In a world increasingly shaped by artificial intelligence, few challenges are as critical—or as complex—as understanding how intelligent systems actually work. At the forefront of this mission stands Suraj Srinivas, a research scientist whose career reflects intellectual courage.
Suraj Srinivas: From Curiosity to Contribution – A Purpose-Driven Research Journey
Suraj Srinivas’ journey into artificial intelligence began not with hype, but with curiosity and discipline. His academic roots trace back to India, where he completed his Bachelor of Engineering in Electronics and Communication Engineering from PES Institute of Technology. This foundation in engineering systems soon evolved into a fascination with learning algorithms and data-driven intelligence.
He pursued a Master of Science in Machine Learning at the Indian Institute of Science (IISc), Bangalore, working closely with Prof. R. Venkatesh Babu at the Video Analytics Lab. At a time when deploying deep learning on edge devices was considered impractical, Suraj was already tackling neural network compression, publishing pioneering research between 2015 and 2017. Years later, this work would quietly power the AI capabilities embedded in everyday smartphones.
Challenging the Black Box: PhD Research at EPFL and Idiap
The defining turn in Suraj Srinivas’ career came during his PhD in Machine Learning at EPFL (École polytechnique fédérale de Lausanne), conducted in collaboration with the Idiap Research Institute under the mentorship of Prof. François Fleuret.
Here, Suraj confronted one of AI’s greatest paradoxes:
How can engineers build systems they do not fully understand?
His doctoral thesis, “Gradient-based Methods for Deep Model Interpretability,” did more than propose new tools—it rigorously questioned existing assumptions. By exposing the limitations of popular interpretability methods, Suraj laid the groundwork for more honest and scientifically grounded explanations of neural networks.
This work earned him the EPFL EDEE Outstanding Thesis Award, a recognition reserved for research that combines originality, rigor, and lasting impact.
Suraj Srinivas: Harvard and the Science of Explanation
Driven by a deeper fascination with interpretability, Suraj Srinivas pursued a postdoctoral research fellowship at Harvard University (2022–2024), working with Prof. Hima Lakkaraju, a global authority in responsible and interpretable machine learning.
At Harvard, his research matured into a broader scientific inquiry—what he calls the “science of deep learning.” Rather than relying on intuition or visual tricks, Suraj focused on systematic, experimental investigations that explain why deep models behave the way they do.
His work during this period strengthened his reputation as a researcher who values truth over trend, and clarity over convenience.
At Bosch AI: Interpretability Meets Real-World Impact
In November 2024, Suraj Srinivas joined Bosch Research (BCAI) in Sunnyvale, USA, where his expertise now directly influences real-world systems. As a Suraj Srinivas AI Research Scientist, his current work spans:
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Developing interpretability tools for vision-language models
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Understanding, debugging, and editing complex neural representations
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Solving computer vision challenges for autonomous driving
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Bridging theoretical rigor with industrial deployment
With over 10 years of experience across the full ML pipeline, from data curation and training to model compression and deployment, Suraj brings rare end-to-end insight to applied AI research.
Research That Shapes the Field
Suraj Srinivas is widely recognized for producing research that is both deeply theoretical and practically useful, with publications at elite conferences such as NeurIPS, ICML, and ICLR.
Some of his most influential contributions include:
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Splice
Demonstrates that CLIP representations can be expressed as approximate sparse linear combinations of concept vectors, enabling powerful interpretability tools. -
Full-Gradient Representation (NeurIPS 2019)
Proves that ReLU neural network outputs can be exactly decomposed into layer-wise gradients, redefining saliency methods. -
Explaining Perceptually Aligned Gradients
Offers an experimental theory explaining why noise-robust models produce more human-aligned explanations. -
Data-Free Parameter Pruning
Advances neural network efficiency without requiring access to original training data. -
Forgetting Data Contamination in LLMs
A nuanced study showing that data contamination may not always undermine real-world model performance.