Currently I am a research intern at ECMWF, working with
Dr. Peter Dueben,
and building Deep models to detect anomalies in metrics for their data services. I also spent an
awesome summer at Media.net, working with the Ad-Experience team on malware
detection in web advertisements.
Following the recent success of transformers in multihop and natural language reasoning, we attempt to study
their application in identifying obstructed objects using compositional reasoning in a visual setting.
ECMWF acts a data vendor to several clients, providing
massive amounts of meteorological data to them. We work on building intelligent Anomaly Detection
systems capable of monitoring the log files produced by these services for sudden disruptions
The Bongard problems were introduced by
Mikhail Moiseevich Bongard in 1967, in his classic pattern recognition book. We use the
DeepProbLog framework to model solutions to these problems by evaluating different hypotheses
and their respective likelihoods.
Schizophrenia is a mental disorder whith symptoms including hallucinations and episodes of
psychosis. We develop a Deep Learning Pipeline for automated Schizophrenia detection using
abnormalies in brain-wave patterns captured through EEG Data.
Spike-Timing Dependent Plasticity, or STDP is the proposed theory which aims to relate temporal
spike differences to changes in synaptic weights between participating neurons. We explore how
well STDP works on a model of the Basal ganglia, using the Izhikevich neuron, using SpineCreator
to model the underlying networks.
BlackSwan - Realtime Streaming Anomaly Detection on Time Series [ Code ]
Developed a pipeline for Deep Time Series Anomaly Detection on Server Log files.
Integrated several State of the Art Anomaly Detection and Forecasting algorithms
with a Realtime plotting framework into a python package. This work was funded by
ECMWF's open source program -
Emotion Recognition from Audio Signals [ Code ]
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Developed a Deep Learning pipeline for Emotion recognition and classification using speech
data, on the MELD Dataset. Classified
emotions across various emotions : [Disgust, Fear, Neutral, ...] across a highly unbalanced
data sample. Used Mel-frequency cepstral coefficients (MFCCs) to form speech representations.
Integrated deep text and image processing models to build a Multimodal Sentiment Analysis system that classified emotions on Internet Memes across different categories.
This work was done on the Sem-Eval dataset.
Signature Verification using Siamese Networks [ Code ]
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Developed a Siamese Neural Net that performed few shot signature verification. Taking a
few sample signatures of a person, the model predicted whether the input 'query' signature
fas forged or authentic.
Worked with genomic data from different geographical locations, plotting it after
dimensionality reduction. Demonstrated relations between geographic origin and
DNA structure by generating different plots and identifying aggregations.
Generated word embeddings using GloVe,
and the Large Movie Review
Dataset. Visualized the obtained embeddings on a 2D graph using Principal Component
Analysis (PCA), and checked the obtained embeddings for semantic coherence.