Omdena Chapter Page: India

Omdena India Chapter - Omdena Chapters

Welcome to the India Chapters!

There are 6 active chapters in India:

1. Tamil Nadu, India

2. Hyderabad, India

3. Delhi NCR, India

4. Kutch, Gujarat India

5. Ahmedabad, India

6. New Delhi, India

Apply here to be a chapter lead for other cities and/or universities in India

Upcoming Projects

Ranchi, India Chapter

Project Starts: 30.07.2022

All Data Science Skills Welcome!

Ranchi, India Chapter – Coastal Erosion based Assessment of Sagar Island using Satellite Data and AI/
Ranchi, India Chapter / Lead – Sairam Villers



Shoreline change is perceived as a major problem mainly in the delta region all over the world. Shoreline or coastline is generally defined as the line of contact between land and water body which is dynamic in nature (Pajak and Leatherman 2002). Due to its dynamic nature it is very difficult how much area is accredited or eroded by the time (Fenster et al. 2001). Natural processes and human activities are also responsible for coastline changes (Sesli et al. 2008).Continuous erosion along the shoreline is responsible for losses of economic and natural resources which affects the livelihood of the local community.

The Problem

Over 19.5% of (administrative units of the island), with 15.33% of the population at Sagar Island at high risk (0.70–0.80)

According to an estimation, by 2050, almost  most people  living in Sagar Island will be adversely impacted by rising sea levels

 Sundarbans is a very good instance of the manifestations of climate change, wherein underdevelopment and over-reliance on climate-dependent subsistence have rendered the whole ecosystem vulnerable


Project Goals

  1. Time Series based Erosion Models on Sagar Island using Remote Sensing and Machine Learning
  2. Prediction of Shorelines in Near Future


Project Timeline

Week 1 Week 2 Week 3 Week 4
  • Data collection
  • Generation of Waterbody Indices
– Time Series Based Coastal Erosion Assessment
  • Prediction of Shorelines in near future
  • Visualization and Depliyment


Hyderabad, India Chapter

Project starts: 30.07.2022

All Data Science Skills Welcome!

Hyderabad, India Chapter – AI system for habit building and creating personalized learning nudges, aimed to improve soft skills



 We live in the knowledge economy and skills are your card. Some estimates state that 50% of the jobs will be affected due to automation by 2030. Non-routine cognitive skills, often described as “soft skills”, have been increasing in importance since the first industrial revolution. Soft skills have the potential to provide the only long-term competitive advantage in the job market of the future, and open up equal opportunities to culturally-diverse remote employees. At the same time, soft skills are the hardest to learn due to their abstract nature and context-dependency. The traditional coaching platforms are unscalable by design. Online learning marketplaces and MOOCs provide content that does not stick. Self-improvement apps add up to daily distractions and fail to maintain engagement.

Calibri is a tech-enabled skill development tool for companies, aimed to help engineers get skills, like self-confidence, self-awareness, empathy, communication, influencing, taking ownership, with the use of technology. Calibri is looking for help developing one of its core modules focused on building habits through learning nudges. People learn best in practice. Therefore, there is a need for a smart system that could contextualize small content bits and deliver them in the right moments through the right channel. Employees nowadays are bombarded with hundreds of notifications and the challenge is to win their attention. Everyone has a different personality, learning style and work structure – and thus, needs to be promoted in a personalized way to result in the desired outcome. Remote work rise and novel decentralized data protection techniques provide us with the abundance of behavior data points that can be leveraged.


The Problem

The project aims to create an AI system that can automatically detect daily triggers for habit building for a remote tech employee, and send personalized learning nudges, aimed to improve soft skills


Project Goals

  1. The project aims to create an AI system that can automatically detect daily triggers for habit building for a remote tech employee, and send personalized learning nudges, aimed to improve soft skills


Learning Outcomes


  1. Data Pre-processing
  2. NLP based EDA
  3. Developing and Deploying Dashboards
  4. Time Series Forecasting
  5. Sentiment Analysis
  6. Computer vision and model Building


  1. Project Timeline

Week 1 Week 2 Week 3 Week 4


Ongoing Projects

Benguluru, India Local Chapter – Improve sorting and segregation of waste using computer vision
Benguluru, India Chapter / Lead – Mohammad Yahiya



Development of image recognition techniques to improve the sorting and segregation process of solid waste management.


Solid Waste Management is a universal issue, and at the same time, it is the ‘need of the hour’ project. One of the major contributors to municipal waste is plastic waste and its generation and consumption have increased drastically over the past few years even without developing a strategy to manage the waste generated.  UNEP reports suggest that so far, only 9% of all plastic waste produced after the 1950s was recycled, and the rest ended up either in landfills or in our environment [1]. The current recycling rate, improper management of the generated waste, and its accumulation in the environment pose a massive threat to the marine and land habitat. Studies indicate that even the remote areas of ocean and land ecosystems are affected by the scourge of plastic trash, chemicals and other pollutants. One such example is the Great Pacific Garbage Patch, a marine debris collection spot in the Pacific Ocean where several thousands of tonnes of ocean plastic are estimated to be floating on the surface [2]. The concern is not only about plastic waste: it is about all the trash generated: metallic, e-waste, organic, textiles etc.  We need to adopt different strategies and recovery plans to manage these waste materials in order to reduce the impact they caused on our ecosystem.  


The Problem

The biggest challenge in recycling/re-using waste is sorting and segregating different types of waste since segregation of waste aids in targeted recycling or even decomposition. As an example, segregating a dry metal can from a metal can containing organic matter eases recycling. The necessary action for proper segregation of the waste on a large scale is to identify various materials first. Once identified, neuromorphic tools could be used to sort things based on the identified parameters. However, while there exist several methods to identify different materials such as visual sensors, olfactory sensors as well as spectroscopic tools, there are very few or no attempts at using artificial intelligence to specifically identify materials from the waste, which could then be applied to ease the segregation process. We, therefore, propose to use visual image recognition to first identify objects, in their full form or by parts in order to be used later for segregation. For example, we envisage the identification of different materials such as plastics, metal and paper in a used milk carton, which could lead to proper recycling of plastics, paper and metal.



Project Goals

In this project, participants will be guided to perform the following steps: 

  1. Data Collection through web scraping and creation of image library 
  2. Image Preprocessing for Computer vision 
  3. Annotating Images to reflect the correct waste category 
  4. Computer Vision techniques to identify and classify different waste materials 
  5. Deploying Dashboard and Visualization to make the ML model available to the public 


Project Timeline

Week 1 Week 2 Week 3 Week 4
  •  Find relevant data sources on correct waste segregation in France 

    – Image preprocessing & Annotations (as required)


  • – Exploratory Data Analysis (EDA) 

    Image Annotations  

    – Debug and process videos/images for the training of ML models to detect correct waste categories. 

    – Image Classification model based on the waste category 

  • Start building Streamlit WebApp (with Tableau plots, if needed) 

Finish Integrating WebApp 

-Deploy the App in Cloud Application Platform


Chennai, India Chapter – Electricity Power Outage Analysis in Tamilnadu, India

by Meenakshi Ramaswamy



India is currently undergoing the worst electricity shortage in more than six years. The science behind these ongoing power cuts is simple — increased demand and decreased supply. 


The rising mercury could lead to serious health complications, deaths, water shortage and more. Also May, June, July months are really important for the students attempting various entrance and competitive exams. Nowadays due to online education, students are affected badly by these power cuts and voltage drop issues.


Tamil Nadu is not excluded from this issue. Frequent power cuts across the state including in Chennai are affecting residents already wilting under rising temperature.




The Problem


All of a sudden there is increasing power cuts and voltage drop issues in many residential areas not only in Tamil Nādu, but in entire India. So it is better to understand what causes the increase in consumption or what causes drop in power generation. Also, we would address the load balancing issues or low-voltage problems which affects the appliances. So we will try to address the demand Vs consumption promotion and visualize the same. This analysis will help policy makers to plan ahead.

Project Goals


    1. Collect and pre-process the historical power generation data for the last few years.


    1. Prepare the EDA to understand the co-relation of data. An interactive Plot /Map displaying the relation between supply Vs demand .


    1. An interactive map to visualize the production capacity the state depends on like Coal, Thermal, Atomic, Wind, Green (Solar) etc


    1. Understand or plot the consumption of different sectors like households, educational institutions, Government private organizations , Hospitals, Restaurants / Cafe, Hyper /Supermarket, Malls, Cinema houses, Airport/Railway/Bus stations, Agriculture Farms


    1. Supervised learning model to predict the next year power requirement Vs supply


    1. Measures/steps could be supportive to policymakers to face the challenges in futur



Learning Outcomes


  1. Data Pre-processing / EDA methods


  1. Visualization methods with Python and other libraries seaborn, plotly


  1. Use satellite images to get more inputs, along with the text data. Use tweets.


  1. Supervised learning models and Unsupervised learning models can be used.


  1. Interactive Graphs and Dashboard using Stream lit or Heroku to display district wise outcome


  1. Project Timeline

Week 1 Week 2 Week 3 Week 4
  • Data Collection
  • Pre-processing/EDA
  • Apply various models to understand the relation
Deployment and finetuning


Kutch, India Chapter – Combating Heatwaves with AI and Satellite Images
Kutch, India Chapter / Lead – Chancy Shah


Heatwaves are a distinct meteorological phenomenon, in which temperatures rise above the 90th percentile of the average temperature in a given area and stay there for a prolonged period, usually a week or so. Since they’re defined based on a local average, what counts as a heatwave changes depending on the location. Heatwaves have happened in the past, but climate change is making heatwaves more prolonged, more extreme, and more frequent. The rapid growth of the population and urbanization can increase the frequency and duration of heatwaves due to climate change, raise a series of issues about the increased health risks of sensitive populations to extreme heat, and the effective means of mitigating impacts of heatwaves.


The Problem

Climate change has already hit India hard, causing huge economic and social losses in recent years. The number of heatwave days is persistently increasing in India in the past few decades, according to an ongoing India Meteorological Department study. Severe heatwave conditions are prevailing over half of India with temperatures soaring to upward of 45° Celsius across large swathes of the country. It indicates that global warming has started affecting ambient temperatures significantly. Besides factors like land-use change, increased concretization, and local weather conditions, climate change is a primary cause of the rise of extreme heat events in the country. Unbearably hot weather has become a public health issue, with outdoor workers forced to change work hours and many regions rushing to put in place heat action plans. Heatwaves affect energy consumption, elevate greenhouse gas emissions, and impair water quality by increasing the temperature of water runoff. 


The risk due to heatwave disasters is one of the greatest threats to humans, which will rise further with the increase in temperature as projected to 3–6 °C at the end of the century (IPCC, 2014). Considering its vast implications, the heatwave is one of the major variables cited in the Sustainable Development Goals (SDG) under goal 13 Climate Action, which aims to limit global warming to 1.5 °C and related threats due to heat extremes. 


India will likely face irreversible impacts of climate change, with increasing heat waves, droughts, and erratic rainfall events in the coming years if no mitigation measures are put in place. One of the targets of SDG 13 is to strengthen resilience and improve the mitigation strategies due to climate-related hazards, including heatwave. Being located in the tropical and subtropical region, India is among the world’s most hazardous prone countries to the heatwave. So mitigation measures should be taken urgently to combat heatwaves.


Project Goals

  1. Understanding the causes and impact of Heat WavesDevelop Practical Solutions to combat heatwaves using AI and Satellite images at Home and City Levels


    The Learning Outcomes


    • Satellite Images
    • Geographic Information System (GIS)
    • Machine Learning Algorithms
    • Data Analysis
    • Data Visualization


    Project Timeline

Week 1 Week 2 Week 3 Week 4
  • Causes and Impact of Heat Waves
  • Identity which satellites and sensors can be used for assessing Heat Waves
  • Collection of Satellite Images
  • Secondary Data Collection
  • Explore AI Algorithms
  • Data analysis and Visualization
  • Develop an algorithm for Home and City Level to mitigate heatwaves
– Report on Practical solution at  Home and City level 


Kolkata, India Chapter – Anomaly Detection on the Martian Surface (continuation)
by Rik Dutta


The second-smallest planet in the Solar System comprises a thin atmosphere and has surface features reminiscent both of the impact craters of the Moon and the valleys, deserts, and polar ice caps of Earth.

Recently looking for extraterrestrials in the form of technosignatures has gained new interest. These signatures are measurable properties that provide scientific evidence of past or present extraterrestrial technology. Scientists want to evaluate how far the search for technosignatures has come and what the most promising possibilities for the future are.


The Problem

In this 4-weeks project, the goal is to build a model to analyse and correctly classify all the surface anomalies as captured by multiple satellites.


Project Goals

  1. Collect and pre-process images from multiple satellites, cross-reference them and check which dataset is acceptable.


  1. Label the images with available tools.


  1. Build a model to classify images into categories of anomalies as labelled by the collaborators.


Learning Outcomes

1.Computer Vision

  1. Classification through Machine Learning
  2. Classification with Deep Learning

  1. Project Timeline

Week 1 Week 2 Week 3 Week 4
  • Data Collection
  • Data Labelling and modelling
  • Modelling
 – Testing and finishing up


New Delhi, India Chapter – Increasing Renewable Energy Access in India through AI
New Delhi, India Chapter / Chapter Lead – Shrey Arora


India has vast land reserves and is geographically favourable for solar power projects, but there is still scope to improve the electricity production that relies on coal.


The Problem

This initiative’s goal is to use satellite data in conjunction with other relevant dataset to identify sites that are most suitable for solar panel installation as a greener energy source through machine learning and coverage analysis.


The project results will be made open source. The aim being to help connect and encourage organisations to use AI tools to understand and plan in transition for green energy. We also hope to encourage citizen science by open sourcing the dataset and code.


Project Goals

  1. Finding relevent datasets and EDA,
  2. Map of suitable locations for solar power projects taking into account the climate, area, budget, terrain, and other factors,
  3. Predicting energy output of a proposed solar project,
  4. Open source code.


Completed Projects

Completed Project(s)

Leveraging AI to Analyse the Socio-Economic Impact of Covid19 in India



1. The economic impact of the COVID-19 pandemic in India has been largely disruptive. According to the Ministry of Statistics, India’s growth in the fourth quarter of the fiscal year of 2020 went down by 3.1%.

2. If we could map the shortage and unavailability of vaccines across various states in India vs the wastage of vaccines across the country, this could help in increasing the efficiency of the vaccination. The current total vaccinated Indians across the whole country(shown below), (by our World in Data)

3. A large proportion of the covid19 mortality rate in India is because of failing to early identify the presence of coronavirus and its symptoms. Identifying symptoms of Covid19 and mapping its severity level not only help diagnose the viral disease faster and efficiently but also help to reduce the mortality rate by getting medication much sooner.

The problem: 

In this 4-weeks project, the goal is to build a model to analyse and model the change in economic trend over the past couple of years in India due to the outbreak of covid and correlate it with various factors that contribute to the rise and fall of the economy in India. The NLP-based model can then be used to predict the change in future trends in the economy based on these factors. The sentiment of people is modeled from the historical data which could then be used to predict the real-time sentiment and the factors contributing to that sentiment when a potential third wave or a new outbreak in the society happens.

Other objectives include building computer vision models to early detect the presence of covid through X-ray images or CT scans and classify the severity of Covid-19. The project will be made open-source and the results obtained can help create awareness about the spread of Covid19 and its impact on the Indian economy. The final tracker and the dashboard made can be used as a tool to track vaccine availability across various states.

Project Goals: 

Economic Impact of Covid19 In India

1. Collect covid related data from the various up to date sources

2. Data Preprocessing and Exploratory Analysis

3. An interactive Plot /Map displaying current covid analytics of each state.

4. Forecast the relation between covid vs economic change in past time and estimate the effect of covid in the future through Time-series analysis.

5. Create a sentiment analysis model from historical data and inferred-time daily tweets on #covid19 and #coronavirus to predict the current sentiment.

Shortage and wastage of Covid19 vaccines 

1. Gather and preprocess the available public open-source data on the latest statistics on Covid19 vaccines.

2. NLP-based Tracker to track the vaccinated population.

3. Interactive Dashboard showing the stepwise-wise statistics of active cases, death cases are given the time range, age-group specific distribution.

4. Correlation matrix between total people vaccinated vs total vaccine produced.

5. Correlation matrix between coronavirus (Covid-19), mortality and morbidity rate, burden over radiologists.

6. A Knowledge Graph-based solution to visualise the statewide shortage and availability of COVID19 vaccine.

Early prediction of Covid19 Through Computer Vision

1. Predict the likelihood of covid positivity from chest CT Scan images and respiratory patterns. From demographic and clinical data (the patient’s age and sex, exposure history, symptoms, and laboratory tests) were put into a machine-learning model to classify COVID-19 positivity.

2. Using model predictions to track patient recovery from Multiple CT scans taken during the course of treatment can be used to analyze whether the patient is recovering or worsening.

3. A web application to visualize the severity of covid from chest X-ray images.

The Learning Outcomes: 

1. Data Pre-processing

2. NLP based EDA

3. Developing and Deploying Dashboards

4. Time Series Forecasting

5. Sentiment Analysis

6. Computer vision and model Building



Source Code:

Hyderabad, India Chapter – Text-based healthcare chatbot supporting admitted patients


As we could imagine the plight of a person (who is undergoing treatment) and related family members.

Initially people don’t get many doubts because they don’t know what’s going to happen. But as time progresses and treatment starts, the concerned person gets anxious, raising various doubts, in mind, regarding post treatment complications and actions required to get to the normal lifestyle. It’s very difficult to find the right person to answer these doubts/questions. For e.g. for diet one has to consult a dietician, for post effects consult a different doctor altogether.

This project is addressing the above issue by creating a chatbot which answers these issues at one place.


The problem: 


The objective of this project is first to collect data via the different existing techniques such as web-scraping etc. 

The final deliverable includes but is not limited to creating a chatbot which will not only answer the queries/questions etc., but will also let the patient(s) share their experience which helped him/her to overcome post treatment effects.

The experiences / articles can be read on a web-app which will be another deliverable of this project.


Project Goals: 

Following are (but not limited to) some goals for the projects that defines the problem statement: 


  • Collect the data from various sources (including hospitals, doctors etc)
  • Pre-processing on the collected data 
  • Building different chatbot (using Rasa framework), sentiment analysis, web-app to host articles and experiences shared.
  • Exploratory analysis on data collected using some visualization tools like Tableau, PowerBi etc.


The Tasks & Timeline:

Week 1

Week 2

Week 3

Week 4

Web scraping

Data collection


Data Preprocessing 

Chatbot modeling

Chatbot modeling

Creating FAQs 

Tableau/BI visualization tool 


Initialization of Web-App

Integrating the chatbot 

Deploying the deliverables.

Learning outcomes

  • Web-scraping, Analysis of Textual data, 
  • NLP including Named Entity Recognition NER 
  • Sentiment Analysis, Conversational AI (chatbot using RASA)
  • Visualization/BI Tools  
  • Building web-applications.


Ahmedabad, India Chapter - Anomaly detection on Martian's surface using Deep learning


1. The martian surface is very uneven and hence it contains a lot of anomalies 

2. The objective of this project is to detect the Anomalies on the martian (MARS) surface caused by non-terrestrial artifacts like derbies of MARS lander missions, rovers, etc.

3. Recently looking for extraterrestrials in the form of technosignatures has gained new interest. These signatures are measurable properties that provide scientific evidence of past or present extraterrestrial technology. Scientists want to evaluate how far the search for technosignatures has come and what the most promising possibilities for the future are.


The problem: 

In this 4-weeks project, the goal is to build a model to analyse and model different anomalies on the martian surface


Project Goals: 

– Researching about different kinds of models already present and what the collaborators can do to make it better 

– Understand different deep learning based models 

– Understand the use of CNN’s and pre-trained models


The Tasks & Timeline:

Week 1 Week 2 Week 3 Week 4

– Searching dataset 

-Collection of the dataset

-Image pre-processing

– Exploratory Data Analysis (EDA)

-classification model based on deep learning model 1

-Image Classification model based on different architectures

-Deploy web app using streamlit or django framework

Finish Integrating WebApp

-Deploy the App in Cloud Application Platforms/Heroku



Learning outcomes:

1. Data Pre-processing

2. Model building

3. Use of new models to produce a comparative analysis

Hyderabad, India Chapter - Ai for Road Safety


Each year, more than 1.2 million people die across the globe due to road crashes; there is a pressing need to understand the underlying cause of the problem. As road safety issues are complex; it involves multi-sectoral ranging from the public. The important factors are human errors, driver fatigue, poor traffic sense, mechanical fault of vehicle, speeding and overtaking violation of traffic rules, poor road conditions, traffic congestion, road encroachment. Lack of road safety measures and implementation of road safety laws are a great lack in the country.


The problem: 

To improve road safety measures, collecting and analyzing road accident data from news articles we can understand the major causes for this problem and take necessary precautions. To also overcome traffic congestion, collecting traffic-related data and using computer vision techniques to analyze traffic on a particular road for a period of time and classifying roads into different classes based on traffic, speed, and related factors and therefore rating the safety of the roads. 

Design an AI solution to detect driver’s activities like driver talking on the phone, eating while driving the vehicle, detect drowsiness, distraction, yawn, eye closure and alert him when potential drowsiness situation is detected, from driving style like sudden accelerations or decelerations, sudden braking, sharp turns, set of events like start, stop, speed and turns, Maximum and minimum rpm of the engine, etc., The project results will be made open source. The aim is to help ride-sharing service providers, regulatory bodies, policymakers, and people while educating aspiring data scientists in solving real-world problems.


Project Goals: 

– Analyzing road accidents data from public databases, newspapers, web pages, etc. 2. Build an AI-Based method based on Machine Learning algorithms & Deep Learning to detect traffic accidents in real-time with the use of traffic cameras with high degrees of precision.

– Build a Deep Learning-based solution for the driver monitoring system by studying a person’s posture and body movements, intelligent interior vehicle algorithms can draw conclusions about a person’s alertness, attention and focus.

– A web application to predict the probability of an accident from video and actions of the driver.


The Tasks & Timeline:

Week 1 Week 2 Week 3 Week 4

– NLP Text  Preprocessing

– Web scraping Image, Video and Text Related Data related to road accidents

– Image preprocessing & Annotations

– Exploratory Data Analysis(EDA)

– Image Annotations 

– Debug and process traffic videos for the training of ML models to detect accidents.

– Image Classification model based on driver actions

– Sentiment Analysis Model 

– Image Classification model based on symptoms

– Start building Streamlit WebApp

– Start building Streamlit WebApp 

– Finish Integrating WebApp 

– Deploy the App on Cloud Platforms


Learning outcomes

1. Data Collection through web scraping

2. Annotating Images

3. Dashboards and Visualization

4. Machine Learning

5. Deep Learning/Computer Vision

6. Image Preprocessing

7. Developing and Deploying Dashboards

Kutch, India Chapter

Water Quality Monitoring Dashboard for Kutch Region
The rapid development of urban areas causes many environmental problems.
One of the vital problems is water pollution. It has become a major global
problem as it can lead to many deaths and diseases among human beings.
Polluted water impacts all aspects of life, including mankind, wildlife, and nature.
Modern urban planning is confronted with major challenges such as accelerated
growth and land-use change, unplanned expansion, and water supply
management issues, in that water quality is an important factor as well as
concern to develop a healthy ecosystem.
According to UNICEF, One in nine people worldwide uses drinking water from
unimproved and unsafe sources.  Water quality is one of the main challenges
that societies are facing in the 21st century, threatening human health, limiting
food production, reducing ecosystem functions, and hindering economic growth.
The shortage of freshwater assets is expanding because of the disposal of large
quantities of insufficiently treated, or untreated, wastewater into rivers, lakes,
aquifers, and coastal waters.
Besides, recently rising pollutants like personal care products, pesticides,
pharmaceuticals, and household chemicals, and changing environment patterns
address another water quality challenge, with still unknown long-term impacts on
human wellbeing and biological systems. The 2030 Agenda and Sustainable
Development Goals (SDGs) bring water quality issues to the bleeding edge of
global activity by defining Goal 6 explicitly planning to “ensure availability and
sustainable management of water and sanitation for all” to react to the
squeezing difficulties presented by water quality issues.
The outcome of this project is to develop a centralized dashboard with different
water quality parameters for analysing, interpretation, and visualization in near
real-time using Remote Sensing and AI for better decision making. By developing
a dashboard, decision-makers can easily identify if any parameter is not within
the standard limits then immediate action can be taken for water treatment. This
dashboard will reinforce the ability to monitor water quality more effectively and

India Chapter Leads

Chancy Shah

Chancy has a Master of Science in Geoinformatics and a Bachelor of Technology in Civil Engineering. I have knowledge and skill in Geographic Information System (GIS), Satellite Images, Remote Sensing, Geospatial Data Processing, Machine Learning, GeoAI, Civil Engineering, and Urban Development. She is also the Global Winner of the NASA Space App Challenge 2020 for Best Use of Data. A Global Hackathon hosted by the National Aeronautics and Space Administration (NASA).

Toshita Sharma

Toshita Sharma is during her final year student at Nirma University. She loves to work with the community on technical projects based in AI. Has contributed to several projects in open source programs and now actively working on research. IEEE and Springer are some of the reputed journals to which she has have in. In addition she is an active GDSC lead at her college and a ML facilitator at google crowdsource.

Shrey Arora

I’m studying computer science and am really interested in data-driven projects that create real-world impact.
My interests include ML, creative coding, math and business.

Nikhil Shrestha

Nikhil Shrestha is an ML Engineer at Omdena and Data Science Mentor who lives in Hyderabad, India.
He is a former Maritime Officer and completed his Post Graduation Diploma in Data Science from Purdue University in March 2022.
He loves to play cricket, listen to music and enjoys learning about new technologies and want to use them to solve problems and make the world a better place.

Rik Dutta

Goal-oriented learner working towards making the world a better place with sustainable technology and Artificially Intelligent applications.

Mohammad Yahiya

Mohammad yahiya working as a Data Engineer and a freelance Machine Learning Engineer at Omdena. I also have experience working as a Beta Tester at Coursera for more than a year and supporting instructors in the AI Courses.With a broad skill set that gained from my studies, work experience, and interest in the area of data science. I continuously learn, upgrades my content through professional challenges and research in the areas of Data Architecture, Business Analytics, Machine Learning, Deep Learning and Artificial Intelligence

Sai Phani Krishna

Sai Phani Parsa holds a Master’s degree in Computer Science from the University of Nevada Las Vegas. He has 5+ years of IT industry experience and is currently working as a Data Analyst at Cerner Healthcare Solutions India Pvt Ltd. Sai Phani is very passionate about applying data science concepts on healthcare datasets.

Hardik Seju

Hardik comes from a Mechanical Engineering background and he has always had a keen interest in the tech industry as it is rapidly changing people’s lives. He is very passionate about Data Science, Machine Learning, and AI and he has worked on multiple Omdena challenges. He aims to give back to the community and nature by working on projects leading to SGD goals.

Sairam Kannan

Sairam has completed Master of Technolgy in Remote Sensing and Bachelor of Engineering in Geoinformatics. I have a wide variety of knowledge in
Geographic Information System(GIS), Time Series Modelling of Satellite Images, Urban Sprawling and Geospatial Data. My Pride is to monitor Desertification using Remote Sensing Data. I love to watch and play cricket, watch Indian Music, and make the world more sustainable in nature