraction and its application with optical, SAR, and multitemporal data", International Journal of Remote Sensing, 2016, 37:3584--3610, doi: 10.1080/01431161.2016.1201227

S Marchesini, H Krishnan, BJ Daurer, DA Shapiro, T Perciano, JA Sethian, FRNC Maia, "SHARP: A distributed GPU-based ptychographic solver", Journal of Applied Crystallography, 2016, 49:1245--1252, doi: 10.1107/S1600576716008074

DM Ushizima, HA Bale, EW Bethel, P Ercius, BA Helms, H Krishnan, LT Grinberg, M Haranczyk, AA Macdowell, K Odziomek, DY Parkinson, T Perciano, RO Ritchie, C Yang, "IDEAL: Images Across Domains, Experiments, Algorithms and Learning", JOM, 2016, 68:2963--2972, doi: 10.1007/s11837-016-2098-4

AW Wills, DJ Michalak, P Ercius, ER Rosenberg, T Perciano, D Ushizima, R Runser, BA Helms, "Block Copolymer Packing Limits and Interfacial Reconfigurability in the Assembly of Periodic Mesoporous Organosilicas", Advanced Functional Materials, 2015, 25:4120--4128, doi: 10.1002/adfm.201501059

J Donatelli, M Haranczyk, A Hexemer, H Krishnan, X Li, L Lin, F Maia, S Marchesini, D Parkinson, T Perciano, D Shapiro, D Ushizima, C Yang, JA Sethian, "CAMERA: The Center for Advanced Mathematics for Energy Research Applications", Synchrotron Radiation News, 2015, 28:4--9, doi: 10.1080/08940886.2015.1013413

T Perciano, MW Urban, NDA Mascarenhas, M Fatemi, AC Frery, GT Silva, "Deconvolution of vibroacoustic images using a simulation model based on a three dimensional point spread function", Ultrasonics, 2013, 53:36--44, doi: 10.1016/j.ultras.2012.03.011

Conference Papers

V. Dumont, C. Garner, A. Trivedi, C. Jones, V. Ganapati, J. Mueller, T. Perciano, M. Kiran, and M. Day, "HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization", 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC), November 15, 2021,

E. W. Bethel, C. Heinemann, and T. Perciano, "Performance Tradeoffs in Shared-memory Platform Portable Implementations of a Stencil Kernel", Eurographics Symposium on Parallel Graphics and Visualization, June 14, 2021,

Talita Perciano, Colleen Heinemann, David Camp, Brenton Lessley, E Wes Bethel, "Shared-Memory Parallel Probabilistic Graphical Modeling Optimization: Comparison of Threads, OpenMP, and Data-Parallel Primitives", High Performance Computing, Cham, Springer International Publishing, 2020, 127--145, doi: 10.1007/978-3-030-50743-5_7

Stefano Marchesini, Anuradha Trivedi, Pablo Enfedaque, Talita Perciano, Dilworth Parkinson, "Sparse Matrix-Based HPC Tomography", Computational Science -- ICCS 2020, Cham, Springer International Publishing, 2020, 248--261, doi: 10.1007/978-3-030-50371-0_18

E. Wes Bethel, David Camp, Talita Perciano, Colleen Heinemann, "Performance Analysis of Traditional and Data-Parallel Primitive Implementations of Visualization and Analysis Kernels", Berkeley, CA, USA, 94720, 2020,

C Heinemann, T Perciano, D Ushizima, EW Bethel, "Distributed memory parallel Markov random fields using graph partitioning", Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, 2018, 2018-Jan:3332--3341, doi: 10.1109/BigData.2017.8258318

B Lessley, T Perciano, C Heinemann, D Camp, H Childs, EW Bethel, "DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives", The 8th IEEE Symposium on Large Data Analysis and Visualization - LDAV 2018, 2018,

B Lessley, T Perciano, M Mathai, H Childs, EW Bethel, "Maximal clique enumeration with data-parallel primitives", 2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017, 2017, 2017-Dec:16--25, doi: 10.1109/LDAV.2017.8231847

T Perciano, D Ushizima, H Krishnan, D Parkinson, J Sethian, "FibriPy: A software environment for fiber analysis from 3D micro-computed tomography data", Advanced Materials - TechConnect Briefs 2017, 2017, 1:25--28,

DY Parkinson, DM Pelt, T Perciano, D Ushizima, H Krishnan, HS Barnard, AA MacDowell, J Sethian, "Machine learning for micro-tomography", Proceedings of SPIE - The International Society for Optical Engineering, 2017, 10391, doi: 10.1117/12.2274731

SV Venkatakrishnan, KA Mohan, K Beattie, J Correa, E Dart, JR Deslippe, A Hexemer, H Krishnan, AA MacDowell, S Marchesini, SJ Patton, T Perciano, JA Sethian, R Stromsness, BL Tierney, CE Tull, D Ushizima, DY Parkinson, "Making advanced scientific algorithms and big scientific data management more accessible", IS and T International Symposium on Electronic Imaging Science and Technology, 2016, doi: 10.2352/ISSN.2470-1173.2016.19.COIMG-155

DY Parkinson, K Beattie, X Chen, J Correa, E Dart, BJ Daurer, JR Deslippe, A Hexemer, H Krishnan, AA Macdowell, FRNC Maia, S Marchesini, HA Padmore, SJ Patton, T Perciano, JA Sethian, D Shapiro, R Stromsness, N Tamura, BL Tierney, CE Tull, D Ushizima, "Real-time data-intensive computing", AIP Conference Proceedings, 2016, 1741, doi: 10.1063/1.4952921

T Perciano, DM Ushizima, EW Bethel, YD Mizrahi, D Parkinson, JA Sethian, "Reduced-complexity image segmentation under parallel Markov Random Field formulation using graph partitioning", Proceedings - International Conference on Image Processing, ICIP, 2016, 2016-Aug:1259--1263, doi: 10.1109/ICIP.2016.7532560

D Ushizima, T Perciano, D Parkinson, "Fast detection of material deformation through structural dissimilarity", Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015, 2015, 2775--2781, doi: 10.1109/BigData.2015.7364080

D Ushizima, T Perciano, H Krishnan, B Loring, H Bale, D Parkinson, J Sethian, "Structure recognition from high resolution images of ceramic composites", Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014, 2014, 683--691, doi: 10.1109/BigData.2014.7004292

Book Chapters

H Chang, J J Donatelli, P Enfedaque, G Freychet, M Haranczyk, A Hexemer, Z Hu, O Jain, H Krishnan, D Kumar, X Li, L Lin, M MacNeil, S Marchesini, X Mo, M Noack, K Pande, R Pandolfi, D Parkinson, D M Pelt, T Perciano, D A Shapiro, D Ushizima, C Yang, P H Zwart, J A Sethian, "Building Mathematics, Algorithms, and Software for Experimental Facilities", Handbook on Big Data and Machine Learning in the Physical Sciences, ( 2020) Pages: 189--240 doi: 10.1142/9789811204579_0012