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%\subtitle{Research and Technology}{\faSuitcase}
\subtitle{Research and development}{\faSuitcase}
While the focus of my research is computing technologies, the developed instrumentation enabled major scientific break-throughs achieved by KATRIN~\cite{katrin2019limit} and ASEC~\cite{chili2010thunderstorm} collaborations.
Below are referenced selected peer-reviewed publications which are either authored by me and my students or where we made a significant contribution.
\begin{verycomplexevents}
% We don't list experiments which are handled torugh the software (with exceptions dedicated applications?), only direct impact.
% Skipped:
% buzmakov - just mentions UFO camera. overall not a high ranked paper.
% \subsectiontitle{Technology for the future data acquisition systems}
% ?? make subsections with dates: \eventsection
\datedeventsection{\since{2011}}{High-bandwidth data acquisition and data-driven control}{}
% \research {starting} {} {} {Adoption of}{high-performance and cloud computing for online data processing}
\research {\ivl{2018}{2019}} {UFO,PyHST} {} {Fine-tuning}{of tomographic reconstruction algorithms through micro-benchmarking and performance modeling~\cite{csa2020rtip}}
\research {\ivl{2016}{2017}} {} {CMS} {Participated}{in a case study on applications of GPUs in Level-1 track trigger for the next upgrade of the \refexp{CMS} experiment~\cite{mohr2017cms}}
\technology {\ivl{2016}{2017}} {UFO} {KARA} {Designed}{a full chain of instrumentation for synchrotron imaging beamlines with possibility of online reconstruction and image-based feedback loop~\cite{kopmann2017ufo,stevanovic2015concert}}
\research {\ivl{2015}{2016}} {Alps} {KARA} {Researched}{low-latency communication mechanisms for data-driven control applications~\cite{vogelgesang2016dgma}} %dritschler2014using
\technology {\ivl{2014}{2015}} {Alps} {KARA} {Implemented}{fast DMA drivers with GPUDirect / DirectGMA support~\cite{rota2015dma}}
\research {\ivl{2013}{2014}} {UFO,PyHST} {KARA,ESRF} {Reviewed}{asymptotically fast methods of tomographic reconstruction well-fitted for GPU architectures~\cite{rshkarin2015}}
\research {\ivl{2011}{2013}} {UFO} {KARA} {Researched}{software architectures for online processing of image streams~\cite{vogelgesang2012ufo}}
\technology {\ivl{2011}{2013}} {Alps} {KARA} {Developed}{streaming data acquisition platform for scientific cameras~\cite{caselle2013camera}}% (readout, debugging, storage) / :readout framework, camera drivers, absraction, streaming storage, scripting & debugging
\datedeventsection{\since{2007}}{Parallel architectures, performance analysis, and software optimization}{verycomplexevents}
\technology {\ivl{2020}{2021}} {CCPi} {UoM} {Applied}{methods of approximate computing to enable reconstruction of large datasets using memory-intensive regularization methods~\cite{ametova2021neutron}}
\research {\ivl{2017}{2018}} {PyHST} {} {Researched}{performance imbalances and hidden parallelism in GPU architectures; proposed how they can be exploited to speed-up tomographic reconstruction~\cite{csa2018sbac}} % Balancing part is new, everything else was done earlier...
\technology {\ivl{2014}{2017}} {UFO} {KARA} {Investigated}{viable compromises between reconstruction quality and parallelization capabilities of tomographic algorithms~\cite{ashkarin2015}}
\technology {\ivl{2013}{2014}} {UFO} {} {Developed}{parallel algorithms for $\mu$PIV (micro-particle velocimetry)~\cite{cavadini2018upiv}}
\technology {2010} {MRSES} {ASEC} {Leveraged}{PoweXCell architecture for MRSES feature selection algorithm (5000x speed-up compared to MATLAB prototype)}
\technology {\ivl{2009}{2010}} {PyHST} {KARA,ESRF} {Optimized}{PyHST tomographic reconstruction framework~\cite{csa2011pyhst}}
\technology {\ivl{2009}{2010}} {DictHW} {} {Implemented}{a digital image correlation and tracking algorithm for GPUs} %(10x speed-up)
\technology {\ivl{2007}{2008}} {XMLBench} {} {Carried out}{a performance study of open-source XML frameworks~\cite{csa2009xmlbench}}
\datedeventsection{\since{1999}}{Digitization, data organization, and distributed control systems}{verycomplexevents}
\research {\since{2019}} {KaaS} {KATRIN} {Researching}{cloud technologies for highly heterogeneous control systems in large-scale scientific experiments [in preparation]} % KATRIN paper to be added, ADEI-cloudificatin separated as stand-alone point
\research {\ivl{2015}{2017}} {WAVe} {KARA} {Researched}{remote visualization techniques for large and time-resolved tomographic volumes~\cite{ntj2017wave,losel2020biomedisa}} % Compression could be continuation of this (19/20)
\research {\ivl{2013}{2015}} {ADEI} {ASEC} {Researched}{emerging web technologies for management and visualization of terabyte-scale archives with time-series} % This is about ADEI2 attempts (but no publication)
\technology {\ivl{2011}{2014}} {ADEI} {ASEC} {Converted}{KATRIN data management system into a full flagged platform for time-series exploration and analysis} % ADEI analysis, etc.
\technology {\ivl{2008}{2010}} {ADEI} {KATRIN} {Developed}{data management components of KATRIN control system~\cite{csa2010adei}} % Lets assume it includes ADEI/Control
\technology {\ivl{2007}{2008}} {} {KATRIN} {Stabilized}{KATRIN slow control system for production use~\cite{katrin2015detector}}
\technology {\ivl{2005}{2006}} {ADAS} {ASEC} {Developed}{a data acquisition system for particle detector networks~\cite{csa2009sevan}}
\research {\ivl{2002}{2004}} {ADAS} {ASEC,KATRIN} {Researched}{network protocols for heterogeneous slow control systems~\cite{eppler2004opc}}
\technology {\ivl{1999}{2001}} {} {} {Evaluated}{hardware-accelerated neural networks for trigger applications~\cite{vardanyan2001sand}}
\end{verycomplexevents}
% \research {\since{2020}} {Group - Application }
% \research {\ivl{2016}{2017}} {Group - Supervised a case study on use of GPUs in L1 track trigger for the next upgrade of CMS experiment~\cite{mohr2017cms}.}
% \technology {\ivl{2011}{2016}} {Participated in the development of streaming data acquisition platform aimed at control systems with image-based feedback loops and was responsible for the software part~\cite{caselle2013camera,rota2015dma}.}
% \technology {\ivl{2007}{2009}} {Developed data management components of KATRIN slow control system~\cite{katrin2015detector}.}
% \technology {\ivl{2005}{2007}} {Developed distributed DAQ system for particle detector networks~
%\cite{csa2009sevan}.}
% \technology {\ivl{2002}{2005}} {Designed a high-performance extension for OPC XML-DA protocol aimed on slow control systems with high sampling rates.}
% \technology {\ivl{2020}{2021}} {Collaborated with scientists from University of Manchester on optimization of their tomography software: suggested methods of approximate computing to enable also reconstruction of large datasets~\cite{ametova2021neutron}.}
% \research {\ivl{2012}{2019}} {Analyzed major parallel architectures from AMD, NVIDIA, and Intel using micro-benchmarking techniques. Build a performance model of the back-projection algorithm and fine-tuned the parallel implementation for each architecture accordingly. The measured speed-ups over the state-of-the-art parallel solutions range from 2 to 7 times depending on the considered architecture. Mixed precission.}
% \technology {\ivl{2011}{2019}} {A new control-system for high-speed synchrotron imaging with online reconstruction and image-based feedback loop~\cite{kopmann2017ufo}}
% \research {\ivl{2013}{2017}} {With a group of 1 PhD and 2 Master students we have investigated iterative reconstruction algorithms to improve reconstruction of noisy or undersampled data. We searched for practical solutions providing viable balance between resulting quality and parallelization capabilities~\cite{ashkarin2015}. The selected algorithms were adapted for the GPU architecture and integrated into the UFO framework.}
% \research {\ivl{2013}{2015}} {Supervised a Master student researching methods of tomographic reconstruction which are both assymtotically faster than traditionally used \emph{Filtered Back Projection} and suitable for execution on GPU architectures~\cite{rshkarin2015}.}
% \research {\ivl{2011}{2014}} {With a group of 2 PhD and several Master students we developed a novel architecture for pipelined processing of image-streams which leveraged available parallelism in GPUs (and other massively parallel architectures) and was scaling across multiple GPUs and multi-GPU nodes automatically~\cite{vogelgesang2012ufo}.}
% \technology {\ivl{2009}{2010}} {Collaborated with researchers from ESRF facility on high-speed tomographic reconstruction and improved performance of PyHST software by factor of 30~\cite{csa2011pyhst}.}
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