Andrea Bertozzi directs the Applied Mathematics program at UCLA, currently ranked number 2 in the US (US News, NRC S-rankings) with significant emphasis on imaging science. In her own research she has worked on problems in remote sensing and hyperspectral imagery, geometric methods in image processing, document exploitation, road inpainting, standoff detection, and real time control of atomic force microscopy. She also has a laboratory with multi-vehicle robotics testbed for design and implementation of cooperative control algorithms. She oversees a research group of about 15 PhD students and postdocs.

Andrea L. Bertozzi and Arjuna Flenner, Diffuse interface models on graphs for classification of high dimensional data submitted 2011.

Michael Moeller, Todd Wittman, Andrea L. Bertozzi, and Martin Burger A Variational Approach for Sharpening High Dimensional Images, submitted 2010.

M. Gonzalez, X. Huang, B. Irvine, D. S. Hermina Martinez, C. H. Hsieh, Y. R. Huang, M. B. Short, and A. L. Bertozzi A Third Generation Micro-vehicle Testbed for Cooperative Control and Sensing Strategies accepted in ICINCO 2011.

W. Gao and A. L. Bertozzi, Level set based multispectral segmentation with corners, accepted in SIAM J. Imag. Sci., 2011.

A. Chen, T. Wittman, A. Tartakovsky, and A. Bertozzi, Boundary Tracking Through Efficient Sampling accepted in AMRX, 2011.

I am interested generally in structure-function relationships in the human brain related to cognition, and in the development of imaging technologies, chiefly MRI and EEG to observe brain activity. My current projects use exploratory data analysis methods to expose large scale networks that describe elements of cognitive process and machine learning tools to understand how these functional networks interact in cognitive process. Our experimental models include attentional control, hypnosis, addictive substances (cigarettes) and other behavioral interventions.

Recent papers available on line:

PK Douglas, S Harris, A Yuille and MS Cohen, “Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief.” NeuroImage. 2010.

PO Harvey, J Lee, MS Cohen, SA Engel, DC Glahn, KH Nuechterlein, JK Wynn and MF Green, “Altered dynamic coupling of lateral occipital complex during visual perception in schizophrenia.” NeuroImage. 2010.

A Anderson, JS Labus, EP Vianna, EA Mayer and MS Cohen, “Common component classification: What can we learn from machine learning?” NeuroImage. 2010.

J Lee, MS Cohen, SA Engel, D Glahn, KH Nuechterlein, JK Wynn and MF Green, “Regional brain activity during early visual perception in unaffected siblings of schizophrenia patients.” Biological Psychiatry, 68(1): p. 78-85. 2010.

Keywords: information extraction, multimodal, statistics, sensors, computation, visual physiology, dynamics, EEG

Dr. Coppola is interested in developing computational approaches for the integrated analysis of genetic, genomic, phenotypic, and imaging data in well-characterized series of patients with neurodegenerative disorders. His long-term goal it to develop a personalized medicine approach to neurological diseases.

• Nagahara AH, Merrill DA, Coppola G, Tsukada S, Schroeder BE, Shaked GM, Wang L, Blesch A, Kim A, Conner JM, Rockenstein E, Chao MV, Koo E, Geschwind DH, Masliah E, Chiba AA, Tuszynski MH. Neuroprotective Effects of BDNF in Rodent and Primate Models of Alzheimer’s Disease. Nat Med, 2009;15:331-7. PMCID: PMC2838375.
• Konopka G, Bomar JM, Winden K, Coppola G, Gao F, Peng S, Preuss TM, Wohlschlegel JA, Geschwind DH. Human-specific transcriptional regulation of CNS development genes by FOXP2. Nature, 2009;462:213-217. PMCID: PMC2778075
• Michaelevski I, Segal-Ruder Y, Rozenbaum M, Medzihradszky KF, Shalem O, Coppola G et al. Signaling to Transcription Networks in the Neuronal Retrograde Injury Response. Sci Signal 2010 Jul 13;3:ra53. PMCID: PMC Journal – In Process
• Li S, Overman JJ, Katsman D, Donnelly CJ, Twiss JL, Giger RJ, Coppola G, Geschwind DH, Carmichael ST. Molecular control of axonal sprouting after stroke: an age-related sprouting transcriptome in cortical neurons. Nat Neurosci 2010;13:1496-504. PMCID: PMC Journal – In Process

Keywords: multimodal, analysis, computation, personalized medicine, information extraction

Stanley Osher received his Phd degree in 1966 from New York University's Courant Institute of Mathematical Sciences. He is a Professor of Mathematics, Computer Science and Electrical Engineering at UCLA. He is also an Associate Director of the NSF funded Institute for Pure and Applied Mathematics. He is a member of the National Academy of Sciences, the American Academy of Arts and Sciences and is one of the top 25 most highly cited researchers in both mathematics and computer sciences. He has received numerous academic honors and has co-founded three successful companies, each based largely on his own (joint) research. His current interests mainly involve information science which includes image processing, compressed sensing and machine learning

Papers from CAM Website

Luders E, Thompson PM, Katherine L. Narr, Alen Zamanyan, Yi-Yu Chou, Boris Gutman, Ivo D. Dinov, Arthur W. Toga (2010). The link between callosal thickness and intelligence in healthy children and adolescents, NeuroImage, 2011 Feb 1;54(3):1823-30. Epub 2010 Oct 13.

Chiang MC; Katie McMahon; Greig de Zubicaray; Nicholas Martin; Ian Hickie; Arthur Toga; Margaret Wright; Thompson PM (2010). Genetics of White Matter Development: A DTI Study of 705 Twins and Their Siblings Aged 12 to 29, NeuroImage, 2011 Feb 1;54(3):2308-17. Epub 2010 Oct 13.

Thomason ME, Thompson PM (2011). Diffusion Imaging, White Matter and Psychopathology, Annual Review of Clinical Psychology, 2011.

Rogers J, Kochunov P, Zilles K, Shelledy W, Lancaster J, Thompson PM, Duggirala R, Blangero J, Fox PT, Glahn D (2010). On the genetic architecture of cortical folding and brain volume in primates, NeuroImage, 1103-1108, Nov 15 2010.

My lab has been working on ultrasensitive, superresolution single molecule spectroscopy and imaging methods for biological studies using semiconductor quantum dots (qdots) and single organic dyes. The lab has introduced quantum dots for biological studies and single molecule fluorescence resonance energy transfer (smFRET) for conformational dynamics studies of biological macromolecules. Our work on quantum dots has led to the founding of Quantum Dot Corporation (later acquired by Invitrogen Inc., now Life Technologies). In recent years my team has developed novel peptide-coating technology (patent pending), unique conjugation chemistries, various approaches for specific targeting of qdots to cellular proteins, a variety of novel detectors, a a variety of novel spectroscopic and imaging modalities, and fast superresolution imaging method.
We have been using these advanced tools to study protein folding, transcription by RNA polymerase, diffusion of single membrane proteins in live cells, conformational dynamics of membrane proteins, and the development of the hide-brain of zebrafish.

Dertinger, T., Heilemann, M., Vogel, R., Sauer, M. & Weiss, S. (2010) Superresolution optical fluctuation imaging with organic dyes. Angewandte Chemie International Edition (accepted), 49(49), 9441-3. PMID: 21031383

Dertinger, T., Colyer, R., Vogel, R., Enderlein, J. & Weiss, S. (2010). Achieving increased resolution and more pixels with Superresolution Optical Fluctuation Imaging (SOFI). Optics Express, 18(18). 18875-85. PMID: 20940780

Dertinger, T., Colyer, R., Iyer, G., Weiss, S. & Enderlein, J. (2009). Fast, background-free, 3D super-resolution optical fluctuation imaging (SOFI). Proceedings of the National Academy of Sciences, 106(52), 22287-92, PMID: 20018714

Michalet, X., Colyer, R.A., Antelman, J., Siegmund, O.H., Tremsin, A., Vallerga, J.V. & Weiss, S. (2009). Single-quantum dot imaging with a photon counting camera. Current Pharmaceutical Biotechnology, 10(4), 543-58. PMID: 19689323

Keywords: single molecule spectroscopy, single molecule imaging, superresolution imaging, information extraction, multimodal, statistics, sensors & detectors, dynamics

My research interests are Computer Vision, Machine Learning, and Computational Neuroscience. I have roughly 250 publications on these topics. My interests in computer vision include image parsing/understanding, object detection, and image segmentation. My research on medical images has included tumor detection, brain region detection, MS lesion detection, and have involved multimodal processing (i.e. combining different sources). I have also applied machine learning methods to the analysis of fMRI data for medical diagnosis and cognitive tasks. I also published one paper on detecting particles in accelerators for high energy particle physics. My papers can be found online:

http://www.stat.ucla.edu/~yuille/publications.html