I am a Computer Science PhD student at the University of California, Berkeley. Applications of machine learning in computational genomics, neuroscience and natural language processing are of particular interest to me.

Publications

• Ruchir Rastogi^, Aniketh Janardhan Reddy^, Ryan Chung, and Nilah M. Ioannidis. “Fine-tuning sequence-to-expression models on personal genome and transcriptome data”. bioRxiv (2024): 2024-09. In review. [Preprint] [Code]

• Aniketh Janardhan Reddy^, Xinyang Geng^, Michael H. Herschl^, Sathvik Kolli, Aviral Kumar, Patrick D. Hsu, Sergey Levine, and Nilah M. Ioannidis. “Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization.” Advances in Neural Information Processing Systems 38 (NeurIPS 2024). Accepted. [Preprint]

• Lucas Ferreira DaSilva, Simon Senan, Zain Munir Patel, Aniketh Janardhan Reddy, Sameer Gabbita, Zach Nussbaum, César Miguel Valdez Córdova, Aaron Wenteler, Noah Weber, Tin M. Tunjic, Talha Ahmad Khan, Zelun Li, Cameron Smith, Matei Bejan, Lithin Karmel Louis, Paola Cornejo, Will Connell, Emily S. Wong, Wouter Meuleman, Luca Pinello. “DNA-Diffusion: Leveraging Generative Models for Controlling Chromatin Accessibility and Gene Expression via Synthetic Regulatory Elements.” bioRxiv (2024): 2024-02. In review. [Preprint]

• Aniketh Janardhan Reddy^, Michael H. Herschl^, Sathvik Kolli, Amy X. Lu, Xinyang Geng, Aviral Kumar, Patrick D. Hsu, Sergey Levine, and Nilah M. Ioannidis. “Pretraining strategies for effective promoter-driven gene expression prediction.” bioRxiv (2023): 2023-02. [Preprint] [Code]

• Aniketh Janardhan Reddy and Leila Wehbe. 2021. Can fMRI reveal the representation of syntactic structure in the brain? Advances in Neural Information Processing Systems 35 (NeurIPS 2021). [Paper] [Code]

• Aniketh Janardhan Reddy, Gil Rocha, and Diego Esteves. 2018. DeFactoNLP: Fact Verification using Entity Recognition, TFIDF Vector Comparison and Decomposable Attention. Proceedings of the First Workshop on Fact Extraction and VERification (FEVER). [Paper] [Code]

• Diego Esteves, Aniketh Janardhan Reddy^, Piyush Chawla^, and Jens Lehmann. 2018. Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web. Proceedings of the First Workshop on Fact Extraction and VERification (FEVER). [Paper] [Code]

• Aniketh Janardhan Reddy, Monica Adusumilli, Sai Kiranmai Gorla, Lalita Bhanu Murthy Neti and Aruna Malapati. 2018. Named Entity Recognition for Telugu using LSTM-CRF. WILDRE4 at LREC 2018. [Paper] [Code]

• Diego Esteves, Anisa Rula, Aniketh Janardhan Reddy, and Jens Lehmann. 2018. Toward Veracity Assessment in RDF Knowledge Bases: An Exploratory Analysis. J. Data and Information Quality 9, 3, Article 16 (February 2018), 26 pages. [Paper]

^Equal contribution.

Projects

• I am advised by Prof. Nilah Ioannidis and we are currently working on building better gene expression prediction models and on optimizing sequences to obtain beneficial gene expression profiles.

• Worked with Prof. Leila Wehbe on understanding the representation of language syntax in the human brain using functional magnetic resonance imaging (fMRI) and machine learning.

• Utilized fMRI and machine learning techniques to identify which brain regions are involved in attention modulation. I was guided by Dr. Sridharan Devarajan at the Cognition Lab, Indian Institute of Science, Bangalore, India.

• Developed an automated fact verification system called DeFactoNLP which could not only assess the veracity of a claim but also retrieve supporting evidences from Wikipedia - https://github.com/DeFacto/DeFactoNLP

• Built a system which could automatically ascertain the trustworthiness of a website - https://github.com/DeFacto/WebCredibility

• Participated in the development and benchmarking of DeFacto, a triple verification framework.

• I worked on the recognition of named entities in texts written in Indian languages. My work on Named Entity Recognition (NER) in Telugu texts using an LSTM-CRF classifier can be found at https://github.com/anikethjr/NER_Telugu.

• I built a C++ based framework to build artificial neural networks. It can be found at https://github.com/anikethjr/NeuralNets.

• An FP Tree implementation which I coded using C++ can be found at https://github.com/anikethjr/FPTree.

• TeChess is a chess bot which I built in early 2017. It makes use of PVS search and its code can be found at https://github.com/anikethjr/TeChess.

• I coded the ID3, Reduced Error Pruning, Random Forests and AdaBoost algorithms in C++. The code is at https://github.com/anikethjr/decision-tree.

• Two cloth simulation models, one which uses the spring mass model and the other which uses an internal energy model, implemented in C++ using OpenGL are at https://github.com/anikethjr/Cloth-Simulation.

• An interactive application to draw Bezier Curves and generate surfaces of revolution can be found at https://github.com/anikethjr/BezierCurves. It was implemented in C++ and uses OpenGL.

• I built a framework which implements L-Systems using OpenGL. The code can be found at https://github.com/anikethjr/LSystems.

• To experiment with 3D modelling using OpenGL and Blender, I built a children’s playground using them. The code for this project is at https://github.com/anikethjr/Playground-OpenGL.

• A Python implementation of the SVD and CUR algorithms can be found at https://github.com/anikethjr/SVD_CUR.

• An implementation of the Apriori algorithm can be found at https://github.com/anikethjr/Apriori.

• I built semantic and syntactic vector space models using Python. The code for this project is at https://github.com/anikethjr/VSM.

• Some games which I made in Python using CodeSkulptor can be found at https://github.com/anikethjr/Python-Games.