Aditya Ahuja
[ News  |  Teaching  |  Work Experience  |  Research  |  Projects ]


I am a senior CS undergraduate at BITS Pilani, Goa and an undergraduate researcher at the APPCAIR Lab, BITS Goa (collaborating with TCS Research) where I am working on Neuro Symbolic Modeling under the supervision of Prof. Ashwin Srinivasan.

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.

I am also the president of Society for Artificial Intelligence and Deep Learning (SAiDL), where we discuss upcoming Deep Learning research and work on projects. I've taught several courses related to Machine Learning and Deep Learning to freshmen students.

I've also participated in a lot of algorithmic programming competitions on online judges, writing efficient code to solve resource-constrained algorithmic challenges.


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[ Sep '20 ]

Got a Pre-Placement Offer from Media.net for Fall 2021!

[ Aug '20 ]

Excited to be one of the 150 Indian undergrads selected for the Google Research AI Summer School!

[ Jul '20 ]

Co-organised the Summer Symposium on AI Research with 3000+ registrations, inviting top AI researchers as speakers.

[ Jul '20 ]

I'll be a TA for the iXperience summer Data Science program!

[ May '20 ]

I'll be mentoring a summer project with Ajay Subramian for the SAiDL-Season-of-Code.

[ May '20 ]

I'll be working as a ML research intern at ECMWF as part of their summer program - ESoWC.

[ May '20 ]

I'll be interning at Media.net (Directi) as part of their Ad-Experience team.

[ Jan '20 ]

I'll be working on a sponsored project with TCS Research on Neuro-Symbolic Modeling.

[ Jan '20 ]

I'll be TAing the Machine Learning Course at BITS Pilani, Goa.

[ May '19 ]

I'll be a mentor for this summers' Machine Learning QSTP course along with Rijul and Saura.



[ Jan '20 - Current ]

APPCAIR Lab & TCS Research - Undergratuate Researcher.

[ May '20 - Sep '20 ]

ECMWF - Machine Learning Research Intern.

[ May '20 - Jun '20 ]

Media.net (Directi) - Summer SWE Intern.

[ May '19 - Jul '19 ]

Bank of Maharashtra, Head Office - Summer SWE Intern.

[ Feb '19 - Apr '19 ]

Pixxel - Machine Learning Intern.



[ Fall '20 ]

BITS F464: Machine Learning - Teaching Assistant (TA).

[ Summer '20 ]

iXperience: Data-Science Program - Teaching Assistant (TA).

[ Spring '20 ]

BITS F464: Machine Learning - Teaching Assistant (TA).

[ Fall '19 ]

Technology Incubator Programme, BITS Pilani - Project Mentor.

[ Summer '19 ]

Quark-QSTP: Introduction to Machine Learning - Instructor.



Compositional Reasoning and Visual Understanding on Videos
Supervisors : Prof. Ashwin Srinivasan, Dr Shirish Karande

In Progress.


Anomaly Detection in Streaming Time Series Data
Supervisor : Dr Peter Dueben
[ Mid-Term Report ]

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 and failures.


Developing a framework to model solutions for Bongard Problems
Supervisors : Prof. Ashwin Srinivasan, Dr Lovekesh Vig

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.


Detecting schizophrenia using Electroencephalography Signals
Supervisor : Prof. Amalin Prince

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.


Implementing Spiking-Time Dependent Plasticity on SpineCreator
Supervisor : Prof. Basabdatta Sen Bhattacharya
[ Report ] [ Poster ]

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 - ESoWC.


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.


Memotion Sentiment Analysis
[ Code ] [ Preprocessing, Model ]

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 ] [ 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.


Visualizing Genomic data
[ Code ] [ HTML ]

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.


Image Generation using GANs
[ Code ] [ HTML ]

Developed a Generative Adversarial Network (GAN) to generate new instances of the CIFAR Dataset.


Generating Word Embeddings
[ Code ] [ HTML ]

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.


Conway's Game of Life
[ Code ]

A modular implementations of Conway's Game of Life in Python with common patterns like Still lifes, Oscillators, and Spaceships.





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