Current Project Openings
(Updated on February 21, 2026)
DREAM Committee: Nazli Ikizler Cinbis
and Erkut Erdem
Memory-efficient and Real-time Continuous Sign Language
Translation
Name of the faculty mentor(s): Hacer Yalim
Keles (hacerkeles@gmail.com)
Project Description: The goal of this
project is to design and implement a memory-efficient and
real-time Continuous Sign Language Translation (CSLT) model
that operates under realistic computational constraints.
Current transformer-based CSLT models achieve strong
translation accuracy but require high GPU memory and offline
processing, which limits their deployment in real-world
scenarios such as edge devices or low-latency applications.
Students will develop a compressed and optimized CSLT
framework that integrates model pruning, knowledge
distillation, and lightweight temporal modeling. The project
will investigate how architectural simplifications—such as
structured attention head pruning, token compression, layer
reduction, and student–teacher distillation—affect
translation accuracy under strict memory and latency
budgets. The central research question is: Can a continuous
sign language translation model operate in real time with
significantly reduced memory consumption without sacrificing
translation quality? Students are expected to: 1. Reproduce
a strong baseline CSLT model, 2. Define clear memory and
latency constraints, 3. Implement pruning and distillation
strategies, and 4. Conduct systematic performance–efficiency
trade-off analysis
Number of students: 2-3 students
Requirements and Additional Info:
Required: Strong Python programming skills, solid
understanding of Data Structures and Algorithms, basic
knowledge of Machine Learning and Deep Learning Preferred:
Experience with PyTorch, familiarity with Transformers,
basic knowledge of model compression (pruning, quantization,
distillation) Motivation: Interest in efficient deep
learning, sign language technologies, and deploying AI
systems in real-world constrained environments.
Enhancing Prostate Region/Lesion Segmentation in CT/PET
Imaging via Super-Resolution Techniques: A Comparative
Study on Institutional and Benchmark Datasets
Name of the faculty mentor(s): Aydın Kaya
(aydinkaya83@gmail.com)
Project Description: Accurate segmentation
of prostate physical region and lesions in CT and PET
imaging plays a critical role in diagnosis, staging,
radiotherapy planning, and treatment response monitoring.
However, the inherently limited spatial resolution and noise
characteristics of PET, as well as partial volume effects in
CT/PET imaging, often degrade segmentation performance. This
project aims to systematically investigate whether
super-resolution (SR) techniques can improve downstream
prostate lesion segmentation accuracy in multimodal CT/PET
imaging. The study will leverage both a newly collected
institutional prostate CT/PET dataset and publicly available
benchmark datasets to ensure methodological robustness and
generalizability. Deep learning-based super-resolution
models will be applied and optimized to enhance image
quality prior to segmentation. The impact of SR
preprocessing will be quantitatively evaluated by comparing
segmentation performance (e.g., Dice similarity coefficient,
Hausdorff distance, etc) against baseline models trained on
native-resolution images. Multiple experimental
configurations will be explored, including: (i) applying SR
as a preprocessing step before segmentation, (ii) joint
SR-segmentation pipelines, and (iii) modality-specific
versus multimodal SR strategies. Statistical analyses will
be conducted to determine whether performance gains are
consistent across datasets and imaging modalities. By
systematically evaluating the interaction between image
resolution enhancement and segmentation performance, this
project seeks to provide evidence-based guidance on the
utility of super-resolution techniques in prostate CT/PET
imaging workflows. The findings are expected to contribute
to more reliable automated delineation systems and
ultimately support improved clinical decision-making in
prostate cancer management.
Number of students: 1 student
Requirements and Additional Info:
Machine learning, deep learning.
ContVAR: Protein Function Prediction with Contrastive
Representation Learning
Name of the faculty mentor(s): Tunca Doğan
(tuncadogan@gmail.com)
Project Description: This project tackles a
bioinformatics problem: explaining how genomic mutations
that produce single– amino-acid variants alter protein
function—an essential issue in biomedicine and
biotechnology. Most current predictors can flag variants as
pathogenic or broadly gain-/loss-of-function, but they
cannot specify exactly which biological roles are affected.
We propose ContVAR, an AI framework for fine- grained
functional interpretation of protein variants that
integrates self-supervised contrastive learning with
sequence-based representation learning. At its core, ContVAR
learns variant-sensitive embeddings from protein sequences
using a masked language modelling (MLM) objective, allowing
it to capture contextual constraints and functional signals
without requiring large datasets containing variant-specific
function labels. We leverage deep mutational scanning data
to support self-supervised training and to improve
discrimination between function-altering and neutral
variants. The resulting embeddings are then used in a
supervised module trained to map representations to Gene
Ontology functional profiles learned from natural proteins.
We will validate ContVAR on independent benchmarks and
resources, and demonstrate utility through targeted case
studies in biologically important settings such as the p53–
MDM2 pathway.
Number of students: 2 students
Requirements and Additional Info:
Machine/deep/representation learning, bioinformatics,
language models, transformers
A System-Level Simulation Framework for In-Network
Computing on High-Performance Networks
Name of the faculty mentor(s): Kayhan İmre
(ki@hacettepe.edu.tr)
Project Description: This project aims to
design and implement a comprehensive discrete event
simulation framework to analyze the performance of
In-Network Computing (INC) capabilities within
High-Performance Computing (HPC) environments. As data
movement becomes a primary bottleneck in exascale computing,
offloading computation to network switches (INC) is a
critical research area. The project focuses on three key
objectives:
1. Framework Development: Developing a
scalable simulation environment using the open-source ns-3
network simulator.
2. Topology Implementation: Modeling
next-generation HPC network topologies, specifically
Fat-Tree, Dragonfly, and Dragonfly+.
3. Algorithm
Validation: Simulating a novel INC-accelerated Parallel
Quicksort algorithm to benchmark performance gains.
Methodology & Resources: Students will not be starting from
zero. An in-house reference prototype, developed from
scratch (without simulation libraries), currently simulates
both the Conventional and INC-accelerated Parallel Quicksort
algorithms on a Fat-Tree topology.
• Phase 1: Analyze
the existing in-house prototype to understand the
INC-accelerated sorting logic.
• Phase 2: Port and
expand this logic into the ns-3 ecosystem, enabling standard
protocol support and more complex network models.
•
Phase 3: Implement Fat-Tree and/or Dragonfly/Dragonfly+
topologies and compare the ns-3 simulation results against
the in-house prototype for validation.
Number of students: 2 students
Requirements and Additional Info: 1) Strong
C++ skills (Essential for ns-3 development). 2) Computer
Networks concepts (Routing, Topologies, Packet Switching).
3) Familiarity with Linux environment and Git.
Evaluation of Structured and Unstructured DNN Pruning
Across Heterogeneous GPU Architectures
Name of the faculty mentor(s): Suleyman
Tosun (stosun@hacettepe.edu.tr)
Project Description: Traditional research
in Deep Neural Network (DNN) pruning typically treats
structured and unstructured methods as distinct paradigms.
While unstructured pruning offers higher theoretical
compression, it often introduces irregular sparsity that may
degrade performance on general-purpose architectures like
GPUs. Conversely, structured pruning aligns better with
hardware data layouts but may sacrifice model accuracy. This
project aims to conduct a comprehensive empirical
investigation into how these two pruning types interact with
diverse GPU architectures. By utilizing established
benchmark datasets, we will quantify the trade-offs between
model sparsity and real-world computational throughput
(latency, energy consumption, and memory bandwidth). Key
Objectives:
Architectural Analysis: Evaluating the "irregularity
penalty" of unstructured pruning across various GPU
generations to identify specific hardware constraints where
structured methods are more beneficial. Comparative
Benchmarking: Utilizing standard datasets to provide a
rigorous baseline of performance- to-accuracy ratios.
Number of students: 2 students
Requirements and Additional Info: None
AI-Driven Blockchain Prediction Markets for Collective
Intelligence and Forecasting Accuracy
Name of the faculty mentor(s): Adnan Özsoy,
Cemil Zalluhoğlu (adnan.ozsoy@hacettepe.edu.tr,
cemil@cs.hacettepe.edu.tr)
Project Description: This project proposes
a blockchain-based, AI-enhanced prediction market platform
where participants trade tokenized shares on the outcomes of
real-world events (e.g., elections, scientific
breakthroughs, or technology trends), while machine learning
models continuously analyze external data sources (news,
social media, economic indicators) to generate probabilistic
forecasts and detect market inefficiencies. Smart contracts
on a public or permissioned blockchain will ensure
transparent order matching, automated settlement, and
tamper-proof recording of trades and outcomes, while oracles
securely feed verified real-world results into the system.
The platform will integrate large language models for
semantic signal extraction, time-series models for trend
forecasting, and reinforcement learning agents to simulate
and evaluate trading strategies. A key research goal is to
study the interaction between decentralized market
incentives and AI-generated signals, measuring whether AI
guidance improves collective forecasting accuracy or
introduces bias or herding. The project will include a web
dashboard, data ingestion pipelines, on-chain analytics, and
an evaluation framework comparing market prices, AI
predictions, and ground truth outcomes over time. Student 1
will design and implement the blockchain smart contracts and
on-chain market logic, Student 2 will develop the AI models
for data ingestion, forecasting, and signal extraction,
Student 3 will build the web platform, dashboards, and
evaluation framework integrating both components.
Number of students: 3 students
Requirements and Additional Info: A strong
willingness to work and actively contribute to the project.
Generative Motion Priors for Robust Navigation in
Highly Constrained Environments: The BARN Challenge on
Duckiebots
Name of the faculty mentor(s): Ozgur Erkent
(ozgurerkent@hacettepe.edu.tr)
Project Description: The goal of this
project is to develop a state-of-the-art autonomous
navigation algorithm for mobile robots operating in dense,
cluttered, and unseen environments. Specifically, students
will tackle The BARN (Benchmark Autonomous Robot Navigation)
Challenge, an ICRA-affiliated competition that tests a
robot's ability to navigate from a start to a goal without
collisions in complex obstacle courses. The project will
focus on Diffusion-based Generative Models to generate
motion trajectories. Unlike classical planners (like DWA) or
standard Reinforcement Learning, diffusion models can learn
complex, multi-modal distributions of "successful" paths,
allowing the robot to "imagine" and execute safe
trajectories even in extremely tight spaces.
Number of students: 3 students
Requirements and Additional Info: Required:
Strong Python programming skills, Data Structures, and
Algorithms. Preferred: Knowledge of ROS (Robot Operating
System), familiarity with Deep Learning frameworks
(PyTorch/TensorFlow). Motivation: Interest in Generative AI,
Robotics, and hardware-software integration.
Scaling CROSSBAR System for Real World Discovery:
Knowledge-Graph Expansion, LLM Agents, and Grounded
Biomedical Question Answering
Name of the faculty mentor(s): Tunca Doğan
(tuncadogan@gmail.com)
Project Description: Biomedical discovery
remains constrained by fragmented repositories, inconsistent
metadata, and limited support for integrative,
mechanism-oriented reasoning. CROssBARv2
(https://crossbarv2.hubiodatalab.com/) addressed these
challenges by unifying heterogeneous biomedical sources into
a provenance-rich knowledge graph with standardized
ontologies, rich metadata, and vector embeddings, and by
providing interactive exploration and CROssBAR-LLM—a
natural-language interface that grounds LLM outputs in the
underlying graph to reduce hallucinations. This foundation
enables scalable access to integrated biomedical evidence
and supports downstream tasks such as hypothesis generation,
drug repurposing, and protein function prediction.
This follow-on project will scale CROssBAR along two axes:
(i) knowledge expansion and (ii) next- generation
interaction and reasoning. We will substantially enlarge the
graph by integrating additional biological resources and
modalities, introducing new node/edge types to represent
richer molecular, cellular, and functional relationships,
while preserving maintainability. In parallel, we will
enable more powerful human–system interaction through
agentic, grounded mechanistic QA that can browse both the
CROssBAR graph and the scientific literature, consolidate
evidence, and return traceable, citation- ready
explanations.
Concretely, the work will be organized as: WP1 data
onboarding and schema extension (new node/edge types,
harmonization, provenance); WP2 an MCP (Model Context
Protocol) server for CROssBAR to expose standardized,
tool-callable capabilities (graph queries, hybrid retrieval,
provenance/evidence export); WP3 graph-native GraphRAG and
literature-aware agents for grounded molecular-mechanism
answers; WP4 advanced knowledge-graph visualization and UX
for interpretable exploration; and WP5 systematic evaluation
(use cases, QA benchmarks, grounding/citation quality,
usability).
Number of students: 3 students
Requirements and Additional Info: Advanced
level programming (Python), Motivation to learn data
representation, agentic LLMs, knowledge graphs and NoSQL
databases.
Domain-Specific Modeling Infrastructure for an OSS
Quality Assessment Tool
Name of the faculty mentor(s): Prof. Dr.
Ayça Kolukısa, Dr. Tuğba Erdoğan, Dr. Nebi Yılmaz, Res.
Asst. Aslı Taşgetiren (atarhan@cs.hacettepe.edu.tr,
aslitasgetiren@cs.hacettepe.edu.tr)
Project Description: This project focuses
on designing and implementing the modeling infrastructure of
an open-source software (OSS) quality modelling evaluation
tool. The tool enables users to define quality models
through a domain-specific language (DSL) derived from a
formally defined meta-model. These definitions must be
parsed, validated, transformed into a Entity–Relationship
(E-R) representation, and persisted in the backend
system.
The student(s) will (1) design and implement a DSL parsing
mechanism that translates user-defined specifications into
an E-R representation, (2) develop validation mechanisms to
enforce structural constraints and consistency rules derived
from the meta-model, and (3) implement backend services that
support creation, storage, retrieval, and integrity control
of defined structures. The outcome will be a robust modeling
engine that ensures correctness, consistency, and
maintainability within the tool..
Number of students: 3 students
Requirements and Additional Info: Students
should have: Strong Java knowledge, familiarity with
database design and E-R modeling and basic knowledge of
parsing concepts or compiler fundamentals.
Automated Quality Evaluation Infrastructure for
Open-Source Software
Name of the faculty mentor(s): Prof. Dr.
Ayça Kolukısa, Dr. Tuğba Erdoğan, Dr. Nebi Yılmaz, Res.
Asst. Aslı Taşgetiren (atarhan@cs.hacettepe.edu.tr,
tugbagurgen@cs.hacettepe.edu.tr,
nebi.yilmaz@hacettepe.edu.tr,
aslitasgetiren@cs.hacettepe.edu.tr)
Project Description: Assessing the quality
of open-source software (OSS) requires the systematic
collection and processing of both repository-level and
code-level metrics. While many tools extract partial
information (e.g., repository statistics), integrating
static code analysis, repository mining, metric
normalization, and comparative reporting into a single
evaluation pipeline remains a practical challenge.
This
project focuses on designing and implementing the evaluation
infrastructure of an OSS quality assessment tool developed
using Java (Spring Boot), React, and PostgreSQL. The
student(s) will (1) investigate and benchmark suitable
static analysis tools (e.g., SonarQube, PMD, CK, Understand,
Radon), (2) design mechanisms to extract repository metrics
via the GitHub API or mining libraries (PyDriller), and (3)
implement backend services that collect, normalize, store,
and aggregate metrics within a PostgreSQL-backend system.
The outcome will be a modular evaluation layer that supports
automated metric extraction, score computation, and
comparative analysis across OSS projects (traditional
software and AI-based repositories).
Students will
conduct a small-scale comparative study of static analysis
tools as part of the project and document integration
trade-offs (accuracy, performance, extensibility,
licensing). Some related works:
https://github.com/nirhasabnis/gitrank [1] &
https://gomstory.github.io/oss-aqm/#/compare [2]
[1]
Hasabnis, Niranjan. "GitRank: a framework to rank GitHub
repositories." Proceedings of the 19th International
Conference on Mining Software Repositories. 2022.
[2]
Madaehoh, Arkom, and Twittie Senivongse. "OSS-AQM: An
open-source software quality model for automated quality
measurement." 2022 International Conference on Data and
Software Engineering (ICoDSE). IEEE, 2022.
Number of students: 3 students
Requirements and Additional Info: Students
should have: Solid Java knowledge, a basic understanding of
Git and open-source development workflows, and familiarity
with REST APIs and relational databases Preferred (not
mandatory): Experience with static analysis tools, basic
knowledge of software metrics (e.g., complexity, cohesion,
coupling), and interest in empirical software engineering.
Efficient Multimodal Retrieval for Video Question
Answering with Adaptive Modality Selection
Name of the faculty mentor(s): Gulden
Olgun
Project Description: This project aims to
develop an AI-based retrieval system for educational videos
that can answer user queries by adaptively selecting the
most informative modality. Instead of always relying solely
on transcript data or only on visual information, the system
first analyzes the query and determines whether the answer
should be retrieved primarily from the transcript, from
visual keyframes, or from a combination of both. The main
motivation is that many instructional and lecture videos
contain important information that is not fully captured in
speech alone, while processing all visual content with large
multimodal models is computationally expensive. Therefore,
the project will focus on building an efficient retrieval
pipeline that selectively uses transcript-based retrieval
and keyframe-based multimodal reasoning to improve answer
accuracy while controlling token usage, computational cost,
and latency.
Number of students: 2 students
Requirements and Additional Info: None