When you join the Institute for Computational and Data Sciences, you join a community of data science and information technology scholars. Here you can keep in touch as an alumni or learn about alumni as a current student.
How would you describe your experience while at UB getting your degree?
UB and CDSE fostered an environment that allowed me to tailor my education to explore areas of interest related to various high performance computing methodologies and technologies, while also providing focused mentorship relating to my chosen research project. Their faculty and infrastructure made for a great way to hone my technical skills in the constantly evolving technological ecosystem, while simultaneously informing me about various theoretical topics along the way.
Where are you at now?
Data Engineer at Regeneron.
How has your degree has helped you get there?
CDSE exposed me to a variety of techniques, perspectives and technologies and provided the cluster-computing resources which allowed me to practice and perfect my abilities.
What made you choose UB for your PhD?
The best thing I find about UB is the cross-culture interaction in the student body and the wide variety of opportunities which we get here. According to me, these are the components for successful personal and professional development. Besides these things, I also found the research areas of the faculty members at UB in alignment with the topics which I wanted to pursue further. These things made UB an easy choice
What do you like most about CDSE at UB?
I find CDSE to be a unique program with a lot of things to offer. The CDSE program is a perfect combination of applied mathematics and statistical analysis coupled with high performance computational techniques for handling problems related to big data and Machine Learning. Also, the interdisciplinary nature of the research enables us to interact with people from different backgrounds and learn a lot of new things
What are you working on?
Currently, I am working on modeling the ice sheet dynamics for parts of Greenland Icesheet. The approach I am following currently is totally data driven without involving much of the Physics. In modeling terms, the problem boils down to a spatio-temporal modeling analysis for data which is non-uniformly distributed both in space as well as in time. Kernel related learning methods, Hierarchical models, Splines and Multiscale methods are some of the ideas I am looking at in my research.
How was your experience while at UB getting your degree?
The time I spent at UB during my PhD was surely the most productive time of my career. The wide variety of courses available at UB coupled with efficient guidance from my mentors helped me reach my goals well within the time duration I planned. Besides setting me up for a bright career ahead, my time at UB also provided me the opportunity to make some very good friends who supported me throughout my journey.
Where are you at now?
Right now I am a postdoctoral researcher at the Data Intensive Studies Center at Tufts University
How your degree has helped you get there?
I think a PhD degree in Computational and Data Enables Science from UB prepared me well for a research based career in the broad field of data science. With appropriate training in Applied Math, Computations and Statistical modeling, I now feel comfortable enough to handle problems related to data science with confidence. So a postdoc in a data science program was the natural next step.