Perciano is a Research Scientist in the Machine Learning and Analytics group and the Computational Biosciences group, at Lawrence Berkeley National Laboratory. She conducts research in the areas of image analysis, machine learning, quantum algorithms and machine learning, probabilistic graphical models, 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, chemistry, among others. She earned her doctorate in Computer Science from the University of São Paulo in 2012.
More info about research projects
Journal Articles
Jan Balewski, Mercy G Amankwah, Roel Van Beeumen, E Wes Bethel, Talita Perciano, Daan Camps,
"Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs",
Journal,
February 10, 2024,
14,
doi: https://doi.org/10.1038/s41598-024-53720-x
Zhe Bai, Abdelilah Essiari, Talita Perciano, Kristofer E Bouchard,
"AutoCT: Automated CT registration, segmentation, and quantification",
Software X,
January 5, 2024,
26,
doi: https://doi.org/10.1016/j.softx.2024.101673
E Wes Bethel, Mercy G Amankwah, Jan Balewski, Roel Van Beeumen, Daan Camps, Daniel Huang, Talita Perciano,
"Quantum computing and visualization: A disruptive technological change ahead",
Journal,
November 6, 2023,
43,
doi: https://doi.org/10.1109/MCG.2023.3316932
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
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
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
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
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
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
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
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
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
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