Find Academic Supervisors

Jochen GarckeView profile

Professor

• Numerical Methods in Science and Technology
• Machine Learning
• Sparse Grids
• +5 more

Prof. Dr. Jochen Garcke is a faculty member at the Institute for Numerical Simulation, University of Bonn, with a dual affiliation at Fraunhofer SCAI's Department of Numerical Data-Based Prediction. His work bridges numerical simulation and machine learning, focusing on high-dimensional problems, sparse grids, and optimal control. Key research themes: Sparse grids, machine learning for simulations, reinforcement learning, uncertainty quantification Teaching includes courses on Numerical Methods in Science and Technology and Scientific Computing , emphasizing practical machine learning applications. Recent publications explore hybrid models combining data-driven and physics-based approaches in automotive engineering, wind turbines, and geoscientific modeling. His group employs adaptive sparse grids, graph algorithms, and spectral methods to tackle challenges in crash simulations, fluctuating renewable energy systems, and turbulent flow analysis. Collaborations span Fraunhofer SCAI and industry 4.0 initiatives.

Chadi BarakatView profile

Researcher

• Internet Measurement
• Network Traffic Analysis
• Quality of Experience (QoE)
• +3 more

Chadi Barakat is a Senior Researcher (Directeur de Recherche) at Université Côte d'Azur's Inria research center, leading the DIANA project-team. He holds a PhD in Computer Science from University of Nice Sophia Antipolis (2001) and Habilitation (HDR) in 2009, with academic credentials from Lebanese University (1997) and French institutions. PhD: Computer Science (2001), University of Nice Sophia Antipolis HDR: Computer Science (2009), University of Nice Sophia Antipolis Master's: Computer Science (1998), University of Nice Sophia Antipolis BSc: Electrical & Electronics Engineering (1997), Lebanese University His research focuses on Internet measurement and traffic analysis , with significant contributions to Quality of Experience (QoE) modeling, 5G/ICN/SDN network architectures , and network performance evaluation . Recent articles highlight browser-based network monitoring, fidelity-aware network emulation, and ray tracing optimization for radio frequency mapping. He has supervised 12 PhD students to completion and currently directs the Academy of Excellence 'Networks, Information, and Digital Society' at Université Côte d'Azur. His work has received multiple best paper awards at CNSM, CloudNet, and SECON conferences, while serving as associate editor for Elsevier Computer Networks journal and active in ACM/IEEE conference committees. Director, Academy of Excellence 'Networks, Information, and Digital Society' (2025-present) Senior IEEE Member (2010) & ACM Senior Member (2018) General Co-Chair: ACM IMC 2022, ACM CoNEXT 2012 Guest Editor: IEEE JSAC special issue on Internet Sampling

Katharina EggenspergerView profile

Researcher

• Automated Machine Learning (AutoML)
• Hyperparameter Optimization (HPO)
• Tabular Machine Learning
• +1 more

Katharina Eggensperger is an Early Career Research Group Leader at the University of Tübingen , leading the AutoML for Science group within the Cluster of Excellence Machine Learning for Science . She previously completed her Ph.D. at the University of Freiburg under Frank Hutter and Marius Lindauer (2022), and actively contributes to the AutoML community through open-source tool development and competition leadership. Co-developer of AutoML.org tools Faculty member of IMPRS-IS Chair for multiple AutoML workshops/conferences (2019-2025) Her research focuses on automated machine learning (AutoML) with specific attention to: AutoML Systems Hyperparameter Optimization Tabular Machine Learning Scientific Applications of ML She has organized multiple AutoML schools and conferences, including serving as Program Chair for AutoML 2024 and Non-archival Track Chair for AutoML 2025. Her work emphasizes making machine learning accessible through automation while maintaining scientific rigor and interpretability, particularly for tabular data applications. Katharina actively recruits PhD students through IMPRS-IS and collaborates with institutions like the University of Freiburg and Cyber Valley .

• Digital Communications
• Signal Processing
• Networking
• +3 more

Mahsa Derakhshani is a Senior Lecturer (equivalent to Associate Professor) in Digital Communications at Loughborough University's Wolfson School of Mechanical, Electrical and Manufacturing Engineering. She leads research in the Signal Processing and Networks Research Group (SPNRG) and serves as an Associate Editor for the IET Signal Processing Journal. Her academic journey includes a PhD from McGill University (2013), followed by postdoctoral roles at the University of Toronto and Imperial College London. Key awards include the Royal Academy of Engineering/The Leverhulme Trust Research Fellowship (2020-21) and NSERC Postdoctoral Fellowships (2015-2017). Research interests focus on digital communications, machine learning for signal processing, wireless networks, and reinforcement learning applications. Recent work addresses challenges in satellite communications, OTFS modulation, and opto-physiological monitoring. She has authored over 70 publications spanning topics like NOMA systems, MIMO optimization, and edge-assisted live streaming. Education: PhD (McGill, 2013), MSc (Sharif University, 2008), BSc (Sharif University, 2006) Affiliations: IEEE Senior Member, IET Member, Fellow of the Higher Education Academy Grants & Funding: Leverhulme Trust, Royal Academy of Engineering, NSERC Labs/Teams: Active in SPNRG and collaborates on projects involving 5G/6G networks, satellite systems, and biomedical signal processing. Current research emphasizes AI-driven solutions for communication networks and wearable health monitoring technologies.

• Machine Learning
• Graph Neural Networks
• Data Attribution
• +4 more

Jiaqi Ma is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC), holding dual appointments in the School of Information Sciences and the Siebel School of Computing and Data Science. Their research focuses on machine learning, graph neural networks, and data attribution, with particular emphasis on fairness in AI, large language models, and scalable algorithms. Ma has contributed to frameworks like dattri for efficient data attribution and GraSS for scalable influence functions. Research interests include graph learning (e.g., structural information analysis in text-attributed graphs), fairness in ML models (e.g., mitigating disparities in unlearning processes), and ethical AI applications. Collaborations span topics like data curation (DCA-Bench benchmark) and reinforcement learning transparency (A Snapshot of Influence). Key contributions include improving data removal while maintaining fairness (Fair Machine Unlearning), analyzing LLMs' graph structural utilization, and developing open-source tools like OpenHexAI for explainable ML evaluation. No scientific awards are explicitly listed, but their work reflects significant contributions to trustworthy AI and machine learning systems. Advising and grants: While specific students/grants are unlisted, Ma leads research teams advancing graph learning (e.g., Graph Learning Indexer platform) and safety-critical AI (e.g., unsafe data detection via attribution). Their work bridges theoretical foundations (e.g., influence functions) with practical applications (e.g., MNL model rankings).

Andrea BonariniView profile

Professor

• Artificial Intelligence
• Robotics
• Machine Learning
• +4 more

Andrea Bonarini is a Professor at the Department of Electronics, Information, and Bioengineering (DEIB) within the School of Industrial and Information Engineering at Politecnico di Milano. His research focuses on Artificial Intelligence , Robotics , and Human-Robot Interaction , particularly in assistive technologies for children with disabilities and autism therapy. Key Research Areas: Reinforcement Learning, Social Robotics, Affective Computing, Accessibility in Robot Design Notable Projects: Development of frameworks like Mushroomrl and datasets like POLIMI-ITW-S for robotics research His recent publications highlight advancements in playful robotics for neurodevelopmental disorders, emotion projection in non-bio-inspired robots, and safety experiments for small social robots interacting with children. He also contributes to ethical, social, and psychological challenges in robotics applications.

Miroslava KavgicView profile

Associate Professor

• Sustainable Building Engineering
• Carbon Capture Materials
• HVAC Systems Optimization
• +6 more

Miroslava Kavgic is an Associate Professor in the Department of Civil Engineering at the University of Ottawa. She holds a Ph.D. (United Kingdom), M.Sc. (United Kingdom), and B.Sc. (Serbia), and is a Professional Engineer (P.Eng.). Her research focuses on sustainable building engineering, carbon-negative materials, and energy-efficient design for remote communities. She leads the Centre for Indigenous Community Infrastructure at uOttawa, emphasizing culturally appropriate solutions. Education: Ph.D. in Environmental Design and Engineering (University College London, 2013) M.Sc. in Environmental Design and Engineering (University College London, 2006) B.Sc. in Mechanical Engineering (Serbia) Research Interests: Carbon capture building materials (e.g., hempcrete composites) Bioclimatic design strategies for net-zero buildings Urban energy modeling to decarbonize cities Renewable energy systems integration Advanced HVAC controls and energy efficiency Her recent publications (2021–2025) emphasize: Phase change material applications in building envelopes Machine learning for energy demand prediction Optimization algorithms like MEVO for building performance Hybrid renewable energy systems Labs/Teams: Active in the Centre for Indigenous Community Infrastructure, focusing on Northern communities' sustainable infrastructure. Collaborates with industry on building design innovations.

• Advanced Machining
• Engineering Design and Optimization
• Bio-Engineering
• +6 more

Robert Heinemann is a Senior Lecturer in the Department of Mechanical and Aerospace Engineering at the University of Manchester, affiliated with the School of MACE. His work focuses on advanced machining, tool condition monitoring, and sustainable manufacturing processes. He holds a PhD from the University of Manchester Institute of Science and Technology (2004) and has extensive research experience in drilling technology, carbon-based coatings, and environmental benign machining. Education: Diplom Ingenieur (Dipl.-Ing. FH) in Mechanical Engineering, University of Paderborn, Germany (1999) MSc in Electronic Engineering and Engineering Management, University of Paderborn/Bolton University (2001) PhD in Mechanical Engineering, University of Manchester Institute of Science and Technology (2004) Research interests include: Drilling and reaming technology for minimally invasive surgery Development of diamond-like carbon coatings for cutting tools Process and tool optimization for aerospace and biomedical applications Environmental sustainability in manufacturing design His research outputs emphasize adaptive drilling strategies, deep learning applications in process monitoring, and sustainable manufacturing practices aligned with UN SDGs. He leads the Laser Processing Research Centre (LPRC), focusing on laser-based machining innovations. Scientific achievements include a Leverhulme Trust Early Career Fellowship (2010) and contributions to over 40 peer-reviewed articles. He advises 9 postgraduate research students and collaborates on multi-disciplinary projects addressing industrial challenges in composites and precision engineering.

• AI Algorithms
• Economic Inequality
• Pricing Algorithms
• +3 more

Shunyuan Zhang is an Assistant Professor at Harvard Business School with research focusing on AI algorithms, economic inequality, and computer vision applications in business contexts. His work examines how algorithmic systems impact economic outcomes, particularly in sharing economy platforms like Airbnb. His research interests include AI algorithms, economic inequality, pricing algorithms, machine learning, computer vision, and the sharing economy. Zhang's work often combines technical computer vision approaches with economic analysis to understand platform dynamics. Zhang's recent publications demonstrate a strong focus on the intersection of AI, fairness, and economic outcomes. His work analyzes how algorithmic pricing affects racial disparities on platforms like Airbnb, and how visual content impacts demand in the sharing economy. His research employs sophisticated methodologies including deep learning, structural modeling, and causal inference. He has published in top journals and working paper series, with notable work including 'Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb' and 'What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features.' Zhang collaborates extensively with leading researchers at Carnegie Mellon University and University of Toronto, particularly on topics related to algorithmic fairness and platform economics. His work has significant implications for both academic understanding and practical policy recommendations regarding algorithmic systems in marketplace contexts.

Shufang ZhuView profile

Lecturer

• Artificial Intelligence
• Formal Methods
• Automated Reasoning
• +3 more

Dr. Shufang Zhu is a Lecturer (Assistant Professor equivalent) at the Department of Computer Science, University of Liverpool , and an Associate Member at the University of Oxford’s Department of Computer Science . Previously, she held roles including Senior Research Associate at Oxford (2023–2024) and Postdoctoral Researcher at Sapienza Università di Roma (2020–2022). She earned her Ph.D. in Software Engineering from East China Normal University (ECNU, 2020) under Prof. Geguang Pu, with a visiting Ph.D. at Rice University (2016–2018) under Prof. Moshe Y. Vardi. Education: B.Sc./Ph.D. in Software Engineering from ECNU (2010–2020). Scholarships include the Chinese Scholarship Council (CSC) and UT Austin EECS Rising Star (2022). Her research focuses on interdisciplinary areas of Formal Methods and Artificial Intelligence , emphasizing automated reasoning, planning, and synthesis. Key topics include temporal logics (LTL/LTLf), symbolic synthesis frameworks, and applications in reactive systems. Notable work addresses finite-trace specifications, best-effort strategies, and coordination in multi-agent systems. Teaching: Lecturer for Game-Theoretic Approach to Planning and Synthesis (European Summer School) and Foundations of Self-Programming Agents (Oxford). She also supervises funded Ph.D. positions, including a 2025 deadline for CSC-Liverpool scholarships. Awards: Future Digileader (Digital Futures, 2023), UT Austin EECS Rising Star (2022). Erdős number ≤3 via Moshe Y. Vardi. Collaborations: Co-chair of AAAI 2023 symposium on temporal logics in AI. Active in open-source tools like LydiaSyft for LTLf synthesis. Engages with academic networks through Google Scholar, DBLP, and GitHub.

Liqiang WangView profile

Professor

• Big Data Analytics
• High-Performance Computing (HPC)
• Parallel Computing
• +3 more

Liqiang Wang is a Professor in the Department of Computer Science at the University of Central Florida (UCF), where he directs the Big Data Lab. Previously, he served as faculty at the University of Wyoming (2006-2015). He holds a Ph.D. in Computer Science from Stony Brook University (2006) and spent a visiting research period at IBM T.J. Watson Research Center (2012-2013). His research focuses on big data analytics, high-performance computing, parallel systems optimization, and applying deep learning to detect programming errors and enhance model robustness. Education: Ph.D., Computer Science, Stony Brook University (2006); Visiting Researcher, IBM Watson (2012-2013). Research Interests: Improving accuracy and security of big data models, optimizing parallel computing systems (HPC, Cloud, GPUs), program analysis for concurrency errors, and deep learning applications in anomaly detection and adversarial robustness. Notable projects include scalable LSQR algorithms for seismic tomography and the OpenMP Analysis Toolkit (OAT) for concurrency error detection. Key Awards: NSF CAREER Award (2011), Castagne Faculty Fellowship (2013-2015), UCF Mid-Career Refresh Award (2020), and grants including a $50K NSF CIVIC-PG grant (2022) and Google/Meta donations. Advising and Grants: Supervises over 20 Ph.D./M.S. students and has secured grants totaling over $100K. Notable collaborations include seismic tomography with NCAR and cloud computing optimization. Labs/Teams: Director of UCF’s Big Data Lab, collaborating on projects like Parallel LSQR and Anti-Neuron Watermarking.

Kourosh DavoudiView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Deep Learning
• +3 more

Kourosh Davoudi is an Associate Professor of Computer Science at Ontario Tech University's Faculty of Science. He holds a PhD in Computer Science from York University with a focus on Machine Learning and Data Mining. Prior to joining Ontario Tech in 2019, he was a postdoctoral research fellow at the University of Waterloo's Department of Management Sciences. His research interests span Natural Language Processing, Deep Learning, Reinforcement Learning, Graph Mining, and Machine Learning. He actively supervises graduate students in these areas and teaches courses such as Data Mining and Artificial Intelligence. His research emphasizes practical applications of AI techniques in areas like outbreak detection, sentiment analysis, and automated grading systems. Recent work includes innovations in neural document segmentation, vision-language models, and hybrid outbreak detection using social media data. His publications consistently address challenges in algorithm design, explainable AI, and domain-specific NLP applications. Dr. Davoudi has contributed to conferences such as COLING, EMNLP, and IEEE transactions, focusing on interdisciplinary applications of machine learning. His work bridges theoretical advancements with real-world problems in healthcare, education, and social media analysis.

M. Hadi AminiView profile

Assistant Professor

• Distributed Machine Learning
• Federated Learning
• Interdependent Networks
• +7 more

M. Hadi Amini is an Assistant Professor at Florida International University's Knight Foundation School of Computing and Information Sciences. He founded and directs the Sustainability, Optimization, and Learning for InterDependent networks (SOLID) laboratory, focusing on cyber-physical-social systems and distributed AI applications. Ph.D., Electrical and Computer Engineering (2019), Carnegie Mellon University M.Sc., Electrical and Computer Engineering (2015), Carnegie Mellon University M.Sc. (2013), Tarbiat Modares University B.Sc. (2011), Sharif University of Technology His research spans federated learning, interdependent network optimization, and AI applications in smart cities , energy systems , and healthcare . Recent work emphasizes privacy-preserving techniques, quantum encryption, and blockchain integration for secure distributed learning. The 15 most recent publications highlight trends in large language models , edge computing , medical imaging security , and infrastructure resilience , with interdisciplinary emphasis across computer science, systems engineering, and urban planning. Best Paper Award, IEEE Conference on Computational Science & Computational Intelligence (2019) Best Journal Paper Award, Springer Nature Operations Research Forum (2021) Excellence in Teaching Award, FIU (2020) Multiple Best Reviewer Awards, IEEE Transactions NSF Travel Awards (2019) As Associate Editor for Frontiers in Communications and Networks and book series editor for Sustainable Interdependent Networks , he actively shapes research discourse. His lab has secured $3.6M in federal/state funding for AI-driven infrastructure projects.

• Transportation Engineering
• Urban Planning
• Sustainable Mobility
• +5 more

Ravi Seshadri is an Associate Professor in the Transport Division at the Department of Technology, Management and Economics, Technical University of Denmark (DTU). His research focuses on designing equitable, efficient, and sustainable mobility solutions with a focus on fiscal instruments like congestion pricing and tradable permits, as well as emerging mobility modes such as shared and demand-responsive transit. He employs methods from transportation network equilibria, dynamic traffic assignment, and agent-based simulation. His research interests span transportation economics, urban planning, and intelligent transportation systems. Key areas include evaluating the impacts of automated mobility-on-demand systems, optimizing tolling strategies using predictive control and reinforcement learning, and integrating multi-modal transportation networks through game-theoretical frameworks. His work emphasizes real-world applications in urban freight systems, e-commerce logistics, and sustainable urban mobility policies. Recent projects include studying congestion pricing schemes via agent-based microsimulation, analyzing behavioral responses to decarbonization policies, and developing frameworks for tradable credit systems with peer-to-peer trading. He has contributed to both theoretical advancements (e.g., robust traffic assignment models) and applied tools like the SimMobility simulation platform. Ravi's research demonstrates a strong focus on bridging transportation engineering with policy analysis, using cutting-edge computational methods to address complex urban mobility challenges. His work spans academic publications, industry collaborations, and policy consultations to advance sustainable transportation systems.

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +7 more

Vahid Behzadan is an Assistant Professor in Data Science and Computer Science at the University of New Haven's Tagliatela College of Engineering. He leads the SAIL Lab, focusing on AI safety and security, particularly in autonomous systems like driverless cars and smart cities. His work addresses adversarial attacks on machine learning and reinforcement learning, with applications in cybersecurity and healthcare. Behzadan has held prior positions at Kansas State University, University of Nevada Reno, and University of Birmingham, UK. He holds a Ph.D. in Computer Science and an M.S. from the University of Nevada Reno, and a B.Eng. from the University of Birmingham. His research spans AI ethics, cybersecurity, and complex systems. Behzadan advises the UNH hacking team and actively participates in policy initiatives, including Connecticut's AI Working Group and the Connecticut AI Alliance. He has contributed to over 30 peer-reviewed articles and frequently engages in media discussions on topics like AI safety, facial recognition, and cybersecurity threats. Key research areas include adversarial machine learning, AI forensics, and ethical AI design. His work bridges theoretical advancements with real-world applications in transportation, healthcare, and national security. Behzadan collaborates with organizations such as the Transportation Research Laboratory (TRL) and Open Web Application Security Project (OWASP).