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Nathan Melton
Nathan Melton
Nathan Melton is a Postdoctoral Scholar at LBNL. He is an experienced data scientist focusing on using various computational and statistical methods to aid x-ray scientists at the Advanced Light Source (LBL) in analysing and collecting data. He has experience with linear regression models, gaussian process modeling, statistical modeling, PCA, NMF, Neural Networks, Convolutional Neural Networks, and k-means clustering. He is also skilled in data visualization and hi work has led to several publications and presentations around the world.
Tess Smidt
Tess Smidt
Tess Smidt is the 2018 Alvarez Postdoctoral Fellow in Computing Sciences. Her current research interests include building neural networks from first-principles for rich data types (such as those found in scientific data sets) and accelerating existing techniques and creating new capabilities for computational chemistry and material science.
Tess earned her PhD in physics from UC Berkeley in 2018 working with Professor Jeffrey B. Neaton. As a graduate student, she used quantum mechanical calculations to understand and systematically design the geometry and corresponding electronic properties of atomic systems.
During her PhD, Tess spent a year as an intern on Google’s Accelerated Science Team where she developed a new type of convolutional neural network, called Tensor Field Networks, that can naturally handle 3D geometry and properties of physical systems.
As an undergraduate at MIT, Tess engineered giant neutrino detectors in Professor Janet Conrad's group and created a permanent science-art installation on MIT's campus called the Cosmic Ray Chandeliers, which illuminate upon detecting cosmic-ray muons.
Dani Ushizima
Dani Ushizima
Daniela Ushizima is a Staff Scientist at LBNL and a Data Scientist at the Berkeley Institute for Data Science (BIDS) at UC Berkeley. She leads the Image Processing/Machine Vision team for the Center of Advanced Mathematics for Energy Research Applications (CAMERA) at LBNL.
Ushizima is the recipient of the U.S. Department of Energy Early Career award (2016-2020). Her research aims to aggregate value to scientific images obtained at DOE facilities by exploring advances in computer vision and machine learning to accelerate science discovery in materials sciences. As part of scientific teams, she has tackled images across domains (ImageXD), experiments, algorithms and learning. As an example, she has been developing pyCBIR, a image recommendation software that drives search, matches and sorting using scientific images across science domains.
Recently, she was nominated by peers to the LBNL Director's Award (2017), and LBNL Women@Lab (2018) for her work on data science and machine learning, as well as activities on scientific diplomacy with the U.S. Dept. of State TechWomen and other educational efforts, such as Software Carpentry and Black Girls Code. Through the cooperation with UC Berkeley, she has designed algorithms with medical doctors and pathologists improve medical imaging understanding; such work led to her nomination to Science without Borders Special Researcher by CNPq/Brazil for her R&D accomplishments in biomedical image analysis.
Mathew Cherukara
Mathew Cherukara
Mathew Cheruka received his Ph.D in materials science and engineering from Purdue University with an emphasis on computational materials science, and a bachelors in materials engineering from the Indian Institute of Technology (IIT) Madras. He is now an assistant scientist at the Center for Nanoscale Materials at Argonne National Laboratory where he performs X-ray coherent diffraction imaging (CDI) experiments and X-ray fluorescence mapping to study dynamic processes at slow and ultra-fast (sub-ns) timescales. He uses inputs from X-ray imaging techniques to build experimentally informed models that can in turn be used to make predictions at spatio-temporal scales the experiment cannot access. Underlying both the analysis of data and model development are machine learning techniques that accelerate the process of data abstraction and model development. In particular, Mathew builds AI models based on deep convolutional neural network (CNN) encoders to rapidly translate X-ray imaging data to real-space structure and lattice strain information.
He is the recipient of research awards from the Materials Research Society (MRS), Defense Threat Reduction Agency (DTRA) and the College of Engineering at Purdue.
Marcus Noack
Marcus Noack
Before coming to Berkeley Lab, Noack was pursing a Ph.D. in theoretical and mathematical physics at the University of Oslo.
Originally from an East German town about 10 minutes from the Polish border, Noack earned his Bachelors and Masters degrees in geophysics from Friedrich-Schiller-Universität Jena.
As a graduate student in Germany, he became interested in theoretical physics, applied math and computing. He channeled these newfound interests into his Masters thesis and developed math methods for ray tracing, wave front tracking and wave propagation. At the University of Oslo, he continued this work and picked up experience in mathematical optimization and high performance computing.
“All of this knowledge will come in handy as I work on x-ray scattering,” he adds.
In his free time, Noack likes to spend time outdoors fishing, climbing, hiking, camping, snowboarding and traveling.
Sergei Kalinin
Sergei Kalinin
Sergei Kalinin is a distinguished staff member at the Center for Nanophase Materials Sciences at Oak Ridge National Laboratory. He received his MS degree from Moscow State University in 1998 and Ph.D. from the University of Pennsylvania (with Dawn Bonnell) in 2002. His research presently focuses on the applications of big data and artificial intelligence methods in atomically resolved imaging by scanning transmission electron microscopy and scanning probes for applications including physics discovery and atomic fabrication, as well as mesoscopic studies of electrochemical, ferroelectric, and transport phenomena via scanning probe microscopy.
Sergei has co-authored >650 publications, with a total citation of >33,000 and an h-index of >90. He is a fellow of MRS, APS, IoP, IEEE, Foresight Institute, and AVS; a recipient of the Blavatnik Award for Physical Sciences (2018), RMS medal for Scanning Probe Microscopy (2015), Presidential Early Career Award for Scientists and Engineers (PECASE) (2009); Burton medal of Microscopy Society of America (2010); 4 R&D100 Awards (2008, 2010, 2016, and 2018); and a number of other distinctions.
Steve Whitelam
Steve Whitelam
Steve Whitelam is a scientist in the Theory Facility at the Molecular Foundry. He studies nanoscale self-assembly using statistical mechanics and machine learning
Subramanian Sankaranarayanan
Subramanian Sankaranarayanan
Subramanian Sankaranarayanan is a Joint Appointment/Group Leader in the Theory and Modeling Group at Argonne National Laboratory
Ph.D., University of South Florida
Postdoc, Harvard University
Research interest is in the following areas:
Machine learning to bridge the electronic, atomistic and mesoscopic scales
Integrate atomistic (and continuum) simulations with ultrafast X-ray imaging
Inverse design for materials discovery and machine learning to predict metastable phase diagrams
Applications of interest include tribology, corrosion, neuromorphic computing, energy storage and thermal management
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