Aniruddha Adiga

24 papers A* 4A 1B 2Misc 5Journal 9Unranked 3
YearRankTypeTitle / Venue / Authors
2025 — conf
Causal Analysis of Graph Signals for Brain Effectome Inference.
IEEECONF
Srikar Mutnuri, Aniruddha Adiga, Srinivasan Venkatramanan, Madhav V. Marathe
2025 A* conf
IGraSS: Learning to Identify Infrastructure Networks from Satellite Imagery by Iterative Graph-constrained Semantic Segmentation.
IJCAI
Oishee Bintey Hoque, Abhijin Adiga, Aniruddha Adiga, Siddharth Chaudhary, Madhav V. Marathe, S. S. Ravi, Kirti Rajagopalan, Amanda Wilson, Samarth Swarup
2025 J jnl
IGraSS: Learning to Identify Infrastructure Networks from Satellite Imagery by Iterative Graph-constrained Semantic Segmentation.
CoRR
Oishee Bintey Hoque, Abhijin Adiga, Aniruddha Adiga, Siddharth Chaudhary, Madhav V. Marathe, S. S. Ravi, Kirti Rajagopalan, Amanda Wilson, Samarth Swarup
2024 J jnl
Challenges of COVID-19 Case Forecasting in the US, 2020-2021.
PLoS Comput. Biol.
Velma K. Lopez, Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O'Dea, Madeline Adee, Turgay Ayer, Jagpreet Chhatwal, Ozden O. Dalgic, Mary A. Ladd, Benjamin P. Linas, Peter P. Mueller, Jade Xiao, Johannes Bracher, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Khoa Le, Anja Mühlemann, Jarad Niemi, Evan L. Ray, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Sen Pei, Jeffrey Shaman, Teresa K. Yamana, Samuel R. Tarasewicz, Daniel J. Wilson, Sid Baccam, Heidi Gurung, Steve Stage, Brad Suchoski, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Lily Wang, Yueying Wang, Shan Yu, Lauren Gardner, Sonia Jindal, Maximilian Marshall, Kristen Nixon, Juan Dent, Alison L. Hill, Joshua Kaminsky, Elizabeth C. Lee, Joseph Chadi Lemaitre, Justin Lessler, Claire P. Smith, Shaun Truelove, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Dean Karlen, Lauren A. Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan M. Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana L. Pastore y Piontti, Alessandro Vespignani, Xinyue Xiong, Robert Walraven, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Graham Casey Gibson, Daniel Sheldon, Ajitesh Srivastava, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav V. Marathe, Akhil Sai Peddireddy, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Lijing Wang, Pragati V. Prasad, Jo W. Walker, Alexander E. Webber, Rachel B. Slayton, Matthew Biggerstaff, Nicholas G. Reich, Michael A. Johansson
2023 Misc conf
A Multi-Team Multi-Model Collaborative Covid-19 Forecasting Hub for India.
WSC
Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav V. Marathe, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Ambedkar Dukkipati, Tony Gracious, Shubham Gupta, Nihesh Rathod, Rajesh Sundaresan, Sarath Yasodharan, Kantha Rao Bhimala, Vidyadhar Mudkavi, Gopal Krishna Patra, Siva Athreya
2023 J jnl
Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support.
Int. J. High Perform. Comput. Appl.
Parantapa Bhattacharya, Jiangzhuo Chen, Stefan Hoops, Dustin Machi, Bryan L. Lewis, Srinivasan Venkatramanan, Mandy L. Wilson, Brian Klahn, Aniruddha Adiga, Benjamin Hurt, Joseph Outten, Abhijin Adiga, Andrew S. Warren, Young Yun Baek, Przemyslaw J. Porebski, Achla Marathe, Dawen Xie, Samarth Swarup, Anil Vullikanti, Henning S. Mortveit, Stephen G. Eubank, Christopher L. Barrett, Madhav Vinayak Marathe
2023 A* conf
Phase-Informed Bayesian Ensemble Models Improve Performance of COVID-19 Forecasts.
AAAI
Aniruddha Adiga, Gursharn Kaur, Lijing Wang, Benjamin Hurt, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Bryan L. Lewis, Madhav V. Marathe
2022 A* conf
CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting.
AAAI
Lijing Wang, Aniruddha Adiga, Jiangzhuo Chen, Adam Sadilek, Srinivasan Venkatramanan, Madhav V. Marathe
2022 B conf
Enhancing COVID-19 Ensemble Forecasting Model Performance Using Auxiliary Data Sources.
IEEE Big Data
Aniruddha Adiga, Gursharn Kaur, Benjamin Hurt, Lijing Wang, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Bryan L. Lewis, Madhav V. Marathe
2022 J jnl
Evaluating the Utility of High-Resolution Proximity Metrics in Predicting the Spread of COVID-19.
ACM Trans. Spatial Algorithms Syst.
Zakaria Mehrab, Aniruddha Adiga, Madhav V. Marathe, Srinivasan Venkatramanan, Samarth Swarup
2021 A* conf
All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting.
KDD
Aniruddha Adiga, Lijing Wang, Benjamin Hurt, Akhil Sai Peddireddy, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Bryan Leroy Lewis, Madhav V. Marathe
2021 A conf
Cohorting to Isolate Asymptomatic Spreaders: An Agent-Based Simulation Study on the Mumbai Suburban Railway.
AAMAS
Alok Talekar, Sharad Shriram, Nidhin K. Vaidhiyan, Gaurav Aggarwal, Jiangzhuo Chen, Srinivasan Venkatramanan, Lijing Wang, Aniruddha Adiga, Adam Sadilek, Ashish Tendulkar, Madhav V. Marathe, Rajesh Sundaresan, Milind Tambe
2021 Misc conf
Informing University Covid-19 Decisions Using Simple Compartmental Models.
WSC
Benjamin Hurt, Aniruddha Adiga, Madhav V. Marathe, Christopher L. Barrett
2020 J jnl
Cohorting to isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway.
CoRR
Alok Talekar, Sharad Shriram, Nidhin K. Vaidhiyan, Gaurav Aggarwal, Jiangzhuo Chen, Srinivasan Venkatramanan, Lijing Wang, Aniruddha Adiga, Adam Sadilek, Ashish Tendulkar, Madhav V. Marathe, Rajesh Sundaresan, Milind Tambe
2020 Misc conf
Epoch Estimation from a Speech Signal Using Gammatone Wavelets in a Scattering Network.
ICASSP
Pavan Kulkarni, Jishnu Sadasivan, Aniruddha Adiga, Chandra Sekhar Seelamantula
2020 — conf
Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting.
IEEE BigData
Lijing Wang, Aniruddha Adiga, Srinivasan Venkatramanan, Jiangzhuo Chen, Bryan L. Lewis, Madhav V. Marathe
2020 J jnl
Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting.
CoRR
Lijing Wang, Aniruddha Adiga, Srinivasan Venkatramanan, Jiangzhuo Chen, Bryan L. Lewis, Madhav V. Marathe
2020 J jnl
Models for COVID-19 Pandemic: A Comparative Analysis.
CoRR
Aniruddha Adiga, Devdatt P. Dubhashi, Bryan L. Lewis, Madhav V. Marathe, Srinivasan Venkatramanan, Anil Vullikanti
2019 Misc conf
Nonlinear Multi-scale Super-resolution Using Deep Learning.
ICASSP
Kenneth Tran, Ashkan Panahi, Aniruddha Adiga, Wesam A. Sakla, Hamid Krim
2018 Misc conf
Wavelet-Based Reconstruction for Unlimited Sampling.
ICASSP
Sunil Rudresh, Aniruddha Adiga, Basty Ajay Shenoy, Chandra Sekhar Seelamantula
2017 J jnl
An Alternating Minimization Strategy for Sparse Blind Deconvolution - Convergence Analysis and Concentration Inequalities.
CoRR
Aniruddha Adiga, Chandra Sekhar Seelamantula
2014 — conf
An alternating ℓp - ℓ2 projections algorithm (ALPA) for speech modeling using sparsity constraints.
DSP
Aniruddha Adiga, Chandra Sekhar Seelamantula
2014 J jnl
Auditory-motivated Gammatone wavelet transform.
Signal Process.
Arun Venkitaraman, Aniruddha Adiga, Chandra Sekhar Seelamantula
2014 B conf
Controlled blurring for improving image reconstruction quality in flutter-shutter acquisition.
ICIP
Suraj Srinivas, Aniruddha Adiga, Chandra Sekhar Seelamantula