Find Academic Supervisors

Stephan GünnemannView profile

Professor

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

Prof. Stephan Günnemann is a Professor of Data Analytics and Machine Learning at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He leads the Munich Data Science Institute as Executive Director and directs the Konrad Zuse School of Excellence in Reliable AI. His research focuses on enhancing the reliability of machine learning systems, particularly in graph-based and temporal data analysis. Prof. Günnemann holds a PhD from RWTH Aachen University (2012) and has held postdoctoral and senior research positions at Carnegie Mellon University (USA), Simon Fraser University (Canada), and Siemens AG. He founded the Emmy Noether Research Group at TUM in 2015 and has been recognized with prestigious awards including the Heinz Maier-Leibnitz Medal (2022) and the ACM SIGKDD Best Paper Award (2018). His research interests span adversarial robustness, graph neural networks, and molecular data analysis. Recent work emphasizes certifiable AI safety, efficient data pruning, and uncertainty estimation in heterogeneous systems. He has contributed to over 150 peer-reviewed publications, with a focus on foundational ML challenges and real-world applications. Key Awards: Heinz Maier-Leibnitz Medal (2022), Google Faculty Award (2020), DFG Emmy Noether Programme (2015) Leadership Roles: Executive Director of Munich Data Science Institute, Director of Konrad Zuse School Past Roles: Postdoctoral Fellow at CMU, Researcher at Siemens His lab actively explores cutting-edge AI topics such as graph representation learning, quantum chemistry simulations, and trustworthy ML systems. Current projects include developing certifiable defense mechanisms against adversarial attacks and scalable molecular generation frameworks.

• Machine Learning
• Robotics
• Human-Robot Interaction
• +4 more

Joseph Camp is a Professor of Comparative Pathobiology and Secretary of Faculties at Purdue University. His work spans interdisciplinary areas including machine learning, robotics, and human-robot interaction. He leads the Global Engineering Program and holds affiliations across technical and administrative roles. His research focuses on reinforcement learning, multi-agent systems, and applying large language models to enhance decision-making and action anticipation in robotics and healthcare contexts. Key research interests include neuro-symbolic systems for short-context action prediction, robust reinforcement learning under noisy feedback, and geometric reasoning for zero-shot robotic grasping. His work also explores disentanglement in concept-residual models and transfer learning in multi-agent environments. He has published extensively on topics ranging from probabilistic imitation learning to bio-inspired robot locomotion design. While no specific awards or grants are listed, his contributions to human-robot interaction (HRI) and AI-driven medical devices reflect a strong focus on practical applications. His lab’s recent work integrates large language models with traditional robotics algorithms to improve adaptability in dynamic environments. No advising relationships or student names are explicitly mentioned in the provided texts.

Vasha DuTellView profile

Lecturer

• Computer Vision
• Deep Learning
• Natural Scene Statistics
• +3 more

Dr. Vasha DuTell is a Postdoctoral Research Associate at the Laboratory for Atmospheric Experiments (LAE) and Lecturing Faculty at UC Berkeley's School of Information. She holds a PhD in Vision Science from UC Berkeley, where she collaborated with Drs. Bruno Olshausen and Martin Banks on natural scene statistics and human vision modeling. Her postdoctoral work at MIT CSAIL focused on spatiotemporal and peripheral vision modeling under Drs. Ruth Rosenholtz and Bill Freeman. Her research combines computer vision and deep learning to address environmental challenges like contrail detection from satellite and ground-based imagery. She develops segmentation models for contrail tracking and studies their environmental impacts. Vasha also teaches computer vision at UC Berkeley, bridging academic instruction with cutting-edge research. Her recent publications span topics such as peripheral vision modeling, perceptual straightness in machine learning models, and high-fidelity tracking systems. These studies emphasize interdisciplinary approaches, merging computational methods with biological insights to advance vision science and environmental monitoring. Dr. DuTell’s work has contributed to foundational datasets like Coco-periph and frameworks like GramStatTexNet, which explore human-machine perception gaps and texture model statistics. Her efforts aim to enhance both theoretical understanding and practical applications in vision systems and climate research.

Armando Solar-LezamaView profile

Professor

• Program Synthesis
• Neurosymbolic Programming
• Machine Learning
• +5 more

Armando Solar-Lezama is a Professor at the MIT Schwarzman College of Computing , Associate Director and COO of MIT CSAIL, and leads the Computer-Aided Programming Group . He earned his BS in Computer Science and Mathematics from Texas A&M University and PhD (2008) from UC Berkeley under Rastislav Bodik. His research focuses on program synthesis at the intersection of Programming Systems and Artificial Intelligence, with recent work on neurosymbolic programming. Education: BS (Texas A&M), PhD (UC Berkeley) Labs: CSAIL, Center for Deployable Machine Learning His research explores automated reasoning and learning to reduce programming effort, including the development of the Sketch programming language. Current neurosymbolic work combines deep learning with logical reasoning for applications in multi-agent systems, RNA splicing, and interpretable policy generation. Recent projects include VLMaterial for procedural asset generation and CRUXEval for code evaluation benchmarks. Scientific awards include: Robin Milner Young Researcher Award (2024) Best Paper (PLDI 2005, PPoPP 2016) Outstanding Paper (EMNLP 2023) He advises graduate students through MIT's EECS PhD program and has developed courses like 6.820 Foundations of Program Analysis and Program Synthesis . His work impacts software synthesis automation, programming language design, and deployable machine learning.

Yunzhu LiView profile

Assistant Professor

• Robotic Perception
• Robot Learning
• Computer Vision
• +5 more

Yunzhu Li is an Assistant Professor of Computer Science at Columbia University, leading the Robotic Perception, Interaction, and Learning Lab (RoboPIL). Previously, she held positions at UIUC and Stanford, and completed her PhD at MIT under advisors Antonio Torralba and Russ Tedrake, with a bachelor's from Peking University. Her research focuses on robotic manipulation, embodied intelligence, and multi-modal perception, emphasizing physics-inspired models and foundation models for generalizable robot learning. RoboPIL develops structured world models for deformable objects, integrates multi-modal sensing (vision, touch, audio), and explores long-horizon embodied interactions. Key research directions include structured world models for manipulable objects, embodied intelligence through foundation models, and multi-modal perception fusion. Recent talks highlight advancements in robot learning, including workshops at ICRA, NUS, and RSS. Her lab actively engages students in PhD, Master's, and undergraduate programs, emphasizing technical strength and self-motivation. Scientific awards include the Best Paper Award at ICRA 2024 Workshop and Sony Faculty Innovation Award. Her work has been featured in venues like Science, Nature Electronics, and IEEE journals. Professional service includes organizing workshops on structured world models and embodied agents, and serving on program committees for CVPR, ICLR, and RSS.

Christos PapadimitriouView profile

Professor

• Theoretical Computer Science
• Algorithms
• Computational Complexity
• +8 more

Christos Papadimitriou is the Donovan Family Professor of Computer Science and Provost's Senior Faculty Teaching Scholar at Columbia University. He previously held the C. Lester Hogan Professorship at UC Berkeley (1996–2017) and taught at Harvard, MIT, Stanford, UC San Diego, and the National Technical University of Athens. His research bridges theory and practice, focusing on algorithms, complexity, computational biology, AI/ML, game theory, and neuroscience. He pioneered algorithmic game theory and explores brain-mind interfaces using neuronal assembly models. Education: BS in EE (Athens Polytechnic, 1972), PhD in EECS (Princeton, 1976) Awards: IEEE John von Neumann Medal (2016), Gödel Prize (2012), National Academy of Sciences/Engineering membership, 9 honorary doctorates Books: Elements of the Theory of Computation , Computational Complexity , Algorithms , and novels like Logicomix His work applies computational lenses to biology, economics, and language, emphasizing formal models for emergent cognition. Recent research includes neuronal assembly-based computation, fairness in ML, and game-theoretic dynamics.

Andrew NereView profile

Assistant Professor

• Machine Learning
• Computer Architecture
• Embedded Systems
• +3 more

Andrew Nere serves as Assistant Professor of Computer Science in the Math & Computer Science Department at Western Colorado University, teaching courses including Introduction to Web Design, Computer Science I, and Software Entrepreneurship since joining the faculty in Fall 2022. His industry background includes co-founding Thalchemy (2013), a startup specializing in embedded machine learning for wearable and environmental sensor applications, alongside prior internships at IBM and Qualcomm. His educational credentials feature: PhD in Electrical Engineering from University of Wisconsin-Madison (2013) MS in Electrical Engineering from University of Wisconsin-Madison (2010) B.S. in Computer Engineering from St. Cloud State University (2007) Nere's research centers on hardware-software co-design for efficient AI deployment, with dual focus on neuromorphic computing architectures and practical embedded systems implementation. He bridges theoretical neuroscience with engineering solutions, particularly for resource-constrained environments like fitness trackers and environmental sensors where computational efficiency is paramount. His work emphasizes translating academic research into real-world applications rather than pure theoretical exploration. Analysis of his 2010-2013 publications reveals consistent innovation in brain-inspired computing hardware, with recurring themes of GPU-accelerated neural simulations, specialized cache architectures for AI workloads, and energy-efficient neuromorphic designs. The research demonstrates strong interdisciplinary collaboration across computer architecture, neuroscience, and machine learning communities, frequently targeting hardware acceleration for cognitive computing tasks. Scientific recognition includes: Best Paper Nomination at IEEE International Symposium on Workload Characterization (2012) Best Paper in Track at International Parallel and Distributed Processing Symposium (2011) Nere brings substantial industry experience to academia, having served as university collaborator on the DARPA/IBM SyNAPSE project modeling brain functionality in computing systems. His startup Thalchemy exemplifies his commitment to applied research translation, while his current Software Entrepreneurship course provides students with practical business development frameworks for technology ventures. He actively leverages Gunnison Valley's natural environment for both recreation and potential research applications, noting particular interest in environmental sensing opportunities afforded by the region's wilderness proximity and diverse ecosystems.

Habib HamamView profile

Professor

• Optics
• Biomedical Engineering
• Wireless Communication
• +7 more

Habib Hamam is a Professor in the Department of Electrical Engineering at the University of Moncton's Faculty of Engineering. His research spans optics, biomedical engineering, wireless communication, and AI applications. Specializations: Diffractive elements, optical interconnections, biomedical engineering, hybrid fiber/wireless systems, and AI-driven optimization. Contact: Email habib.hamam@umoncton.ca | Phone (506) 858-4762 Recent publications highlight his focus on AI integration across healthcare, energy systems, and education, combined with blockchain for security and transparent data management. Key subdomains include medical imaging, renewable energy optimization, and IoT scalability. Notable research trends involve deep learning for diagnostics (e.g., brain tumors, diabetic retinopathy), blockchain applications in smart grids and fisheries, and quantum-inspired algorithms for energy forecasting.

• Sustainable Energy Systems
• Mathematical Optimization
• Industrial Process Engineering
• +3 more

Frank Pettersson is a Senior Lecturer at the Faculty of Natural Sciences and Engineering, Department of Process and Systems Engineering, Åbo Akademi University. His research aligns with UN Sustainable Development Goals (SDGs), focusing on sustainable energy systems and industrial process optimization through mathematical modeling. Expertise Areas: Mathematical Optimization, Hydrogen-Based Steelmaking, Biogas Digestate Recycling, Energy Storage Systems, Gas Distribution Networks, Blast Furnace Efficiency His recent work analyzes hydrogen transition in steel plants, water scarcity in power sectors, and nutrient recycling in biogas systems. Key methodologies include Mixed-Integer Linear Programming (MILP) and systems engineering approaches. Academic supervision spans Energy Technology diploma theses (2013–2017) and ongoing Master’s student mentorship (2025). While no specific scientific awards are listed, his publications demonstrate impact in sustainable industrial solutions and energy systems.

• Artificial Intelligence
• Machine Learning
• Automotive Production
• +8 more

Prof. Dr. Alexander Schiendorfer is a faculty member at Technische Hochschule Ingolstadt within the Faculty of Industrial Engineering , focusing on AI-based Optimization in Automotive Production . His research bridges Artificial Intelligence with manufacturing and industrial engineering , particularly in constraint programming, self-organizing systems, and machine learning applications for composite materials. His work spans pedagogical innovation in machine learning education, real-time manufacturing analytics , and AI for energy systems . Recent publications highlight applications in gas grid management, synthetic data frameworks, and defect analysis in autonomous driving sensors. He leads teams at AImotion Bavaria , collaborating on projects like SmartManPy for synthetic manufacturing data and MORL agents for multi-objective energy optimization. His methodological contributions include certainty groups for neural network confidence estimation and hierarchical resource allocation algorithms.

Musbah AbdulgaderView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +3 more

Musbah Abdulgader is an Assistant Professor in the Department of Computer Science at Norfolk State University, affiliated with the College of Science, Engineering and Technology. His research focuses on interdisciplinary applications of artificial intelligence, cybersecurity, and smart systems. Academic Rank: Assistant Professor Department: Computer Science University: Norfolk State University Research Interests His work spans Artificial Intelligence , Machine Learning , Cybersecurity , Data Science , and Smart Systems . Key areas include emotion recognition, healthcare technology, blockchain systems, and energy management. Publication Trends Recent publications (2024-2019) emphasize AI applications in education and healthcare, blockchain-based voting , cyber-physical systems , and grey wolf optimization in smart energy contexts. Earlier works focus on fuzzy logic and cybersecurity protocols . Advising & Grants No student advising or grant information is currently available in the provided texts.

RamyaaView profile

Assistant Professor

• Theory of Computation
• Computational Complexity
• Logic
• +7 more

Ramyaa is an Assistant Professor in the Department of Computer Science & Engineering at New Mexico Tech. Her research focuses on the intersection of computation, logic, and emerging models of computation, including implicit complexity theory and biologically inspired neural networks. She also explores machine learning applications in real-world problems and interpretable AI. Education: Ph.D. in Computer Science, with a focus on theoretical foundations. Research Interests: Implicit complexity (relating logical complexity to computational resource constraints), formalization of computational models (e.g., biological neural networks), machine learning (including neural network training, adversarial robustness, and program synthesis), and applications in healthcare, security, and education. Her work emphasizes adaptable complexity measures for novel computational frameworks. Recent Trends in Publications: Recent work includes reinforcement learning for astrophysical data workflows, biologically inspired sleep algorithms for neural networks, and machine learning applications in nutritional epidemiology. She also explores educational technology, such as games for teaching logic and interactive programming tutorials. Grants & Advising: Advises research in machine learning, theoretical computer science, and interdisciplinary applications. Active in grant-seeking for projects at the intersection of theory and applications. No listed students, but collaborates widely with researchers globally. Affiliations: Fellow at the Simons Institute for the Theory of Computing (Berkeley), organizer of conferences like ICALT and CSR, and contributor to initiatives like WiCS at NMT. Teaches courses in formal languages, automata theory, and reinforcement learning.

• Wind Energy
• Hybrid Renewable Energy Systems
• Energy Transition
• +13 more

Professor Alireza Maheri is a Personal Chair in the School of Engineering at the University of Aberdeen, where he has been a faculty member since 2016. He previously served as a Senior Lecturer at Northumbria University and held a research position at the University of Bristol. His academic journey includes a BSc from Shiraz University, an MSc from Amirkabir University of Technology, and a PhD from UWE Bristol, all in Mechanical Engineering. BSc in Mechanical Engineering, Shiraz University MSc in Mechanical Engineering - Energy Conversion, Amirkabir University of Technology PhD in Mechanical Engineering - Design and Simulation of Adaptive Aero-structures, UWE Bristol His research focuses on renewable and sustainable energy systems, with key interests in wind energy, hybrid renewable energy systems (HRES), multidisciplinary design optimization, offshore wind farm decommissioning, and smart energy systems. He leads several high-impact research projects funded by EU-InterReg, Net Zero Technology Centre, and Innovate UK, among others. His work integrates advanced computational methods with real-world energy challenges, particularly in off-grid and island communities. The 15 most recent publications reflect a strong trend in optimization of hybrid energy systems, decommissioning cost modeling, decision support platforms, and algorithmic development for renewable integration. His research spans both technical innovation—such as ant colony algorithms and metaheuristic assessments—and practical implementation, including hydrogen production and load planning for machinery. Professor Maheri has received recognition through research grants, including a project funded by the Royal Aeronautical Society. He has also secured substantial funding from national and international bodies, demonstrating sustained excellence and impact. Net Zero Technology Centre: 'Data for Net Zero (D4NZ)', £992k, 2022–2025, CoI EU-InterReg: 'DecomTools', £240k, 2018–2023, PI National Decommissioning Centre PhD Studentship: 'DSS for Oil & Gas Decommissioning', £72k, 2019–2022, PI Scottish Funding Council SPRe-Industrial Collaboration: £78k, 2019–2023, PI Carnegie Trust PhD Scholarship: 'Wind Turbine Smart Blades', £60k, 2019–2022, Principal Supervisor He supervises a wide range of PhD students on topics including smart blades, hydrogen systems, energy management, and decommissioning. He is also actively involved in teaching, particularly as Course Coordinator for Energy Systems Integration (EG554T and EG554U). His leadership extends to editorial roles in journals such as Energies and Renewable Energy , and he has chaired multiple international conferences. He serves on research proposal review panels for EPSRC, Royal Society, and DAAD, and has acted as an external examiner for MSc programs at Cranfield University. Professor Maheri leads the Applied Dynamics and Structures Research Group and is affiliated with the Centre for Energy Transition. He collaborates with institutions and industries across the UK, Europe, China, Mauritius, and Jordan, fostering international research partnerships in sustainable energy and engineering design.

Ioannis MarinakisView profile

Professor

• Stochastic Optimization
• Combinatorial Optimization
• Evolutionary Algorithms
• +3 more

Ioannis Marinakis is a Professor at the School of Production and Management Engineering, Technical University of Crete, specializing in Stochastic Optimization and Applications. He holds a B.A., M.A., and Ph.D. from the same department (1999–2005). His research focuses on metaheuristics, evolutionary algorithms, supply chain optimization, and large-scale systems. Education: B.A. (1999), M.A. (2001), Ph.D. (2005) in Production and Management Engineering, Technical University of Crete. Research Interests: Explores stochastic and combinatorial optimization, nature-inspired algorithms, supply chain design, game theory, and vehicle routing. He has contributed to projects involving operations research and automatic control. Labs/Teams: Leads the Decision Science Laboratory,专注于开发决策支持系统.

Seibu Mary JacobView profile

Senior Lecturer

• Mathematics Education
• Engineering Education
• Machine Learning
• +14 more

Dr Seibu Mary Jacob is a Senior Lecturer in Analytical Methods/Engineering Mathematics at the School of Computing, Engineering and Digital Technologies, Teesside University, where she has been since 2014. She is a Senior Fellow of the Higher Education Academy (HEA) and leads the pedagogic research group STEAMEDULAB@TU. She teaches modules including Engineering Mathematics, Probability & Statistics, Statistical Analysis, and History/Philosophy of Mathematics at both undergraduate and postgraduate levels, including Masters project supervision. Education: BSc in Mathematics MSc in Mathematics BEd in Mathematics Education PhD in Mathematics Education (focus: Critical Thinking in Online Engineering Mathematics) Postgraduate Diploma in Computer Applications (PGDCA) Her research lies at the intersection of applied mathematics, statistical modeling, machine learning, and educational technology. She investigates how AI, especially Generative AI, can enhance student engagement, critical thinking, and inclusive learning. Her work also extends into cybersecurity, where she applies machine learning to detect Android malware, spam emails, and cyber-hate using techniques such as graph neural networks, fuzzy systems, and adversarial defense mechanisms. The recent publications reveal a strong trend in applying machine learning to security challenges—particularly in adversarial environments—while maintaining a parallel focus on pedagogical innovation and the impact of digital tools on education. Her interdisciplinary approach combines technical rigor with social responsibility, emphasizing diversity, inclusion, and community impact. Scientific Awards and Recognitions: Senior Fellow of the Higher Education Academy (HEA) Microsoft Certified Educator (MCE) Dr Jacob is actively involved in PhD student supervision and community projects promoting STEM education, especially for marginalized groups. She is passionate about policy development and curriculum internationalization in mathematics and engineering education. Her research has been supported through institutional affiliations and collaborations, particularly with IEEE, where several of her works are published. She leads the STEAMEDULAB@TU research group, which focuses on innovation in digital pedagogy, including projects on collaborative learning models, U-learning, and learner engagement through technology-enhanced environments.