Academic Positions

  • 2016 2015

    Graduate Student Researcher

    University of California, Merced,
    Applied Mathematics Unit

  • 2015 2012

    Teaching Assitant

    University of California, Merced,
    Applied Mathematics Unit

  • 2012 2010

    Instructor

    University of Sri Jayewardenepura,
    Dept. of Mathematics, Sri Lanka

Educational Qualifications

  • Ph. D.2017

    Ph. D. in Applied Mathematics

    University of California, Merced, USA

  • B. Sc. ( Special )2010

    Bachelor of Science in Mathematics

    University of Sri Jayewardenepura, Sri Lanka

  • B. Sc. ( Hons )2008

    Bachelor of Science in Information Technology

    Sri Lanka Institute of Information Technology, Sri Lanka

Work Experiences

  • 2016
    SAMSI Industrial Math/Stat Modeling Workshop, NCSU, NC, USA
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    About thirty students selected from a national pool work in teams on projects presented by nonacademic scientists. The workshop exposes mathematics and statistics students to current research problems from government labs and industry as well as to a team approach to problem solving. The students learn to interact with scientists outside their discipline, allocate tasks among team members, and communicate results through both oral presentations and written reports. 2016 research reports inlucding my group report (using remote sensing for bathymetry estimation in coastal environments) can be found here .
  • 2009
    Training at Ansell Lanka (Pvt) Ltd., Sri Lanka
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    Underwent a one-month industrial training at Ansell Lanka (Pvt) Ltd., Sri Lanka, where I performed a defect analysis to improve the quality of their glove manufacturing process. The major objectives of this project were to: (1) Identify the major defects of Professional Health Care (PHC) gloves, (2) Determine the process average and average outgoing quality of PHC gloves over the time, (3) Analyze the relationships between major defect types, (4) Give an opportunity to make corrective decisions to improve the process quality, (5) Identify the variation of FIFO (First In First Out) failed lots, missing lots in dipping and packing sections over the time. At the end of this project, management of the Ansell Lanka (Pvt) Ltd. was able to make corrective decisions regarding the quality of the PHC gloves. Results of this study revealed the major defects and their relationships of the PHC gloves. Furthermore, we realized which defect types should reduce to improve the process quality efficiently. Also, the results of this analysis were used to show the quality performance to the Quality Assurance Review Board.

Achievements and Awards

  • 2017 Mar
    Spring-Summer Research Travel Fellowship
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    The Applied Mathematics Research Travel Fellowship provides support to outstanding continuing doctoral students in the Applied Mathematics Graduate Program at UC Merced in order to facilitate the dissemination of research results needed to complete a high quality Ph.D. dissertation. The award is intended to enable students to travel to a scientific meeting.
  • 2017 Jan
    SIAM Student Travel Award for SIAM Conference on Optimization
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    Get more details from SIAM conference on optimization.
  • 2016 Nov
    School of Natural Sciences Dean’s Distinguished Scholars Fellowship
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    Get more details from School of Natural Sciences, UC Merced.
  • 2016 Nov
    Open Data Science Conference (ODSC) West Scholarship
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    See more details at ODSC-West.
  • 2016 Oct
    NSF Student Travel Fellowship for IEEE GlobalSIP 2016 Conference
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    GlobalSIP 2016 grants are funded by the US National Science Foundation and fellowships are awarded based on the paper quality.
  • 2016
    Spring-Summer Research Travel Fellowship
    image
    The Applied Mathematics Research Travel Fellowship provides support to outstanding continuing doctoral students in the Applied Mathematics Graduate Program at UC Merced in order to facilitate the dissemination of research results needed to complete a high quality Ph.D. dissertation. The award is intended to enable students to travel to a scientific meeting.
  • 2015 - 2016
    Graduate Student Opportunity Program Fellowship
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    The Graduate Student Opportunity Program (GSOP) forms an important link in the continuum of support for academically promising graduate students at UC Merced. This award assists recipients in acquiring and developing advanced research skills that will result in a significant achievement for the student. It is designed to improve mentoring for UC Merced (UCM) doctoral students who will not be at the dissertation stage during the award period, but who are currently engaged in research with a faculty mentor. The award is expected to increase the number of graduate students who complete their Ph.D. degree and successfully acquire a faculty appointment.
  • 2015
    Summer Research Travel Fellowship
    image
    The Applied Mathematics Summer Research Travel Fellowship provides support to outstanding continuing doctoral students in the Applied Mathematics Graduate Program at UC Merced in order to facilitate the dissemination of research results needed to complete a high quality Ph.D. dissertation. The award is intended to enable students to travel to a scientific meeting.
  • 2005 - 2009
    Five Scholarships in recognition of superior academic performance during B. Sc. (Hons) Degree in IT
    image
    SLIIT's unique scholarship schemes, designed to maximize students' potential while providing a valuable boost for their academic progress and development, seeks to equip students with the necessary skills to meet the challenges of emerging trends within the relevant industries today, and recognises the growing need to produce top notch professionals who will make an important contribution to these industries. ( Read more )
  • 1995
    Grade 5 Scholarship with a Cash Prize.
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    The grade 5 scholarship examination is a highly competitive Sri Lankan examination conducted by the Department of Examinations of the Ministry of Education. Based on the results of the exam, students could transfer to prominent national schools in Sri Lanka. ( Read more )

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[18] Non-convex sparse optimization for photon-limited imaging,

L. Adhikari and R. Marcia,
Peer-Reviewed PaperProceedings of M.Sc./Ph.D. Forum in the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, 2017.

Abstract

While convex optimization for low-light imaging has received some attention by the imaging community, non-convex optimization techniques for photon-limited imaging are still in their nascent stages. In this thesis, we developed a stage-based non-convex approach to recover high-resolution sparse signals from low-dimensional measurements corrupted by Poisson noise. We incorporate gradient-based information to construct a sequence of quadratic subproblems with an -norm (0 p 1) penalty term to promote sparsity. The proposed methods lead to more accurate and high strength reconstructions in medical imaging applications such as bioluminescence tomography and fluorescence lifetime imaging.

[17] Non-convex Shannon entropy for photon-limited imaging,

L. Adhikari and R. Marcia,
Peer-Reviewed PaperAccepted, 2017.

Abstract

Accurate reconstruction of high-dimensional sparse signals from low-dimensional low-count photon observations is a challenging nonlinear optimization problem. Recent work in nonconvex optimization has shown that sparse signals can be recovered accurately by minimizing the p-norm (0 p 1) regularized negative Poisson log-likelihood function. In this paper, we propose to regularize the negative Poisson log-likelihood by the generalized nonconvex Shannon entropy function. Moreover, the non-separable Shannon regularization function is approximated using its first-order Taylor series at each iteration. We explore the effectiveness and efficiency of the proposed approach using numerical experiments.

[16] Biomedical signal recovery: Genomic variant detection in family lineages,

M. Banuelos, R. Almanza, L. Adhikari, S. Sindi, and R. F. Marcia
Peer-Reviewed Paper Proceedings of 2017 IEEE 5th Portuguese Meeting on Bioengineering, 2017.

Abstract

Structural variations (SVs) - genomic rearrangements such as insertions, deletions and duplications - represent an important class of genomic variation. These mutations have been associated with both genetic diseases (e.g., cancer) and promoting genetic diversity. The common approach to detecting SVs in an unknown genome involves sequencing fragments of the genome, comparing them to a reference genome, and predicting SVs based on identified discordant fragments. However, detecting SVs from traditional DNA sequencing is challenging due to the presence of errors and biases in the DNA sequencing process as well as problems aligning sequences to a reference genome. The majority of existing methods use hierarchical relationships to detect these genetic changes, but often post-process this information. Our work aims to improve on existing SV detection methods in three ways: First, we use a continuous relaxation of admissible solutions to apply gradient-based optimization techniques. Second, since SVs are rare, we incorporate an sparsity-promoting penalty term. Third, we improve on our previous work by using a blockcoordinate descent approach to predict variants in families of individuals. We demonstrate the effectiveness of our method on a variety of simulated datasets and real genomes of a two parenttwo child family.

[15] Nonconvex Regularization Based Sparse Recovery and Demixing with Application to Color Image Inpainting,

F. Wen, L. Adhikari, P. Liu, R. Marcia and W. Yu,
Peer-Reviewed Paper Submitted, 2017.

Abstract

This work addresses the recovery and demixing problem of signals that are sparse in some general dictionary. Involved applications include source separation, image inpainting, super-resolution, and restoration of signals corrupted by clipping, saturation, impulsive noise, or narrowband interference. We employ the -norm for sparsity inducing and propose a constrained -minimization formulation for the recovery and demixing problem. This nonconvex formulation is approximately solved by two efficient first-order algorithms based on proximal coordinate descent and alternative direction method of multipliers (ADMM), respectively. The new algorithms are convergent in the nonconvex case and scale well for high-dimensional problems. A convergence condition of the new ADMM algorithm has been derived. Furthermore, extension of the two algorithms for multi-channels joint recovery has been presented, which can further exploit the joint sparsity pattern among multi-channel signals. Numerical experiments on inpainting showed that the new algorithms can achieve considerable performance gain over the -minimization algorithms (an improvement of more than 8dB in terms of peak-signal noise ratio).

[14] Sparse reconstruction for fluorescence lifetime imaging microscopy with Poisson noise,

L. Adhikari, A. D. Kim and R. Marcia,
Peer-Reviewed Paper Proceedings of 2016 IEEE Global Conference on Signal and Information Processing, 2016.

Abstract

We present a novel, three-stage method to solve the fluorescence lifetime imaging problem under low-photon conditions. In particular, we reconstruct the fluorophore concentration along with its support and fluorescence lifetime from the time-dependent measurements of scattered light exiting the domain. Because detectors used for these problems are photon counting devices, measurements are corrupted by Poisson noise. Consequently, we explicitly consider Poisson noise in conjunction with SPIRAL- a sparsity-promoting nonconvex optimization method to solve this problem. We demonstrate the effectiveness of the proposed three-stage method through numerical experiments in 2D fluorescence lifetime imaging.

[13] Nonconvex sparse Poisson intensity reconstruction for time-dependent bioluminescence tomography,

L. Adhikari, A. D. Kim and R. Marcia,
Peer-Reviewed PaperProceedings of 2016 International Symposium on Information Theory and Its Applications, 2016.

Abstract

This paper concerns time-dependent bioluminescence imaging under low-photon conditions. In this problem, one seeks to reconstruct sources of light contained within a tissue sample from noisy boundary measurements of scattered light. The main challenge in this problem lies in processing signals that are constrained by partial differential equations. In this paper, we propose a novel two-stage method to recover timedependent bioluminescent sources from boundary measurements corrupted by Poisson noise. Numerical experiments demonstrate the effectiveness of the proposed approach.

[12] Trust-region methods for nonconvex sparse recovery optimization,

L. Adhikari, R. Marcia, J. B. Erway and R. J. Plemmons
Peer-Reviewed PaperProceedings of 2016 International Symposium on Information Theory and Its Applications, 2016.

Abstract

We solve the sparse recovery problem by transforming the objective function into an unconstrained differentiable function and apply a limited-memory trust-region method. Unlike gradient projection-type methods, which uses only the current gradient, our approach uses gradients from previous iterations to obtain a more accurate Hessian approximation. Preliminary numerical experiments with simulated compressive sensing 1D data are provided to illustrate that our proposed approach eliminates spurious solutions more effectively while improving the computational time to converge in comparison to standard approaches.

[11] Limited memory trust-region methods for sparse relaxation,

L. Adhikari , J. Erway, S. Lockhart and R. Marcia,
Peer-Reviewed PaperAccepted, 2017.

Abstract

In this paper, we solve the sparse recovery problem by transforming the objective function of this problem into an unconstrained differentiable function and apply a limited-memory trust-region method. Unlike gradient projection-type methods, which uses only the current gradient, our approach uses gradients from previous iterations to obtain a more accurate Hessian approximation. Numerical experiments show that our proposed approach eliminates spurious solutions more effectively while improving the computational time to converge.

[10] Sparse diploid spatial biosignal recovery for genomic variation detection,

M. Banuelos, L. Adhikari, A. Fujikawa, J. Sahagún, K. Sanderson, M. Spence, R. Almanza, S. Sindi and R. Marcia,
Peer-Reviewed Paper Accepted to 2017 IEEE International Symposium on Medical Measurements and Applications.

Abstract

Structural variants (SVs) - rearrangements of regions of the genome such as inversions, insertions, deletions and duplications - are present in the genomes of all individuals. Commonly, SVs are detected by comparisons of an (unknown) test genome with a known reference through a sequencing and mapping process. Because humans are diploid (2 of each chromosome), individuals may be heterozygous (1 copy) or homozygous (2 copies) for each variant. If an individual is sequenced at low-coverage it may be difficult to distinguish heterozygous SVs from erroneous mappings. Our approach aims to improve SV detection in three ways. First, our formulation explicitly predicts the number of copies of each potential SV present. Second, we analyze related individuals simultaneously - a parent and a child - and enforce relationships in the copy number of SV predictions when appropriate. Finally, we solve a constrained optimization equation consisting of a negative Poisson log-likelihood objective function with an penalty term to promote sparsity. Despite increasing the complexity of the SV problem formulation by considering copy number, our method decreases the false positive rate despite a large amount of error from both DNA sequencing and mapping.

[9] Nonconvex regularization for sparse genomic variant signal detection,

M. Banuelos, L. Adhikari, A. Fujikawa, J. Sahag, K.Sanderson, M. Spence, R. Almanza, S. Sindi and R. Marcia,
Peer-Reviewed Paper Accepted to 2017 IEEE International Symposium on Medical Measurements and Applications.

Abstract

Recent research suggests an overwhelming proportion of humans have genomic structural variants (SVs): rearrangements of regions in the genome such as inversions, insertions, deletions and duplications. The standard approach to detecting SVs in an unknown genome involves sequencing paired-reads from the genome in question, mapping them to a reference genome, and analyzing the resulting configuration of fragments for evidence of rearrangements. Because SVs occur relatively infrequently in the human genome, and erroneous read-mappings may suggest the presence of an SV, approaches to SV detection typically suffer from high false-positive rates. Our approach aims to more accurately distinguish true from false SVs in two ways: First, we solve a constrained optimization equation consisting of a negative Poisson log-likelihood objective function with an additive penalty term that promotes sparsity. Second, we analyze multiple related individuals simultaneously and enforce familial constraints. That is, we require any SVs predicted in children to be present in one of their parents. Our problem formulation decreases the false positive rate despite a large amount of error from both DNA sequencing and mapping. By incorporating additional information, we improve our model formulation and increase the accuracy of SV prediction methods.

[8] Constrained variant detection with SPaRC: Sparsity, Parental Relatedness, and Coverage,

M. Banuelos, R. Almanza, L. Adhikari, R. Marcia and S. Sindi,
Peer-Reviewed PaperProceedings of International Conference of the IEEE Engineering in Medicine and Biology Society, 2016.

Abstract

Structural variants (SVs) are rearrangements of DNA sequences such as inversions, deletions, insertions and translocations. The common method for detecting SVs has been to sequence data from a test genome and map it to a reference genome. More recently, DNA sequencing studies may consist of hundreds, or even thousands of individuals, some of which may be related. In order to boost the signal of true SVs, we simultaneously analyze parent-child trios. In order to improve our ability to identify SVs, we boost the true SV signals by simultaneously analyzing parent and child genomes. Our algorithmic formulation SPaRC employs realistic criteria such as sparsity of SVs, relatedness between individuals and variable sequencing coverage throughout the genome.

[7] Sparse genomic structural variant detection: exploiting parent-child relatedness for signal recovery,

M. Banuelos, R. Almanza, L. Adhikari, R. Marcia and S. Sindi,
Peer-Reviewed PaperProceedings of 2016 IEEE Workshop on Statistical Signal Processing, 2016.

Abstract

Structural variants (SVs) rearrangements of an individuals' genome are an important source of heterogeneity in human and other mammalian species. Typically, SVs are identified by comparing fragments of DNA from a test genome to a known reference genome, but errors in both the sequencing and the noisy mapping process contribute to high false positive rates. When multiple related individuals are studied, their relatedness offers a constraint to improve the signal of true SVs. We develop a computational method to predict SVs given genomic DNA from a child and both parents. We demonstrate that enforcing relatedness between individuals and constraining our solution with a sparsity-promoting penalty (since SV instances should be rare) results in improved performance. We present results on both simulated genomes as well as two-sequenced parent-child trios from the 1000 Genomes Project.

[6] Bounded sparse photon-limited image recovery,

L. Adhikari and R. Marcia,
Peer-Reviewed PaperProceedings of 2016 IEEE International Conference on Image Processing, 2016.

Abstract

In photon-limited image reconstruction, the behavior of noise at the detector end is more accurately modeled as a Poisson point process than the common choice of a Gaussian distribution. As such, to recover the original signal more accurately, a penalized negative Poisson log-likelihood function and not a least-squares function is minimized. In many applications, including medical imaging, additional information on the signal of interest is often available. Specifically, its maximum and minimum amplitudes might be known . This paper describes an approach that incorporates this information into a sparse photon-limited recovery method by the inclusion of upper and lower bound constraints. We demonstrate the effectiveness of the proposed approach on two different low-light deblurring examples.

[5] Sparse signal recovery methods for variant detection in next-generation sequencing data,

M. Banuelos, R. Almanza, L. Adhikari , S. Sindi and R. Marcia,
Peer-Reviewed PaperProceedings of 2016 IEEE International Conference on Acoustics, Speech and Signal Processing.

Abstract

Recent advances in high-throughput sequencing technologies have led to the collection of vast quantities of genomic data. Structural variants (SVs) rearrangements of the genome larger than one letter such as inversions, insertions, deletions, and duplications are an important source of genetic variation and have been implicated in some genetic diseases. However, inferring SVs from sequencing data has proven to be challenging because true SVs are rare and are prone to low-coverage noise. In this paper, we attempt to mitigate the deleterious effects of low-coverage sequences by following a maximum likelihood approach to SV prediction. Specifically, we model the noise using Poisson statistics and constrain the solution with a sparsity-promoting penalty since SV instances should be rare. In addition, because offspring SVs inherit SVs from their parents, we incorporate familial relationships in the optimization problem formulation to increase the likelihood of detecting true SV occurrences. Numerical results are presented to validate our proposed approach.

[4] Analysis of p-norm regularized subproblem minimization for sparse photon-limited image recovery,

A. Orkusyan, L. Adhikari , J. Valenzuela and R. Marcia,
Peer-Reviewed PaperProceedings of 2016 IEEE International Conference on Acoustics, Speech and Signal Processing.

Abstract

Critical to accurate reconstruction of sparse signals from low-dimensional low-photon count observations is the solution of nonlinear optimization problems that promote sparse solutions. In this paper, we explore recovering high-resolution sparse signals from low-resolution measurements corrupted by Poisson noise using a gradient based optimization approach with non-convex regularization. In particular, we analyze zero-finding methods for solving the p-norm regularized minimization subproblems arising from a sequential quadratic approach. Numerical results from fluorescence molecular tomography are presented.

[3] p-th power total variation regularization in photon-limited imaging via iterative reweighting,

L. Adhikari and R. Marcia,
Peer-Reviewed PaperProceedings of 2015 European Signal Processing Conference.

Abstract

Recent work in -norm regularized sparsity recovery problems (where ) has shown that signals can be recovered with very high accuracy despite the fact that the solution to these nonconvex optimization problems are not necessarily the global minima but are instead potentially local minima. In particular, -norm regularization has been used effectively for signal reconstruction from measurements corrupted by zero-mean additive Gaussian noise. This paper describes a p-th power total variation () regularized optimization approach for image recovery problems in photon-limited settings using iterative reweighting. The proposed method iteratively convexifies a sequence of nonconvex subproblems using a weighted TV approach and is solved using a modification to the FISTA method for TV-based denoising. We explore the effectiveness of the proposed method through numerical experiments in image deblurring.

[2] Nonconvex reconstruction for low-dimensional fluorescence molecular tomographic Poisson observations,

L. Adhikari, D. Zhu, C. Li, and R. Marcia,
Peer-Reviewed PaperProceedings of 2015 IEEE International Conference on Image Processing.

Abstract

As an emerging near-infrared molecular imaging modality, fluorescence molecular tomography (FMT) has great potential in resolving the molecular and cellular processes in 3D objects through the reconstruction of the injected fluorescence probe concentration. In practice, when a charge-coupled device (CCD) camera is used to obtain FMT measurements, the observations are corrupted by noise which follows a Poisson distribution. To reconstruct the original concentration, the standard least-squares function for data-fitting is not a suitable objective function to minimize since this model assumes measurement noise which follows a Gaussian distribution. Rather, in this paper, we minimize a negative log-likelihood function to more accurately model the CCD camera shot noise. Furthermore, we exploit the presence of the flourescence in only small regions of the 3D object by introducing a nonconvex penalty term that promotes sparsity in the reconstruction. This paper proposes a method to solve the FMT reconstruction problem from low-dimensional and low-mean photon count measurements. Using simulated data, we validate the effectiveness of the proposed non-convex Poisson-based reconstruction method for FMT inverse problems.

[1] Nonconvex relaxation for Poisson intensity reconstruction,

L. Adhikari and R. Marcia,
Peer-Reviewed PaperProceedings of 2015 IEEE International Conference on Acoustics, Speech and Signal Processing.

Abstract

Critical to accurate reconstruction of sparse signals from low-dimensional Poisson observations is the solution of nonlinear optimization problems that promote sparse solutions. Theoretically, non-convex -norm minimization () would lead to more accurate reconstruction than the convex -norm relaxation commonly used in sparse signal recovery. In this paper, we propose an extension to the existing SPIRAL- algorithm based on the Generalized Soft Thersholding (GST) function to better recover signals with mostly nonzero entries from Poisson observations. This approach is based on iteratively minimizing a sequence of separable subproblems of the nonnegatively constrained, -penalized negative Poisson log-likelihood objective function using the GST function. We demonstrate the effectiveness of the proposed method, called SPIRAL-, through numerical experiments.

Presentations

  • Merced 2017

    Nonconvex Sparse Recovery Methods,

    Ph.D. defense talk at UC Merced, Merced, USA on March 24, 2017.

  • NOLA 2017

    Non-convex sparse optimization for photon-limited imaging,

    IEEE International Confer. on Acoustics, Speech and Signal Processing (ICASSP 2017), New Orleans, USA on March 6, 2017.

  • Washington 2016

    Sparse reconstruction for fluorescence lifetime imaging microscopy with Poisson noise,

    IEEE Global Conference on Signal and Information Processing (GlobalSIP), Washington, DC, USA on December 8, 2016.

  • Monterey 2016

    Nonconvex sparse Poisson intensity reconstruction for time-dependent bioluminescence tomography,

    International Symposium on Information Theory and Its Applications (ISITA), Monterey, CA, USA on November 01, 2016.

  • Monterey 2016

    Trust-region methods for nonconvex sparse recovery optimization,

    International Symposium on Information Theory and Its Applications (ISITA), Monterey, CA, USA on November 01, 2016.

  • Phoenix 2016

    Bounded sparse photon-limited image recovery,

    IEEE International Conference on Image Processing (ICIP 2016), Phoenix, Arizona, USA on September 28, 2016.

  • Tokyo 2016

    Limited-memory trust-region methods for sparse reconstruction,

    International Conference on Continuous Optimization (ICCOPT 2016), Tokyo, Japan on August 8, 2016.

  • Shanghai 2016

    Analysis of p-norm regularized subproblem minimization for sparse photon-limited image recovery,

    IEEE International Confer. on Acoustics, Speech and Signal Processing (ICASSP 2016), Shanghai, China on March 22, 2016.

  • Merced 2016

    Fluorescence-lifetime imaging microscopy (FLIM) with Poisson noise,

    Applied Math Optimization Seminar at UC Merced, Merced, USA on May 11, 2016.

  • Merced 2016

    Nonconvex relaxation for Poisson intensity reconstruction,

    Central Valley SIAM Regional Conference at UC Merced, Merced, USA on April 29, 2016.

  • Merced 2015

    Time-independent and time-dependent fluorescence optical tomography,

    Applied Math Optimization Seminar at UC Merced, Merced, USA on Nov 16, 2015.

  • Quebec City 2015

    Nonconvex reconstruction for low-dimensional fluorescence molecular tomographic Poisson observations,

    2015 IEEE International Conference on Image Processing (ICIP 2015), Quebec City, Canada on Sep 29, 2015.

  • Nice 2015

    p-th power total variation regularization in photon-limited imaging via iterative reweighting,

    3rd European Signal Processing Conference (EUSIPCO 2015), Nice, France on Sep 03, 2015.

  • Pittsburgh 2015

    Nonconvex relaxation for photon-limited sparse optimization,

    22nd International Symposium on Mathematical Programming (ISMP 2015), Pittsburgh, USA on July 16, 2015.

  • Merced 2014

    Introduction to the mathematics of medical imaging - part II,

    Applied Math Optimization Seminar at UC Merced, Merced, USA on Dec 08, 2014.

  • Merced 2014

    Introduction to the mathematics of medical imaging - part I,

    Applied Math Optimization Seminar at UC Merced, Merced, USA on Dec 01, 2014.

Teaching History

  • Spring 2015

    Teaching Assistant at Department of Applied Mathematics, UC Merced.

    Math 131: Numerical Analysis I.

  • Fall 2014

    Teaching Assistant at Department of Applied Mathematics, UC Merced.

    Math 24: Introduction to Linear Algebra & Differential Equations.

  • Fall 2013

    Teaching Assistant at Department of Applied Mathematics, UC Merced.

    Math 140: Mathematical Methods for Optimization.

  • Spring 2013

    Teaching Assistant at Department of Applied Mathematics, UC Merced.

    Math 32: Probability and Statistics.

  • Fall 2012

    Teaching Assistant at Department of Applied Mathematics, UC Merced.

    Math 21: Calculus I.

  • 2012 July 2011 Sep

    An Instructor at Department of Mathematics, University of Sri Jayewardenepura, Sri Lanka.

    Courses taught : Computer Programming (C++). Visiting lecturer to conduct Mathematics lab session, Faculty of Medical Sciences, University of Sri Jayewardenepura, Sri Lanka.

  • 2011 Aug 2010 Sep

    An Instructor at Department of Mathematics, University of Sri Jayewardenepura, Sri Lanka.

    Courses taught : Calculus I/II, Numerical Methods I/II, Abstract Algebra, Optimization I, Applicable Mathematics.

Mailing Address:

School of Natural Sciences,
Department of Applied Mathematics,
University of California, Merced,
5200 North Lake Road,
Merced, CA 95343,
USA.

At My Office

You can find me at my office COB 396-4, which is located at 3rd floor of class room and office bulding at UC Merced.

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