resume

1. Research
Sep’18-Now Medical Imaging & Deep Learning Gevaert Lab, Stanford
- Worked on imaging bio-marker segmentation and 3-D Neural Au-
toencoder feature analysis using Keras models.
- Developed a model incorporating registered patch based approach
for hassle-free training in disease classification.(∼ 3% error)
- Designing a Multi-class model for neural images with molecular
characteristics for early diagnosing dementia and Alzheimer’s.
Apr-Jul’19 Meta-gradient Learning| Reinforcement Learning Stanford
-Designed Meta-gradient method for policy estimation on toy Markov
Reward Processes (MRP) and ATARI games
-Proposed and evaluated primal-dual meta-objective alleviating dou-
ble sampling; learning discount factors by 10% better than baseline
Work Experience and Internships
Jun-Sep’18 Summer Intern| GPU & DL NVIDIA Corporation| Santa Clara, CA
-Developed a Deep learning solution to optimize GPU testbench &
coverage( 20X impr.). Neural net model in PyTorch for prediction
-Trained feature segmentation algo improving control (Turing GPU)
2016-May’17 Research Assistant| CV Indian Institute of Science|CGRA Team
-Designed a reconfigurable Vector Processor for streaming kernels.
( 3X perf over scalar processors)
-Synthesized real time Face recognition Neural Net in C++, Python
Projects
Jun’18-Now Natural Language| Sentiment classification & NER Praxis Inc.
-Developed NLP models for VR responses sentiment and entity recog.
-Devised ensemble model (in PyTorch )(Acc. 95%) tailored to
increment empathetic response after VR experience.
Sep-Dec’18 Artificial Intelligence| Robotic Digit Mimicking Stanford
-Developed a CNN to estimate and learning actions at all pen states
-Devised algorithm (in Python ) to trace out simple digits (Acc. 98%)
Apr-Jun’18 Deep Learning| Neural Net Approaches to DNA Denoising Stanford
-CNN approach (U-Net architecture) predicting entire denoised DNA
sequence (0.05% error on reference sequences)
-RNN model predicting localized nucleotide substitution/deletion;
model in Keras, TF (2.8% error/seq.)
Apr-Jun’18 Conv Net| Artwork Classification and Style Transfer Stanford
-Designed high accuracy artistic media & emotion classification so-
lution ; model in TensorFlow & Transfer Learning(close to VggNet)
-Performed style transfer transforming media/emotion of images.
Sep-Dec’17 Machine Learning| Supervised autonomous driving Stanford
-Formulated end-to-end steer and throttle driving control from
recorded raw images, trained CNN ( TensorFlow ) with LSTM ends.
-Worked on improving "performance metrics" to increase max speed
without offshoot & image processing NVIDIA architecture.
Achievements
2016 CDNLive Best Paper Award
Functional safety analysis verification solution
2015 Runner up Paper APOGEE
Robust Iris segmentation hardware module
Electives/Online Courses
Creative Thinking|AI in Imaging|Computer Vision| Data Science| NLP
Abhishek
Roushan
Stanford, CA
aroushan@stanford.edu
650-300-9151
Interested in AI/ Deep learning
applications in data & imaging
Education
Stanford University
MS in Electrical Engineering |
Depth: Software & Hardware
Systems| Jun 2019 | GPA:3.74/4.00
BITS Pilani, India
B.E in Electronics & Instrumentation
Depth: Computer & Hardware
systems| Jun 2016| GPA: 9.45/10.00
Skills
Languages: Python, C/C++, OpenCV,
MATLAB, Java, Scala, Perl, LATEX
Deep Learning.: PyTorch, Tensorflow,
Keras, Google Cloud Platform, AWS
WebDev : HTML, JS
Other: MySQL, NLTK, Git, Data Mining,
MapReduce, SLAM
Miscellaneous
Coursework
Machine Learning/Deep Learning
Artificial Intelligence/ Reinf. Learning
CNNs/Generative Networks
Computer Systems
Computer Vision
Natural Language Processing (NLP)
Algorithmic Machine Learning
Data Mining
Virtual Reality
Links
Github: https://bit.ly/2C5Bzff
LinkedIn: https://bit.ly/2QCuua0
Quora: https://bit.ly/2IQuwZr
Strengths
Dilligent • Quick Learner • Proactive
Life Ideology
"Things won’t change if you don’t
change the way to look at them!"