ValleyML is an organization dedicated to bringing much quality, job-ready, and affordable, cutting-edge professional training programs for graduating students and working professionals.

Our Motto

Thriving career for everyone!

Our Team


Tirthajyoti Sarkar

Tirthajyoti works as a Data Science Engineering Manager at Adapdix. Corp., where he builds machine learning models and solves customer problems using edge analytics in the domain of semiconductors and manufacturing. Previously, he worked for more than a decade at Fairchild and ON Semiconductor developing world’s most advanced power semiconductor technologies and products as well as applying artificial intelligence techniques for design automation and control problems. Tirthajyoti is a Sr. Member of IEEE and serves as a co-chair of the semiconductor committee of PSMA - world’s largest consortium of power electronics companies. He has been associated with ValleyML for the past couple of years teaching boot camps in ML and data science. He has published multiple books on data science and authored independent Python libraries on the topics of synthetic data generation, regression, web scraping, etc. Tirthajyoti holds a Ph.D. in Electrical Engineering from the Univ. of Illinois and a B.Tech in Instrumentation Engineering from IIT Kharagpur, India.


Siddha Ganju

Siddha Ganju, an AI researcher who Forbes featured in their 30 under 30 list, is a Self-Driving Architect at Nvidia. As an AI Advisor to NASA FDL, she helped build an automated meteor detection pipeline for the CAMS project at NASA, which ended up discovering a comet. Previously at Deep Vision, she developed deep learning models for resource constraint edge devices. Her work ranges from Visual Question Answering to Generative Adversarial Networks to gathering insights from CERN's petabyte-scale data and has been published at top-tier conferences including CVPR and NeurIPS. She has served as a featured jury member in several international tech competitions including CES. As an advocate for diversity and inclusion in technology, she speaks at schools and colleges to motivate and grow a new generation of technologies from all backgrounds. She is also the author of O'Reilly's Practical Deep Learning for Cloud, Mobile and Edge.

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Bhairav Mehta

Bhairav Mehta is Principal Data Science Manager at Microsoft Azure Edge, Platform and Security team. He manages team of Data Scientists and build security tools and services for Azure that relates to AI Security, Hyper-personalization and Operating system security. Apart from his job at Microsoft he is professor at Sofia University and other institutes in the San Francisco-Bay area. Bhairav also has founded a startup Erudition Inc. that provides education in Emerging technologies to Silicon Valley community for last 6 years. Before Microsoft Bhairav worked at Apple (10 years), Qualcomm (3 years) and other companies. Bhairav has many graduate degrees in MS Computer Science (Georgia Tech), MS Statistics and MBA (Cornell), MS Systems Engineering (Rochester).

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Avinash Pujala

Avinash Pujala received his Ph.D. in Neuroscience from Brown University in partnership with the National Institutes of Health. During his postdoctoral years he began using Machine Learning methods (computer vision) for tracking and identifying animals in high speed images. He developed innovative ways to quantify animal behavior and found a novel a way to represent complex motor patterns in simplified low-dimensional space as a way of relating this activity to neuronal activation patterns in the brain. He became increasingly interested in the applications and potential of neural networks and following a fellowship in Artificial Intelligence began working as a Data Scientist at XOKind Inc, a startup based in San Diego. At XOKind Avinash primarily uses Natural Language Processing (NLP) to extract relevant information from unstructured text data, build knowledge graphs and augment the intelligent features in a planning app focused in the travel and leisure space.


Geetika Sharma

Geetika is a Senior Data Scientist in CFIA’s AI Lab, responsible for researching ways to automate processes at the CFIA and then deploying end-to-end Machine Learning Pipelines in Azure. She has been nominated for the President’s National Award 2021 for her contributions geared at Innovation and Science at the Agency. She has been a Teaching Assistant at the School of Computer Science in Carleton University for courses in Artificial Intelligence, Neural Network, and Software Engineering. She also recently was a Teaching Assistant at the AI4Good Lab ’21, where she led group sessions and delivered a lecture for the entire cohort.

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Subhashree Radhakrishnan

Subhashree Radhakrishnan is a Deep Learning Engineer in Intelligent Video Analytics Team at NVIDIA. She is one of the core developers of Transfer Learning Toolkit 3.0 released from NVIDIA that provides a code-free framework and pre-trained models for users to train, prune and deploy models on the edge. She also works on developing DL models targeting Smart City usecases such as pedestrian detection, tracking and action recognition. She is a co-inventor for several patents in the domain of Deep learning and Scene understanding. Outside of NVIDIA, Subhashree provides mentorship on AI projects as part of programs such as MIT generator, WIT. Prior to NVIDIA, Subhashree earned her Masters from Virginia Tech specializing in Deep Learning and computer vision with a focus on Video Understanding.

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Priyanka Makhijani

Priyanka Makhijani is a part of the Engineering and Analytics team at DeepNorth. Her work largely involves gathering quality metrics to develop reports, data visualizations, statistical inference on large data and perform data cleaning and validation to get actionable insights and provide ongoing visibility to the dashboards. Apart from that she is also involved in NLP and conversational AI projects. She was a Deep Learning Engineer (Intern) at Siemens Healthineers USA working on medical images. Priyanka is a Computer Science graduate (MS) from Illinois Institute of Technology Chicago.


Aanshul Sadaria

Aanshul works as a software engineer at Google. He works in cloudsearch team (Search for Enterprise) providing enterprise solutions to clients for search. He completed his graduation from IIIT Hyderabad in 2020 and had primarily researched there in Data Science and Analytics and had major contributions in Machine Learning as well. He did his internship at Adobe where he working in Deep Learning for Computer Vision and the project got approved for patent and is under filing. His primary passion is data structures and algorithms and he was also a teaching assistant at his alma mater for DSA, ML and AI courses. During his research span at IIIT, he worked on Elections 2019 and its intersection with social media, the project got multiple publications, poster presentations and caught attraction by multiple news houses as well. He has also worked on traffic violations analysis using e-challan receipts and the project got published and is also to undergo a poster presentation.


Pooja Voladoddi

Pooja Voladoddi was born in Bijapur, India, in 1991. She received the B.Tech degree in Telecommunication Engineering from Ramaiah Institute of Technology, Bangalore, India in 2013. She received her M.S degree in Electrical Engineering from University of Southern California in 2014, where she specialized in vision-based robotics and machine learning. She has held research intern roles at USC Institute for Creative Technologies, Robotic Embedded Systems Laboratory, USC and Nvidia, working on projects related to robot navigation and 3D scanning hardware. Since 2015, she has been working in the AI/ML software industry in Silicon Valley. As part of Knowles Intelligent Audio, she helped ship Keyword-Spotting systems for consumer mobile phones. As part of Cisco, she worked on voice wake-up system and data pipeline for enterprise meeting devices, and in addition, shipped call control and notification features for Cisco 730 series smart headsets. Currently, as a part of McD Tech Labs, she's working at the intersection of speech recognition, natural language understanding and software to help bring intelligent experiences to hundreds of McDonald's drive-thrus in the USA. Pooja is also a part of the Global Shapers community, working with the Palo Alto chapter to help the local community prepare for ramifications of the fourth industrial revolution.

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Swetha Mandava

Swetha Mandava is a Machine Learning engineer at where she is working on building a trusted search engine that summarizes the web for you. Prior to this, as a Senior Deep Learning Engineer at NVIDIA, she worked on optimizing deep learning algorithms and developing language models to run on massive GPU clusters. She received her M.S in Electrical and Computer Engineering focusing on Machine learning from Carnegie Mellon University.


Akshit Arora

Akshit is a deep learning solutions architect at NVIDIA. He works on deploying machine learning and deep learning algorithms at scale. He also helps accelerate deep learning pipelines using NVIDIA GPUs at various consumer internet companies. Previously at CU Boulder, he developed deep learning models to understand how students learn on an online learning platform. His work also includes predicting weather using LSTMs and automatically completing a 3-D painting in virtual reality using sketch-RNN. He is interested in creative applications of machine learning/deep learning and the wide set of possibilities it presents.

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Devika Jadhav

Devika Jadhav is a Machine Learning engineer at Ivani. She works on developing, testing and evaluating experimental algorithms to detect human occupancy in a space. Most of her time is spent analyzing data to uncover hidden patterns in them. As a master’s student at Carnegie Mellon University (CMU), she worked on deep learning models for facial recognition. As an undergraduate student at Manipal University, she worked on fault detection in electrical circuits using machine learning.


Saurabh Jain

Saurabh is a Machine Learning engineer at JPMorgan Chase. He primarily works on building intelligent information extraction systems that work on legal documents at scale. He is a Master’s in Computer Science from the State University of New York at Buffalo. At grad school, he focused on robotics and self driving vehicles, working on DARPA grants for swarm robotics as well as using deep learning and simulation for training self driving vehicles in different scenarios.