Find Academic Supervisors

Ziad MallatView profile

Professor

• Cardiovascular biology
• immune mechanisms of cardiovascular disease
• atherosclerosis
• +3 more

Professor Ziad Mallat is a distinguished Professor of Cardiovascular Medicine at the University of Cambridge, where he serves as the BHF Professor of Cardiovascular Medicine since 2010. He is affiliated with the Victor Phillip Dahdaleh Heart & Lung Research Institute and is a Principal Investigator in the Cardiovascular and Respiratory Medicine section of the Department of Medicine. Elected as a Fellow of the Academy of Medical Sciences (FMedSci) in 2020, Professor Mallat has established himself as a leading figure in cardiovascular immunology research. Professor Mallat received his MD and degree in Cardiovascular Disease from University of Paris VI, and his PhD from University of Paris VII. He was appointed Director of Research at Inserm, France, in 2007. His academic journey has positioned him at the forefront of cardiovascular research, with particular expertise in immune mechanisms of cardiovascular disease. Professor Mallat's research focuses on the immune mechanisms leading to cardiovascular inflammation in atherosclerosis, post-ischaemic injury (such as myocardial infarction), and aortic aneurysm and dissection. His laboratory develops novel immuno-modulatory and vaccination-based strategies to promote protective immune responses, aiming to radically change the management and treatment of these common and serious cardiovascular diseases. His approach combines basic science research, pre-clinical experimentation in animals, and translation clinical investigation in patients. Analysis of Professor Mallat's recent publications reveals a strong focus on the intersection of immunology and cardiovascular disease, particularly investigating regulatory T cells, B cell responses in atherosclerosis, and immune-modulatory therapies. His work spans from fundamental mechanisms to clinical translation, with several studies examining therapeutic approaches like low-dose interleukin-2 for cardiovascular inflammation. Fellow of the Academy of Medical Sciences (FMedSci) Co-Editor of Atherosclerosis journal Editorial Board member of Circulation Research and JCI Insight Professor Mallat has published over 250 papers in peer-reviewed journals, with landmark contributions including identifying prominent roles for specific pathogenic and regulatory immune pathways in atherosclerosis and post-ischaemic remodeling. His basic research activities are complemented by proof-of-concept clinical trials using immune-modulatory therapies in patients with coronary artery disease. He maintains active collaborations with the British Heart Foundation and is involved in the Cambridge BHF Centre of Research Excellence.

Guimu GuoView profile

Assistant Professor

• Parallel Computing
• Graph Mining
• GPGPU
• +2 more

Guimu Guo is an Assistant Professor in the Department of Computer Science at Rowan University's College of Science & Mathematics. His research focuses on parallel and distributed computing techniques for large-scale graph mining problems, with applications in bioinformatics and transportation engineering. Ph.D. in Computer Science from University of Alabama at Birmingham M.Sc. in Computer Science from Tongji University Dr. Guo has published extensively in top-tier venues like VLDB, ICDE, and IEEE BigData. His work spans graph mining algorithms, parallel computing, and interdisciplinary applications in transportation and genomics. He actively mentors PhD and Master's students, offering fully funded positions. Key research trends include: Advancing GPU-accelerated graph decomposition techniques Developing distributed frameworks for subgraph querying and task concurrency Exploring parallel algorithms for frequent pattern mining and clique-like subgraphs Scientific Recognition: NSF CRII Award UAB Outstanding PhD Student Award Alabama GRSP Awards (Rounds 15 & 16) Teaching spans from foundational object-oriented programming to advanced graduate courses in parallel programming. His lab group has produced significant contributions to subgraph mining, transportation simulation, and genome assembly systems.

Wayne MyrvoldView profile

Professor

• Philosophy of Physics
• Philosophy of Science
• Probability
• +5 more

Wayne Myrvold is a Professor in the Department of Philosophy at Western University, Faculty of Arts and Humanities. His research lies at the intersection of philosophy of physics and philosophy of science, with a focus on quantum mechanics, statistical mechanics, and the foundations of probability. His primary research interests include: Philosophy of Physics, especially quantum mechanics and relativity Foundations of probability and its role in science Statistical mechanics and thermodynamics Quantum state realism and ontology Unification and scientific explanation Epistemology of scientific inference Myrvold's recent publications, including his 2021 book Beyond Chance and Credence , explore hybrid conceptions of probability that bridge epistemic and physical interpretations. His articles span topics such as quantum collapse theories, Bell’s Theorem, and the nature of the wavefunction, reflecting a deep engagement with both technical and philosophical dimensions of modern physics. His work frequently appears in leading journals like Studies in History and Philosophy of Modern Physics , Physical Review A , and Synthese . He is the Subject Editor for Quantum Mechanics in the Stanford Encyclopedia of Philosophy and has co-authored key entries, including the comprehensive overview of Bell’s Theorem. His scholarly contributions emphasize rigorous analysis of foundational concepts in physics and their philosophical implications. Myrvold holds a BSc from McGill University and a PhD from Boston University. He maintains an active research profile with a continually updated list of publications available via Google Scholar. He advises graduate students in philosophy of science and supervises research in foundational physics. While specific grant details are not listed, his sustained publication output suggests active research support. He is affiliated with the Rotman Institute of Philosophy, which supports interdisciplinary research in philosophy of science.

Paolo PrandoniView profile

Lecturer

• Signal Processing
• Audio Processing
• Image Processing
• +5 more

Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.

Somak DuttaView profile

Associate Professor

• Statistics
• Machine Learning
• Genomics
• +5 more

Somak Dutta is an Associate Professor at Iowa State University conducting interdisciplinary research at the intersection of statistical methodology, machine learning, and agricultural sciences. His work addresses critical challenges in plant breeding, genomics, and environmental modeling through innovative quantitative approaches. His academic foundation includes: PhD in Statistics, University of Chicago, 2015 M.Stat (First Division with Distinction), Indian Statistical Institute, Kolkata, 2010 B.Stat (Honours with Distinction), Indian Statistical Institute, Kolkata, 2008 Dr. Dutta specializes in developing advanced statistical frameworks for high-dimensional data analysis, Bayesian variable selection, and spatial modeling. His research directly impacts plant breeding programs through applications in phenotyping, haploid induction systems, drought response mechanisms, and genetic architecture studies across key crops including maize, soybean, and mungbean. He also contributes to meteorological science through machine learning applications for severe weather verification. Analysis of his 2023-2025 publications reveals two dominant research streams: (1) agricultural applications focusing on genomic selection, phenotyping technologies, and stress response modeling in crops, and (2) meteorological innovations applying machine learning to severe thunderstorm wind verification and precipitation forecasting. Both streams demonstrate his signature approach of developing novel statistical methodologies tailored to domain-specific challenges. No scientific awards were documented in the provided materials. Information regarding student advising, grant funding, laboratory infrastructure, or collaborative teams was not available in the source documentation.

Yazan OtoumView profile

Part-Time Lecturer

• Internet of Things (IoT) Security
• Artificial Intelligence for Cybersecurity
• Federated Learning
• +4 more

Yazan Otoum is a Part-Time Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa and concurrently an Assistant Professor in the School of Computer Science and Technology at Algoma University . A licensed Professional Engineer in Ontario, he is internationally recognized for his interdisciplinary work at the intersection of cybersecurity, artificial intelligence, and the Internet of Things . Education Ph.D. in Electrical and Computer Engineering, University of Ottawa (September 2022) M.Sc. in Network Engineering and Management, DePaul University (December 2009) Research Interests Dr. Otoum’s research program is dedicated to securing the rapidly expanding IoT ecosystem. His core themes include: Scalable meta-learning models that adapt to evolving threats in resource-constrained IoT devices. Federated and transfer learning to enable privacy-preserving, collaborative intrusion detection across heterogeneous networks. Healthcare IoT (IoMT) security, ensuring safe and trustworthy medical devices and data streams. Smart-city infrastructures , where AI-driven security safeguards critical urban services. His recent work leverages large language models (LLMs) , blockchain , and differential privacy to push the boundaries of next-generation cyber-defence mechanisms. Publication Trends Across 23 peer-reviewed works (2017-2025), a clear evolution is evident: early studies established foundational deep-learning intrusion detection frameworks (DL-IDS), followed by federated and transfer-learning paradigms tailored for IoT and IoMT. The latest 2024-2025 publications integrate cutting-edge generative AI and blockchain techniques, highlighting a shift toward holistic, scalable, and privacy-preserving security ecosystems for IoT, Internet of Vehicles, and healthcare domains. Professional Recognition & Service Licensed Professional Engineer (P.Eng), Ontario Certifications: CEH, CCNA, CHFI, ISO 27001 Lead Implementer Peer reviewer for IEEE, ACM, and Elsevier journals Invited speaker and mentor in cybersecurity education initiatives Teaching & Mentorship Dr. Otoum currently teaches Data Science and Data Structures and Algorithms at the University of Ottawa. His office hours are held Mondays 11:30 AM–1:30 PM in SITE room 4075. While specific student advisees are not listed, he is actively engaged in mentoring emerging researchers and practitioners in secure AI and IoT systems. Labs & Teams Operating at the intersection of academia and industry, Dr. Otoum collaborates with multidisciplinary teams spanning embedded systems, AI laboratories, and healthcare technology partners, fostering innovation that transitions seamlessly from theory to real-world deployment.

• deep learning
• convolutional neural network
• deep features
• +5 more

Fabrizio Falchi is a researcher at the Artificial Intelligence for Media and Humanities (AIMH) Lab of the Institute of Information Science and Technologies (ISTI) within Italy's National Research Council (CNR). He also maintains an associate position at the Biorobotics Institute of Scuola Superiore Sant'Anna. His work focuses on developing advanced multimedia retrieval systems, with the VISIONE platform being his most notable contribution, which has won international competitions including the Video Browser Showdown in 2024 and placed second in 2023. Falchi's educational background includes: Ph.D. in Information Engineering from University of Pisa (Italy) Ph.D. in Informatics from Faculty of Informatics of Masaryk University of Brno (Czech Republic) M.B.A. from Scuola Superiore Sant'Anna in Pisa His research spans deep learning, convolutional neural networks, deep features extraction, similarity search algorithms, distributed indexing systems, multimedia information retrieval, computer vision applications, and peer-to-peer systems. Falchi has made significant contributions to fine-grained visual understanding, cross-modal retrieval (particularly image-text matching), and robustness of deep learning systems against adversarial attacks. His work demonstrates a strong focus on practical applications of these technologies, particularly in video retrieval systems and safety monitoring solutions. Analysis of Falchi's recent publications reveals a strong focus on video and image retrieval systems, with the VISIONE platform being central to his work. His research shows increasing emphasis on fine-grained understanding in computer vision, cross-modal retrieval, and addressing practical challenges like cross-resolution face recognition. Recent work demonstrates innovation in making these systems more efficient through techniques like knowledge distillation (ALADIN) and leveraging virtual worlds for training data. His publications consistently bridge theoretical advances with practical applications in surveillance, safety monitoring, and multimedia search. Falchi's work has received significant recognition: Best paper award at CBMI 2024 for 'Is ClLIP the main roadblock for fine-grained open-world perception?' VISIONE 2024 won the Video Browser Showdown competition in Amsterdam VISIONE obtained second place at Video Browser Showdown 2023 in Bergen Best Paper Award for 'Learning Safety Equipment Detection using Virtual Worlds' at CBMI 2019 Falchi collaborates extensively with researchers at ISTI-CNR, particularly within the AIMH Lab. His work on VISIONE involves collaboration with Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Claudio Gennaro, Nicola Messina, Lucia Vadicamo, and Claudio Vairo. As co-chair of Ital-IA 2023, the 3rd National Conference on Artificial Intelligence, he plays an active role in the academic community. He is a member of ACM (since 2012), the Computer Vision Foundation, the Italian Association for Computer Vision Pattern Recognition and Machine Learning (CVPL), and the CINI Lab on Artificial Intelligence and Intelligent Systems. Falchi is a key member of the Artificial Intelligence for Media and Humanities (AIMH) Lab at ISTI-CNR, where he leads research on video retrieval systems. The lab has developed the award-winning VISIONE platform, which combines multiple scientific results in content-based video retrieval. His team focuses on developing systems that enable users to search for target videos using textual prompts, drawing objects and colors, or images as query examples. The lab's work demonstrates strong interdisciplinary collaboration, bridging computer science with practical applications in media, safety monitoring, and urban environments.

• Biomedical Acoustics
• Acoustic Cavitation
• Therapeutic Ultrasound
• +6 more

Dr. Kenneth Bader is an Associate Professor in the Department of Radiology at the University of Chicago's Pritzker School of Medicine. He leads the Biomedical Acoustics Development and Engineering Research Laboratory (BADER Lab), focusing on translating therapeutic ultrasound into clinical applications for non-invasive treatment of cardiovascular and cancerous diseases. His work bridges physics, engineering, and medicine, with emphasis on developing innovative ultrasound-based therapies. Education: B.S. in Physics from Grand Valley State University (2005), Ph.D. in Physics from the University of Mississippi (2011) Current Funding: Principal Investigator on two major NIH R01 grants (R01EB035230 and R01HL133334) totaling nearly a decade of continuous research support Lab Affiliation: BADER Lab (baderlab.uchicago.edu) Dr. Bader's research centers on acoustic cavitation and histotripsy for combinatorial ablation and enhanced drug delivery strategies targeting pathologies resistant to standard interventions. He develops multi-modal imaging approaches combining diagnostic ultrasound and magnetic resonance imaging to assess bubble activity and resultant tissue changes. His work spans fundamental bubble dynamics modeling to translational applications in thrombosis and cancer treatment. Key areas include chronic thrombus ablation, histotripsy-enhanced drug delivery, sonochemical reactions for cancer therapy, and MR-guided transurethral prostate ablation. His publication record demonstrates consistent productivity with over 40 publications since 2012, showing an upward trajectory with 25 publications in the last five years (2020-2025). His work appears in high-impact journals including IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control; Physics in Medicine & Biology; and Journal of Ultrasound in Medicine. His research shows strong interdisciplinary collaboration across engineering, physics, radiology, and vascular medicine. Dr. Bader serves as Principal Investigator on two major NIH-funded projects: Imaging Feedback for Histotripsy Renal Tumor Ablation (2024-2028) and Treatment of chronic venous thrombosis with histotripsy and thrombolytics (2017-2028). These projects represent significant sustained funding for developing ultrasound-based therapies for cancer and vascular diseases. His work has generated substantial interest in the scientific community, with multiple publications receiving over 50 citations, particularly his foundational work on bubble dynamics in histotripsy. The BADER Lab represents a hub for ultrasound research at the University of Chicago, developing both theoretical models and practical applications of therapeutic ultrasound. The lab focuses on translating laboratory discoveries into clinical practice, with particular emphasis on making treatments more effective while minimizing invasiveness. Current work includes developing AI approaches for ultrasound image analysis, novel transducer designs, and combination therapies that leverage both mechanical and biochemical effects of ultrasound.

Kavita BalaView profile

Professor

• Computer Vision
• Artificial Intelligence
• Computer Graphics
• +6 more

Kavita Bala is the 17th Provost of Cornell University and a Professor of Computer Science. She previously served as the inaugural Dean of the Cornell Ann S. Bowers College of Computing and Information Science, leading its transition to a degree-granting college by 2025, and as Chair of Cornell’s Department of Computer Science. Her academic leadership includes expanding faculty, establishing research programs like the Bowers CIS Undergraduate Research Experience (BURE), and securing a new research facility for computing and information science. Education: B.Tech (IIT Bombay), M.S. and Ph.D. (MIT, Computer Science) Bala’s research focuses on computer vision, artificial intelligence, and computer graphics , with groundbreaking work in material and style recognition using deep learning. Her innovations in crowdsourced training data and differentiable rendering have advanced visual search technologies and translucent material modeling, powering her startup GrokStyle. She pioneered AI techniques applied to environmental monitoring through projects like MONITRS and AllClear , addressing Earth observation challenges. Her scientific awards include: American Academy of Arts and Sciences (2025) SIGGRAPH Computer Graphics Achievement Award (2020) IIT Bombay Distinguished Alumnus Award (2021) ACM Fellow (2019) SIGGRAPH Academy Fellow (2020) As Provost, Bala drives strategic initiatives like the Cornell AI Initiative , creating interdisciplinary minors in AI and AI in Society, and establishing the Schmidt AI in Science postdoctoral program. She co-chaired a task force for generative AI guidelines in education.

• Biomaterials
• Biomechanics
• Materials Science
• +4 more

Cecilia Persson is a Professor at Uppsala University in the Department of Materials Science and Engineering; Biomedical Engineering. She leads the BioMaterial Systems (BMS) research group within the Division of Biomedical Engineering, focusing on the development of new biomaterials through additive manufacturing. She also directs a Competence Centre in Additive Manufacturing for the Life Sciences and the national Research Technology Platform WISE Additive. 2018, Professor in Materials Science, Uppsala University 2015, Docent (Assoc. Prof.) in Engineering Science with Specialization in Materials Science, Uppsala University 2009, PhD in Mechanical Engineering, University of Leeds 2004, MSc in Materials Engineering, European degree (EEIGM) with triple diploma Persson's research focuses on biomaterials, biomechanics, materials science, and additive manufacturing. Her work takes an integrated approach to solving clinical and sustainability problems, combining materials science, mechanical and biological engineering with new technologies like 3D printing and machine learning. Key research areas include magnesium-based alloys for bone substitutes, titanium-based alloys for permanent implants, and machine learning methods to enhance manufacturing efficiency. Analysis of her recent publications shows a strong emphasis on additive manufacturing of biomaterials, particularly magnesium and titanium alloys. Her work explores microstructure control, mechanical properties optimization, antibacterial properties, and patient-specific implant design. The research demonstrates a clear trajectory toward more sustainable, patient-adapted medical solutions using advanced manufacturing techniques. Persson has received funding from prestigious organizations including the Swedish Research Council (VR), the Knut and Alice Wallenberg Foundation (KAW), the Swedish Foundation for Strategic Research (SSF), Sweden's Innovation Agency (VINNOVA), and the EU. As an academic leader, Persson has served as Section Dean of Engineering (2020-2023), President of the Scandinavian Society of Biomaterials (2019-2023), and Coordinator of EU Innovative Training Network NU-SPINE (2019-2023). Her BioMaterial Systems research group takes an integrated approach to solving clinical and sustainability problems, bridging fundamental scientific mechanisms with high societal relevance.

Niklas Mattsson-CarlgrenView profile

Associate Professor

• Alzheimer's Disease
• Neurodegenerative Diseases
• Tau Pathology
• +7 more

Niklas Mattsson-Carlgren serves as an Associate Professor and Senior Lecturer at Lund University's Faculty of Medicine, Department of Clinical Sciences. He holds multiple significant roles including Deputy Research Team Manager and Principal Investigator for several major research projects. His affiliations extend to the Wallenberg Centre for Molecular Medicine (WCMM), LU Profile Area: Proactive Ageing, and MultiPark: Multidisciplinary Research Focused on Parkinson's Disease. Dr. Mattsson-Carlgren's research primarily focuses on Alzheimer's disease and other neurodegenerative conditions, with additional work on acute brain injuries such as those following cardiac arrest. His expertise spans neurochemistry, including the regulation of pain and sleep. He employs biochemical measurements, neuroimaging, and cognitive testing to study disease processes in vivo across the spectrum from preclinical disease to advanced dementia. His work aims to improve diagnostic and prognostic methods in clinical practice, enhance clinical trial design for novel therapies, and deepen understanding of disease mechanisms. His recent publications demonstrate a strong emphasis on plasma biomarkers for Alzheimer's disease detection and monitoring, with particular focus on tau and amyloid pathology. The research shows increasing integration of machine learning approaches with traditional biomarker analysis, reflecting a trend toward more sophisticated diagnostic tools that combine multiple data sources for improved accuracy in predicting disease progression. As Principal Investigator for multiple significant projects including 'Longitudinal plasma biomarkers, cognitive data and amyloid PET to improve early prevention of Alzheimer's disease' and 'Validation of an MTBR tau immunoassay for CSF and plasma,' he leads substantial research efforts funded by organizations including Familjen Rönströms stiftelse, Eli Lilly and Company, and the Knut and Alice Wallenberg Foundation. He also serves as supervisor for PhD students, notably for 'Studies of induced neuronal cells in Alzheimer's disease.' Dr. Mattsson-Carlgren is actively involved in the neuroscience community, serving on the program committee for Neuroscience Day 2025. His research contributes to UN Sustainable Development Goals related to health and wellbeing. His work has gained significant attention, with multiple papers being picked up by news outlets and referenced across social media platforms including X (Twitter) and Bluesky.

• Topological Data Analysis
• Stochastic Geometry
• Large Deviations Theory
• +4 more

Christian Hirsch is an Associate Professor for Data Science and Statistics at Aarhus University, where he studies random networks motivated from biology and health sciences through techniques from topological data analysis and stochastic geometry. He is a member of the Stochastics group at the Department of Mathematics and holds additional affiliations as an Associate Fellow of the Aarhus Institute for Advanced Studies, and with the AU DIGIT Centre and the AU Quantum Campus. Current Position: Associate Professor for Data Science and Statistics, Aarhus University Previous Positions: Assistant Professor at University of Groningen and University of Mannheim Postdoctoral Experience: Aalborg University, LMU Munich, WIAS Berlin Education: PhD from Ulm University Christian Hirsch's research focuses on the statistical foundations of topological data analysis, large deviations theory in stochastic geometry, and percolation theory of spatial random networks. His work bridges theoretical mathematics with practical applications in data science, particularly in analyzing complex structures through topological methods. He investigates how topological features form and disappear in growing data structures, developing statistical tests to determine whether observed patterns are significant or merely random occurrences. His recent publications reveal a strong trend toward applying topological data analysis to increasingly complex structures, with significant focus on statistical validation of topological features. Hirsch has made substantial contributions to understanding the probabilistic behavior of persistent homology, developing functional central limit theorems and large deviation principles for topological functionals. His work spans theoretical foundations in stochastic geometry while finding applications in materials science, neural networks, and wireless communication systems. As an educator, Hirsch teaches graduate courses including Topological Data Analysis, Stochastic Geometry, Monte Carlo Simulation, Markov Decision Processes, Probability Theory, and Stochastic Processes. He has supervised numerous PhD, MSc, and BSc students, with several of his former students securing academic positions at institutions like University of Leiden, Tokyo Institute of Technology, and Budapest University of Technology. Hirsch leads a research group within the Stochastics group at Aarhus University, collaborating extensively with researchers across Europe and North America. His work demonstrates how topological methods can provide rigorous statistical insights into complex data structures, making significant contributions to both theoretical mathematics and practical data analysis techniques.

Marcus GerholdView profile

Assistant Professor

• Model-Based Testing
• Software Reliability
• Critical Infrastructures
• +7 more

Marcus Gerhold is an Assistant Professor in the Formal Methods and Tools group at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on model-based testing for software reliability in critical infrastructures, particularly railway systems, alongside significant contributions to game design and programming language analysis. His educational background includes: PhD in Computer Science from University of Twente (2018): Choice and Chance: Model-based Testing of Stochastic Behaviour MSc in Mathematics from Friedrich Schiller Universität Jena (2013): Embeddings of Weighted Morrey Spaces BSc in Mathematics from Friedrich Schiller Universität Jena (2011): Entropy-, Approximation- and Kolmogorov Numbers on Quasi-Banach Spaces Gerhold's research integrates theoretical model-based testing with practical critical infrastructure applications . His work on railway conformance testing addresses EULYNX controller validation, while his game design research explores affective mirroring in NPCs and procedural dungeon generation. The code modernity analysis stream leverages static analysis to quantify legacy code evolution across languages like Python and PHP, revealing version identification challenges through deep learning. Publication trends show consistent focus on model-based testing methodologies (40%), railway safety applications (25%), and innovative game design/code analysis (35%). Recent work increasingly incorporates AI/ML techniques for UML assessment and Python version identification, while maintaining rigorous formal methods foundations. He actively mentors 63 students across all academic levels and contributes to major research initiatives: STORM_SAFE (ERDF, 2024): Daily Supervisor for WP1/WP2 on software reliability for critical infrastructures ZORRO (KIC grant, 2023): Daily Supervisor for WP4 on zero downtime in cyber-physical systems MISSION (MSCA RISE, 2021-2025): Interim coordinator (early 2024) for space systems modeling As part of the Formal Methods and Tools research group, Gerhold participates in European collaborations while serving on SAC-SVT 2024 and FormaliSE 2023 program committees.

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 .

Alexander RodríguezView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Time Series Analysis
• +7 more

Alexander Rodríguez is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on advancing AI methods for modeling complex spatiotemporal dynamics, particularly in applications related to population health and community resilience. He specializes in machine learning, time series analysis, uncertainty quantification, and multi-agent systems, with an emphasis on scientific modeling and data-driven decision-making. Recent contributions include keynote talks at AAMAS 2025 (Autonomous Agents for Social Good workshop), presentations at the US National Academies Symposium, and invited talks at AAAI 2025 on topics like knowledge-guided machine learning and public health prediction. He co-organizes AAMAS 2025 as sponsorship co-chair and leads initiatives in AI for science and epidemic forecasting. His publications emphasize neural networks for time series forecasting, biomedical foundation models, and epidemic surveillance systems. Notable work includes 'Neural Conformal Control for Time Series Forecasting' (AAAI 2025) and 'Deepcovid: An operational deep learning-driven framework for explainable real-time forecasting' (2021). No scientific awards explicitly listed in available texts. His research group actively collaborates on grants related to AI applications in public health and infrastructure resilience, with a focus on data-centric methodologies and multi-agent systems.