Vikas K. Garg

I'm an Assistant Professor at Aalto University, Finland, and Chief Scientist at YaiYai.

vgarg@mit.edu, vgarg@csail.mit.edu Connect on LinkedIn

Previously, I led research and engineering efforts at IBM Research, Amazon A9, and Microsoft Research. I hold a PhD in Computer Science (EECS) from MIT, where I worked closely with Tommi Jaakkola (PhD supervisor), Cynthia Rudin, Regina Barzilay, and Stefanie Jegelka. While at MIT, I was closely associated with the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) Consortium, and also served as a BP Technologies Energy Fellow. I also collaborated extensively with Adam Kalai, Ofer Dekel, and Lin Xiao.

###### Select Publications

My research interests include (geometric and topological) deep learning, quantum computing, generative models, physics-inspired models, and learning under uncertainty or resource constraints along with their intersections with optimization and game theory. I'm particularly passionate about applications of generative AI in biodesign and drug discovery.

V. Garg. Generative AI for graph-based drug design: Recent advances and the way forward, Current Opinion in Structural Biology, 2024. (PDF)

K. Kogkalidis, J.-P. Bernardy, and V. Garg. Algebraic Positional Encodings, NeurIPS, 2024 (**Spotlight**).

G. Mercatali(+), Y. Verma(+), A. Freitas, and V. Garg. Diffusion Twigs with Loop Guidance for Conditional Graph Generation, NeurIPS, 2024.

T. A. Pham and V. Garg. What do Graph Neural Networks learn? Insights from Tropical Geometry, NeurIPS, 2024.

K. Brilliantov, A. Souza, and V. Garg. Compositional PAC-Bayes: Generalization of GNNs with persistence and beyond, NeurIPS, 2024.

Y. Verma, A. Souza, and V. Garg. Topological Neural Networks go Persistent, Equivariant, and Continuous, ICML, 2024. (PDF)

R. Karczewski, A. Souza, and V. Garg. On the Generalization of Equivariant Graph Neural Networks, ICML, 2024. (PDF)

Y. Verma, M. Heinonen, and V. Garg. ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs, ICLR, 2024 (**Oral**). (PDF)

Y. Jiang, C. Zhou, V. Garg(+), and A. Oulasvirta(+). Graph4GUI: Graph Neural Networks for Representing Graphical User Interfaces, CHI, 2024. (PDF)

J. Immonen(+), A. Souza(+), and V. Garg. Going beyond persistent homology using persistent homology, NeurIPS, 2023 (**Oral**). (PDF)

T. Garipov, S. De Peuter, G. Yang, V. Garg, S. Kaski, and T. Jaakkola. Compositional Sculpting of Iterative Generative Processes, NeurIPS, 2023. (PDF)

Y. Verma, M. Heinonen, and V. Garg. AbODE: Ab initio antibody design using conjoined ODEs, ICML, 2023. (PDF)

I. Moflic, V. Garg, and A. Paler. Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation, APS, 2023. (PDF)

D. Alvarez-Melis (+*), V. Garg(+*), and A. Kalai (+*). Are GANs overkill for NLP?, NeurIPS, 2022 (**Spotlight**). (PDF)

Y. Verma, S. Kaski, M. Heinonen, and V. Garg. Modular Flows: Differential Molecular Generation, NeurIPS, 2022. (PDF)

A. Souza, D. Mesquita, S. Kaski, and V. Garg. Provably expressive temporal graph networks, NeurIPS, 2022. (PDF)

G. Mercatali, A. Freitas, and V. Garg. Symmetry-induced Disentanglement on Graphs, NeurIPS, 2022. (PDF)

V. K. Garg(*), A. Kalai(*), K. Ligett(*), and S. Wu(*). Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization , AISTATS, 2021 (**Oral**). (PDF)

V. K. Garg, S. Jegelka, and T. Jaakkola. Generalization and Representational Limits of Graph Neural Networks, ICML, 2020 (**Oral - Virtual**). (PDF)

V. K. Garg and T. Jaakkola. Predicting deliberative outcomes, ICML, 2020 (**Oral - Virtual**). (PDF)

J. Ingraham, V. K. Garg, R. Barzilay, and T. Jaakkola. Generative Models for Graph-Based Protein Design, NeurIPS, 2019. (PDF) (Code)

V. K. Garg and T. Jaakkola. Solving graph compression via optimal transport, NeurIPS, 2019. (PDF)

V. K. Garg and T. Pichkhadze. Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms, NeurIPS, 2019. (PDF)

V. K. Garg, O. Dekel, and L. Xiao. Learning SMaLL Predictors, NeurIPS, 2018. (PDF)

V. K. Garg and A. Kalai. Supervising Unsupervised Learning, NeurIPS, 2018 (**Spotlight**). (PDF)

V. K. Garg, L. Xiao, and O. Dekel. Sparse Multi-Prototype Classification, UAI, 2018. (PDF)

V. K. Garg and T. Jaakkola. Local Aggregative Games, NIPS, 2017. (PDF)

V. K. Garg and T. Jaakkola. Learning Tree Structured Potential Games, NIPS, 2016. (PDF)

V. K. Garg, C. Rudin, and T. Jaakkola. CRAFT: ClusteR-specific Assorted Feature selecTion, AISTATS, 2016. (PDF) (Code)

S. Shankar, V. K. Garg, and R. Cipolla. Deep Carving: Discovering Visual Attributes by Carving Deep Neural Nets, CVPR, 2015. (PDF)

R. Kondor, N. Teneva, and V. K. Garg. Multiresolution Matrix Factorization, ICML, 2014. (PDF)

V. K. Garg, T. S. Jayram, and B. Narayanaswamy. Online Optimization with Dynamic Temporal Uncertainty, AAAI, 2013. (PDF)

S. Kpotufe and V. K. Garg. Adaptivity to Local Smoothness and Dimension in Kernel Regression, NIPS, 2013. (PDF)

V. K. Garg, M. N. Murty, and Y. Narahari. Novel Biobjective Clustering (BiGC) Based on Cooperative Game Theory, TKDE, 2013. (PDF)

P. Agrawal(+), V. K. Garg(+), and R. Narayanam. Link Label Prediction in Signed Social Networks, IJCAI, 2013. (PDF)

[(+): Equal Contribution, (*): Alphabetical Order]

K. Kogkalidis, J.-P. Bernardy, and V. Garg. Algebraic Positional Encodings, NeurIPS, 2024 (

G. Mercatali(+), Y. Verma(+), A. Freitas, and V. Garg. Diffusion Twigs with Loop Guidance for Conditional Graph Generation, NeurIPS, 2024.

T. A. Pham and V. Garg. What do Graph Neural Networks learn? Insights from Tropical Geometry, NeurIPS, 2024.

K. Brilliantov, A. Souza, and V. Garg. Compositional PAC-Bayes: Generalization of GNNs with persistence and beyond, NeurIPS, 2024.

Y. Verma, A. Souza, and V. Garg. Topological Neural Networks go Persistent, Equivariant, and Continuous, ICML, 2024. (PDF)

R. Karczewski, A. Souza, and V. Garg. On the Generalization of Equivariant Graph Neural Networks, ICML, 2024. (PDF)

Y. Verma, M. Heinonen, and V. Garg. ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs, ICLR, 2024 (

Y. Jiang, C. Zhou, V. Garg(+), and A. Oulasvirta(+). Graph4GUI: Graph Neural Networks for Representing Graphical User Interfaces, CHI, 2024. (PDF)

J. Immonen(+), A. Souza(+), and V. Garg. Going beyond persistent homology using persistent homology, NeurIPS, 2023 (

T. Garipov, S. De Peuter, G. Yang, V. Garg, S. Kaski, and T. Jaakkola. Compositional Sculpting of Iterative Generative Processes, NeurIPS, 2023. (PDF)

Y. Verma, M. Heinonen, and V. Garg. AbODE: Ab initio antibody design using conjoined ODEs, ICML, 2023. (PDF)

I. Moflic, V. Garg, and A. Paler. Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation, APS, 2023. (PDF)

D. Alvarez-Melis (+*), V. Garg(+*), and A. Kalai (+*). Are GANs overkill for NLP?, NeurIPS, 2022 (

Y. Verma, S. Kaski, M. Heinonen, and V. Garg. Modular Flows: Differential Molecular Generation, NeurIPS, 2022. (PDF)

A. Souza, D. Mesquita, S. Kaski, and V. Garg. Provably expressive temporal graph networks, NeurIPS, 2022. (PDF)

G. Mercatali, A. Freitas, and V. Garg. Symmetry-induced Disentanglement on Graphs, NeurIPS, 2022. (PDF)

V. K. Garg(*), A. Kalai(*), K. Ligett(*), and S. Wu(*). Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization , AISTATS, 2021 (

V. K. Garg, S. Jegelka, and T. Jaakkola. Generalization and Representational Limits of Graph Neural Networks, ICML, 2020 (

V. K. Garg and T. Jaakkola. Predicting deliberative outcomes, ICML, 2020 (

J. Ingraham, V. K. Garg, R. Barzilay, and T. Jaakkola. Generative Models for Graph-Based Protein Design, NeurIPS, 2019. (PDF) (Code)

V. K. Garg and T. Jaakkola. Solving graph compression via optimal transport, NeurIPS, 2019. (PDF)

V. K. Garg and T. Pichkhadze. Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms, NeurIPS, 2019. (PDF)

V. K. Garg, O. Dekel, and L. Xiao. Learning SMaLL Predictors, NeurIPS, 2018. (PDF)

V. K. Garg and A. Kalai. Supervising Unsupervised Learning, NeurIPS, 2018 (

V. K. Garg, L. Xiao, and O. Dekel. Sparse Multi-Prototype Classification, UAI, 2018. (PDF)

V. K. Garg and T. Jaakkola. Local Aggregative Games, NIPS, 2017. (PDF)

V. K. Garg and T. Jaakkola. Learning Tree Structured Potential Games, NIPS, 2016. (PDF)

V. K. Garg, C. Rudin, and T. Jaakkola. CRAFT: ClusteR-specific Assorted Feature selecTion, AISTATS, 2016. (PDF) (Code)

S. Shankar, V. K. Garg, and R. Cipolla. Deep Carving: Discovering Visual Attributes by Carving Deep Neural Nets, CVPR, 2015. (PDF)

R. Kondor, N. Teneva, and V. K. Garg. Multiresolution Matrix Factorization, ICML, 2014. (PDF)

V. K. Garg, T. S. Jayram, and B. Narayanaswamy. Online Optimization with Dynamic Temporal Uncertainty, AAAI, 2013. (PDF)

S. Kpotufe and V. K. Garg. Adaptivity to Local Smoothness and Dimension in Kernel Regression, NIPS, 2013. (PDF)

V. K. Garg, M. N. Murty, and Y. Narahari. Novel Biobjective Clustering (BiGC) Based on Cooperative Game Theory, TKDE, 2013. (PDF)

P. Agrawal(+), V. K. Garg(+), and R. Narayanam. Link Label Prediction in Signed Social Networks, IJCAI, 2013. (PDF)

[(+): Equal Contribution, (*): Alphabetical Order]