Lovekesh Vig       

EDUCATION

• • Doctor of Philosophy: 2002-2006

Vanderbilt University, Nashville, TN, USA

Thesis Title "Multiple Robot Coalition Formation"

• • Master of Computer Applications: 1999-2002

University of Delhi, Delhi, India

• • Bachelor of Science in Physics (Hons): 1996-1999

St. Stephens College, Delhi, India

WORK EXPERIENCE

•  TCS Research, New Delhi, India

   Senior Scientist, 2015 - 2019

   Principal Scientist, 2019 -2023

   Chief Scientist, 2023 -present

• Bloomberg, R&D, New York, NY, USA

  Financial Software Developer, Feb 2007-Dec 2007

• Jawaharlal Nehru University, New Delhi, India

  Assistant Professor, School of Computational and Integrative Sciences

  Dec 2007-Sep 2015

 PUBLICATIONS

Refereed Journal Publications

•  Lovekesh Vig and Julie A. Adams, "Multi-Robot Coalition Formation", IEEE Transactions on Robotics, 22 (4), 637-649, 2006.

• Lovekesh Vig and Julie A. Adams, “Coalition Formation: From Software Agents to Robots”, in Journal of Robotic and Intelligent Systems 50(1), 85-118, 2007.

• Ashish Gupta, Lovekesh Vig and David C. Noelle, “A Dual Association Model for the Extinction of Animal Conditioning”, Neurocomputing,  74 (17),  3531-3542, 2011.

• Ashish Gupta, Lovekesh Vig and David C. Noelle, “A Cognitive Model for Generalization during Sequential Learning ”, Journal of Robotics, Special Issue on Cognitive and Neural Aspects of Robotics, 2011.

• Lovekesh Vig, Ashish Gupta and Abhinandan Basu, “A Neurocomputational model for the role of hunger in Dopamine mediated actions”, 20(4), 373-393, Journal of Intelligent Systems, 2011

• Ashish Gupta, Lovekesh Vig, David C. Noelle, “A Cognitive Model for Automaticity in Motor Skill Learning”, Biologically Inspired Cognitive Architectures, Vol. 2, pp. 1-12, 2012

• Manoj Agarwal, Lovekesh Vig,  and Naveen Kumar, “Multiple Objective Robot Coalition Formation”, Journal of Expert Systems and Applications , 20(4), 3736-3747, 2014 (This paper was selected to be presented at the “Hot off the press track” at GECCO 2014)

• Amit Srivastava, Rupali Chopra, Shafat Ali, Shweta Agarwal, Lovekesh Vig, and Ramesh Bamezai, "Inferring population structure and relationships using minimal independent markers in Y-chromosome: a hybrid approach of recursive feature selection for hierarchical clustering", Nucleic Acids Research, 42 (15), 2014

• Manoj Agarwal, Naveen Kumar, and Lovekesh Vig, "Parallel Multi Objective Coalition Formation", Expert Systems and Applications, 42 (21), 7797-7811, 2015

• Rajan Srivastava, Sudhanshu Shankar, Lovekesh Vig, Pradipta Bandyopadhyaya " A combination of Monte Carlo Temperature Basin Paving and Graph Theory: Water cluster low energy structures and completeness of search", Journal of Chemical Sciences, 2016

• Kushal Veer Singh and Lovekesh Vig, "Improved Prediction of Missing Protein Interactome Links via Anomaly Detection", Applied Network Science, 2017

• Narendhar Gugulothu, Vishnu Tv, Pankaj Malhotra, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff, "Predicting Remaining Useful Life Using Time Series Embeddings based on recurrent neural networks", International Journal of Prognostics and Health Management (IJPHM), 2018. 

• Priyanka Gupta, Vishnu TV,  Pankaj Malhotra, Lovekesh Vig, and Gautam Shroff, Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks, Springer Journal of Healthcare Informatics , 2019

• Ashwin Srinivasan, Lovekesh Vig and Michael Bain, Logical Explanations for Deep Relational Machines Using Relevance Information, Journal of Machine Learning Research (JMLR), 2019

• Ashwin Srinivasan, Lovekesh Vig and Gautam Shroff, Constructing generative logical models for optimisation problems using domain knowledge, Machine Learning Journal (MLJ), 2019 

• Ashwin Srinivasan, Tirtharaj Dash, Lovekesh Vig, Incorporating Symbolic Domain Knowledge into Graph Neural Networks, Machine Learning Journal (MLJ), 2021

Refereed Conference Publications

•  Lovekesh Vig and Julie A. Adams, "A Framework for Multi-Robot Coalition Formation", In the Proceedings of the second Indian International Conference on Artificial Intelligence, Pune, India, 2005.

•  Lovekesh Vig and Julie A. Adams, “The Effect of Coalition Imbalance on Multi-Robot Teams ”, In the Proceedings of the third Indian International Conference on Artificial Intelligence, Tumkur, India, 2009

• Lovekesh Vig , Ashish Gupta and Abhinandan Basu, “On the Relation between Hunger, Dopamine and Action Rate”, In the Proceedings of the fourth Indian International Conference on Artificial Intelligence, Tumkur, India, 2011

• Manoj Aggarwal, Lovekesh Vig and Naveen Kumar,  “A Multiple Objective approach to robot Coalition Formation”, International Conference of Intelligent Robotic Application , Aachen, Germany, 2011

•  Ashish Gupta and Lovekesh Vig, “A Dual Association Model for Acquisition and Extinction” In Proceedings of the tenth Conference of the Italian Association of Artificial Intelligence,: 139-150, Palermo, Italy, 2011

• Ankit Verma and Lovekesh Vig, "Deep Convolutional Neural Networks for Gender Recognition", International Conference on Soft Computing and Machine Intelligence, New Delhi,  2014

• Pankaj Malhotra, Lovekesh Vig, Gautam Shroff and Puneet Agarwal, "Long Short Term Memory Networks for Anomaly Detection in Time Series", European Symposium on Artificial Neural Networks, Bruges, 2015

• Sucheta Chouhan and Lovekesh Vig, "Anomaly Detection in ECG Time Signals through Deep Long Short-Term Memory based Recurrent Neural Network Architecture" , DSAA, Paris,  2015

• Urminder Singh, Sucheta Chouhan, A. Krishnamachari and Lovekesh Vig, "Ensemble of Deep Long Short Term Memory Networks for Labelling Origin of Replication Sequences", DSAA, Paris,  2015

• Ankit Verma, Ramya Hebbalaguppe, Lovekesh Vig, Swagat Kumar and Ehtesham Hassan, "Pedestrian Detection via Mixture of CNN Experts and Thresholded Aggregate  Channel Features", IEEE International Conference on Computer Vision (ICCV) Workshop on Assistive Technologies, Santiago, 2015

• Gaurangi Anand, Ah Kazmi, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, Puneet Agarwal,"Deep Temporal Features to Predict Repeat Buyers", NIPS  Workshop on Learning for E-Commerce, 2015, Montreal

• Mohit Yadav, Pankaj Malhotra, S. Sriram, Gautam Shroff, Lovekesh Vig, "Augmenting Time series data with ODE models for Anomaly Detection", NIPS Workshop on Time Series Analysis, 2015, Montreal

• Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, Gautam Shroff, "LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection", ICML Workshop on Anomaly Detection, 2016, New York

• Pankaj Malhotra, Vishnu TV, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, Gautam Shroff

• "Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder", KDD Workshop on ML for Prognostics and Health Management, 2016, San Francisco

• Ashwin Srinivasan, Gautam Shroff, Lovekesh Vig, Sarmila Saikia, Puneet Agarwal, "Generation of Near Optimal Solutions using ILP guided sampling" ,(* Best Paper Award) ILP 2016, London    

• Perla Ramakrishna, Ramya Hebbalaguppe, Gaurav Gupta, Geetika Sharma, Ehtesham Hassan, Monika Sharma, Lovekesh Vig, Gautam Shroff. "An AR Inspection Framework: Feasibility Study with Multiple AR Devices", ISMAR, Merida, 2016 

• Ankit Verma, Monika Sharma, Ramya Hebbalaguppe, Ehtesham Hassan, Lovekesh Vig, "Automatic Container Code Recognition  via Spatial Transformer Networks and Connected Component Region Proposals", ICMLA, Anaheim, 2016

• Anirban Chakraborti, Kiran Sharma, Aditeya Pandey, Kaushal Paneri, Sidharth Verma, Gunjan Sehgal, Bindu Gupta, Geetika Sharma, Lovekesh Vig, Puneet Agarwal, Gautam Shroff, "Spatio-Temporal Analysis of Ethnic Conflicts and Human Rights Violations in Africa and Middle East", SBP-BRIMS, Washington DC, 2016

• Sarmimala Saikia, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Puneet Agarwal, Richa Rawat, " Knowlege-Rich Deep Networks for Optimization by Inclusion of ILP-constructed Features ", NIPS Workshop on Cognitive Computing and Neuro Symbolic Integration, Barcelona, 2016

• Karamjit Singh, Garima Gupta,  Lovekesh Vig, Gautam Shroff, and Puneet Agarwal, "Deep Convolutional Neural Networks for Pairwise Causality",  What If? NIPS workshop on on hypothetical and counterfactual interventions, Barcelona, 2016

• Mohit Yadav, Lovekesh Vig, and Gautam Shroff, "Learning and Knowledge Transfer with Memory Networks for Machine Comprehension", EACL, Valencia, 2017

• Pankaj Malhotra, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff, "TimeNet: Using Pre-trained Embeddings based on LSTM-Autoencoders for Time-Series Classification and Clustering", ESANN, Bruges, 2017 

• Monika Sharma, Oindrila Saha, Anand Sriram, Shirish Karande,and Lovekesh Vig, "Crowdsourcing for Chromosome Segmentation and Deep Classification", CVMI Workshop, CVPR, Hawaii, 2017

• Narendhar Gugulothu, Vishnu Tv, Pankaj Malhotra, Puneet Agarwal, Lovekesh Vig, and Gautam Shroff "Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks", 2nd ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management, Halifax, 2017

• Vishnu Tv, NarendharGugulothu, Pankaj Malhotra, Puneet Agarwal, Lovekesh Vig and Gautam Shroff, Explainable Deep Learning for Health Monitoring of Complex Systems  AI4IOT Workshop, IJCAI, Melbourne, 2017

• Gaurav Gupta, Swati J, Monika Sharma, and Lovekesh Vig,  Data Extraction from Handmarked Inspection Sheets Workshop on Camera Based Document Analysis and Recognition, ICDAR, Kyoto, 2017

• Lovekesh Vig, Ashwin Srinivasan, Michael Bain, and Ankit Verma, "An Investigation into the Role of Domain-Knowledge on the Use of Embeddings", ILP, Orleans, 2017

• Ashwin Srinivasan, Lovekesh Vig and Gautam Shroff, "Mode-Directed Neural-Symbolic Modelling" , ILP, Orleans, 2017

• Swati J, Gaurav Gupta, Mohit Yadav, Monika Sharma, and  Lovekesh Vig, Siamese Networks for Chromosome Classification, Bioimage Computing workshop, ICCV, Venice 2017

• Somdyuti Paul and Lovekesh Vig, "Deterministic Policy Gradient Based Robotic Path Planning with Continuous Action Spaces", Workshop on Vision in Practice on Autonomous Robotics, ICCV , Venice, 2017

• Mayur Patidar, Puneet Agarwal, Lovekesh Vig, Gautam Shroff, "Correcting Linguistic Training Bias in an FAQ-bot using LSTM-VAE", ECML-PKDD  Data Mining and Natural Language Processing (DMNLP) Workshop, Macedonia, 2017

• Prerna Khurana, Puneet Agarwal, Ashwin Srinivasan, Gautam Shroff, and Lovekesh Vig, "Hybrid BiLSTM-Siamese network for FAQ Assistance", CIKM Case Studies Track,Singapore 2017

• S Vishal, Mohit Yadav, Lovekesh Vig and Gautam Shroff, "Information Bottleneck inspired method for chat text segmentation", IJCNLP, Taiwan, 2017.

• D. Vishwanath, Lovekesh Vig, Puneet Agarwal and Gautam Shroff, "MEETING BOT: Reinforcement Learning for Dialogue Based Meeting Scheduling", AAAI 2018, DEEPDIAL Workshop on Reasoning and Learning for Human-Machine Dialogues, New Orleans, 2018.

• Sakti Saurav, Pankaj Malhotra, Vishnu Tv, Narendhar Gugulothu, Lovekesh Vig, Puneet Agarwal, Gautam Shroff, "Online Anomaly Detection with Concept Drift Adaptation using Recurrent neural Networks", IKDD, Conference on Data Science (CoDS), Goa, 2018.

• Prerna Khurana, Puneet Agarwal, Gautam Shroff, Lovekesh Vig, "Resolving Abstract Anaphora Implicitly in Conversational Assistants using a Hierarchically stacked RNN", KDD (Applications Track), London, 2018

• Monika Sharma, Swati J, Lovekesh Vig, "Automatic Chromosome Classification using Deep Attention Based Sequence Learning of Chromosome Bands", IJCNN, Rio, 2018

• S. Vishal, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff, "Prosocial or Selfish?: Multi-Behaviour Agents for Contract Negotiation using Reinforcement Learning", IJCAI Workshop on Automated Negotiating, Stockholm, 2018

• Priyanka Gupta, Pankaj Malhotra, Lovekesh Vig, and Gautam Shroff, "Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks", IJCAI Workshop in Knowledge Discovery in Healthcare, Stockholm, 2018

• Priyanka Gupta, Pankaj Malhotra, Lovekesh Vig, and Gautam Shroff, Using Features From Pre-trained TimeNET For Clinical Predictions, IJCAI Workshop in Knowledge Discovery in Healthcare, Stockholm, 2018

• Narendhar Gugulothu, Pankaj Malhotra, Lovekesh Vig and Gautam Shroff, "Sparse Neural Networks for Anomlay Detection in High Dimensional Time Series", IJCAI Workshop on  AI4IOT, Stockholm, 2018 

• Vishnu TV, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, "Deep Ordinal Regression for Remaining Useful Estimation from Censored Data", ICML Workshop on Deep Learning for Safety-Critical Applications in Engineering Systems,  Stockholm, 2018

• Arindam Chowdhury and Lovekesh Vig, "An efficient end-to-end neural model for handwritten text recognition ", BMVC, 2018

• Swati J, Monika Sharma, and Lovekesh Vig, "Automatic Classification of Low Resolution Classification Images", ECCV Workshop on Bio-Image Computing, Munich, 2018

• Monika Sharma; Abhishek Vermaa; Lovekesh Vig, "Learning to Clean: A GAN Perspective", International Workshop on Robust Reading, ACCV, Perth,  2018 

• Rohit Rahul; Arindam Chowdhury; Animesh Animesh; Samarth Mittal; Lovekesh Vig, "Reading Industrial Inspection Sheets by Inferring Visual Relations",  International Workshop on Robust Reading, ACCV, Perth,  2018  

• Vishwanath D; Rohit Rahul; Gunjan Sehgal; Swati; Arindam Chowdhury; Monika Sharma; Lovekesh Vig; Gautam Shroff; Ashwin Srinivasan, "Deep-Reader: End-to-end framework for relevant text extraction from real image documents with Natural Language Interface",  International Workshop on Robust Reading, ACCV , Perth, 2018

• Tirthraj Dash, Ashwin Srinivasan, Lovekesh Vig, Oghenejokpeme I. Orhobor,  and Ross King Large-Scale Assessment of Deep Relational Machines, 28th International Conference on ILP 2018, Ferrara, Italy,  2018,***Best Student Paper

• Mayur Patidar, Puneet Agarwal, Lovekesh Vig, and Gautam Shroff,  Automatic Conversational Helpdesk Solution using Seq2Seq and Slot-filling Models., CIKM, Turin, 2018

• Sarmimala Saikia, Richa Verma, Puneet Agarwal, Gautam Shroff, Lovekesh Vig and Ashwin Srinivasan, Evolutionary RL for Container Loading, ESAAN, Bruges,  2018

• Arijit Ukil, Pankaj Malhotra, Soma Bandyopadhyay, Tulika Bose, Ishan Sahu, Ayan Mukherjee, Lovekesh Vig, Arpan Pal, and Gautam Shroff, Fusing Features based on Signal Properties and TimeNet for Time Series Classification, ESAAN, Bruges, 2019

• Kathan Kashiparekh, Jyoti Narwariya, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification , IJCNN,  Budapest, 2019

• Monika Sharma, Shikha Gupta, Arindam Chowdhury and Lovekesh Vig. ChartNet: Visual Reasoning over Statistical Charts using MAC-Networks, IJCNN,  Budapest, 2019

• Saurabh Srivastava, Puneet Agarwal, Gautam Shroff, Lovekesh Vig, Hierarchical Capsule Based Neural Network Architecture for Sequence Labeling, IJCNN, Budapest, 2019

• Diksha Garg, Priyanka Gupta, Pankaj Malhotra, Lovekesh Vig and Gautam Shroff, Sequence and Time Aware Neighborhood for Session-based Recommendations, SIGIR, Paris, 2019 

• Ankit Sharma, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff , "MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population",  Workshop on Do the right thing”: machine learning and causal inference for improved decision making, Neurips 2019, Montreal

• Vishnu T. V. , Pankaj Malhotra, Jyoti Narwariya, Lovekesh Vig, Gautam Shroff, Meta-Learning for Black Box Optimization, ECML 2019 (Main Track)

• Shubham Palliwal, Vishwanath D, Rohit Rahul, Monika Sharma, Lovekesh Vig,  TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images, ICDAR 2019, Sydney (Main Track)

• Kushagra Mahajan, Monika Sharma, Lovekesh Vig, Character Keypoint based homography estimation in Scanned Documents for Efficient Information Extraction,   CBDAR Workshop ICDAR 2019

• Priyanka Gupta, Diksha Garg, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, NISER: Normalized Item and Session Representations with Graph Neural Networks, International Workshop on Graph Representation Learning and its Applications, CIKM ’19

• Jyoti Narwariya, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, T. V. Vishnu, Meta-Learning for Few-Shot Time Series Classification, CoDS, 2020 (Main Track)

• Diksha, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, BCD4Rec: Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation, Workshop on Offline Reinforcement Learning, Neurips 2020

• Kushagra Mahajan, Monika Sharma, Lovekesh Vig, Rishab Khincha, Soundarya Krishnan, Adithya Niranjan, Tirtharaj Dash, Ashwin Srinivasan, Gautam Shroff , CovidDiagnosits: Deep Diagnosis of Covid-19 Patients using Chest X-Rays, International Workshop on Thoracic Image Analysis, MICCAI , 2020

• Soundarya Krishnan, Rishab Khincha, Lovekesh Vig, Tirtharaj Dash, Ashwin Srinivasan, A Case Study of Transfer of Lesion Knowledge, MIL3D Workshop, MICCAI 2020

• Kushagra Mahajan, Monika Sharma, Lovekesh Vig, Meta-DermDiagnosis: Few-Shot Skin Disease Identification Using Meta-Learning, CVPR-W, 2020

• Vibhor Gupta, Jyoti Nariwala, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, Handling Variable Dimension Time Series with Graph Neural Networks, AI4IoT workshop IJCAI 2020

• Jyoti Nariwala, Pankaj Malhotra, Vishnu TV, Lovekesh Vig, Gautam Shroff, Graph Neural Network for Leveraging Industrial Equipment Structure: An application to remaining useful life estimation, AI4IoT Workshop, IJCAI 2020

• Priyanka Gupta, Ankit Sharma, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, “CauSeR: Causal Session-based Recommendations for Handling Popularity Bias”, CIKM 2021 (Main Track)

• Manu Sheoran, Meghal Dani, Monika Sharma and Lovekesh Vig, “DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detection”, BMVC 2021 Main Track

• Atharva Sonwane, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan and Tirtharaj Dash, “Solving Visual Analogies using Neural Algorithmic Reasoning”, IJCLR 2021, (Progress Paper)

• Arushi Jain, Shubham Palliwal, Monika Sharma, Lovekesh Vig, TSR-DSAW: Table Structure Recognition via Deep Spatial Association of Words, ESAAN 2021, (Main Track)

• Vibhor Gupta, Jyoti Narwariya, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, "Continual Learning for Multivariate Time Series Tasks with Varying Input Dimensions", ICDM 2021 (Main Track) 

• Mrinal Rawat, Ramya Hebbalaguppe, Lovekesh Vig, “ PnPOOD: Out of Distribution Detection for Text Classification via Plug and Play Data Augmentation”, ICML Workshop on Uncertainty and Robustness in Deep Learning, 2021

• Garima Gupta, Lovekesh Vig, Gautam Shroff, “DRTCI: Learning Disentangled Representations for Temporal Causal Inference”, ICML Workshop on Neglected Assumptions in Causal Inference, 2021

• Vaibhav Varshney, Rajat Kumar, Mayur Patidar, Lovekesh Vig, Gautam Shroff; “ Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection”, NAACL 2022 (Main Track)

• Rajat Kumar, Vaibhav Varshney, Mayur Patidar, Lovekesh Vig, Gautam Shroff; “Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering”, NAACL 2022 (Main Track)

• Manu Sheoran, Meghal Dani, Monika Sharma, Lovekesh Vig, “ Efficient Anchor-free Universal Lesion Detection in CT-Scans”, ISBI 2022 (Main Track).

• Jyoti Narwariya, Chetan Verma, Pankaj Malhotra, Lovekesh Vig, Easwara Subramanian, SanjayBhat: “Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs”, AAAI Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD), AAAI 2022

• Diksha Garg, Pankaj Malhotra, Anil Bhatia, Sanjay Bhat, Lovekesh Vig, Gautam Shroff: “Learning to Liquidate Forex: Optimal Stopping via Adaptive Top-K Regression”, Workshop on AI in Financial Services: Adaptiveness, Resilience & Governance, AAAI 2022

• Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar, Shirish Karande, Lovekesh Vig, Venkatrammana Runkana, "Real-time Health Monitoring of Heat Exchangers using Hypernetworks and PINNs', Workshop on Machine Learning and the Physical Sciences,  Neurips, 2022

• Ritam Majumdar, Vishal Sudam Jadhav, Anirudh Deodhar, Shirish Karande, Lovekesh Vig, Venkataramana Runkana, "Physics Informed Symbolic Networks", Workshop on The Symbiosis of Deep Learning and Differential Equations, Neurips, 2022

• Rajaswa Patil, Manasi Patwardhan, Shirish Karande, Lovekesh Vig, Gautam Shroff, "Exploring Dimensions of Generalizability and Few Shot Transfer for Text-to-SQL Semantic Parsing", Transfer Learning for NLP Workshop, Neurips, 2022

• Manu Sheoren, Meghal Dani, Monika Sharma, Lovekesh Vig, "Handling Domain Shift for Lesion Detection via Semi-Supervised Domain Adaptation",  Workshop on Computer Vision for Medical Computing,  ACCV, 2022

• Rishabh Patra, Ramya Hebbalaguppe, Tirtharaj Dash, Lovekesh Vig, Gautam Shroff, "Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic data pruning", Spotlight Paper, Algorithms (Main) Track, WACV 2023

• Shabbirhussain Bhaisaheb, Shubham Paliwal, Rajaswa Patil, Manasi Patwardhan, Lovekesh Vig and Gautam Shroff, "Program Synthesis for Complex QA on Charts via Probabilistic Grammar Based Filtered Iterative Back-Translation", EACL 2023 (Main Track)

• Shubham Palliwal, Manasi Patwardhan and Lovekesh Vig, "Generalization of Fine Granular Extraction from Charts", ICDAR 2023 (Main Track)

• Aseem Arora, Shabbirhussain B., Harshit Nigam, Manasi Patwardhan, Lovekesh Vig, and Gautam Shroff , "Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Leat-to-Most Prompting", GenBench workshop, EMNLP, 2023

• Brahmavar, S. B., Srinivasan, A., Dash, T., Krishnan, S. R., Vig, L., Roy, A., & Aduri, R. "Generating Novel Leads for Drug Discovery Using LLMs with Logical Feedback". Proceedings of the AAAI Conference on Artificial Intelligence 2024 (Main Track). 

• Muskan Gupta Priyanka Gupta Jyoti Narwariya, Lovekesh Vig Gautam Shroff, "SCM4SR: Structural Causal Model-based Data Augmentation for Robust Session-based Recommendation ", SIGIR, 2024 (Short Paper)

• Rudra Patil, Manasi Patwardhan, Lovekesh Vig, Gautam Shroff, "Beyond Retrieval: Topic-based alignment of scientific papers to research proposal", ACL Workshop on Scholarly Document Processing (SDP), 2024 

• Soham Chitnis, Manasi Patwardhan, Ashwin Srinivasan, Lovekesh Vig, Gautam Shroff, "Generating Refinements of Reviews given Guidelines", ACL Workshop on Scholarly Document Processing (SDP), 2024 

• Shubham Gandhi, Manasi Patwardhan, Jyotsana Khatri, Lovekesh Vig, Raveendra Kumar M, ICSE LLM4Code workshop, 2024 

• Harshit Nigam, Manasi Patwardhan, Lovekesh Vig, Gautam Shroff,  "An Interactive Co-Pilot for Accelerated Research Ideation", NAACL2024 HCI NLP workshop, 2024 

• Arpita Kundu Saiyam Jogani Manasi Patwardhan Indrajit Bhattacharya Lovekesh Vig Gautam Shroff, "Checker Augmented Retriever and Reference-free Scorer for QA over Long Scientific Documents with Distinct Answer Types", Scientific Document Understanding Workshop, AAAI 2024. 

•  "Acceleron: A tool for Accelerating Research Ideation", AI for Accelerating Science and Engineering workshop, AAAI 2024

 Refereed Book Chapters

• Lovekesh Vig and Julie A. Adams, "Market Based Multi-Robot Coalition Formation", Distributed Autonomous Robotic Systems (DARS), Minneapolis, editors R. Voyles and M. Gini, Minneapolis, 2006.

• Lovekesh Vig and Julie A. Adams, "Issues in Multi-Robot Coalition Formation", Third International Multi-Robot Systems Workshop, From Swarms to Intelligent Automata, Washington D.C., editors Lynne E. Parker, Frank E. Schneider and Allan C. Schultz, 2005.

Books

• Lovekesh Vig, “Multiple Robot Teaming”, VDM Verlag, May 2008.