Find Academic Supervisors

• Machine Learning
• Statistical Methods
• Data Science
• +3 more

Dr. Daniel Horn is affiliated with the Department of Statistics at the Technical University of Dortmund, specifically within the Faculty of Statistics. He is part of the working group led by Prof. Dr. Andreas Groll and serves as the Study Coordinator for the B.Sc. and M.Sc. Data Science programs. His research focuses on machine learning algorithms, statistical methods for big data, and optimization techniques, with a particular emphasis on hyperparameter tuning, ensemble methods, and robust outlier detection. Key research areas include the development of efficient algorithms for high-dimensional data analysis, such as tree ensembles for ordinal prediction, kernelized support vector machines, and model-based optimization frameworks like mlrMBO. His work also addresses industrial applications of machine learning, emphasizing practical qualification concepts for data-driven production processes. Dr. Horn's publications span topics such as multi-objective optimization, robust outlier detection (RODD), and the Contextual Shift Method (CSM). His contributions highlight advancements in both theoretical methodologies and applied computational tools, supporting data science education and industrial innovation. Contact: dhorn@statistik.tu-dortmund.de

Abdullah AlsalehView profile

Lecturer

• Natural Language Processing
• Arabic Language Processing
• Semantic Similarity
• +3 more

Abdullah Alsaleh is a Lecturer at King Abdulaziz University's Faculty of Engineering and Physical Sciences, School of Computer Science. He holds a Bachelor's and Master's degree in Information Technology from Queensland University of Technology. PhD student at University of Leeds Research focuses on semantic similarity in Arabic texts (especially Quran) using Transformers-based models Areas include dataset development for Quranic analysis, model performance evaluation, and hyperparameter tuning His work addresses the lack of Arabic-specific semantic similarity datasets and explores optimization of pre-trained Arabic language models. No scientific awards or publications listed in the available text.

• Control
• Optimization
• Data Science
• +3 more

Victor Zavala Tejeda is the Baldovin-DaPra Professor in the Department of Chemical and Biological Engineering at the University of Wisconsin-Madison and a senior computational mathematician at Argonne National Laboratory. He holds a B.Sc. from Universidad Iberoamericana (2003) and a Ph.D. from Carnegie Mellon University (2008), both in chemical engineering. His research focuses on control, optimization, data science, and their applications to energy and environmental systems. He has received numerous awards, including the NSF and DOE Early Career awards, PECASE, and the Vilas Mid-Career Award. His work spans sustainable energy systems, plastic recycling technologies, and process automation. Education: PhD in Chemical Engineering, Carnegie Mellon University (2008) B.Sc. in Chemical Engineering, Universidad Iberoamericana (2003) Research interests: Control systems and optimization for energy infrastructure Data-driven modeling of chemical processes Design of sustainable plastic recycling methods Integration of machine learning in process operations Notable awards include the Harvey Spangler Award (2024), Vilas Mid-Career Award (2023), and the Presidential Early Career Award (2019). His recent publications address challenges in circular economy, energy storage, and computational topology for material analysis. Teaching and advising involve advanced courses in chemical engineering and special topics in process systems engineering. His research group collaborates on projects related to decarbonization, waste-to-energy systems, and autonomous manufacturing processes.

Basheer QolomanyView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Network Science
• +7 more

Basheer Qolomany is an Assistant Professor at Howard University's College of Medicine, Department of Medicine. He also holds academic roles at institutions including the University of Cincinnati, University of Nebraska, and Kennesaw State University. His research focuses on AI-driven solutions for computational medicine, cybersecurity, smart health systems, and data analytics. He earned his Ph.D. in Computer Science from Western Michigan University (2018) and has taught courses in AI, cybersecurity, and data science across multiple universities. Dr. Qolomany's expertise includes network science, evolutionary computation, natural language processing, and big data analytics. His work addresses challenges in precision medicine, disease diagnosis via wearable sensors, and optimizing therapeutic strategies. He has secured grants totaling over $150,000, including a co-PI role in a 2021-2023 project on peripheral artery disease detection and a PI role in a 2020-2021 study analyzing Bitcoin dynamics via Twitter data. His publications span AI optimization techniques, smart building energy management, and cybersecurity in autonomous systems. He actively contributes to interdisciplinary collaborations, including the Center for Sickle Cell Disease and the Center for Applied Data Science and Analytics at Howard University.

• Machine Learning
• Operations Research
• Optimization
• +3 more

Silvia Villa is an Associate Professor in the Department of Mathematics (DIMA) at the University of Genoa (UniGe). Her teaching responsibilities include courses such as Game Theory, Machine Learning, Optimization and Operations Research, and Operations Research at both undergraduate and master's levels. Her research focuses on optimization theory and its applications in machine learning, inverse problems, and regularization methods. She has contributed to advancements in iterative regularization techniques, stochastic optimization algorithms, and convex optimization frameworks. Her work often bridges theoretical foundations with practical applications in signal processing and data science. Her research interests emphasize structured optimization problems, including low-complexity regularizers, bilevel optimization, and sparse recovery. Key methodologies in her studies include primal-dual dynamics, proximal algorithms, and variance reduction techniques. Recent trends in her publications highlight advancements in unrolled deep networks for sparse signal restoration, adaptive optimization strategies, and convergence analysis of stochastic methods. Villa's collaborative efforts span interdisciplinary projects involving applied mathematics, computer science, and engineering. While no specific awards are listed, her prolific publication record reflects her active role in the optimization and machine learning communities. She has advised multiple research projects but no formal student names are provided in the available data.

David Kristjanson DuvenaudView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• AGI Governance
• +3 more

David Duvenaud is an Associate Professor at the University of Toronto, affiliated with the Faculty of Arts and Science. He is a founding member of the Vector Institute and holds a Schwartz Reisman Chair in Technology and Society. His research focuses on AGI governance, existential risk mitigation, and probabilistic machine learning. Previously, he conducted postdoctoral research at Harvard and earned his Ph.D. from the University of Cambridge under advisors Carl Rasmussen and Zoubin Ghahramani. Education: Ph.D. (University of Cambridge), M.Sc. (University of British Columbia) Research Interests: Neural ODEs, AI ethics, probabilistic modeling, and scalable generative systems. His work bridges foundational machine learning theory with real-world applications, including chemical design and alignment science. Key contributions include LLM Processes, sabotage evaluations for frontier models, and neural ODE frameworks. He teaches advanced machine learning courses (e.g., STA414, CSC2547) and advises students like James Requeima and Raymond Douglas. His awards include a Sloan Fellowship and distinguished paper recognitions. Current projects address systemic risks posed by incremental AI progress, emphasizing long-term societal impacts and governance mechanisms.

Bo ShenView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Additive Manufacturing
• +4 more

Bo Shen is an Assistant Professor in the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology (NJIT). His research focuses on machine learning, deep learning, additive manufacturing, and data analytics with applications in solar physics, healthcare, and materials science. He holds a Ph.D. in Industrial and Systems Engineering from Virginia Tech (2022) and a B.S. in Statistics from the University of Science and Technology of China (2017). His work bridges theoretical advancements in AI with practical industrial and scientific challenges. Education: Ph.D., Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, 2022 B.S., Statistics, University of Science and Technology of China, 2017 Research Interests: Prof. Shen’s research spans deep learning , optimization algorithms , additive manufacturing process analytics , and solar physics data modeling . He develops novel methods for model explainability, anomaly detection in manufacturing, and high-dimensional data analysis. His work often integrates physics-based principles with machine learning frameworks. Publications: His recent articles address topics like neural operator learning for fatigue prediction, Bayesian optimization in additive manufacturing, and deep learning-driven solar image reconstruction. These reflect a trend toward applying advanced AI techniques to solve interdisciplinary problems in engineering and science. Awards/Grants: No awards explicitly listed, but his prolific publication record suggests active research engagement. Details on grants or lab affiliations are not provided in the text. Labs/Teams: While specific lab affiliations are not mentioned, his work aligns with NJIT’s strategic research focus areas in data analytics and advanced manufacturing.

Anna MaView profile

Assistant Professor

• Applied Mathematics
• Computational Mathematics
• Optimization
• +3 more

Anna Ma is an Assistant Professor in the Department of Mathematics at the University of California, Irvine (UCI). Her research focuses on optimization, computational mathematics, and algorithm design with applications to inverse problems, signal processing, and high-dimensional data analysis. Key research interests include iterative algorithms (e.g., randomized Kaczmarz methods), tensor recovery, stochastic gradient descent, and robust recovery techniques for noisy data. Her work emphasizes algorithmic efficiency, convergence guarantees, and applications in machine learning and data science. Recent publications highlight advancements in quantile-based iterative methods, tensor linear systems, and robust low-rank recovery. These studies address challenges in handling missing data, noisy observations, and scalable solutions for large-scale systems. No scientific awards are explicitly mentioned in the provided text. Her research has been supported by grants focused on algorithm development and data-driven methodologies. Anna collaborates on projects involving multidisciplinary teams and contributes to advancing numerical methods for modern data analysis challenges.

Heike TrautmannView profile

Professor

• Data Science
• Artificial Intelligence
• Multiobjective Optimization
• +4 more

Prof. Heike Trautmann is a Professor of Data Science: Statistics and Optimization at the University of Münster, holding the Chair of Statistics and Optimization within the Department of Information Systems and Statistics, School of Business & Economics. She also serves as Vice Dean for Internationalization at the School. Her academic career includes roles such as Pascal Professor at Leiden University (2017) and a visiting position at the University of Twente (2021–2026). She earned her PhD (2004) and Habilitation (2013) in Statistics from TU Dortmund, focusing on optimization methodologies. Her research interests span Multiobjective Optimization, Evolutionary Algorithms, Automated Algorithm Selection, Data Stream Mining, and Social Media Analytics. Notable projects include the COSEAL consortium for algorithm selection, the Benchmarking Network for optimization heuristics, and the ERCIS Social Media Analytics Competence Center addressing online disinformation. She has led initiatives like MODERAT! (automated comment moderation) and Algorithmisierung und gesellschaftliche Interaktion (societal impact of algorithms). Awarded multiple best paper prizes, including at CBI 2019 and PPSN XIV 2016, she emphasizes interdisciplinary collaboration. Her work bridges technical advancements with societal implications, reflected in contributions to AI ethics and platform regulation. Active in international conferences (e.g., EMO, GECCO), she maintains roles in program committees and advisory boards such as CLAIRE and ACM SIGEVO. Funded projects include EU initiatives on algorithm configuration and DAAD collaborations on platform regulation. Her research outputs span over 50 peer-reviewed articles since 2012, focusing on algorithmic innovation and real-world applications in optimization and digital media.

• Artificial Intelligence
• Game AI
• Machine Learning
• +4 more

Ivan Bravi is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on artificial intelligence, particularly in the context of game AI and automated playtesting. He explores topics such as statistical forward planning, reinforcement learning, and evolutionary optimization for game agents. Bravi's work emphasizes developing intelligent systems capable of navigating complex game environments, including card games like Splendor and unforgiving grid-based simulations like the Game of Life. His contributions include the Rinascimento framework, which employs event-value functions and hyperparameter tuning to enhance agent decision-making. His publications highlight advancements in agent optimization, behavioral expressivity, and self-adaptive algorithms. While no awards are listed, his active research in game AI underscores a commitment to pushing boundaries in computational decision-making and adaptive learning. Bravi collaborates with academic and industry partners to refine AI-driven solutions for gaming and beyond, leveraging interdisciplinary approaches within the School's vibrant research ecosystem.

Sam SilvaView profile

Assistant Professor

• Atmospheric Chemistry
• Computational Modeling
• Air Quality
• +3 more

Sam Silva is Assistant Professor in Earth Sciences, Civil and Environmental Engineering, and Population and Public Health Sciences at the University of Southern California. He leads the Atmospheric Composition and Earth Data Science group, investigating atmospheric chemistry's role in air quality and climate change through computational modeling and machine learning. Silva holds a PhD in Environmental Engineering and Computation from MIT and a BSc in Physics and MSc in Atmospheric Science from the University of Arizona. Previously, he was a Linus Pauling Distinguished Postdoctoral Fellow and Research Data Scientist at Pacific Northwest National Laboratory. Research focuses on atmospheric chemical composition using computer models, data science, and machine learning to address global environmental challenges. Work emphasizes aerosol-cloud interactions, chemical cycling dynamics, climate-health relationships, and equitable modeling approaches. Publications demonstrate strong trends in developing machine learning frameworks for atmospheric and climate science, with applications in chemical transport modeling, health impact assessments, and biogeochemical cycling. Recent work advances interpretable AI methods while addressing urban air quality-climate interactions.

Kristian KerstingView profile

Professor

• Statistical Relational Learning
• Machine Learning
• Data Mining
• +3 more

Kristian Kersting is a Professor at the University of Bonn, Germany, and affiliated with Fraunhofer IAIS. His research focuses on Statistical Relational Learning, Machine Learning, and their applications in Robotics and Computational Biology. He earned his PhD from Albert-Ludwigs-University of Freiburg under Luc De Raedt and was a postdoc at MIT's CSAIL. Award highlights include the ECCAI 2006 Dissertation Award and ECML-06 Best Paper Award. He has supervised numerous students, including those working on relational sequence alignments and robotics navigation policies. He co-organized key workshops like MLG-2007 and served on program committees for ECML/PKDD, ICML, and AAAI. His contributions span probabilistic logic learning, including Bayesian Logic Programs and Logical Hidden Markov Models. Recent work includes neuro-symbolic systems, causal reasoning, and benchmarks in emotion detection and AI safety.

Long ChenView profile

Lecturer

• Digital Twin Technology
• Systems Engineering
• Design Change Management
• +2 more

Dr. Long Chen is a Lecturer at the School of Architecture, Building and Civil Engineering, Loughborough University. He holds a PhD and BEng, with professional affiliations including MCIOB and A.M.ASCE. Previously, he was a Postdoctoral Research Associate at Imperial College London's Centre for Systems Engineering and Innovation (CSEI), where he contributed to the CDBB-funded project 'Analysing Systems Interdependencies using a Digital Twin' and the Alan Turing Institute/Lloyd’s Register Foundation's 'Grand Challenge III: Data-Driven Design Under Uncertainty'. His research focuses on digital twin-driven approaches for design change management in complex systems and image-based digital twinning for as-built buildings. Key research areas include Systems Engineering, Data-Centric Engineering, and Building Information Modeling. Dr. Chen's publications primarily explore machine learning fairness, Bayesian computation, and computer vision techniques, with recent works addressing algorithmic bias mitigation, transformer calibration, and semantic segmentation robustness. His research demonstrates consistent applications in probabilistic modeling and adaptive systems. He maintains active collaborations with Imperial College London and the Alan Turing Institute, leveraging his expertise in systems interdependencies and digital twin implementations.

Kirthevasan KandasamyView profile

Assistant Professor

• Machine Learning
• Game Theory
• Computer Systems
• +3 more

Kirthevasan Kandasamy is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison, affiliated with the School of Computing, Data & Information Sciences. His research focuses on theoretical machine learning, game theory, and their applications in computer systems. He holds a PhD in Machine Learning from Carnegie Mellon University and a B.Sc. in Electronics & Telecommunications Engineering from the University of Moratuwa, Sri Lanka. Education: PhD in Machine Learning, Carnegie Mellon University (2018) B.Sc. in Electronics & Telecommunications Engineering, University of Moratuwa, Sri Lanka Research Interests: Machine Learning Theory Game Theory and Mechanism Design Data Sharing Incentives Optimization in Computer Systems Automated Machine Learning Recent Trends in Articles: Focuses on mechanisms for data sharing, pricing strategies in markets, and online learning algorithms. Recent work explores Nash incentive-compatible mechanisms, collaborative estimation under uncertainty, and resource allocation in distributed systems. Awards: Facebook Fellowship (2017/18) Siebel Scholarship (2017/18) CMU Presidential Fellowship (2015/16) Advising: Currently supervising PhD students in Statistics and Computer Science, including Keran Chen, Alex Clinton, and Joon Suk Huh. Also mentors undergraduate researchers like Peter Yang.

Nikos GianniotisView profile

Researcher

• probabilistic machine learning
• dimensionality reduction
• Bayesian inference
• +1 more

Nikos Gianniotis serves as a Staff Scientist in the Astroinformatics group at the Heidelberg Institute of Theoretical Studies (HITS), leveraging his computer science expertise to develop advanced machine learning solutions for astronomical data analysis. His role focuses on probabilistic modeling and algorithmic innovation within a dedicated research environment. His academic credentials include: PhD in Computer Science from the University of Birmingham (2007) MSc in Natural Computation (with Distinction) from the University of Birmingham (2003) BSc in Computing Science (First class) from the University of Aberdeen (2002) Dr. Gianniotis specializes in probabilistic machine learning , Bayesian inference , and dimensionality reduction techniques applied to astronomical challenges. His research bridges computational statistics and astrophysics, with emphasis on interpretable models for complex observational data. Recent work demonstrates sophisticated integration of Gaussian processes with time-series analysis for cosmic phenomena. Analysis of his 15 most recent publications (2016-2024) reveals consistent innovation in machine learning for astronomy, particularly in time-delay estimation for active galactic nuclei, spectral analysis, and time-series dimensionality reduction. His methodology frequently combines Gaussian processes, variational inference, and neural architectures like autoencoders to address astronomical data challenges. No scientific awards were documented in the provided materials. While no student advisement or grant details were specified, his publications indicate active collaboration within the astroinformatics community. The Astroinformatics group at HITS provides his primary research environment, focusing on scalable computational frameworks for next-generation astronomical datasets from missions like Kepler.