Before that, I spent an awesome summer as a research intern at ECMWF, working with
Dr. Peter Dueben,
and building Deep models to detect anomalies in ECMWF's massive data services. I also spent a
summer at Media.net, working with the Ad-Experience team on malware
detection in web advertisements.
We present a survey of ways in which domain knowledge has been included when constructing models with neural
networks. The inclusion of domain-knowledge is of special interest not just to constructing scientiﬁc assistants,
but also, many other areas that involve understanding data using human-machine collaboration. In many such instances,
machine-based model construction may beneﬁt signiﬁcantly from being provided with human-knowledge of the domain
encoded in a sufﬁciently precise form. This paper examines two broad approaches to encode such knowledge–as logical
and numerical constraints–and describes techniques and results obtained in several subcategories under each of these
ECMWF (Europe's largest meteorological research institute) 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
This work was supported by a grant of £5,000 from ECMWF.
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.
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 ]
[ HTML ]
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.
A modular implementations of Conway's Game of Life
in Python with common patterns like Still lifes, Oscillators, and Spaceships.
Signature Verification using Siamese Networks [ Code ]
[ HTML ]
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.
If you're a fresher interested in Machine Learning then check out the resources listed in the
SAiDLSummer Assignment to get started!
[ PDF ]
If you're a junior/senior at BITS Goa, IIT Delhi or IIIT Delhi, then check out the
Meta Learning Course!
[ Labs ]