Aaditya (Adi) Prakash
Manager, Machine Learning Research at PathAI
Machine Learning Research at PathAI Inc. and Adjunct Faculty at BayPath University.
I am a Machine Learning (Deep Learning) researcher with interests in applications of computer vision in healthcare. I have a Ph.D. in Computer Science from Brandeis University with a specialization in applications of deep learning in vision and language and a Biomedical Engineering degree. I currently work at PathAI as a Research Lead. In the past, I have worked at Microsoft Research, Qualcomm Research, and Philips Research. More at https://iamaaditya.github.io/about
PathAI
https://www.linkedin.com/company/pathai
Manage and guide a team of engineers to explore novel applications of deep learning in digital pathology.
https://media-exp1.licdn.com/dms/image/C560BAQGeznS6rFv64A/company-logo_100_100/0/1646415278593?e=1655942400&v=beta&t=6b3p_9cyhONXLPL__tsnQRVH_Qe_IktqA03WpguHa9M
Manager, Machine Learning Research
PathAI
https://www.linkedin.com/company/pathai
Explore novel methods in Machine Learning (architectures, loss functions, etc) for various tasks in digital pathology. Solve problems in Multiple Instance Learning, Semantic Segmentation, cell classification and other associated tasks.
https://media-exp1.licdn.com/dms/image/C560BAQGeznS6rFv64A/company-logo_100_100/0/1646415278593?e=1655942400&v=beta&t=6b3p_9cyhONXLPL__tsnQRVH_Qe_IktqA03WpguHa9M
Senior Machine Learning Scientist and Research Lead
PathAI
https://www.linkedin.com/company/pathai
Design and develop deep learning-based models for region and cell-based prediction of various end-points.
https://media-exp1.licdn.com/dms/image/C560BAQGeznS6rFv64A/company-logo_100_100/0/1646415278593?e=1655942400&v=beta&t=6b3p_9cyhONXLPL__tsnQRVH_Qe_IktqA03WpguHa9M
Machine Learning Scientist
Philips
https://www.linkedin.com/company/philips
Research in Deep learning techniques in NLP, especially paraphrasing, text simplifciation and question answering.
Cambridge, Massachusetts
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Research Scientist
Philips
https://www.linkedin.com/company/philips
Research application focussed in applying deep learning to problems of NLP. Designing new techniques for memory networks.
Cambridge, MA
https://media-exp1.licdn.com/dms/image/C510BAQG4O5wCG6zf2Q/company-logo_100_100/0/1519855869410?e=1655942400&v=beta&t=B6OEsS22r5gouNId3ad_vo5VudkpeNvii1uRxOXR9uM
Research Intern
Microsoft
https://www.linkedin.com/company/microsoft
Efficient training for convolutional neural networks.
Redmond, WA
https://media-exp1.licdn.com/dms/image/C560BAQE88xCsONDULQ/company-logo_100_100/0/1618231291419?e=1655942400&v=beta&t=lzKVkVuf-McwnYzrrz9ib7uQFSgowMUHhH_KDAbw3y0
Research Intern
Qualcomm
https://www.linkedin.com/company/qualcomm
– Explored model parallelism for convolutional neural networks
– Architecture learning for reduced model complexity
Greater San Diego Area
https://media-exp1.licdn.com/dms/image/C560BAQHHmRgEadFrdA/company-logo_100_100/0/1611250856145?e=1655942400&v=beta&t=OlYNWFjd-lqOEJ82mAVDViNdhY9vT1MpPZalKn2mWA4
Research Intern
Infosys
https://www.linkedin.com/company/infosys
I worked on very large datasets and applied various distributed algorithms and machine learning techniques to derive insights.
https://media-exp1.licdn.com/dms/image/C4E0BAQHS0k_nLjbnKA/company-logo_100_100/0/1625111766048?e=1655942400&v=beta&t=_b8eB1amUsPIWGQQrld7_RXII7y7ZwTsYOC0NUptzBQ
Senior Systems Engineer
Doctor of Philosophy (PhD)
Thesis:
Robust and Efficient Techniques in Deep Learning

PDF: https://github.com/iamaaditya/research-papers-slides-posters/blob/master/thesis/thesis_adi.pdf

Video of the defense: https://youtu.be/wOdSNSmqU1s?t=115
Deep Learning
https://media-exp1.licdn.com/dms/image/C4E0BAQFCXJxHmEvr6w/company-logo_100_100/0/1573679502766?e=1655942400&v=beta&t=6r3PrR_4QeScn7yAZOq5O5TrBtEPMJM-yFfMf-dNcpI
Brandeis University
https://www.linkedin.com/school/brandeis-university/
Master of Arts (M.A.)
Machine Learning, Statistical NLP, Computational Biology, Computational Neuroscience, Computational Semantics, Advanced Data Structures, Distributed Systems.
Computer Science
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Brandeis University
https://www.linkedin.com/school/brandeis-university/
BS
Applying Medicine and healthcare to engineering concepts and products. This is a multidisciplinary STEM field that blends biology and engineering.
Biomedical/Medical Engineering
https://media-exp1.licdn.com/dms/image/C4D0BAQEpxjZeqPfFtw/company-logo_100_100/0/1519880937252?e=1655942400&v=beta&t=EJTyxJb-1P2e5vIFnKhJVs2AAlbm3aVU573CorgOu1E
Bharath University
https://www.linkedin.com/school/bharath/
A well-trained Convolutional Neural Network can easily be pruned without significant loss of performance. This is because of unnecessary overlap in the features captured by the network's filters. Innovations in network architecture such as skip/dense connections and Inception units have mitigated this problem to some extent, but these improvements come with increased computation and memory requirements at run-time. We attempt to address this problem from another angle - not by changing the network structure but by altering the training method. We show that by temporarily pruning and then restoring a subset of the model's filters, and repeating this process cyclically, overlap in the learned features is reduced, producing improved generalization. We show that the existing model-pruning criteria are not optimal for selecting filters to prune in this context and introduce inter-filter orthogonality as the ranking criteria to determine under-expressive filters. Our method is applicable both to vanilla convolutional networks and more complex modern architectures, and improves the performance across a variety of tasks, especially when applied to smaller networks.
RePr: Improved Training of Convolutional Filters
CVPR
https://arxiv.org/abs/1811.07275
CNNs are poised to become integral parts of many critical systems. Despite their robustness to natural variations, image pixel values can be manipulated, via small, carefully crafted, imperceptible perturbations, to cause a model to misclassify images. We present an algorithm to process an image so that classification accuracy is significantly preserved in the presence of such adversarial manipulations. Image classifiers tend to be robust to natural noise, and adversarial attacks tend to be agnostic to object location. These observations motivate our strategy, which leverages model robustness to defend against adversarial perturbations by forcing the image to match natural image statistics. Our algorithm locally corrupts the image by redistributing pixel values via a process we term pixel deflection. A subsequent wavelet-based denoising operation softens this corruption, as well as some of the adversarial changes. We demonstrate experimentally that the combination of these techniques enables the effective recovery of the true class, against a variety of robust attacks. Our results compare favorably with current state-of-the-art defenses, without requiring retraining or modifying the CNN.
Deflecting Adversarial Attacks with Pixel Deflection
CVPR
https://arxiv.org/abs/1801.08926
As deep neural networks (DNNs) have been integrated into critical systems, several methods to attack these systems have been developed. These adversarial attacks make imperceptible modifications to an image that fool DNN classifiers. We present an adaptive JPEG encoder which defends against many of these attacks. Experimentally, we show that our method produces images with high visual quality while greatly reducing the potency of state-of-the-art attacks. Our algorithm requires only a modest increase in encoding time, produces a compressed image which can be decompressed by an off-the-shelf JPEG decoder, and classified by an unmodified classifier.
Protecting JPEG Images Against Adversarial Attacks
IEEE DCC
https://arxiv.org/abs/1803.00940
In this paper, we examine the different measures of Fault Tolerance in a Distributed Simulated Annealing process. Optimization by Simulated Annealing on a distributed system is prone to various sources of failure. We analyse simulated annealing algorithm, its architecture in distributed platform and potential sources of failures. We examine the behaviour of tolerant distributed system for optimization task. We present possible methods to overcome the failures and achieve fault tolerance for the distributed simulated annealing process. We also examine the implementation of Simulated Annealing in MapReduce system and possible ways to prevent failures in reaching the global optima. This paper will be beneficial to those who are interested in implementing a large scale distributed simulated annealing optimization problem of industrial or academic interest. We recommend hybrid tolerance technique to optimize the trade-off between efficiency and availability.
Measures of Fault Tolerance in Distributed Simulated Annealing
Proceedings of "International Conference on Perspective of Computer Confluence with Sciences"
http://arxiv.org/abs/1212.3295
Novel technique to improve memory representation in memory networks. This allows to scale the memory networks beyond toy tasks. We were able to achieve the best results compared to various other memory networks for clinical inference on a MIMIC III dataset.
Condensed Memory Networks for Clinical Diagnostic Inferencing
AAAI
https://arxiv.org/pdf/1612.01848v1.pdf
Visual Question Answering using highway networks. We modify the highway networks to allow multimodal learning. We show that this achieves (as of May 2016) state-of-the-art on VQA Challenge.
Highway Networks for Visual Question Answering
CVPR (VQA Workshop)
http://gpgpu.cs-i.brandeis.edu/highway.pdf
Paraphrase generation using various sequence to sequence neural algorithms. We proposed a novel technique of stacking LSTM units and show that this method outperforms various other sequence to sequence technique for paraphrase generation. We experimented with MSCOCO, PPDB and WikiAnswers dataset.
Neural Paraphrase Generation with Stacked Residual LSTM Networks
COLING
https://arxiv.org/pdf/1610.03098v3.pdf
. This
model reconstructs SOM to show strength between variables as the threads of a cobweb
and illuminate inter-scenario analysis. While Radar Graphs are very crude representation
of spider web, this model uses more lively and realistic cobweb representation to take into
account the difference in strength and length of threads. This model allows for visualization
of highly unstructured dataset with large number of dimensions, common in Bigdata
sources.
Reconstructing Self Organizing Maps as Spider Graphs for better visual interpretation of large unstructured datasets
Infosys Lab Briefings
https://arxiv.org/ftp/arxiv/papers/1301/1301.0289.pdf
Incorporating semantic knowledge into JPEG compression by using CNN. We developed a novel method which captures all semantic regions in a given image and a system to combine image such that final outcome is a JPEG where objects of interest are compressed with more bits and hence better quality.
Semantic Perceptual Image Compression using Deep Convolution Networks
Data Compession Conference
https://arxiv.org/pdf/1612.08712v1.pdf
Qualcomm
Roberto Padovani Scholarship
Brandeis Univesity
Outstanding Teaching Fellow
For being a single person team in top five position. Most teams had several members.
VQA Workshop
Honorable Spotlight Award Visual Question Answering Challenge
Top ranked student in the University
Bharath University
University Gold Medalist
Algorithms
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Computational Neuroscience
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Computational Semantics
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Distributed Systems
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Information Retrieval
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Statistical approaches to NLP
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Biosignal Processing
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Biostatistics
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Computer Modelling in Biomedical
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Medical Physics
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Numerical Methods
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Biology
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Chemistry
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Maths
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Physics
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A simple Demo of Visual Question answering which uses pretrained models (see models/CNN and models/VQA) to answer a given question about the given image.
Visual Question Answering in Demo
https://github.com/iamaaditya/VQA_Demo
Final project for computational semantics course, aiming to detect logical fallacies in natural language. It can detect three kinds of logical fallacies, and in the future, we plan to extend it to parse more sophisticated sentence structures, and detect more fallacies.
Fallacy Hunter
https://github.com/amsa/fallacy-hunter
While unstructured data is abundant, free and hidden with information the tools of analyzing the same are still nascent and cost of converting them to structured form is very high. Machine  learning is used to classify unstructured data but comes with issues of speed and space constraints. Self-Organizing maps are fastest machine learning algorithm but their visualization powers are limited. We have presented a naturally intuitive method to visualize SOM outputs which facilitates multi-variable analysis and is also highly scalable.
Visualization of Unstructured Bigdata
http://aaditya.info/research
Liked by Aaditya (Adi) Prakash
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Plan to attend Atomwise scientist Aryan Pedawi's #ACSSpring2022 presentation on work with Atoms Hossam Ashtawy and Brandon Andersson, "Uncertainty…
Liked by Aaditya (Adi) Prakash
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Day 10 of Ken Jee's #66daysofdata How can you use #machinelearning and #artificialintelligence to save lives with medical imaging? When I learned…
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We are excited to announce our partnership with Ultivue under which we will collaborate on the co-development of unique spatial AI-powered imaging…
Scientific Python
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https://www.linkedin.com/groups/2326082
Algorithms (O)
https://static-exp1.licdn.com/sc/h/4h1ldzvvebtszyx75ve7dst1v
https://www.linkedin.com/groups/1920463
Brandeis Sciences
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https://www.linkedin.com/groups/1353447
Distributed Systems Developers
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https://www.linkedin.com/groups/88759
Monte Carlo Simulation and Risk Modelling
https://static-exp1.licdn.com/sc/h/4h1ldzvvebtszyx75ve7dst1v
https://www.linkedin.com/groups/1518367
Distributed Computing: Apache Spark Hadoop TensorFlow Kafka Streaming AWS NoSQL K8s Terraform DevOps
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https://www.linkedin.com/groups/2390941
Alumni Stanford On-Line Classes
https://static-exp1.licdn.com/sc/h/4h1ldzvvebtszyx75ve7dst1v
https://www.linkedin.com/groups/4221292
Applied Mathematics
https://media-exp1.licdn.com/dms/image/C5607AQE-QjtWtVXOJQ/group-logo_image-shrink_200x200/0/1631421144313?e=1647691200&v=beta&t=l0rDlO2iefxHaYsBFXrkN6iqiqDVPKHt7yOHUrWarLk
https://www.linkedin.com/groups/52380
Brandeis Network of Technologists
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https://www.linkedin.com/groups/3645
Quantitative Analysts
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https://www.linkedin.com/groups/100063
RDataMining: R and Data Mining
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