Find Academic Supervisors

Peter HaasView profile

Professor

• Data Management
• Machine Learning
• Simulation
• +6 more

Peter Haas is a Professor at the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, with an adjunct role in Industrial Engineering. Previously, he spent 30 years as a Principal Research Staff Member at IBM Research and held a consulting professorship in Management Science and Engineering at Stanford University. His research focuses on applying probability and statistics to data management, simulation of complex systems, and machine learning scalability. Education : PhD, Operations Research, Stanford University, 1986 MS, Statistics, Stanford University, 1984 MS, Environmental Engineering, Stanford University, 1979 SB, Engineering and Applied Physics, Harvard University, 1978 Research Interests : Haas’s work spans stochastic systems, probabilistic databases (e.g., MCDB and SimSQL), sampling techniques, and simulation optimization. He pioneered methods for managing uncertain data and scalable machine learning, including compressed linear algebra for declarative systems. His recent focus includes in-database decision support and hybrid simulation metamodeling with neural networks. Key Contributions : He developed the Online Aggregation framework (SIGMOD 1997), which earned a Test-of-Time Award in 2007. His work on matrix factorization and distributed stochastic gradient descent (DSGD) revolutionized large-scale machine learning. He also advanced techniques for estimating distinct-values and correlation discovery in databases. Awards : A six-time recipient of IBM’s Pat Goldberg Memorial Award, he is an ACM and INFORMS Fellow. His honors include the VLDB Best Paper Award (2016), EDBT Best Paper (2018), and recognition in Communications of the ACM. Advising & Grants : He advises four current PhD students and has graduated Matteo Brucato. His IBM career included over 30 patents, including foundational work for DB2’s sampling capabilities and IBM Watson analytics. He leads the DREAM Lab, focusing on data systems for exploration and analytics. Labs/Teams : Directs the Data systems Research for Exploration, Analytics, and Modeling (DREAM) Lab, advancing projects like Splash (health system simulation) and SuDocu (document summarization by example).

Alan A. StockerView profile

Professor

• Computational Neuroscience
• Bayesian Inference
• Sensory Adaptation
• +4 more

Alan A. Stocker is a Professor in the Department of Psychology at the University of Pennsylvania, with affiliations in the Neuroscience Graduate Group, Bioengineering Graduate Group, and Computational Neuroscience Initiative. He leads the Computational Perception and Cognition (CPC) Laboratory, focusing on how prior beliefs and expectations shape sensory perception through Bayesian inference and efficient coding principles.

• Machine Learning
• Bayesian Optimization
• Gaussian Processes
• +4 more

Ti John is a Research Fellow at Aalto University's Department of Computer Science within the School of Science. He is affiliated with Professor Marttinen's research group and the Probabilistic Machine Learning group led by Professor Samuel Kaski. His work connects with the Finnish Center for Artificial Intelligence (FCAI) and the Helsinki Institute for Information Technology (HIIT). Dr. John's research focuses on machine learning, particularly Bayesian optimization, Gaussian processes, and point process models. His work spans theoretical developments in neural processes and practical applications in healthcare analytics and large language models. He has made significant contributions to equivariant neural processes, causal mediation analysis in healthcare, and interpretability of additive models. His publication record shows consistent output with 17 publications between 2021-2024, including multiple papers at top AI conferences like NeurIPS, ICML, and ICLR. His research demonstrates strong interdisciplinary connections between statistical modeling, artificial intelligence, and healthcare applications. Active reviewer for NeurIPS, ICLR, AISTATS Reviewer for Journal of Machine Learning Research Member of Finnish Center for Artificial Intelligence project Dr. John has been actively contributing to the machine learning community through peer review and conference participation, demonstrating expertise across multiple subfields of artificial intelligence and statistical modeling.

Adrian PerrigView profile

Professor

• Network Security
• Cybersecurity
• Distributed Systems
• +3 more

Adrian Perrig is a Full Professor at the Department of Computer Science at ETH Zürich. He leads research in network security, distributed systems, and internet architecture, focusing on projects like the SCION secure internet architecture and its commercialization through Anapaya Systems. His work emphasizes secure communication, denial-of-service defense, and public key infrastructure (PKI) innovations. Affiliations: ETH Zürich, Institute for Information Security Key Contributions: SCION, SAGE, RHINE, F-PKI Research interests include path-aware networks, cryptographic protocols, and resilient systems. His publications span over 295 results since 2005, with notable awards including the Best Paper Award (CoNEXT 2021) and ANRP 2023. He has contributed to foundational work in secure routing, DNS security, and GPU attestation. Scientific awards include Best Paper Awards at CoNEXT and ACM SIGCOMM, as well as recognition for applied networking research. His work bridges academia and industry, addressing challenges in global network security and scalability.

John ClarkView profile

Professor

• Cybersecurity
• Software Engineering
• Artificial Intelligence
• +5 more

John Clark is a Professor of Computer and Information Security at the University of Sheffield since 2017 and Director of the Siemens Digital MINE. Previously, he held roles as Professor of Critical Systems at the University of York (1992–2017) and worked at Logica in security R&D. He studied Mathematics and Applied Statistics at the University of Oxford. His research focuses on cybersecurity, software engineering, and AI applications, particularly in threat modeling, intrusion detection, quantum cryptanalysis, and secure autonomous systems. Clark leads the Security of Advanced Systems research group and has secured grants totaling over £36 million. Notable projects include the EPSRC-funded DAASE (2012–2019) and the Active Building Centre (2018–2022). His work on phishing detection (e.g., analyzing user behavior) and malware analysis has been widely recognized. He has been awarded the Royal Society Wolfson Merit Award (2013), GEECO medals (2005, 2013), and multiple best-paper prizes. Clark’s research spans theoretical and applied domains, including evolutionary computation for cryptanalysis, robotic system security, and smart grid protection. His labs explore areas like digital twin authentication and privacy-aware energy theft detection. He has supervised numerous grants and maintains active collaborations with industry and academia.

Chew Lock YueView profile

Professor

• Complex Systems
• Nonlinear Dynamics
• Quantum Physics
• +5 more

Professor Chew Lock Yue is an Associate Dean (Students) in the College of Science and a Full Professor in the School of Physical & Mathematical Sciences at Nanyang Technological University (NTU). He holds a B.Eng (Hons) in Electrical Engineering from the National University of Singapore (1991), an M.Sc in Electrical Engineering from the University of Southern California (1997), and a Ph.D. in Theoretical Physics from NUS (2004). His research focuses on complex systems, nonlinear dynamics, quantum thermodynamics, and urban systems modeling. Current projects include thermodynamics of information processing, machine learning integration with complex systems, and statistical physics of sea-level rise. Professional roles span technical leadership at DSO National Laboratories (1992-2005), academic appointments since 2005 (Assistant Professor to Full Professor), and administrative roles including Cluster Deputy Director at NTU’s Data Science & Artificial Intelligence Research Centre (2018-2021). He has received multiple teaching awards, including the Nanyang Award for Excellence in Teaching (2007) and the Best Faculty Mentor Award (2013). Research interests also encompass social-ecological systems, quantum heat engines, and spatial agglomeration patterns in urban contexts. His work bridges physics with interdisciplinary challenges like climate modeling and machine learning, with over 150 publications in peer-reviewed journals. Active in education, he teaches courses on quantum mechanics, nonlinear dynamics, and statistical physics.

Qiang JiView profile

Professor

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +3 more

Qiang Ji is a Professor in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI), directing the Intelligent Systems Laboratory (ISL). He holds IEEE and IAPR Fellowships. Dr. Ji's research focuses on AI, computer vision, Bayesian methods, and robotics, with contributions to causal discovery, 3D reconstruction, and Tibetan multi-dialect speech recognition. He previously served as an NSF program director managing machine learning and computer vision initiatives. His academic journey includes positions at the University of Nevada, Reno, and visiting roles at institutions like Carnegie Mellon's Robotics Institute. Education: PhD in Electrical Engineering from the University of Washington. Research interests span machine learning, probabilistic graphical models, and human-computer interaction. Notable contributions include Bayesian adversarial learning, knowledge-augmented deep learning, and physics-aware human motion prediction. His work bridges theoretical advancements with applied systems like gaze estimation and facial action unit detection. Awards: IEEE Fellow (202?), IAPR Fellow (202?). Professional roles include conference committee chairs and editorial board memberships. Key research themes include uncertainty quantification, causal inference, and cross-domain learning challenges.

Jonas BeskowView profile

Professor

• Multimodal Interaction
• Speech Synthesis
• Robotics
• +3 more

Jonas Beskow is a Professor and Head of Division at the Division of Speech, Music and Hearing at KTH Royal Institute of Technology. His research focuses on multimodal interaction, speech synthesis, robotics, and human-robot interaction. He leads the Learning style variation in nonverbal behaviour for social robots and agents project as part of Digital Futures, a cross-disciplinary research center. His work involves developing social robots like the Furhat head and advancing technologies for gesture synthesis, audio-driven motion, and adaptive intelligent systems. He holds roles as Co-PI for the Advanced Adaptive Intelligent Systems (AAIS) and Adaptive Intelligent Homes (AIH) projects. His research spans robotics, computer graphics, and clinical applications such as dementia detection through multimodal patient behavior analysis. Beskow also contributes to educational initiatives, supervising courses in computer science and engineering, including degree projects in machine learning and systems engineering. Publications highlight innovations in gesture generation, speech-driven animation, and socially-aware robotics. Collaborations with institutions like Stockholm University and RISE Research Institutes drive interdisciplinary solutions. His work bridges artificial intelligence, human-computer interaction, and assistive technologies, emphasizing ethical and societal impacts of emerging digital systems.

• Natural Language Processing
• Large Language Models
• Soft Robotics
• +4 more

Peiyi Wang is an Assistant Professor at Peking University's School of Electronics Engineering and Computer Science, Institute for Artificial Intelligence. With strong research output spanning both natural language processing and robotics, Wang maintains significant collaborations with Southern University of Science and Technology and National University of Singapore, particularly in soft robotics research with Professor Cecilia Laschi. Additionally, Wang is actively involved with DeepSeek-AI, contributing to several major language model initiatives including DeepSeek-R1 and DeepSeek-V2. Peking University, School of EECS, Institute for Artificial Intelligence (Primary) Southern University of Science and Technology (Collaborative) National University of Singapore (Collaborative) DeepSeek-AI Research Organization Dr. Wang's research spans two primary domains with significant intersection points. In natural language processing, Wang focuses on large language model reasoning capabilities, mathematical verification, uncertainty estimation, and preference alignment. The robotics work centers on soft robotics, particularly origami-inspired designs, strain-based modeling, and control systems for continuum manipulators. These domains converge in Wang's work on vision-language models, embodied AI, and multimodal reasoning systems. Recent work demonstrates particular innovation in mathematical reasoning verification (Math-Shepherd), soft robotic control systems, and red teaming frameworks for language model safety. Wang's publication record shows remarkable productivity, with over 40 publications between 2021-2025 across top-tier venues including ACL, EMNLP, CVPR, and IEEE Transactions on Robotics. The work demonstrates consistent progression from foundational NLP tasks to increasingly sophisticated multimodal and reasoning systems. The most recent publications (2024-2025) show particular emphasis on mathematical reasoning verification, soft robotics control, and language model safety evaluation. While specific awards aren't documented in the provided materials, Wang's work has clearly gained significant recognition through acceptance at top-tier conferences and collaborations with leading researchers in both NLP and robotics fields. Wang's research demonstrates strong interdisciplinary connections, bridging theoretical NLP work with practical robotics applications. The work with DeepSeek-AI suggests active industry collaboration while maintaining strong academic research output. Current research directions appear focused on improving language model reasoning reliability while developing novel soft robotic systems that can interact safely and effectively with complex environments.

Oliver DeussenView profile

Professor

• Visualization
• Computer Graphics
• Digitalization Processes
• +4 more

Oliver Deussen is a Professor of Visual Computing at the University of Konstanz, recognized by the German Informatics Society (GSI) as a Fellow for his contributions to computer science. His research focuses on visualization, robotics, and environmental modeling, particularly in plant and landscape representation. He has pioneered methods in image manipulation and robotic painting, emphasizing digitalization's societal impacts. His work spans computational biology (e.g., schooling fish behavior) and AI-driven creative technologies. Research Interests: Visualization techniques, swarm behavior analysis, robotic creativity, and interdisciplinary applications of computer science. He explores how computational methods can model natural systems and enhance human-machine interaction. Awards: Fellow of the German Informatics Society (GSI) His research often bridges theory and practice, with contributions to SLAM frameworks, style transfer algorithms, and uncertainty visualization tools. Collaborations in robotics and biology reflect his commitment to applied computational research.

• Robotics and Autonomous Systems
• Computer Vision
• Machine Learning
• +7 more

Dr. Sen Wang is an Associate Professor in Robotics and Autonomous Systems at Imperial College London's Department of Electrical and Electronic Engineering, affiliated with I-X (Imperial's AI initiative), the Grantham Institute, and the Robotics Forum. He directs the Sense Robotics Lab and founded the MSc in Artificial Intelligence Applications and Innovation. His research focuses on advancing robotic autonomy through probabilistic and machine learning methods, addressing challenges in unstructured environments such as underwater infrastructure inspection and climate change solutions. Key projects include leading the £18M UKRI ORCA Hub, developing underwater robotics for offshore energy infrastructure inspection, and achieving the first autonomous wind farm foundation inspection at EDF's Blyth site. He holds editorial roles at IEEE Transactions on Robotics and other journals. Research interests span robotics, computer vision, SLAM, and AI applications, with recent work emphasizing underwater systems, sensor fusion, and safety-critical autonomy. His publications bridge theoretical advancements with real-world deployments in marine robotics and environmental monitoring. Awards: 2024 AI Most Influential Scholar Award Honourable Mention Grants: £18M ORCA Hub funding (UKRI) Labs: Sense Robotics Lab, I-X AI Initiative

Rohan TabishView profile

Assistant Professor

• Real-Time Systems
• Embedded Systems
• Multicore Processors
• +5 more

Rohan Tabish is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), specializing in real-time systems, embedded systems, and cybersecurity. His work focuses on developing predictable and secure software frameworks for multi-core and heterogeneous architectures. Education: Ph.D. in Computer Science and Engineering Master's in Computer Science and Engineering B.Sc. in Electrical Engineering with Telecommunications specialization Research Interests: Real-Time Task Scheduling Fault Tolerance in Embedded Systems Inter-Core Communication Frameworks Memory Bandwidth Management Cyber-Physical Systems Scratchpad-Centric Operating Systems Awards: Outstanding Paper & Best Paper Award (RTSS 2020) Outstanding Paper & Best Student Paper Award (RTSS 2020) Best Presentation Award (RTAS 2016) Nominated for Best Paper Award (ECRTS 2019) Teaching: CS 431: Embedded Systems (Instructor, 2015-2018) CS 424: Real-Time Systems (Instructor, 2019) CS 438: Communication Networks (TA, 2019) Labs/Teams: Active member of the Real-Time Systems Lab (RTSL) at UIUC, focusing on safety-critical embedded systems and real-time software frameworks.

Frances YungView profile

Research Fellow

• Discourse Relations
• Computational Linguistics
• Natural Language Processing
• +4 more

Dr. Frances Yung is a Postdoctoral Researcher at Saarland University's Department of Language Science and Technology within the Department of Computer Science. She has been affiliated with Prof. Vera Demberg's research group since April 2017 and is currently working on the DFG-funded SFB-1102 project "Information Density and Linguistic Encoding," specifically on project B2 "Cognitive modelling of information density for discourse relations." She is pursuing her habilitation, indicating career progression toward a higher academic position in the German university system. Dr. Yung's research focuses on discourse relations at the intersection of NLP, corpus linguistics, and experimental psycholinguistics. Her work explores how information density affects discourse relation marking through cognitive modeling approaches. She has developed expertise in discourse parsing, resource construction, annotation aggregation, and experimental pragmatics, with particular attention to multilingual aspects of discourse phenomena. Her research combines computational modeling with experimental methods to understand how speakers produce and comprehend discourse relations. Analysis of Dr. Yung's recent publications reveals a strong focus on discourse relation resources, particularly multilingual corpora like DiscoGeM 2.0 covering English, German, French, and Czech. Her work increasingly incorporates crowdsourcing methodologies and examines how large language models can be leveraged for discourse annotation tasks. She has made significant contributions to understanding the challenges of implicit discourse relation annotation and the biases introduced by different task designs in crowdsourcing environments. Active reviewer for major computational linguistics conferences (ACL, EMNLP, NAACL, EACL, COLING, IJCNLP) and workshops since 2016 Served as area chair for Sigdial 2024 Regular service on program committees for discourse-related workshops Dr. Yung has supervised multiple Master's theses on topics related to discourse relations, implicit relation identification, and domain adaptation. Her teaching portfolio includes courses on crowdsourcing linguistic annotations, discourse relations from cognitive and NLP perspectives, and recent advances in discourse processing. She has also served as a teaching assistant for data science and AI courses, demonstrating her commitment to interdisciplinary education at the intersection of computer science and linguistics.

• Environmental Microbiology
• Molecular Diagnostics
• Water Quality
• +3 more

Prof. Andreas Farnleitner is a Professor at TU Wien, affiliated with the Environmental Microbiology and Molecular Diagnostics Research Group. His research focuses on microbial water quality, molecular diagnostics, and environmental microbiology. Key areas include PCR-based methods for fecal pollution tracking, antimicrobial resistance in aquatic environments, and climate change impacts on waterborne pathogens. He has contributed to advancements in DNA aptamer technology for rapid water quality monitoring and employs machine learning for predictive modeling in hydrology. His work spans collaborations across Europe, addressing One Health challenges and wastewater-based epidemiology. Recent projects include studies on Vibrio cholerae in Austrian bathing waters and the biostability of drinking water resources. Prof. Farnleitner leads interdisciplinary initiatives to improve water safety and environmental management. Publications highlight his expertise in microbial source tracking, climate change effects on infection risks, and antibiotic resistance patterns. His team develops innovative diagnostic tools and models for environmental monitoring, with applications in both academic and applied settings. Current projects emphasize the integration of molecular biology and engineering to address global water quality challenges.

Lateef AkanjiView profile

Senior Lecturer

• Petroleum Engineering
• Multiphase Flow in Porous Media
• Enhanced Oil Recovery (EOR)
• +7 more

Dr. Lateef Akanji is a Senior Lecturer in the Department of Petroleum Engineering at the School of Engineering, University of Aberdeen, where he has been contributing since 2014. He previously served as Lecturer and Head of the Petroleum Technology Research Group at the University of Salford, Assistant Professor at King Saud University, and Visiting Lecturer at the University of Leoben. His academic journey includes a PhD from Imperial College London and degrees from the University of Ibadan. University: University of Aberdeen School: School of Engineering Position: Senior Lecturer, Petroleum Engineering Email: l.akanji@abdn.ac.uk Education: PhD, Petroleum Engineering, Imperial College London M.Sc., Petroleum Engineering, University of Ibadan B.Sc. (Honours), Petroleum Engineering, University of Ibadan DIC (Diploma of Imperial College) Research Interests: Dr. Akanji's research centers on multiphase flow in porous and permeable media, with applications in enhanced oil recovery (EOR) in clastic, carbonate, and unconventional shale reservoirs. His work integrates theoretical, experimental, and computational fluid dynamics, utilizing platforms like Python, C++, and Fortran. He is pioneering the application of artificial intelligence in petroleum engineering, particularly in EOR screening and production optimization. His research includes pore-scale modeling, gas-lift systems, and nuclear reactor flow dynamics. Publication Trends: His recent publications (2025–2021) reflect a strong focus on fluid displacement in porous media, shale reservoir characterization, AI applications in energy, and nuclear safety. Notable themes include computational modeling of multiphase flow, biosurfactant EOR, and advanced numerical methods for reservoir simulation. Scientific Awards and Honors: Fellow of the Higher Education Academy (FHEA) Chartered Engineer (CEng) Chartered Petroleum Engineer European Engineer (Eur Ing) Member of the Energy Institute (MEI) Advising and Grants: Dr. Akanji supervises numerous PhD students in areas such as AI-based production optimization, permeability upscaling, and biosurfactant EOR. He leads research funded by PTDF, TETFUND, Sonangol, and Elphinstone, focusing on high-pressure high-temperature flow loops, gas-lift pilot rigs, and neuro-fuzzy screening systems. His collaborative projects involve institutions in the UK, Austria, and Australia. Laboratories and Research Platforms: He contributes to the development of the Complex System Modelling Platform (CSMP++), a C++-based API for simulating multi-physics flow in porous systems, co-developed with ETH Zurich and Montanuniversität Leoben. He also leads a technology innovation platform for EOR, including experimental rigs for biosurfactant screening and gas-lift stability testing.