Assessing the Effects of a Soft Cut-Off in the Twitter Social Network. - Saptarshi Ghosh 001; Ajitesh Srivastava; Niloy Ganguly (Networking (2), 2011)

Effects of a soft cut-off on node-degree in the Twitter social network. - Saptarshi Ghosh 001; Ajitesh Srivastava; Niloy Ganguly (Comput. Commun., 2012)

Degree distributions of evolving alphabetic bipartite networks and their projections. - Niloy Ganguly; Saptarshi Ghosh 001; Tyll Krueger; Ajitesh Srivastava (Theor. Comput. Sci., 2012)

A graph-based topic extraction method enabling simple interactive customization. - Ajitesh Srivastava; Axel J. Soto; Evangelos E. Milios (ACM Symposium on Document Engineering, 2013)

Text clustering using one-mode projection of document-word bipartite graphs. - Ajitesh Srivastava; Axel J. Soto; Evangelos E. Milios (SAC, 2013)

Understanding Evolution of Inter-Group Relationships Using Bipartite Networks. - Saptarshi Ghosh 001; Sudipta Saha; Ajitesh Srivastava; Tyll Krueger; Niloy Ganguly; Animesh Mukherjee 001 (IEEE J. Sel. Areas Commun., 2013)

Influence in social networks: A unified model? - Ajitesh Srivastava; Charalampos Chelmis; Viktor K. Prasanna (ASONAM, 2014)

Computational models of technology adoption at the workplace. - Charalampos Chelmis; Ajitesh Srivastava; Viktor K. Prasanna (Soc. Netw. Anal. Min., 2014)

Social Influence Computation and Maximization in Signed Networks with Competing Cascades. - Ajitesh Srivastava; Charalampos Chelmis; Viktor K. Prasanna (ASONAM, 2015)

A hybrid design for high performance large-scale sorting on FPGA. - Ajitesh Srivastava; Ren Chen; Viktor K. Prasanna; Charalampos Chelmis (ReConFig, 2015)

The unified model of social influence and its application in influence maximization. - Ajitesh Srivastava; Charalampos Chelmis; Viktor K. Prasanna (Soc. Netw. Anal. Min., 2015)

Mining Large Dense Subgraphs. - Ajitesh Srivastava; Charalampos Chelmis; Viktor K. Prasanna (WWW (Companion Volume), 2016)

Computing competing cascades on signed networks. - Ajitesh Srivastava; Charalampos Chelmis; Viktor K. Prasanna (Soc. Netw. Anal. Min., 2016)

Spatio-Temporal Modeling of Criminal Activity. - Michail Misyrlis; Chung Ming Cheung; Ajitesh Srivastava; Rajgopal Kannan; Viktor K. Prasanna (SocialSens@CPSWeek, 2017)

OSCAR: Optimizing SCrAtchpad reuse for graph processing. - Shreyas G. Singapura; Ajitesh Srivastava; Rajgopal Kannan; Viktor K. Prasanna (HPEC, 2017)

How to Stop Violence Among Homeless: Extension of Voter Model and Intervention Strategies. - Ajitesh Srivastava; Robin Petering; Rajgopal Kannan; Eric Rice; Viktor K. Prasanna (ASONAM, 2018)

FActCheck: Keeping Activation of Fake News at Check. - Ajitesh Srivastava; Rajgopal Kannan; Charalampos Chelmis; Viktor K. Prasanna (AAMAS, 2018)

Behind-the-Meter Solar Generation Disaggregation using Consumer Mixture Models. - Chung Ming Cheung; Wen Zhong; Chuanxiu Xiong; Ajitesh Srivastava; Rajgopal Kannan; Viktor K. Prasanna (SmartGridComm, 2018)

On Minimizing the Completion Times of Long Flows Over Inter-Datacenter WAN. - Mohammad Noormohammadpour; Ajitesh Srivastava; Cauligi S. Raghavendra (IEEE Commun. Lett., 2018)

Optimizing Inter-Datacenter Tail Flow Completion Times using Best Worst-case Routing. - Max Noormohammadpour; Ajitesh Srivastava; Cauligi S. Raghavendra (Allerton, 2019)

RecANt: Network-based Recruitment for Active Fake News Correction. - Ajitesh Srivastava; Rajgopal Kannan; Charalampos Chelmis; Viktor K. Prasanna (IEEE BigData, 2019)

Parallel edge-based sampling for static and dynamic graphs. - Kartik Lakhotia; Rajgopal Kannan; Aditya Gaur; Ajitesh Srivastava; Viktor K. Prasanna (CF, 2019)

On Predicting Crime with Heterogeneous Spatial Patterns: Methods and Evaluation. - Chuanxiu Xiong; Ajitesh Srivastava; Rajgopal Kannan; Omkar Damle; Viktor K. Prasanna; Erroll Southers (SIGSPATIAL/GIS, 2019)

SPEC2: SPECtral SParsE CNN Accelerator on FPGAs. - Yue Niu 001; Hanqing Zeng; Ajitesh Srivastava; Kartik Lakhotia; Rajgopal Kannan; Yanzhi Wang; Viktor K. Prasanna (HiPC, 2019)

Design and Implementation of Knowledge Base for Runtime Management of Software Defined Hardware. - Hongkuan Zhou; Ajitesh Srivastava; Rajgopal Kannan; Viktor K. Prasanna (HPEC, 2019)

Accurate, Efficient and Scalable Graph Embedding. - Hanqing Zeng; Hongkuan Zhou; Ajitesh Srivastava; Rajgopal Kannan; Viktor K. Prasanna (IPDPS, 2019)

Predicting memory accesses: the road to compact ML-driven prefetcher. - Ajitesh Srivastava; Angelos Lazaris; Benjamin Brooks; Rajgopal Kannan; Viktor K. Prasanna (MEMSYS, 2019)

Parallel Totally Induced Edge Sampling on FPGAs. - Akshit Goel; Sanmukh R. Kuppannagari; Yang Yang 111; Ajitesh Srivastava; Viktor K. Prasanna (PARCO, 2019)

Network-based intervention strategies to reduce violence among homeless. - Ajitesh Srivastava; Robin Petering; Nicholas Barr; Rajgopal Kannan; Eric Rice; Viktor K. Prasanna (Soc. Netw. Anal. Min., 2019)

Reuse Kernels or Activations?: A Flexible Dataflow for Low-latency Spectral CNN Acceleration. - Yue Niu 001; Rajgopal Kannan; Ajitesh Srivastava; Viktor K. Prasanna (FPGA, 2020)

QTAccel: A Generic FPGA based Design for Q-Table based Reinforcement Learning Accelerators. - Rachit Rajat; Yuan Meng 001; Sanmukh R. Kuppannagari; Ajitesh Srivastava; Viktor K. Prasanna; Rajgopal Kannan (FPGA, 2020)

Towards High Performance, Portability, and Productivity: Lightweight Augmented Neural Networks for Performance Prediction. - Ajitesh Srivastava; Naifeng Zhang; Rajgopal Kannan; Viktor K. Prasanna (HiPC, 2020)

A Framework for Task Mapping onto Heterogeneous Platforms. - Ta-Yang Wang; Ajitesh Srivastava; Viktor K. Prasanna (HPEC, 2020)

GraphSAINT: Graph Sampling Based Inductive Learning Method. - Hanqing Zeng; Hongkuan Zhou; Ajitesh Srivastava; Rajgopal Kannan; Viktor K. Prasanna (ICLR, 2020)

RAOP: Recurrent Neural Network Augmented Offset Prefetcher. - Pengmiao Zhang; Ajitesh Srivastava; Benjamin Brooks; Rajgopal Kannan; Viktor K. Prasanna (MEMSYS, 2020)

MemMAP: Compact and Generalizable Meta-LSTM Models for Memory Access Prediction. - Ajitesh Srivastava; Ta-Yang Wang; Pengmiao Zhang; César Augusto Fonticielha De Rose; Rajgopal Kannan; Viktor K. Prasanna (PAKDD (2), 2020)

Compiler Abstractions and Runtime for Extreme-scale SAR and CFD Workloads. - Connor Imes; Alexei Colin; Naifeng Zhang; Ajitesh Srivastava; Viktor K. Prasanna; John Paul Walters (ESPM2@SC, 2020)

FASTHash: FPGA-Based High Throughput Parallel Hash Table. - Yang Yang 111; Sanmukh R. Kuppannagari; Ajitesh Srivastava; Rajgopal Kannan; Viktor K. Prasanna (ISC, 2020)

Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemic. - Ajitesh Srivastava; Viktor K. Prasanna (CoRR, 2020)

Data-driven Identification of Number of Unreported Cases for COVID-19: Bounds and Limitations. - Ajitesh Srivastava; Viktor K. Prasanna (CoRR, 2020)

Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJα Model. - Ajitesh Srivastava; Tianjian Xu; Viktor K. Prasanna (CoRR, 2020)

Monte Carlo Tree Search for Task Mapping onto Heterogeneous Platforms. - Ta-Yang Wang; William Chang; Ajitesh Srivastava; Rajgopal Kannan; Viktor K. Prasanna (HiPC, 2021)

A Robot Walks into a Bar: Automatic Robot Joke Success Assessment. - Ajitesh Srivastava; Naomi T. Fitter (ICRA, 2021)

The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021). - Bijaya Adhikari; Ajitesh Srivastava; Sen Pei; Sarah Kefayati; Rose Yu; Amulya Yadav; Alexander Rodríguez; Arvind Ramanathan; Anil Vullikanti; B. Aditya Prakash (KDD, 2021)

Decoupling the Depth and Scope of Graph Neural Networks. - Hanqing Zeng; Muhan Zhang; Yinglong Xia; Ajitesh Srivastava; Andrey Malevich; Rajgopal Kannan; Viktor K. Prasanna; Long Jin; Ren Chen (NeurIPS, 2021)

GenMAT: A General-Purpose Machine Learning-Driven Auto-Tuner for Heterogeneous Platforms. - Naifeng Zhang; Ajitesh Srivastava; Rajgopal Kannan; Viktor K. Prasanna (PEHC@SC, 2021)

The EpiBench Platform to Propel AI/ML-based Epidemic Forecasting: A Prototype Demonstration Reaching Human Expert-level Performance. - Ajitesh Srivastava; Tianjian Xu; Viktor K. Prasanna (CoRR, 2021)

Accurate, efficient and scalable training of Graph Neural Networks. - Hanqing Zeng; Hongkuan Zhou; Ajitesh Srivastava; Rajgopal Kannan; Viktor K. Prasanna (J. Parallel Distributed Comput., 2021)

Accelerating Large Scale Real-Time GNN Inference using Channel Pruning. - Hongkuan Zhou; Ajitesh Srivastava; Hanqing Zeng; Rajgopal Kannan; Viktor K. Prasanna (Proc. VLDB Endow., 2021)

Computational Models for Cascades in Massive Graphs. - Ajitesh Srivastava; Charalampos Chelmis; Viktor K. Prasanna (Massive Graph Analytics, 2022)

Shape-based Evaluation of Epidemic Forecasts. - Ajitesh Srivastava; Satwant Singh; Fiona Lee (IEEE Big Data, 2022)

Fine-grained address segmentation for attention-based variable-degree prefetching. - Pengmiao Zhang; Ajitesh Srivastava; Anant V. Nori; Rajgopal Kannan; Viktor K. Prasanna (CF, 2022)

epiDAMIK 5.0: The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery. - Bijaya Adhikari; Amulya Yadav; Sen Pei; Ajitesh Srivastava; Sarah Kefayati; Alexander Rodríguez; Marie-Laure Charpignon; Anil Vullikanti; B. Aditya Prakash (KDD, 2022)

"This Bot Knows What I'm Talking About!" Human-Inspired Laughter Classification Methods for Adaptive Robotic Comedians. - Carson Gray; Trevor Webster; Brian Ozarowicz; Yuhang Chen; Timothy Bui; Ajitesh Srivastava; Naomi T. Fitter (RO-MAN, 2022)

ReSemble: Reinforced Ensemble Framework for Data Prefetching. - Pengmiao Zhang; Rajgopal Kannan; Ajitesh Srivastava; Anant V. Nori; Viktor K. Prasanna (SC, 2022)

TransforMAP: Transformer for Memory Access Prediction. - Pengmiao Zhang; Ajitesh Srivastava; Anant V. Nori; Rajgopal Kannan; Viktor K. Prasanna (CoRR, 2022)

Random Forest of Epidemiological Models for Influenza Forecasting. - Majd Al Aawar; Ajitesh Srivastava (CoRR, 2022)

C-MemMAP: clustering-driven compact, adaptable, and generalizable meta-LSTM models for memory access prediction. - Pengmiao Zhang; Ajitesh Srivastava; Ta-Yang Wang; César A. F. De Rose; Rajgopal Kannan; Viktor K. Prasanna (Int. J. Data Sci. Anal., 2022)

Spatio-Temporal Attention in Multi-Granular Brain Chronnectomes For Detection of Autism Spectrum Disorder. - James Orme-Rogers; Ajitesh Srivastava (ICASSP, 2023)

epiDAMIK 6.0: The 6th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery. - Bijaya Adhikari; Alexander Rodríguez; Amulya Yadav; Sen Pei; Ajitesh Srivastava; Marie-Laure Charpignon; Anil Vullikanti; B. Aditya Prakash (KDD, 2023)

Estimating Temporal Trends using Indirect Surveys. - Ajitesh Srivastava; Juan Marcos Ramirez; Sergio Díaz-Aranda; José Aguilar 001; Antonio Ortega; Antonio Fernández Anta; Rosa Lillo-Rodriguez (CoRR, 2023)

DTW+S: Shape-based Comparison of Time-series with Ordered Local Trend. - Ajitesh Srivastava (CoRR, 2023)

Detection of Delays and Feedthroughs in Dynamic Networked Systems. - Sina Jahandari; Ajitesh Srivastava (IEEE Control. Syst. Lett., 2023)

Adjusting for Unmeasured Confounding Variables in Dynamic Networks. - Sina Jahandari; Ajitesh Srivastava (IEEE Control. Syst. Lett., 2023)

Behind-the-Meter Solar Generation Disaggregation at Varying Aggregation Levels Using Consumer Mixture Models. - Chung Ming Cheung; Sanmukh Rao Kuppannagari; Ajitesh Srivastava; Rajgopal Kannan; Viktor K. Prasanna (IEEE Trans. Sustain. Comput., 2023)

Nowcasting Temporal Trends Using Indirect Surveys. - Ajitesh Srivastava; Juan Marcos Ramirez; Sergio Díaz-Aranda; José Aguilar 001; Antonio Fernández Anta; Antonio Ortega; Rosa Elvira Lillo (AAAI, 2024)

Acoustic-to-Articulatory Inversion for Dysarthric Speech: are Pre-Trained Self-Supervised Representations Favorable? - Sarthak Kumar Maharana; Krishna Kamal Adidam; Shoumik Nandi; Ajitesh Srivastava (ICASSP Workshops, 2024)

epiDAMIK 2024: The 7th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery. - Alexander Rodríguez; Bijaya Adhikari; Ajitesh Srivastava; Sen Pei; Marie-Laure Charpignon; Kai Wang 071; Serina Chang; Anil Vullikanti; B. Aditya Prakash (KDD, 2024)

Global Prediction of COVID-19 Variant Emergence Using Dynamics-Informed Graph Neural Networks. - Majd Al Aawar; Srikar Mutnuri; Mansooreh Montazerin; Ajitesh Srivastava (CoRR, 2024)

Task-Agnostic Federated Learning. - Zhengtao Yao; Hong Nguyen; Ajitesh Srivastava; José Luis Ambite (CoRR, 2024)

Simultaneous Weight and Architecture Optimization for Neural Networks. - Zitong Huang; Mansooreh Montazerin; Ajitesh Srivastava (CoRR, 2024)

Challenges of COVID-19 Case Forecasting in the US, 2020-2021. - 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; et al. (PLoS Comput. Biol., 2024)

Dynamics-Based Feature Augmentation of Graph Neural Networks for Variant Emergence Prediction. - Majd Al Aawar; Srikar Mutnuri; Mansooreh Montazerin; Ajitesh Srivastava (AAAI, 2025)

Aligning Time-series by Local Trends: Applications in Public Health. - Ajitesh Srivastava (AAAI, 2025)

Peer Disambiguation in Self-Reported Surveys using Graph Attention Networks. - Ajitesh Srivastava; Aryan Shetty; Eric Rice (CoRR, 2025)

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression. - Mansooreh Montazerin; Majd Al Aawar; Antonio Ortega; Ajitesh Srivastava (CoRR, 2025)

SWAT-NN: Simultaneous Weights and Architecture Training for Neural Networks in a Latent Space. - Zitong Huang; Mansooreh Montazerin; Ajitesh Srivastava (CoRR, 2025)