Academic and Research Experience

PhD Research — University of Belgrade

Current research is conducted as a PhD candidate at the School of Electrical Engineering, University of Belgrade, focusing on semantic and instance-level segmentation of brain tumors from MRI data.

The research combines state-of-the-art deep learning approaches with rigorous experimentation, careful data preparation, and systematic evaluation in medically relevant contexts.

Applied AI & Computer Vision Research

Academic and research work builds on more than a decade of experience in applied artificial intelligence and computer vision, with a particular focus on medical image analysis.

Research Engagement — Mihajlo Pupin Institute

In addition to doctoral research, this experience includes multi-year research engagement at the Mihajlo Pupin Institute, contributing to national and international projects in areas such as:

Big Data
Semantic Web
real-time systems
recommender systems

This work resulted in authored and co-authored scientific publications, as well as the development of applied research systems bridging theoretical research with data-driven system implementation.

Projects

SOFIA – Software Framework for Intelligent and Adaptive Management of Complex Facilities

-Big Data

-Semantic Web

-Real-Time Systems

Research Gate

Linked Data

SlideWiki – Large-scale pilots for collaborative OpenCourseWare authoring, multiplatform delivery and learning analytics

-Machine/Deep Learning

-EDU Materials Recommender System

-Student failure prediction & prevention

Pupin - Slide Wiki

SlideWiki EU

Publications

Citizen-Centric Linked Data Apps for Emergency Response System

Valentina Janev

Marko M. Dabović

Sanja Vraneš

Nova Science Publishers 2017

One of the most critical aspects of emergency management is the notification system concerning how to get updated and accurate information from the very first stages of the event and how to notify affected people. The citizens, as the simplest level of participation in an emergency scenario, can act as consumers information consumers (e.g. about alarms, affected areas, instructions for handling critical situation), but also as evidence producers (e.g. via phone, social network). With the aim to develop a deeper understanding of potential benefits from using Linked Data technologies in emergency scenarios, in this Chapter we present our current work on building citizen applications for improved emergency response. The paper introduces first the potential areas of applicability of Linked Data in innovative emergency management solutions and points out to early prototypes in Europe based on crowdsourcing, social networks, mobile devices and Open Data. Next, the software requirements and information flow are analyzed for a data ecosystem that facilitates the engagement of citizens leveraging novel approaches such as participatory sensing approach (knowledge from the wisdom of crowds) and cloud-enabled solutions. In order to demonstrate the feasibility of the approach, a prototype application Traffaccs has been developed using state-of-the-art open-source tools and frameworks: the Massachusetts Institute of Technology Panya framework for implementing the mobile client and Dydra graph database for storing the notification messages.

Citizen-centric mobile application for traffic accidents reporting

Marko M. Dabović

Valentina Janev

ICISTS 2017

Nowadays modern mobile phones have a lot of sensors that can be helpful in various situations, for

instance, to facilitate the collaboration between citizens and public services. This paper explores the use of smart phones for producing rich information about traffic accidents in real time. The goal is to examine the Linked Data approach for building a mobile application to be used by citizens for collecting evidence about an exceptional situation (a traffic accident) and for notifying emergency organizations (e.g. the nearest police station) and the wider community. The benefits expected from using the Linked Data in this scenario are related to interlinking data from different sources, semantically enriching the data and sharing the data in a standardized format. This paper presents preliminary results from the design and implementation of a mobile application for Android-based devices.

Deep Convolutional Neural Networks – Concepts and Current Research

Marko M. Dabović

Igor Tartalja

ETRAN 2017

This paper presents the core concepts and architectures of convolutional neural networks, whose application is widespread across various domains. The importance of this class of neural networks is significant, which is why research in this area is of particular interest. The paper systematizes key results from existing research, proposes a consistent terminology within the field, and highlights open challenges and directions for future research.

PhD Research - Brain Tumor Segmentation

Brain tumor semantic segmentation from MRI data
Applied deep learning, computer vision, and medical image analysis
10+ years in artificial intelligence and computer vision research

I am PhD candidate at the School of Electrical Engineering, University of Belgrade, working in applied deep learning and computer vision for medical image analysis, with a primary focus on brain tumor semantic segmentation from MRI data.

The research builds on more than a decade of experience in artificial intelligence and computer vision, and is grounded in state-of-the-art methodologies and benchmarks used in contemporary medical imaging research. This includes familiarity with modern segmentation architectures, evaluation protocols, and competitive research environments such as BraTS-style challenges and other large-scale medical imaging benchmarks.

The work is informed by continuous engagement with current scientific literature, public datasets, and experimental research platforms, enabling rigorous comparison with leading approaches in the field.

The research approach combines methodological rigor with practical experimentation, covering data preparation, model design, training, and evaluation in clinically relevant settings, supported by experience with cloud-based and GPU-accelerated training environments.

Honors & Awards

Scholarship for PhD students

Apr 2016 - Apr 2020

Ministry of Education, Science and Technological Development of the Republic of Serbia

In the period of four years I was receiving a PhD scholarship from the Ministry of Education, Science and Technological Development of the Republic of Serbia. As a PhD student I was involved in the projects of Institute Mihajlo Pupin at Belgrade.

_________

Award for the best students

Sep 2012

Foundation Professor Radovan Ćirić

Licenses

Peer review Seminar

Belgrade, 2019

Participation Certificate, The Center for the Promotion of Science (CPN)

_________

Elsevier eBooks Seminar

Belgrade, 2018

Participation Certificate, National Library of Serbia

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