Aditya Ahuja
Updates  |  Work Experience  |  Teaching  |  Research  |  Projects

I am a senior CS undergraduate at BITS Pilani, Goa where I am affiliated with the APPCAIR Lab (and TCS Research). I was supervised by Prof. Ashwin Srinivasan and worked on Visual Reasoning and Neuro-Symbolic models.

I am currently pursuing my undergraduate thesis at the Visual Computing Group (VCG) at Harvard University, under the supervision of Prof Hanspeter Pfister where I'm working on extending the Connectomics Pipeline . Concurrently, I'm also working at UIUC's Computer Vision and Robotics Lab (CVRL) under the supervision of Prof. Narendra Ahuja.

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, working with the Ad-Experience team on malware detection in web advertisements.

On Campus, I am the president of Society for Artificial Intelligence and Deep Learning (SAiDL), where we discuss upcoming Deep Learning research and work on collaborative projects.

In a past life, I also participated in a lot of algorithmic programming competitions on online judges, writing code to solve resource-constrained algorithmic challenges.

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Check out the CATER Demo and the BlackSwan Demo!

Feb '21

Check out our latest preprint: Incorporating Domain Knowledge into Deep Neural Networks from APPCAIR!

Jan '21

I've started my undergraduate thesis at the Visual Computing Group (VCG), Harvard mentored by Prof. Hanspeter Pfister!

Nov '20

Check out me and Adithya talking about our work at ECMWF. [Video]

Nov '20

I'll be working as a research intern at Computer Vision and Robotics lab (CVRL), UIUC mentored by Prof. Narendra Ahuja!

Sep '20

Got a Pre-Placement Offer from 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

Me and Ajay Subramian are mentoring a summer project for the SAiDL-Season-of-Code.

May '20

Excited to start my research internship at ECMWF as part of their open source summer program - ESoWC!

May '20

I'll be interning at (Directi) as part of their Ad-Experience team over the summer.

Jan '20

I'll be working with APPCAIR and TCS Research on Visual Reasoning and Neuro-Symbolic modelling.

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 '21 - Current

Visual Computing Group (VCG), Harvard - Research Intern.

Nov '20 - Current

Computer Vision & Robotics Lab (CVRL), UIUC - Research Intern.

Jan '20 - Dec' 20

APPCAIR Lab & TCS Research - Undergratuate Researcher.

May '20 - Sep '20

ECMWF - Machine Learning Research Intern.

May '20 - Jun '20 (Directi) - Summer SWE Intern.

May '19 - Jul '19

Bank of Maharashtra, Head Office - Summer SWE Intern.

Feb '19 - Apr '19

Pixxel - Machine Learning Intern.

Spring '21

BITS G513: Meta-Learning - Teaching Assistant (TA) [Graduate course, done in collaboration with IITD and IIITD]

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

We explore compositional reasoning on the CATER dataset by learning action embeddings and by using object centric representations.

[ Full Size ]

[ Full Size ]

[ Full Size ]

[ Full Size ]

Check out the Independent Demo Page for more information!

[ Go to Independent Demo Page ]

Incorporating Domain Knowledge into Deep Neural Networks
Submitted to International Joint Conference on Artificial Intelligence (IJCAI), 2021
Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, Ashwin Srinivasan
[ Preprint ]

Abstract: 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 approaches.

Anomaly Detection in Streaming Time Series Data
Supervisor : Dr Peter Dueben
[ Project | Presentation ]

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 and failures.

This work was supported by a grant of £5,000 from ECMWF.

[ Demo | Full Size ]

[ Demo | Full Size ]

Check out the Independent Demo Page for more information!

[ Go to Independent Demo Page ]

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
[ Project ] [ Presentation ]

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.

Conway's Game of Life
[ Code ]
Demos: Still Life, Oscillators, Acorn Spread, Engine Spread, Guns, Pulsars, Ships Simple, Ships Collision, Ships Destroyed.

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.

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.

If you're a fresher interested in Machine Learning then check out the resources listed in the SAiDL Summer 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 ]

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