Room A322 in Computer Science T-Building,
Konemiehentie 2, Otaniemi campus area, Espoo
- Postal Address:
Aalto University School of Science,
Department of Information and Computer Science,
P.O. Box 15400, FI-00076 Aalto, Finland
- Kunal dot ghosh aat aalto dot fi
- [Github], [LinkedIn], [Flickr], [CV]
Since summer 2018, I am a Doctoral candidate in the Department of Computer Science at Aalto University. I focus on deep generative models (focusing on molecular data).
I recived my MSc (Honours) in the Machine Learning and Data Mining (MACADAMIA
), also from Aalto University.
Currently working on:
Previously I worked on:
- Predicting Molecular Electronic Properties using Deep Learning.
For this project, we are collaborating with Prof. Patrick Rinke
and members of his Computational Electronic Structure Theory (CEST) group,
from the Department of Applied Physics at Aalto University
on the European Union (EU) wide NOMAD project.
- Semi-Supervised Learning with extensions of the Variational Autoencoder (VAE).
Before returning to academia in 2015, I worked at Amazon India as a Software Development Engineer in Test.
Deep Learning, Probabilistic Graphical Models, Generative Models
(Honours Program) Machine Learning and Data Mining (Graduated with Honours, November 2017)
Aalto University, Finland.
Computer Science and Engineering (Graduated 2011)
Visvesvaraya Technological University, India
[New] Deep learning spectroscopy: neural networks for molecular excitation spectra.[url]
Kunal Ghosh, Annika Stuke, Milica Todorović, Peter Bjørn Jørgensen, Mikkel N. Schmidt, Aki Vehtari, and Patrick Rinke
Wiley Advanced Science, 2019.
Chemical diversity in molecular orbital energy predictions with kernel ridge regression[arxiv]
Annika Stuke, Milica Todorović, Matthias Rupp, Christian Kunkel, Kunal Ghosh, Lauri Himanen, Patrick Rinke
Deep Learning for Predicting Molecular Electronic Properties [Link]
Kunal Ghosh, Supervisor : Prof. Aki Vehtari (Dept. of Computer Science), Advisor : Prof. Patrick Rinke (Dept. of Applied Physics)
Masters Thesis, Aalto University, Finland, 2017.
Comparison of the recognition accuracy of Eigen Faces, when applying different dimensionality reduction algorithms on the input images.
Kunal Ghosh, M. GuruPrasad, S. Dharini, and J. L. KiranTej
Bachelor Thesis, Visvesvaraya Technological University, India, 2011.
- Implementing parallel coordinate descent for L1-Regularized Loss Minimization.
In this project we implemented the parallel coordinate descent algorithm as Described in [Bradley et.al 2011] in Apache Spark, (Parallel) Matlab and NumPy. The Spark implementation was deployed the algorithem on
Aalto University's internal compute cluster. A detailed report and code can be found on Github. [Code],[Report PDF]
- Experiments with the EM algorithm..
In this project we derived the update equations of the EM algorithm for a gaussian mixture model and then Implemented the algorithm. The model was then compared with a simple linear model to fit various toy datasets. Finally a detailed report was written with the findings (and the derivations).
- Approximating F0 and F2 of streaming data.
The project was to implement probabilistic counting algorithms, specifically the
Flajolet Martin Algorithm
for approximate F0 (unique elements in a sequence) and to
implement second moment F2 using the algorithm as described in Alon et.al 2002]
useful for computing the Gini coefficient of variation for a data stream.[Code],[Report PDF]
- Comparing the effect on search relevance when using various parameter settings in a Information Retrieval system.
We implemented an Information Retrieval system in Java using Apache Lucene and compared the
Relevance of the Recommendations in a document corpus (relevance (or not) of a document was provided in the test corpus).
We experimented with various parameters in the implementation such as the choice of stemmer, text preprocessing etc. We also compared the Vector Space Model (VSM) with TF-IDF weights and BM25 as Similarity measures.
- Kah-Vis : Visualizing the effect of Bulk Purchase of Coffee on consumption.
The objective of this project was to use principles of effective visualization design (using Munzner's Model and also from works of Edward Tufte).
I visualized a dataset of coffee consumption over a period of time and tried to see if buying coffee in bulk has had any effect on (my) consumption.
The dataset was from my own coffee purchase receipts over a period of 8 months.