Aadesh Neupane
Research Assistant at Brigham Young University
PhD candidate
I am a Computer Science Ph.D. student graduating in August 2022. My current research focuses on designing safe, reliable, and goal-oriented robots and AI agents integrating Linear Temporal Logic and Behaviors Trees. We can create better autonomous agents by validating and verifying their behaviors using our verifiable goal framework research. I am interested in autonomous vehicles, evolutionary robotics, swarm robots, privacy-focused machine learning, active-listening chatbots, and reinforcement learning.
Brigham Young University
https://www.linkedin.com/company/brigham-young-university
* Developed a theoretical framework to specify and evaluate goals using Linear Temporal Logic.
* Developed distributed Grammatical Evolution algorithm and framework which is currently used for evolving collective behavior in swarm robotics using Behaviors Trees.
* Developed a framework for cooperative transport using multiple robots using occlusion-based proofs and robotic vision algorithms.
Provo, Utah Area
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Research Assistant
Pollen Sense, LLC
https://www.linkedin.com/company/pollen-sense-llc
* Remodeled pollen classification computer vision system to use RCNN based segmentation algorithm for fine-grain pollen classification from microscopic images.
* Investigated pollen forecasting system using the historical pollen data, weather forecast, and user reported data.
* Launched a UI platform to label, train and evaluate thousands of microscopic pollen images using unsupervised learning algorithms.
Provo, Utah, United States
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Research Internship
Verisk Information Technologies (http://www.veriskit.com/)
https://www.linkedin.com/company/cotiviti-nepal-
* Developed and maintained an internal ETL tool, Auto Import Platform (AIP) using Bash, Python and Java to increase efficiency in data processing work-flow
* Production deployment of AIP on high-end Oracle RAC server
* Use of AIP enabled 200+ users to free 20 hr/week of data pre-processing task
Hattisar, Kathmandu
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Software Engineer
Inspiring Lab
https://www.linkedin.com/company/inspiring-lab
* Led the project for Nepali Character dataset using semi-supervised approach.  
* Led the customer care chat-bot project for Nepali telecommunication companies using the combination of AIML, NLTK and Neural Nets.
* Led the development of Real Time Vehicle Detection and Tracking (RTVTR) which is in beta testing phase for WWF, Shopping Complex and Traffic management
Kathmandu, Nepal
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Machine Learning Engineer
Verisk Information Technologies
https://www.linkedin.com/company/verscend
* Development and Maintenance of AIP (Automatic Import Platform)
* Linux/Unix Trainer
Hattisar, Kathmandu
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Associate Software Engineer
Verisk Information Technologies
* Development of stand alone linux tools for file processing. 
* Development and maintenance of AIP(Automatic Import Platform) system
* Unix/Linux application development
Hattisar, Kathmandu, Nepal
Intern - Unix/Linux developer
WebSearch Professional
https://www.linkedin.com/company/webserach-professional
* Web application development
Lainchowr, Kathmandu
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Intern
Doctor of Philosophy - PhD
Computer Science
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Brigham Young University
https://www.linkedin.com/school/brigham-young-university/
Master of Science - MS
Computer Science
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Brigham Young University
https://www.linkedin.com/school/brigham-young-university/
Bachelor of Engineering (B.E.)
Computer Engineering
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Kathmandu Vishwavidalaya
https://www.linkedin.com/school/kathmandu-university/
Intermediate
Science
Xavier International College
Kathmandu University Computer Club
Executive Member
Kathmandu University Open Source Community (KUOSC)
Co-ordinator
Animals such as bees, ants, birds, fish, and others are able to perform complex coordinated tasks like foraging, nest-selection, flocking and escaping predators efficiently without centralized control or coordination. Conventionally, mimicking these behaviors with robots requires researchers to study actual behaviors, derive mathematical models, and implement these models as algorithms. We propose a distributed algorithm, Grammatical Evolution algorithm for Evolution of Swarm bEhaviors (GEESE), which uses genetic methods to generate collective behaviors for robot swarms. GEESE uses grammatical evolution to evolve a primitive set of human-provided rules into productive individual behaviors. The GEESE algorithm is evaluated in two different ways. First, GEESE is compared to state-of-the-art genetic algorithms on the canonical Santa Fe Trail problem. Results show that GEESE outperforms the state-of-the-art by (a) providing better solution quality given sufficient population size while (b) utilizing fewer evolutionary steps. Second, GEESE outperforms both a hand-coded and a Grammatical Evolution-generated solution on a collective swarm foraging task.
GEESE: grammatical evolution algorithm for evolution of swarm behaviors
Proceedings of the Genetic and Evolutionary Computation Conference. ACM, 2018
https://dl.acm.org/citation.cfm?id=3205619
Human-Swarm Interaction as Shared Control: Achieving Flexible Fault-Tolerant Systems
International Conference on Engineering Psychology and Cognitive Ergonomics
https://link.springer.com/chapter/10.1007/978-3-319-58472-0_21
Speaker on “AGI: Doom or Boon for Homo Sapiens” Seminar. Talked about the possibilities of Artificial General intelligence (AGI) taking over earth.
CSIT Nepal
Speaker on “AGI: Doom or Boon for Homo Sapiens” Seminar
Automatic Import Platform (AIP) was awarded Best Project of Year.
Verisk Information Technologies
Best Project of the Year
Rookie of the Year
Verisk Information Technologies
Rookie of the Year
AI
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Cryptography
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MCB80.1x Fundamentals of Neuroscience, Part 1: The Electrical Properties of the Neuron
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MCB80.2x, Fundamentals of Neuroscience, Part II: Neurons and Networks
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Machine Learning
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NLP
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Probabilistic Graphical Model
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R
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Weka
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Advance Neural Networks
CS 678
Algorithmic Decision Process
CS 712R
Computational Creativity
CS 673
Computer Vision
CS 650
Deep Neural Nets
CS 501R
Multi-Agent Systems
CS 670
Robotic Vision
EC EN 631
Robust Control
CS 513
It is crucial not only to understand the specialized sub-system of an autonomous vehicle like lane detection, vSLAM, and traffic light detection to build a safe and reliable self-driving car, but understanding the subsystem interaction with each other is equally important. So for our CS704R project, first, we independently implement those specialized subsystems. Second, we combine those modules to build a minimal self-driving car agent in ROS. Finally, we test our agent in a simulated highway environment. Our minimal agent was successful in driving on the highway track.
Minimal Self-Driving Car agent
https://github.com/aadeshnpn/CarND-Capstone
Cryptography and data science research grew exponential with the internet boom. Legacy encryption techniques force users to make a trade-off between usability, convenience, and security. Encryption makes valuable data inaccessible, as it needs to be decrypted each time to perform any operation. Billions of dollars could be saved, and millions of people could benefit from cryptography methods that don’t compromise between usability, convenience, and security. Homomorphic encryption is one such paradigm that allows running arbitrary operations on encrypted data. It enables us to run any sophisticated machine learning algorithm without access to the underlying raw data. Thus, homomorphic learning provides the ability to gain insights from sensitive data that has been neglected due to various governmental and organization privacy rules.In this paper, we trace back the ideas of homomorphic learning formally posed by Ronald L. Rivest and Len Alderman as “Can we compute upon encrypted data?” in their 1978 paper. Then we gradually follow the ideas sprouting in the brilliant minds of Shafi Goldwasser, Kristin Lauter, Dan Bonch, Tomas Sander, Donald Beaver, and Craig Gentry to address that vital question. It took more than 30 years of collective effort to finally find the answer “yes” to that important question.
Homomorphic learning: A privacy-focused approach to machine learning
https://arxiv.org/abs/2009.04587
Current computer vision algorithms using Neural Nets with softmax function can only classify objects in between the labels used for training. If we provide the algorithm with the entirely different image still it will try to classify the image for the labels it knows. It would be amazing if these algorithms could distinguish between the images which are similar to what it has seen before and which are completely different. This problem is known as Open Set recognition problem. Addressing this problem would be tremendous benefits for computer vision as machines will be able to classify the objects more accurately and more robust to fooling images as well as adversarial images. We implement OSDN algorithm using Weibull fitting in the penultimate layers of Neural Nets to address the issue with Open Set recognition problem.
Open Set Deep Network (OSDN)
https://github.com/aadeshnpn/OSDN
Social animals cooperatively transport object which is many times bigger than themselves effectively. Mimicking those behaviors on real robots will have diverse applications in engineering, health care and search and rescue. In this paper, we define different categories of cooperative transport problems and discuss different tools and techniques to tackle them. We then show that occlusion-based cooperative transport techniques are effective when the object is convex and there are enough agents to overcome frictional force. Results show that even with only two robots, the occlusion-based technique is able to transport objects 60\% of the time.
Cooperative Transport in Cozmo robots
http://aadeshnpn.com/wp-content/uploads/2018/09/cooperative-transport-mobile.pdf
This motivation of this project was to compile chapters so that it could be useful for upcoming students in control theory. Different topics in each chapter were divided among the students in CS 712R and as a final project all the chapters were combined to form a book on Control Theory. Specifically I worked on the Dissipative systems chapter of the book along with Dynamical Structure Functions, Lyapunov Functions and stability criteria. I this this book would be useful for students who want to study Control theory with a broader perspective.
Control Theory Notes
http://aadeshnpn.com/wp-content/uploads/2018/09/712-control-textbook.pdf
Reinforcement learning (RL) has recently shown great promise in solving difficult learning task, and achieved above human level performance in promising tasks like Atari games, Go and poker. RL has two broad classes of algorithms: Actor and Critic, with their own merits and demerits. This paper explores the benefits of combining them into Actor-Critic algorithms and demonstrates their ability in solving classic control problems. We use temporal difference learning class of algorithms as critic and policy gradient as actor. Our implementation of Actor-Critic algorithms was able to solve the classic Cartpole problem in 96 time steps. As these algorithms are model-free, it can be use to solve almost any type of RL problems with very few tweaks.
Actor-Critic Reinforcement Learning for Classic Control problem
http://aadeshnpn.com/wp-content/uploads/2018/09/actor-critic-reinforcement.pdf
Various deep learning architectures such as Deep Neural Network (DNN), Convolution Neural Network (CNN), Long short term memory (LSTM) and Recurrent Neural Nets (RNN) have been applied to fields such as natural language processing, speech recognition and computer vision where they have been shown to produce state-of-the-art results. But these advance models have not yet been applied in the context of Nepali language. Trivial problems like automatic recognition of a small piece of a handwritten document is still a challenging task in Nepali. So, we want to apply state-of-the-art deep learning models to recognize handwritten Nepali characters. This might convince researchers to explore these deep models which in turn will help to expand research on Nepali language. After careful analysis of different hyper parameters, we choose four layers deep network with 1024 hidden units in each layer. We achieved 90.18\% accuracy in a test set against 72.3\% accuracy obtained using simple Multi Layer Perceptron (MLP). This result shows a significant improvement above all other methods reported. This trained model can be used for any general purpose application which involves recognizing Nepali characters effectively.
Nepali Character Recognition using Deep Belief Nets
http://aadeshnpn.com/wp-content/uploads/2018/09/nepali-character-recognition.pdf
Creating catchy slogans is a demanding and clearly creative job for ad agencies. The process of slogan creation by humans involves finding key concepts of the company and its products, and developing a memorable short phrase to describe the key concept. We attempt to follow the same sequence, but with an evolutionary algorithm. A user inputs a paragraph describing describing the company or product to be promoted. The system randomly samples initial slogans from a corpus of existing slogans. The initial slogans are then iteratively mutated and improved using an evolutionary algorithm. Mutation randomly replaces words in an individual with words from the input paragraphs. Internal evaluation measures a combination of grammatical correctness, and semantic similarity to the input paragraphs. Subjective analysis of output slogans leads to the conclusion that the algorithm certainly outputs valuable slogans. External evaluation found that the slogans were somewhat successful in conveying a message, because humans were generally able to select the correct promoted item given a slogan.
Slogan Generator
https://arxiv.org/pdf/1809.09563.pdf
One eLibrary per District
http://helpnepal.net/projects
Social Services
KUYRC
I was class co-ordinator of Kathmandu Universiry Red Cross Society. I was actively involved in Blood donation programs, marathons, and public awareness events.
Class Co-ordinator
Science and Technology
HeNN (Help Nepal Network)
* Teaching basic linux administration to public school teachers
* Configuring LTSP servers for eLibrary project
* Community School Visits
* Technology related awareness training to students
Technical Volunteer
Coursera Course Certificates
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Self-Driving Cars Specialization
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Coursera Course Certificates
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Reinforcement Learning Specialization
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Coursera Course Certificates
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Deep Learning Specialization
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Coursera
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Control of Mobile Robots
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Coursera
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Game Theory
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edX Honor Code Certificate for The Fundamentals of Neuroscience Part 3: The Brain
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Coursera Course Certificates
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Data Science  Specialization
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Introduction to Philosophy
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Machine Learning Specialization
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edX
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MCB80.2x, Fundamentals of Neuroscience, Part II: Neurons and Networks
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edX
drive.google.com
8bebc883180d459c82dda52692770791
Linux
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University of Waikato
drive.google.com
More Data Mining With Weka
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Coursera Verified Certificates
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Machine Learning
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Develop4U.co - hire the best App Development company at Develop4U.co(Android,iOS, Web, Mobile,Voice)
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Python Developers Community (moderated)
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Evolving Computers - AI, Chaos, Evolutionary & Generic Algorithms
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Algorithms
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Ubuntu Users Official Group
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The Linux Foundation
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Natural Language Processing
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Computer Vision and Pattern Recognition
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Computer Vision
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Linux Admins
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