CURRICULUM VITAE

• Since November 2018: Doctoral Candidate at LIS
• 2018: M.Sc., Electrical and Computer Engineering, Technische Universität München
  Master Thesis: "Hierarchical Control Structure for Autonomic MPSoCs"
• 05/2018 - 09/2018: Master Thesis at UC Irvine, CA, USA
• 2016: B.Sc. Electrical and Computer Engineering, Technische Universität München
  Bachelor Thesis: "Design, Simulation and Optimization of a Variable Optical Attenuator Driver"
• Intern / Working Student at:
  • DR. JOHANNES HEIDENHAIN GmbH
  • Duschl Ingenieure GmbH & Co KG
  • Elektrotechnik Pichler
• Tutor for:
  • Labcourse Crypto Implementation
  • Lab Electrical Engineering
  • MATLAB in Stochastic Signals

Teaching

Project Laboratory IC Design (SS 2019 - SS 2020)

Digitaltechnik (WS 2020/21 - WS 2023/24)

System-on-Chip Solutions & Architecture (WS 2021/22 - WS 2023/24)

Summer School Workshop (WS 2022/23)

Finished Work

• VHDL Implementation of Line Segment Detector
  (Research Internship, 2024)
• Duckietown-Computer Vision Based Measurements for Performance Analysis
  (Engineering Internship, Hakan SUNGURLU, 2024)
• Development of Obstacle Avoidance Algorithms in Duckietown’s Autonomous Driving Pipeline
  (Bachelor Thesis, Elena Kuznetsova, 2024)
• Duckietown - Development of Platooning Mode and Adaptive Speed Control in Autonomous Driving
  (Bachelor Thesis, Hooman Khosravi, 2024)
• Implementation and Evaluation of Safety Mechanisms for Learning Classifier Tables
  (Research Internship, Yiming Lu, 2024)
• Enabling Task Offloading to FPGAs via DMA Transfers
  (Research Internship, Youqing Gao, 2024)
• Duckiebot Task Offloading to FPGA - Using Xilinx PCIe DMA IP, Python and Docker
  (Research Internship, Daniel Biser, 2024)
• Simulation-Based Tuning of a MISO PI Controller for Autonomous Vehicle Applications
  (Bachelor Thesis, Hannes Vogel, 2023)
• Implementing GPU-Accelerated Lane Detection Algorithm: GPU Performance Slower than CPU in Analysis
  (Research Internship, Jiang Shuai, 2023)
• Revision of Learning Classifier Tables to Handle Temporarily Unachievable Goals
  (Master Thesis, Michael Meidinger, 2023)
• Docker Maintanance for Duckietown
  (Working Student, Teodora Ljubevska, 2023)
• Improved Line Segment Detection in Duckietown’s Autonomous Driving Pipeline
  (Bachelor Thesis, Matthias Schlemmer, 2023)
• Implementation of Learning Classifier Tables for Power Reduction in Autonomous Driving Applications
  (Bachelor Thesis, Ethan Allan, 2023)
• Extending Duckietown Robots Via Learning Classifier Tables: Optimization of Speed in Autonomous Driving
  (Bachelor Thesis, Zara Weir, 2023)
• Development of a Simulation Model for RL-based Task Scheduling in Simulink
  (Bachelor Thesis, Diane Gerber, 2023)
• Recognition of Unachievable Goals in Reinforcement Learning
  (Research Internship, Michael Meidinger, 2023)
• Hardware in the Loop for Reinforcement Learning Investigation
  (Bachelor Thesis, Youssef Sharafaldin, 2023)
• Reward Function Design for Reinforcement Learning
  (Bachelor Thesis, Lara Mehlsam, 2023)
• Hardware Implementation of a Hybrid Reinforcement Learning Environment for Development and Demonstration
  (Bachelor Thesis, Yiming Lu, 2022)
• Developer-Friendly Simulation Environment for Reinforcement Learning-Based MPSoC Runtime Optimization
  (Bachelor Thesis, Jakob Hölzl, 2022)
• RTEMS on Leon3
  (Research Internship, Roberto Ruano Martinez, 2022)
• Porting a Learning Classifier Table (LCT) for Processor Optimization from Hardware to Software and Evaluating its Usability
  (Research Internship, Moritz Thoma, 2022)
• Analysis of Possible DVFS Periodicities in Self-Aware MPSoCs
  (Master Thesis, Thomas Hallermeier, 2022)
• Development of a Self-Adaptive RL-Based Task Mapper for MPSoCs with Flexible Optimization Goals
  (Master Thesis, 2022)
• Development of a User-Friendly Simulation Environment for RL-Based Optimization of MPSoC Runtime Parameters
  (Bachelor Thesis, Eric Christfreund, 2022)
• Development of a Multi-Step Reinforcement Learning Approach for Autonomous DVFS on MPSoCs
  (Master Thesis, Lorenz Völk, 2021)
• Development of a Cooperative Multi-Agent RL Approach for Autonomous DVFS on MPSoCs
  (Master Thesis, Klajd Zyla, 2021)
• Enabling Multi-Core Capabilities in a DVFS Simulation Environment
  (Research Internship, 2021)
• Application Scheduling on Self-aware Embedded Systems
  (Research Internship, Daniel Shkurti, 2021)
• Development of a Debugger for a Reinforcement Learning Paradigm on an MPSoC
  (Research Internship, Klajd Zyla, 2021)
• Development of a SoC Trace Generator on an FPGA for a Trace-Based Simulation Environment
  (Bachelor Thesis, Raphael Mayr, 2020)
• Implementation of a Self-aware MPSoC Platform for Research on Cross-layer Resource Management
  (Working Student, Klajd Zyla, 2020)
• Design of a Trace-Based DVFS Simulation Environment
  (Research Internship, Øivind Bakke, 2020)
• Design of a Hardware-Based Debugger for a Self-Aware SoC Paradigm
  (Bachelor Thesis, Klajd Zyla, 2019)
• Port of a Pedestrian Recognition Software on a VHDL MPSoC
  (Working Student, Ali Younessi, 2019)
• HW-SW Interface Design for a Self-aware SoC Paradigm based on Hardware Machine Learning (IPF)
  (Research Internship, Ozan Sahin, 2019)

2024 SS

• A Survey on Value- and Rule-Based Reinforcement Learning in Hardware
• Effiziente Belohnungszuweisung im ReinforcementLearning

2023 WS

• Challenges and Chances of Reinforcement Learning in Control Applications
• Typen und Einsatzgebiete von Caches in Praktischen Anwendungen
• A Survey on Types of Caching
• A Survey on Safety Guarantee Mechanisms for the eXtended Classifier System
• Cache Coherency Between Compute Nodes

2023 SS

• Reinforcement Learning in Control Problems

2022 WS

• Exploration the State-of-the-art System Resource Management and Future Direction for Multi-core Systems

2022 SS

• Tools for Software Optimization
• Survey on Model-Based Reinforcement Learning

2021 WS

• Adaptive Embedded Systems based on Learning Classifier Systems
• Evolution of P-state Transition Latencies in Modern x86 CPUs

2021 SS

• Explainable AI: A Collection of Interpretable Machine Learning Approaches and Black-Box Explanation Techniques
• Task and Communication Scheduling Mechanisms on NoC-based Platforms

2020 WS

• Learning Classifier Systems in Multistep Reinforcement Learning Problems
• Markov Decision Processes in the Context of Multi-step Learning

2020 SS

• A Survey on Common MPSoC Simulators
• Survey on Debugging Mechanisms for MPSoCs
• A Qualitative Comparison of Common Benchmark Suits, Using Predefined Hardware Focused Metrics

2019 WS

• Interplay of DVFS and DPM for energy minimization of multicore processors
• Advancements in Learning Classifier Systems
• Architectural Techniques for Runtime Power Optimization on MPSoCs
• A Survey on Machine Learning Techniques Used for Predicting Hard Drive Failures in High Performance Centers

2019 SS

• Comparison of Reinforcement Learning Based Multi Agent System Approaches
• Distributed Reinforcement Learning Approaches
• Power Optimization Methodes for MPSoCs