Itilekha podder (Iti)

I am Iti, a Data Scientist and AI researcher with 7 years of experience in developing AI-driven frameworks and end-to-end implementable solutions for industrial and healthcare applications. My expertise lies in leveraging artificial intelligence and machine learning to optimize processes, enhance product quality, and deliver innovative, measurable results. I specialize in designing advanced AI frameworks and analytics models in semiconductor manufacturing to improve efficiency and performance. I am equally passionate about healthcare, focusing on creating AI solutions that enhance medical technologies and significantly improve patient care. My research bridges the gap between theoretical AI advancements and real-world industry applications, driving impactful change. With a proven track record of publishing research papers and delivering end-to-end solutions, I excel at tackling complex challenges and translating cutting-edge innovations into practical, actionable results.

Alongside my technical expertise, I also have a passion for business development. With strong communication and writing abilities, I effectively convey complex concepts, facilitating collaboration and project success.

Work experinece

Data Scientist at Robert Bosch Kft., Budapest, Hungary

(12/2019- Present)

I led the development of data-driven solutions for MEMS sensor manufacturing automation, overseeing a project that reduced production time by 28% and improved efficiency. I also worked on other research projects on early fault prediction and root cause analysis caused by thermal drift in gyroscopes. I developed an unsupervised and semi-supervised wafer map clustering solution to enhance quality control processes.

My role also involved active participation in the Design Review Based on Failure Mode (DRBFM) process, effective stakeholder communication and numerous sprint-related activities. I authored several invention reports and recently submitted a patent for a novel optimization technique in sensor manufacturing.

Researcher in Machine Learning for Signal Processing at Spinal Cord Injury & Artificial Intelligence Lab, ETH Zurich, Switzerland

(02/2023- PrEsent)

My work was to develop a robust multimodal sensor fusion algorithm for blood pressure estimation in Spinal Cord Injury (SCI) individuals. My proposed framework involved data collection, feature extraction, algorithm development (CNN, TL), validation and optimization. Ultimately, my work aimed to enhance patient blood pressure monitoring and clinical decision-making for SCI individuals.

Teaching Assistant, Computer Science at Eötvös Loránd University (ELTE), Budapest, Hungary (09/2021- Present)

I have prepared and taught theoretical and practical courses in software technology and business development, supervised multiple data science-related master theses, and designed and taught practical classes in imperative programming. Additionally, I have mentored bachelor's and master's students, providing guidance and support throughout their academic journeys.

Researcher at Ericsson, Hungary research and development centre (02/2019- 08/2019)

This research work encompassed a comparative analysis of detecting objects in real-time using various object detection models (SSD mobile nets, faster rcnn resnet etc.) and diverse environmental setups (LAN, WIFI , 3G connection, GPU, CPU )to assess the network's computational capacity regarding the accuracy, detection frame, and latency.

Education

Double Degree Doctoral Student in data science at Eötvös Loránd University (ELTE), Budapest, Hungary (Minor in innovation and Business Development) (09/2020- Present)

In collaboration with Eötvös Loránd University, Budapest, Hungary, Robert Bosch Kft., Budapest, Hungary and the European Institute of Innovation and Technology (EIT) Digital Doctoral School, my research work aims to develop an efficient method for AI-based solution implementation for industrial optimization especially in semiconductor manufacturing. I received the EIT Doctoral Scholarship (2020-Present). I applied design science research and process management to develop a standardized data mining process model in Data Science, facilitating digital transformation initiatives. This approach enabled the creation of a robust framework for data-driven solutions, optimizing workflows and enhancing the efficiency of data mining operations.

Double degree Master of Science in computer science (Minor in innovation & entrepreneurship): (08/2017- 01/2020) GPA: 4.64

In collaboration with Delft University of Technology (TU Delft), Delft, Netherlands, Budapest University of Technology and Economics (BME), Budapest, Hungary and the European Institute of Innovation and Technology (EIT) Digital Master School.

Double MSc. degree in digital media technology focusing on Autonomous Vehicles and Innovation and Entrepreneurship knowledge. Attended summer schools in Big Data.