Trainees

Current Trainees are listed in alphabetical order by last name.

 

Alice Lucas

Alice Lucas is a Ph.D. graduate student in the Electrical Engineering and Computer Science department at Northwestern University, working in the Image and Video Processing Laboratory.

She recently graduated from the University of Wisconsin-Madison, where she obtained a B.S. in Applied Math, Engineering and Physics  along with a Computer Science minor.

Her research interests lie in the field of machine learning and deep learning.  More specifically, her work examines the use of deep neural network architectures and probabilistic graphical models as an approach to solving new challenges in image processing and analysis.

Learn more about Alice’s research.

 

Arjun Punjabi

Arjun is a Ph.D. candidate in the Department of Electrical Engineering at Northwestern University.

He is currently a member of the Image and Video Processing Laboratory (IVPL) under the supervision of Prof. Aggelos Katsaggelos, which he joined during his first term at Northwestern.  His research interests include computer vision, biomedical signal processing, and machine learning.

Arjun received a Bachelor of Science degree in Electrical Engineering with a focus in Multimedia Signal Processing from Brown University in May of 2015.

Learn more about Arjun’s research.

 

Neda Rohani

Neda is a Ph.D. candidate in the Electrical Engineering and Computer Science department, under the mentorship of Professor Aggelos Katsaggelos, at Northwestern University.

She received her Bachelor’s and Master’s degrees in Electrical Engineering from Sharif University of Iran.  Her research interests are Machine Learning, Computer Vision, and Signal Processing.

 

NiharikaNiharika Sravan

Niharika is a Ph.D. candidate in the Department of Physics and Astronomy, at Northwestern University.

She is primarily interested in modeling the evolutionary pathways to ‘stripped-envelope supernovae’ (Types IIb, Ib, and Ic). A systematic analysis of possible evolutionary channels can be used to build a database recording properties of progenitor sequences. Such a database can aid in expediting classification, progenitor identification in pre-supernova images and further detailed follow-up and study. It will be an important tool in analyzing the wealth of data on the transient sky that will be available in the future with the advent of rapid-cadence big-data telescopes like the LSST.

Niharika is also interested in modeling the evolution of binary star systems to investigate the channels and mechanisms governing X-ray binaries and binary compact objects. In the past, she has worked on a wide range of topics from re-ioninzation to galaxy evolution.

View Niharika’s website.

 

Vivian Tang

Vivian is a Ph.D. student in Department of Earth and Planetary Sciences, at Northwestern University.

Her work involves investigating deep Earth’s structure beneath East Asia by seismological and geophysical methods. She is originally from Taiwan and received her Master’s degree in Geosciences from National Taiwan University.

 

Vicky Chuqiao Yang

Vicky is a Ph.D. candidate in the Department of Applied Mathematics at Northwestern University.

She works on mathematical models applied to social systems. Some topics of her work include crime in cities and dynamics of political elections. She received her undergraduate degrees from Worcester Polytechnic Institute, in both Mathematical Sciences and Physics.

View Vicky’s website.
Learn more about Vicky’s research.

 

Michael Zevin

Michael is a Ph.D. student in the Department of Physics and Astronomy at Northwestern University.

He is part of the LIGO Scientific Collaboration and has multiple research interests in gravitational wave astrophysics ranging from LIGO detector data analysis to source formation channels of gravitational wave events.

He is originally from the southwest suburbs of Chicago and received undergraduate degrees in Astronomy, Physics, and Music Performance from the University of Illinois at Urbana-Champaign.

Learn more about Michael’s research.