Hello World! I'm David Krasowska.
david:~$ summary
David Krasowska is a Ph.D. student at Northwestern University, advised by Dr. Peter Dinda.
His research journey began with his mentor during his undergraduate degree at Clemson University, Dr. Jon C. Calhoun.
Under his mentor, he collaborated with Argonne to explore lossy compression for optimizations in HPC scientific applications.
He was a visiting student at Argonne National Laboratory under Dr. Julie Bessac and Dr. Franck Cappello.
This research has led to publications and awards. He has received the DOE Computational Science Graduate Fellowship to fund his studies.
At Northwestern, David is exploring processing near memory utilization for datacenters to create a data-centric design.
david:~$ location
Chicago, IL
david:~$ education
Northwestern University (2023-Current) | Computer Science Ph.D. Student
Clemson University (2019-2022) | Computer Engineering BS | GPA: 3.7
Clemson University (2019-2022) | Computer Engineering BS | GPA: 3.7
david:~$ interests
Running. Biking. Hiking. Outdoors. Coding. Friendship. Achievement.
david:~$ resume
david:~$ email
david:~$ number
+1 (843) 283-7758
Experience
Research Assistant
May 2021 - June 2023
Lossy compression research with Argonne National Laboratory and Clemson University FTHPC using the Palmetto Cluster.
Analyzing statistical correlations within datasets in comparison to compression performance.
Presented during Super Computing (SC) ’21 at the 7th International Workshop on Data Analysis and Reduction for Big Scientific Data workshop with a publication.
Presented and won best undergraduate poster during the ACM Student Research Competition at SC ’22.
Contributed to Libpressio, an Argonne library for compression.
High Performance Computing Creative Inquiry
June 2021 - December 2022
Participant in the Student Cluster Competition at SC ’21 and INDYSCC at SC '22.
Collaboration with Dell and Intel to build a cluster with greatest performance per watt.
Azure Cyclecloud was learned due to the competition switching modalities. Set up: schedulers (OpenPBS),
package managers (Spack), applications (Quantum Espresso), and benchmarks (HPCG). Gained knowledge of parallel computing with MPI.
Undergraduate Student Researcher
January 2022 - May 2022
Region of interest compressibility research in collaboration with Los Alamos National Laboratory and Clemson University
FTHPC using the Palmetto Cluster. Determining methods to achieve the highest compressibility for images from a Laser Powdered Fusion
(LPBF) EOS X printer in the SIGMA division within LANL.
Django TRACE Camp Student
June 2020
Bootcamp through Clemson University where I developed multiple projects using Django. Specific tasks were assigned on a weekly basis in
order to strengthen teamwork and web-development skills. Using APIs, creating a chat system, and creating a portfolio were some projects.
Utilized AWS Lightsail in combination with NGINX and Gunicorn projects.
Peer-Reviewed Publications
2023
PDF
Underwood R, Bessac J, Krasowska D, Calhoun JC, Di S, Cappello F. Black-box statistical prediction of lossy compression ratios for scientific data.
The International Journal of High Performance Computing Applications. 2023. doi:10.1177/10943420231179417
D. Krasowska, J. Bessac, R. Underwood, J. C. Calhoun, S. Di and F. Cappello, "Exploring Lossy Compressibility through Statistical Correlations of Scientific Datasets,"
2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7), St. Louis, MO, USA, 2021, pp. 47-53, doi: 10.1109/DRBSD754563.2021.00011.
Skills:
Unix
MPI
CUDA
C
C++
Python
LLVM
VHDL
Databases
RISC-V